Elaboration Likelihood Model

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【精品】消费者行为学基本概念

【精品】消费者行为学基本概念

消费者行为学核心概念的中英文对照表P5消费者行为学consumerbehavior研究个体或群体为满足需要与欲望而挑选、购买、使用或处置产品、服务、观念或经验所涉及的过程。

P5角色理论roletheory许多消费者行为类似于戏剧情节。

由于要扮演许多角色,人们有时候会根据自己当时所处的特定“剧情”改变消费决策。

P7重度使用者(频繁使用者)heavyusersP9关系营销relationshipmarketing在品牌与消费者间建立起维持终身的关系.P11全球营销/消费文化globalconsumerculture在这种文化中世界各地的消费者出于对品牌消费品、电影明星、名人及休闲活动的热爱而联结起来。

P49差别阈限differentialthreshold指感觉系统察觉两种刺激之间的差别或者变化的能力。

P49最小可觉察差别justnoticeabledifference能够察觉到的两种刺激之间的最小差别。

P49韦伯定律Weber'sLawK=△i/I(K为常数【不同感觉常数不同】;△i为产生最小可察觉差别所要求的刺激强度的最小变化量;I为引起变化的刺激强度)P50阈下知觉subliminalperception刺激在消费者的感知水平之下。

P52知觉警惕perceptualvigilance消费者更可能意识到与他们目前需要有关的刺激物。

P52知觉防御perceptualdefense人们看他们所要看的,而不看他们所不想看的。

P57知觉地图perceptualmap画出产品或品牌在消费者心目中“处于”何种位置的形象方式。

P72经典性条件反射classicalconditioning(伊凡·巴普洛夫狗铃声干肉粉分泌唾液)指将一种能够诱发某种反应的刺激与另一种原本不能单独诱发这种反应的刺激想配对,随着时间的推移,因为与能够诱发反应的第一种刺激相联结,第二种刺激会引起类似的反应。

(重点研究包括饥饿、口渴、性唤起以及其他基本内驱力的视觉和嗅觉线索。

国际经济与贸易(双语)-教学大纲

国际经济与贸易(双语)-教学大纲

《国际经济与贸易》教学大纲课程编号:112602B课程类型:□通识教育必修课□通识教育选修课□专业必修课√专业选修课□学科基础课总学时:32 讲课学时: 32学分:2适用对象:金融学(国际金融英文班)先修课程:经济学、金融学一、教学目标本课程的主要目标:本门课程的教学,旨在使学生了解和掌握《国际经济学》中的主要理论和研究方法,并能灵活运用所学的理论和方法研究和分析国际经济领域的问题和现象,认识现象和问题的本质属性。

Through the teaching of this course, students should know and seize the major theories and inquisitive method of the international economics. Meanwhile, students should be able to research and analyze some problem and phenomena and understand the substantial properties in the field of international economy according to the theories and methods studied in this course.二、教学内容及其与毕业要求的对应关系要求学生掌握国际贸易基本理论、基本知识,了解当代国际贸易的热点问题及发展趋势,把握国际贸易理论研究前沿。

学完本课程后,应达到以下基本要求:1、了解国际贸易理论前沿和发展状况,能够理解和掌握国际贸易基本概念、历史、理论、政策、新趋势和新实践等基本知识,掌握国际贸易基本方法和基本知识;2、了解国际贸易实践,通过案例教学把握国际贸易的实际情况,能够理论联系实际解决问题,具有分析和解决国际贸易实际问题的能力和研究、分析和编写报告的能力;3、使学生能够运用所学知识,正确分析和解释国际贸易问题与现象。

混合logit模型

混合logit模型

混合logit模型•研究出行选择行为(选择何种交通方式出行)•研究消费者商品选择行为(选择购买何种商品)•研究顾客的满意度(满意度的影响因素)•研究某种事物的接受度•产品的市场份额估计•支付意愿及选择偏好2 数据描述及研究步骤2.1 数据描述我们利用inschoice.dta来应用条件logit模型、混合logit 模型、随机参数logit模型、潜类别logit模型。

该数据集包含6个变量用于记录250人的可用保险计划和选定计划的信息,各变量的描述如下:•id:用于识别个体•premium:保费(随方案而变),Insurance premium(in $100/month)•deductible:免赔额(随方案而变),Deductible (in $1,000/year)•ine:收入(个人属性),Ine (in $10,000/year)•insurance:保险方案(可选方案),Insurances•choice:选定的保险方案(因变量),Chosenalternative首先,我们看一下数据前10行的格式:. list in 1/10, sepby(id) abbreviate(10)+----------------------------------------------------------+| id premium deductible ine insurancechoice ||----------------------------------------------------------|1. | 12.87 1.70 5.74 Health1 |2. | 13.13 2.14 5.74 HCorp 0 |3. | 1 2.03 2.26 5.74 SickInc 0 |4. | 1 1.65 2.945.74 MGroup 0 |5. | 1 0.87 3.56 5.74 MoonHealth 0 ||----------------------------------------------------------|6. | 2 3.52 1.24 2.89 Health 0 |7. | 2 3.23 1.52 2.89 HCorp 0 |8. | 2 2.81 2.31 2.89 SickInc 0 |9. | 2 1.04 2.58 2.89 MGroup 1 |10. | 2 0.93 3.17 2.89 MoonHealth 0 |+----------------------------------------------------------+然后,查看下数据的基本特征:. sum id premium deductible ine insurance choiceVariable | Obs Mean Std. Dev. Min Max-------------+---------------------------------------------------------id | 1,250 125.5 72.19709 1 250premium | 1,2502.298161 .858024 .0568172 4.348273deductible | 1,2502.194286 .7541999 .334168 4.171037ine | 1,250 4.935434 1.4401650 8.337807insurance | 1,250 3 1.41478 1 5-------------+---------------------------------------------------------choice | 1,250 .2 .4001601 0 12.2 研究步骤本文主要目的是通过inschoice.dta介绍stata估计混合logit模型、潜类别logit模型和随机参数logit模型的方法,同时为了做对比,也将估计条件logit模型,具体流程如下:•估计条件logit模型;•估计混合logit模型;•估计随机参数logit模型;•在估计潜类别logit模型。

elm模型在健康广告中的运用

elm模型在健康广告中的运用

ELM 模型(Elaboration Likelihood Model)是一种信息处理模型,常用于解释和预测个体对信息的接受和态度改变。

在健康广告中,可以运用ELM 模型来提高广告的效果和影响力,以下是一些运用方式:1.引起注意和兴趣:通过吸引人的标题、图像或故事引起受众的注
意,并激发他们对健康问题的兴趣。

2.提供信息:清晰、简洁地传达有关健康问题、产品或服务的关键
信息。

确保信息具有可信度和可靠性。

3.引发思考:通过提问、引用科学研究或提供案例,引发受众对健
康问题的思考,促使他们更加深入地处理信息。

4.增强信念:提供证据和论据,支持广告中的主张,增强受众对健
康建议或产品的信念。

5.激发情感:利用情感诉求,如恐惧、希望或社会认同,来增强受
众对健康问题的关注,并促使他们采取行动。

6.引导行为:明确指出具体的行动步骤或建议,引导受众采取有利
于健康的行为。

7.调整信息处理方式:根据受众的认知需求和处理能力,调整信息
的呈现方式。

对于低卷入者,可以使用简单直接的信息和情感诉求;对于高卷入者,可以提供更多详细的科学证据和理性论证。

8.重复和强化:通过重复关键信息、展示实际效果或用户见证,强
化受众对健康信息的记忆和接受程度。

需要注意的是,在运用ELM 模型时,应确保广告内容的合法性、道德性和真实性,以建立受众的信任和提高广告的有效性。

AIC信息准则

AIC信息准则
ˆ Ey Ex [log(g(x|θ(y)))]
G: collection of “admissible” models (in terms of probability density functions). ˆ θ is MLE estimate based on model g and data y. y is the random sample from the density function f (x). • Model Selection Criterion ˆ Maximizing Ey Ex[log(g(x|θ(y)))]

f (x) log
f (x) dx g(x|θ) f (x) log(g(x|θ))dx

=

f (x) log(f (x))dx −
relative K-L information f : full reality or truth in terms of a probability distribution. g: approximating model in terms of a probability distribution. θ: parameter vector in the approximating model g. • Remark I(f, g) ≥ 0, with I(f, g) = 0 if and only if f = g almost everywhere. I(f, g) = I(g, f ), which implies K-L information is not the real “distance”.
March 15, 2007
-2-
background
Estimation error variance bias parameter vector for the full reality model. is the projection of ϑ onto the parameter space of the approximating model Θk . the maximum likelihood estimate of θ in Θk . Variance ˆ For sufficiently large sample size n, we have n θ − θ

6.1 选择性样本模型

6.1 选择性样本模型

• 具体步骤
– 第一步:利用从全部企业(包括上市和未上市)中随 机抽取的样本,估计上市倾向模型 ;并利用估计结果 计算逆米尔斯比的值。
– 第二步,利用选择性样本观测值和计算得到的逆米尔 斯比的值,将(ρσ1)作为一个待估计参数,估计经理报 酬模型,得到β1的估计。
– 注意,在抽取样本时间必须保证所有选择性样本包含 于全部样本之中。
• 如果采用OLS直接估计原模型:
– 实际上忽略了一个非线性项; – 忽略了随机误差项实际上的异方差性。 – 这就造成参数估计量的偏误,而且如果不了解解释变
量的分布,要估计该偏误的严重性也是很困难的。
6、一点说明
• 如果对截断被解释变量数据计量经济学模型采用 最大似然估计,必须首先求得“截断分布”,为 此,必须存在明确的“截断点”。
• 被解释变量样本观测值受到限制。
二、“截断”数据计量经济学模型
1、思路
• 如果一个单方程计量经济学模型,只能从“掐头” 或者“去尾”的连续区间随机抽取被解释变量的 样本观测值,那么很显然,抽取每一个样本观测 值的概率以及抽取一组样本观测值的联合概率, 与被解释变量的样本观测值不受限制的情况是不 同的。
– 一是,所抽取的部分个体的观测值都大于或者小于某 个确定值,即出现“掐头”或者“去尾”的现象,与 其它个体的观测值相比较,存在明显的“截断点”。
– 二是,所抽取的样本观测值来自于具有某些特征的部 分个体,但是样本观测值的大小与其它个体的观测值 相比较,并不存在明显的“截断点”。
• 样本选择受到限制。
19
3886.0
1313.9
3765.9
44
4140.4
2072.9
2390.2
20
2413.9

Hotelling-model以及两个扩展和多党竞选模型解析

Hotelling-model以及两个扩展和多党竞选模型解析

基本思路: 假定两家厂商生产的同种产品具有质量差异, 及消费者对产品质量偏好服从 [d,e]
区间上的均匀分布,研究二次运输成本下两家厂商的选址和价格竞争的二阶段完全 信息动态博弈问题。 假设: (1)长度为1的“ 线性城市”; (2)交通费用为距离的二次函数,单位交通费用为t (3)消费者对产品质量的偏好 在 [d,e ]区间均匀分布,
w
x1 x2 m或x1 x2 m x1 = x2 = m
m x1 x2或m x1 x2
• 当x2 < m,党派1的最优策略x1为大于x2,小于2m-x2之间的任意数。 • 当x2 = m时,那么党派1的最优策略x1=x2。 • 当x2 > m,党派1的最优策略x1为大于2m-x2 ,小于x2之间的任意数。
1、来源:1838“伯特兰德悖论”———解决:引入产品差异性;特殊空间上的差异。 1929哈罗德·霍特林——经典Hotelling模型。
2、基本假设: (1)产品同质。 (2)决策变量:价格。 (3)成本函数相同,且AC=MC=C0; (4)长度为1的线性市场,两个厂商,消费者均匀分布,每个消费者购买一件商品。 (5)消费者购买商品的交通成本与离商店的距离成比例,单位距离的交通成本为t。
Hotelling模型中企业选址问题——转化成跨国企业选择经营何种品牌 产品的问题。
一、单一 A品牌经营下跨国公司的均衡利润 和基本模型 中 A、 B两企业 进行竞争的结果相同:
二、单一 D品牌经营下跨国公司的均衡利润 1、 先考虑给定 的情况下 ,B企业的两种选择 : 第一种, 在 [ x ,1 ] 区间 内达到利润最大化:
启示 1 跨国公司并购我国企业后,在大多数情况下并不会选择弃
用我国民族品牌,现有民族品牌被弃用的例子,可能是因为

2024年教师资格考试高中学科知识与教学能力英语试题与参考答案

2024年教师资格考试高中学科知识与教学能力英语试题与参考答案

2024年教师资格考试高中英语学科知识与教学能力模拟试题与参考答案一、单项选择题(本大题有30小题,每小题2分,共60分)1、The teacher encourages students to to foster their interest in English literature.A) read extensivelyB) study hardC) focus solely on grammarD) memorize vocabularyAnswer: A) read extensivelyExplanation: Reading extensively is a strategy that can help students to gain a better understanding of English literature. Insisting that students solely focus on grammar or memorize vocabulary would be restrictive and less effective in fostering an interest in the subject.2、In a classroom discussion, the teacher mistakenly called the author ofa famous novel “Jane”. The class pointed out the error, and the teacher should:A)apologize and correct the mistakeB)ignore the student’s interventionC)defend the mistake by dismissing it as a trivial errorD)ASCEND services and report the student to an administratorAnswer: A) apologize and correct the mistakeExplanation: Maintaining a respectful and welcoming classroom environment is crucial. When the teacher makes a mistake, it is important to correct it and apologize to the class. This shows integrity, reinforces the importance of accuracy in academic settings, and strengthens the teacher-student relationship.3、Which of the following sentences is grammatically correct?A. If I am you, I would study harder.B. If I were you, I would study harder.C. If I was you, I would study harder.D. If I will be you, I would study harder.Answer: BExplanation: The correct choice is B. This sentence uses the second conditional form, which is used to talk about hypothetical situations in the present or future. In the second conditional, we use ‘were’ with ‘I’ and ‘he/she/it’ to show that the situation is not real or is unlikely. Options A, C, and D are incorrect because they do not follow the proper structure for the second conditional.4、Choose the sentence where the underlined word is used correctly:A. She was so disinterested in the topic that she fell asleep during the lecture.B. He showed a disinterested concern for the welfare of others, always willing to help.C. The judge listened to both sides of the case with a disinterested air, ensuring fairness.D. Despite being a disinterested party, he still had a lot to say about the matter.Answer: CExpl anation: The correct choice is C. The word ‘disinterested’ means impartial or unbiased, especially in the context of a judge who must remain neutral. In option A, the word should be ‘uninterested’ as it refers to a lack of interest. Option B is incorrect because showing concern for the welfare of others suggests personal interest, which contradicts the meaning of‘disinterested.’ Option D is also incorrect because someone who is disinterested would not have much to say about a matter if they are truly impartial.5、What is the correct tense to use when describing a past event that hasa present result?A. Present perfectB. Simple pastC. Present perfect continuousD. Past perfectAnswer: A. Present perfectExplanation: The present perfect tense is used to describe an action that started in the past and continues to the present, or an action that has a presentresult. For example, “I have finished my homework.”6、Which of the following sentence structures is used to express a condition that is true in the present?A. If + past tense, would + verbB. If + past perfect, would have + past participleC. If + present tense, would + verbD. If + past tense, would + past participleAnswer: C. If + present tense, would + verbExplanation: The correct structure for expressing a condition that is true in the present is “if + present tense, would + verb.” For example, “If it rains, we would stay indoors.” This structure is used to talk about hypothetical or conditional situations that are possible or likely to happen.7、In the teaching of English to senior high school students, which of the following methods is most suitable for fostering critical thinking and analytical skills?A)Memorization of vocabulary listsB)Frequent vocabulary quizzesC)Debate and discussion on complex topicsD)Recitation of literary passagesAnswer: CExplanation: C) Debate and discussion on complex topics is the most suitable method for fostering critical thinking and analytical skills because it encourages students to analyze, evaluate, and construct arguments on varioustopics. This method requires students to think deeply and consider multiple perspectives, which is crucial for developing critical thinking and analytical skills.8、Which of the following is an effective strategy for teaching advanced grammar to senior high school English students?A)Presenting rules through long lectures followed by extensive drillingB)Implementing grammar drills through fill-in-the-blank exercises onlyC)Giving a list of grammar rules to be memorizedD)Using real-life examples and contexts to explain grammar rulesAnswer: DExplanation: D) Using real-life examples and contexts to explain grammar rules is an effective strategy for teaching advanced grammar to senior high school English students. It helps students connect theoretical knowledge with practical situations, making it easier for them to understand and apply the rules in various contexts.9.The sentence “She is not only an excellent teacher but also a caring friend to her students” uses the tense of:A. present simpleB. past simpleC. present perfectD. past perfectAnswer: AExplanation: The sentence “She is not only an excellent teacher but also acaring friend to her students” uses the present simple tense to describe qualities that are true about the teacher. The phrase “not only…but also” is used to connect two a djectives, and both adjectives follow the verb “is,” indicating the present simple tense is the correct choice.10.In the following dialogue, who is asking for help?A. JohnB. MaryC. Mr. SmithD. The teacherDialogue:John: Excuse me, Mr. Smith, I don’t understand the meaning of this part of the text.Mr. Smith: Sure, John. Let me explain it to you.Answer: AExplanation: The correct answer is A, John, because in the dialogue, he is the one who doesn’t understand and is asking Mr.Smith for help. The other options, like Mary, Mr. Smith, and the teacher, are not making the request for help; they are either responding or participating in the conversation.11、Which of the following is NOT a characteristic of communicative language teaching (CLT)?A) Focus on fluency over accuracy.B) Emphasis on learner-centered instruction.C) Use of authentic materials in the classroom.D) Strict adherence to grammatical rules.Answer: D) Strict adherence to grammatical rules.Explanation: Communicative Language Teaching (CLT) emphasizes communication and interaction as both the means and the ultimate goal of learning a language. It focuses on fluency and the ability to communicate effectively, often prioritizing these aspects over strict grammatical correctness. CLT encourages learner-centered activities, the use of real-life materials, and a variety of interactive techniques, which makes option D not a characteristic of CLT.12、In the context of second language acquisition, the term ‘interlanguage’ refers to:A) The native language of the learner.B) The language used by the teacher in the classroom.C) A transitional system that reflects the learner’s current state of L2 knowledge.D) The standard form of the target language.Answer: C) A transitional system that reflects th e learner’s current state of L2 knowledge.Explanation: Interlanguage is a term used in second language acquisition theory to describe the dynamic linguistic system that learners construct as they learn a new language. It is an evolving system that is neit her the learner’sfirst language nor the target language but a unique, intermediate stage of language development. This concept helps explain why learners may make systematic errors and how they progress towards proficiency in the second language.13.The following sentence is an example of which type of sentence structure?A) SimpleB) CompoundC) ComplexD) Compound-complexAnswer: C) ComplexExplanation: A complex sentence contains at least one main clause and one or more dependent clauses. The example se ntence could be “Although it was raining, the students still played football.”14.Which of the following words is the correct past tense form of the verb “go”?A)GrewB)Goin’C)GoneD)GoteAnswer: C) GoneExplanation: The correct past tense form of the verb “go” is “went,” and its past participle is “gone.” Therefore, the correct answer is “Gone.”15、The following sentence is a conditional clause: “If it rains tomorrow,we will cancel the picnic.” In which of the following scenarios would this conditional clause be in the past perfect tense?A) We will cancel the picnic if it had rained yesterday. (X)B) We will cancel the picnic if it rains tomorrow. (X)C) We will cancel the picnic if it had rained this morning. (X)D) We will have canceled the picnic if it had rained yesterday. (✓) Answer: DExplanation: The past perfect tense is used to describe an action that occurred before another past action. In this scenario, the conditional clause is in the past to imply that the picnic cancellation will have already taken place if the rain occurred prior to the picnic. Option D correctly uses the past perfect tense (“would have canceled”).16、Which of the following sentences is an example of a complex-embedded sentence?A) She decided to go for a walk, the weather seemed nice.B) She decided to go for a walk, because the weather seemed nice. (X)C) She decided to go for a walk and the weather seemed nice. (X)D) Even though the weather seemed nice, she decided to go for a walk. (✓) Answer: DExplanation: A complex-embedded sentence contains a main clause and at least one embedded clause, often introduced by subordinating conjunctions like “even though” or “although.” In option D, the clause “Even though the w eatherseemed nice” is the embedded clause, making it a complex-embedded sentence.17、In the following sentence, which phrase structure angle is used to explain the relationship between the words “students” and “expected”?A. Subject-Verb-Object (SVO)B. Prepositional PhraseC. Subject-Object-Verb (SOV)D. Adverbial ClauseAnswer: BExplanation: The phrase “of the students” is a prepositional phrase. It modifies the verb “expected” by indicating whose attitude or expectation is in focus in the sentence.18、Which of the following sentences demonstrates parallel structure in terms of语法功能?A. The teacher encouraged participation and gave constructive feedback.B. The students studied, while门外weren’t allowed in.C. While the students are studying, the teacher is grading papers.D. When the bell rings, the students rush out of the classroom, and the teacher stops the class.Answer: AExplanation: Parallel structure involves using grammatically similar elements to create a balanced and rhythmical effect. Choice A utilizes parallel structure as both “encouraged participation” and “gave constructive feedback” arephrases that complete the action of “encouraged” with similar structure. Choices B and D use dependent and independent clauses respectively, and choiceC uses a dependent clause with independent clause structure.19.What is the most appropriate method to use when teaching a new vocabulary item to high school students?A)Direct translationB)Contextual cluesC)rote memorizationD)Unresponsive questioningAnswer: B) Contextual cluesExplanation: Using contextual clues helps students understand the meaning of new vocabulary within the context of the text or lesson. It encourages active learning and promotes deeper understanding of the language.20.In a high school English class, which of the following activities is best suited for assessing students’ comprehension of a complex literary text?A)Multiple-choice questionsB)Vocabulary matching exercisesC)Oral presentationsD)Short answer questionsAnswer: C) Oral presentationsExplanation: Oral presentations require students to synthesize and express their understanding of a complex literary text. This activity encourages critical thinking, analysis, and communication skills, making it an effectiveway to assess comprehension.21、Which of the following best describes the concept of “modal verbs” in English?A、Nouns that indicate the type of person or thing.B、Verbs that express the level of formality of a sentence.C、Verbs that express probability, ability, permission, and advice.D、Adjectives that modify the quality or state of a noun.Answer: C解析: Modal verbs in English are a group of verbs that express modality, including possible meanings such as ability, permission, necessity, advice, and probability. Therefore, the correct answer is C.22、In the context of English listening skills, which of the following strategies is most effective for identifying key information and details in a spoken text?A、Listening for t he speaker’s accent and dialect.B、Focusing on the overall structure of the speech.C、Noting the intonation and stress patterns in the speech.D、Paying attention to specific information and facts provided.Answer: D解析: Effective listening skills include paying attention to the specific information and facts provided in a spoken text to identify key details and information. Therefore, the correct answer is D.23.Which of the following phrases is used to describe a past event that has already finished in the past?A)“I have gone to the movies last night.”B)“I had gone to the movies last night.”C)“I went to the movies last night and I was tired.”D)“I’m going to the movies last night.”Answer: B) “I had gone to the movies last night.”Explanation: The correct answer is “had gone” because it uses the past perfect tense, which is used to describe a past event that happened before another past event. Options A, C, and D use different tense structures, which are not appropriate for this specific context.24.In the senten ce “She was reading a book when the bell rang,” which clause indicates that the action started first?A)“She was reading a book”B)“She read a book”C)“The bell rang”D)“She went to school”Answer: A) “She was reading a book”Explanation: The clause “She was reading a book” indicates that the action of reading started first, which is followed by the action of the bell ringing, represented by the clause “The bell rang.” Option B uses the past simple tense, which indicates that the actions occurred at different times but not necessarily in sequence. Option C focuses on the bell ringing event. Option D introducesa completely different unrelated event.25.The following sentence is an example of which sentence structure?A. SimpleB. CompoundC. ComplexD. Compound-complexAnswer: C. ComplexExplanation: A complex sentence contains an independent clause and at least one dependent clause. The example sentence is “Although it was raining, the students still played outside.” Here, “Although it was raining” i s a dependent clause, and “the students still played outside” is an independent clause.26.Which of the following is NOT a characteristic of a good vocabulary teaching strategy?A. Encourages students to use new words in different contextsB. Provides students with multiple examples of word usageC. Requires students to memorize a large number of words without contextD. Involves students in activities that promote word retentionAnswer: C. Requires students to memorize a large number of words without contextExplanation: A good vocabulary teaching strategy should avoid rote memorization and instead focus on helping students understand and use words indifferent contexts. Therefore, requiring students to memorize words without context is not an effective strategy. The other options (A, B, and D) are all characteristics of effective vocabulary teaching strategies.27、Which of the following texts is NOT an excellent example for teaching narrative writing skills in high school English?A) “To Build a Fire” by Ja ck London — This text provides a vivid narrative that can be dissected for how to structure a compelling narrative.B) “The Lottery” by Shirley Jackson — This story has a timeless theme and can be used to teach how to build tension and surprise in a narrative.C) “The Road Not Taken” by Robert Frost — This poem is a classic and can be used to expound on narrative poetry rather than a narrative essay.D) “Raymond’s Run” by Toni Cade Bambara —This story can be used to analyze character development and plot structure.Answer: C) “The Road Not Taken” by Robert Frost —This poem is primarily a narrative of a poet contemplating his past decisions, and it is not an example of narrative writing.28、Which of the following activities is most effective for developing students’ comprehension skills when teaching a complex text in high school English?A) Group discussions about the main ideas of the text —This activity encourages students to share their ideas and defend their interpretations.B) Summarizing the text in their own words — This activity helps students toretell the text and understand the main points.C) Writing a summary and responding to a few higher-order thinking questions based on the text — This combines summarization with analysis.D) Reciting the text from memory — This activity helps with memorization but not necessarily with comprehension.Answer: C) Writing a summary and responding to a few higher-order thinking questions based on the text — This combines summarization with analysis, prompting students to think critically about the text.29.Choose the word that best completes the sentence.The student’s performance in the_________was exceptional, which impressed the teacher greatly.A. actB. playC. danceD. performanceAnswer: BExplanation: The correct answer is “play” because it is the correct noun that fits the context of student performance. The other options (act, dance, performance) do not fit as well in the context of a student’s performance in a class setting, as “play” typically refers sp ecifically to a theatrical performance performed by students.30.Which of the following is an example of scaffolding in instruction?A. Teaching a complete lesson at onceB. Providing students with detailed notes and instructionsC. Breaking a complex concept into smaller parts and building upon themD. Asking questions that require students to provide only one-word answersAnswer: CExplanation: The correct answer is “C. Breaking a complex concept into smaller parts and building upon them.” Scaffolding is a teaching technique that involves providing support to students as they move toward a goal. Breaking a complex concept into smaller parts helps students manage the complexity and gradually move towards a full understanding of the material, which is the essence of scaffolding. The other options do not represent scaffolding; instead, they describe different teaching strategies or methods.二、简答题(20分)Question:Explain the importance of vocabulary teaching in high school English language learning and discuss two effective strategies for teaching vocabulary to high school students.Answer:Vocabulary teaching plays a crucial role in high school English language learning for several reasons:1.Foundation for Language Skills: Vocabulary is the building block of language. A strongvocabulary enables students to understand and express themselves more effectively. It is essential for reading comprehension, writing, and speaking.2.Enhances Reading Comprehension: A diverse vocabulary allows students to understand complex texts, grasp the nuances of language, and appreciate literature.3.Facilitates Communication: A rich vocabulary helps students articulate their thoughts and emotions more precisely, both in written and spoken forms.4.Boosts Confidence: As students expand their vocabulary, they become more confident in their language abilities, which can positively impact their self-esteem and motivation.Two effective strategies for teaching vocabulary to high school students are:1.Contextual Teaching: Presenting new words in context helps students understand their meanings and usage. This can be done by incorporating new vocabulary into readings, discussions, and writing activities. For example, when teaching the word “euphemism,” provide examples from various texts and encourage students to identify other euphemisms in their own lives.2.Interactive Vocabulary Games: Engaging students in interactive games can make vocabulary learning enjoyable and memorable. Games like “Word Search,” “Scattergories,” and“Word Association” can help students practice and reinforce new vocabulary in a fun and engaging way.Explanation:The importance of vocabulary teaching in high school English languagelearning is highlighted by the reasons mentioned in the answer. The first reason emphasizes the foundation that vocabulary provides for language skills. The second reason explains how a diverse vocabulary aids in reading comprehension. The third reason highlights the impact of vocabulary on communication, and the fourth reas on emphasizes the positive effects of a rich vocabulary on students’ confidence and motivation.The answer also provides two effective strategies for teaching vocabulary: contextual teaching and interactive vocabulary games. Contextual teaching ensures that students understand the meanings and usage of new words, while interactive vocabulary games make the learning process enjoyable and memorable.三、教学情境分析题(30分)Teaching Context Analysis QuestionPassage:This passage is from the novel “The Great Gatsby” by F. Scott Fitzgerald. The protagonist, Jay Gatsby, has hosted an extravagant party at his mansion, hoping to impress an old flame, Daisy Buchanan. Tom Buchanan, Daisy’s husband, arrives at the party. Tom, who is aware of Gatsby’s identity, cautions Gatsby about Daisy’s true qualities.Context:Teacher Ms. Chen will be teaching a class on the theme of “Isolation and Longing” from the novel “The Great Gatsby.” She plans to use this passageto illustrate the theme and to enhance students’ reading comprehension skills.Task:Ms. Chen decides to ask students to discuss the reasons behind Gatsby’s isolation and the factors that contribute to his longing for Daisy. She wants to encourage open, critical thinking.Teaching Objectives:•To understand the characters’ motivations.•To analyze the literary devices used to convey the characters’ emotions and themes.•To enhance critical thinking and discussion skills.Situation Analysis:1.Identify the Factors Behind Gatsby’s Isolation:•Discuss the potential reasons for Gatsby’s feelings of isolation.•Identify the social dynamics and class differences that contribute to Gatsby’s isolation.•How does the setting of the Great Gatsby during the 1920s reflect the isolation of the character?2.Explain Gatsby’s Longing for Daisy:•Why is Gatsby so captivated by Daisy?•What does Gatsby’s relationship with Daisy represent in the context of the novel and the time period?Question:How can Ms.Chen effectively use the given passage to teach the theme of “Isolation andLonging”? What strategies should she employ to enhance students’ understanding and critical thinking skills?Answer and Explanation:Answer:Ms. Chen can effectively use the following strategies and questions to teach the theme of “Isolation and Longing” and enhance students’ understanding and critical thinking skills:1.Introduction:•Begin with a brief background on the setting and context of the 1920s in the novel “The Great Gatsby”. This will help students understand the social and historicalcontext and how it influences Gatsby’s isolation.2.Discussion Questions:•Encourage students to discuss potential reasons for Gatsby’s isolation.Potential reasons could include the social barriers between the wealthy elite and the middle class, Gat sby’s fabricated background, and his general loneliness.•Ask students to explore the literary devices in the passage (e.g., tone, imagery, symbolism) that contribute to the portrayal of Gatsby’sisolation. For example, the opulent yet superficial parties mightsymbolize the emptiness of Gatsby’s life.•Discuss Gatsby’s longing for Daisy. Why does Gatsby hold on to the past and his illusions about Daisy? Explore how Gatsby’s longing reflects romantic and societal ideals of the 1920s.3.Pair and Group Work:•Pair students to discuss their initial thoughts, then form small groups to share and debate their points. This fosters a collaborative andinteractive learning environment.•Use guided questions to prompt deeper analysis, such as: “What does Gatsby’s fixation on Daisy reveal about his personality and motivations?”or “How does the setting and social context impact Gatsby’s characterand desires?”4.Critical Thinking and Analysis:•Prompt students to think critically about the themes of isolation and longing. As k them to consider how Gatsby’s situation relates to othercharacters or to their own lives.•Encourage students to draw connections between the novel and current issues of social isolation and longing in modern times.5.Summarize and Conclude:•Conclude the lesson by summarizing the key points and encouraging students to share their insights. Ask them to reflect on how the characters’ situations influencethe overall narrative.Explanation:The teaching strategies mentioned are designed to engage students in critical thinking and deep analysis of the text. By discussing potential reasons for Gatsby’s isolation and the literary devices used to convey his emotions, students can gain a deeper understanding of the text and its themes. Pair andgroup work encourages collaborative learning, allowing students to share and build on each other’s ideas. Critical thinking questions prompt students to consider the broader implications of the text, fostering a more nuanced and meaningful interpretation of the novel.四、教学设计题(40分)1.请根据以下教学要求,设计一节45分钟的英语课堂活动。

BinomialLinkFunctions:二项链接功能

BinomialLinkFunctions:二项链接功能
beetles killed were noted. The data are in the
following table:
Example (continued)
> beetle<-read.table("BeetleData.txt",header=TRUE)
> head(beetle)
Dose Num.Beetles Num.Killed
(Intercept) -34.935
2.648 -13.19 <2e-16 ***
Dose
19.728
1.487 13.27 <2e-16 ***
--Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
yi

i 1
n
e
xi T ˆ
• Logit:
pˆ i
• Probit:
pˆ i ( xiT ˆ )
• C Log Log:
pˆ i 1 exp{ exp[ xiT ˆ ]}
1 e
xi T ˆ
Differences in Link Functions
probLowerlogit <- vector(length=1000)
family = binomial) > summary(logitmodel)
> probitmodel<-glm(cbind(Num.Killed,Num.Beetles-Num.Killed) ~ Dose, data = beetle,

[通俗易懂]Multilevel Model

[通俗易懂]Multilevel Model

Consider One State: MO
1850 houses in 115 counties
31% have less than 5 observations (houses)
80% have less than 15 observations
z <- lm(radon ~ county + floor + typebldg , data = MO)
pairs of litters (litter effect) each mouse observed 5 times (time effe3; eG[i] + et[i] + ei
X1and X2 are fixed effects eG[i] is a random effect -- one for each group et[i] is the same for all mice measurements at t
Yi = b0 + b1X1 + b2X2 + eG[i] + ei
X1and X2 are fixed effects eG[i] is a random effect -- one for each group
Other Multilevel Models
There can be more than one random effect
Random effects: Groups Name Variance Std.Dev. county (Intercept) 0.15457 0.39316 Residual 0.78677 0.88700
Random County and Wave Effects

IBM SPSS Modeler 18.2.2 用户指南说明书

IBM SPSS Modeler 18.2.2 用户指南说明书
从命令行启动.......................................................................................................................................... 7 连接到 IBM SPSS Modeler Server ......................................................................................................... 8 连接到 Analytic Server ........................................................................................................................... 9 更改 temp 目录..................................................................................................................................... 10 启动多个 IBM SPSS Modeler 会话........................................................................................................10 IBM SPSS Modeler 界面概览..................................................................................................................... 10 IBM SPSS Modeler 流工作区................................................................................................................ 11 节点选用板............................................................................................................................................ 11 IBM SPSS Modeler 管理器....................................................................................................................12 IBM SPSS Modeler 工程....................................................................................................................... 13 IBM SPSS Modeler 工具栏....................................................................................................................14 自定义工具栏........................................................................................................................................ 15 定制 IBM SPSS Modeler 窗口............................................................................................................... 15 更改流的图标尺寸................................................................................................................................. 16 在 IBM SPSS Modeler 中使用鼠标 ....................................................................................................... 17 使用快捷键............................................................................................................................................ 17 打印............................................................................................................................................................ 18 实现 IBM SPSS Modeler 的自动化............................................................................................................. 18

基于GARCH类模型对美国股市波动性的对比分析

基于GARCH类模型对美国股市波动性的对比分析

DOI:10.19995/10-1617/F7.2024.05.111基于GARCH类模型对美国股市波动性的对比分析李姜悦 沈慈慈 王伟杰(淮北理工学院 安徽淮北 235000)摘 要:为研究美国股市股指的波动性特征,本文选取美国股市的Nasdaq指数和Russel2000指数的日收盘价数据,借助统计软件,利用GARCH类模型进行实证分析。

实证结果表明:Russel2000指数的风险较Nasdaq指数更稳定,更适合投资,且相较GARCH(1,1)模型,满足学生t分布的APARCH(1,1)模型拟合的条件异方差可以更好地反映种股指日对数收益率的波动率情况,因此可选用此模型对两种指数波动率的未来值进行预测,为投资者提供未来投资参考。

关键词:GARCH模型;APARCH模型;非正态性;收益率;波动率;全球金融市场;国际金融风险本文索引:李姜悦,沈慈慈,王伟杰.基于GARCH类模型对美国股市波动性的对比分析[J].商展经济,2024(05):111-116.中图分类号:F831;F830.9 文献标识码:A随着全球金融市场的日益相互联系和国际金融风险的增加,金融市场的波动性成为重要的研究领域。

美国作为全球最大的经济体,其金融市场的波动性对全球经济有着巨大的影响。

为了更好地刻画时间序列的波动率,Bollerslev(1986)[1]对自回归条件异方差(Autoregressive Conditional Heterosc edasticity,ARCH)模型进行了拓展,建立了广义自回归条件异方差模型(Generalized Autoregressive Conditional Heteroscedasticity Model,GARCH模型)。

因为波动性是市场风险的度量,可以反映市场的不确定性,反映投资者对市场的情绪和态度,因此国外学者借助GARCH类模型对股市波动性的研究层出不穷;Edbert和Sigit(2018)[2]基于GARCH类模型对东盟五国的油价波动和股票收益进行了实证分析;Takwi(2023)[3]通过GARCH模型对喀麦隆股市的波动率进行实证分析,表明相比ARCH模型,GARCH模型能够很好地衡量喀麦隆股市的波动率;Maria等(2023)[4]利用英国上市公司高管内幕交易的月度数据分析了内幕交易总量( AIT )与股市波动之间的关系,发现较高的AIT会导致股市波动率的短期上升。

理论-精细加工可能性模型

理论-精细加工可能性模型

精细加工可能性模型(elaboration likelihood model),petty和CacioppoELM源自社会心理学领域的研究, 是由美国心理学家Petty 和Cacioppo( 1986)提出。

该模型着眼于探讨说服过程中个体态度的改变过程, 后来作为一种说服模式被广泛应用于态度、社会传播和消费者行为的研究。

精细加工可能性模型所表达的基本思想是:信息接收者在处理说服性诉求时,既不是一贯的深思熟虑,也不是完全的心不在焉,而是依据评估态度客体的主要优点的动机和能力存在一个精细加工可能性区间即人们越有动机且越能够评估态度客体的主要优点,越可能努力的审查所有可获得的客体相关信息这样当精细加工可能性高时,在面对说服性诉求时,人们可能意向于:(l)留意这些诉求,(2)试着从记忆中存取相关联想、形象和经验;(3)根据记忆中可用的联想来审查和精细加工外部提供的讯息论据;(4)基于他们对诉求中所提取的数据与从记忆中所存取的数据的对比分析,得出关于态度客体的论据的优点的推断;(5)据此形成对态度客体的总体评价或者态度判断。

这一过程被称为中心路径(central route)的说服。

当精细加工可能性很低时,对诉求的接受或拒绝并非基于对一项考虑中的议题相关的信息的深思熟虑,而是基于与态度客体不具有内在联系的正向或者负向的暗示或者是基于说服情境中的各种暗示得出一个简单的推论。

这一说服过程被称为外围路径(peripheral route)的说服ELM认为说服有两种基本路径:中枢路径和边缘路径, 信息接收者因自身的动机、能力和参与( involvement)程度不同而对信息有不同程度的加工处理, 并形成不同持久性的态度。

当信息接收者高度参与主题或者有能力、有动机对主题信息的细节进行仔细审查和深入思考时,就会设法对所有与主题相关的信息进行加工, 这样形成的态度较为持久并能够预测将来的行为。

这种说服实现(或态度改变)的方式被称为中枢路径。

中英文双语外文文献翻译:一种基于...

中英文双语外文文献翻译:一种基于...

中英⽂双语外⽂⽂献翻译:⼀种基于...此⽂档是毕业设计外⽂翻译成品(含英⽂原⽂+中⽂翻译),⽆需调整复杂的格式!下载之后直接可⽤,⽅便快捷!本⽂价格不贵,也就⼏⼗块钱!⼀辈⼦也就⼀次的事!英⽂3890单词,20217字符(字符就是印刷符),中⽂6398汉字。

A Novel Divide-and-Conquer Model for CPI Prediction UsingARIMA, Gray Model and BPNNAbstract:This paper proposes a novel divide-and-conquer model for CPI prediction with the existing compilation method of the Consumer Price Index (CPI) in China. Historical national CPI time series is preliminary divided into eight sub-indexes including food, articles for smoking and drinking, clothing, household facilities, articles and maintenance services, health care and personal articles, transportation and communication, recreation, education and culture articles and services, and residence. Three models including back propagation neural network (BPNN) model, grey forecasting model (GM (1, 1)) and autoregressive integrated moving average (ARIMA) model are established to predict each sub-index, respectively. Then the best predicting result among the three models’for each sub-index is identified. To further improve the performance, special modification in predicting method is done to sub-CPIs whose forecasting results are not satisfying enough. After improvement and error adjustment, we get the advanced predicting results of the sub-CPIs. Eventually, the best predicting results of each sub-index are integrated to form the forecasting results of the national CPI. Empirical analysis demonstrates that the accuracy and stability of the introduced method in this paper is better than many commonly adopted forecasting methods, which indicates the proposed method is an effective and alternative one for national CPI prediction in China.1.IntroductionThe Consumer Price Index (CPI) is a widely used measurement of cost of living. It not only affects the government monetary, fiscal, consumption, prices, wages, social security, but also closely relates to the residents’daily life. As an indicator of inflation in China economy, the change of CPI undergoes intense scrutiny. For instance, The People's Bank of China raised the deposit reserve ratio in January, 2008 before the CPI of 2007 was announced, for it is estimated that the CPI in 2008 will increase significantly if no action is taken. Therefore, precisely forecasting the change of CPI is significant to many aspects of economics, some examples include fiscal policy, financial markets and productivity. Also, building a stable and accurate model to forecast the CPI will have great significance for the public, policymakers and research scholars.Previous studies have already proposed many methods and models to predict economic time series or indexes such as CPI. Some previous studies make use of factors that influence the value of the index and forecast it by investigating the relationship between the data of those factors and the index. These forecasts are realized by models such as Vector autoregressive (VAR)model1 and genetic algorithms-support vector machine (GA-SVM) 2.However, these factor-based methods, although effective to some extent, simply rely on the correlation between the value of the index and limited number of exogenous variables (factors) and basically ignore the inherent rules of the variation of the time series. As a time series itself contains significant amount of information3, often more than a limited number of factors can do, time series-based models are often more effective in the field of prediction than factor-based models.Various time series models have been proposed to find the inherent rules of the variation in the series. Many researchers have applied different time series models to forecasting the CPI and other time series data. For example, the ARIMA model once served as a practical method in predicting the CPI4. It was also applied to predict submicron particle concentrations frommeteorological factors at a busy roadside in Hangzhou, China5. What’s more, the ARIMA model was adopted to analyse the trend of pre-monsoon rainfall data forwestern India6. Besides the ARIMA model, other models such as the neural network, gray model are also widely used in the field of prediction. Hwang used the neural-network to forecast time series corresponding to ARMA (p, q) structures and found that the BPNNs generally perform well and consistently when a particular noise level is considered during the network training7. Aiken also used a neural network to predict the level of CPI and reached a high degree of accuracy8. Apart from the neural network models, a seasonal discrete grey forecasting model for fashion retailing was proposed and was found practical for fashion retail sales forecasting with short historical data and better than other state-of-art forecastingtechniques9. Similarly, a discrete Grey Correlation Model was also used in CPI prediction10. Also, Ma et al. used gray model optimized by particle swarm optimization algorithm to forecast iron ore import and consumption of China11. Furthermore, to deal with the nonlinear condition, a modified Radial Basis Function (RBF) was proposed by researchers.In this paper, we propose a new method called “divide-and-conquer model”for the prediction of the CPI.We divide the total CPI into eight categories according to the CPI construction and then forecast the eight sub- CPIs using the GM (1, 1) model, the ARIMA model and the BPNN. To further improve the performance, we again make prediction of the sub-CPIs whoseforecasting results are not satisfying enough by adopting new forecasting methods. After improvement and error adjustment, we get the advanced predicting results of the sub-CPIs. Finally we get the total CPI prediction by integrating the best forecasting results of each sub-CPI.The rest of this paper is organized as follows. In section 2, we give a brief introduction of the three models mentioned above. And then the proposed model will be demonstrated in the section 3. In section 4 we provide the forecasting results of our model and in section 5 we make special improvement by adjusting the forecasting methods of sub-CPIs whose predicting results are not satisfying enough. And in section 6 we give elaborate discussion and evaluation of the proposed model. Finally, the conclusion is summarized in section 7.2.Introduction to GM(1,1), ARIMA & BPNNIntroduction to GM(1,1)The grey system theory is first presented by Deng in 1980s. In the grey forecasting model, the time series can be predicted accurately even with a small sample by directly estimating the interrelation of data. The GM(1,1) model is one type of the grey forecasting which is widely adopted. It is a differential equation model of which the order is 1 and the number of variable is 1, too. The differential equation is:Introduction to ARIMAAutoregressive Integrated Moving Average (ARIMA) model was first put forward by Box and Jenkins in 1970. The model has been very successful by taking full advantage of time series data in the past and present. ARIMA model is usually described as ARIMA (p, d, q), p refers to the order of the autoregressive variable, while d and q refer to integrated, and moving average parts of the model respectively. When one of the three parameters is zero, the model is changed to model “AR”, “MR”or “ARMR”. When none of the three parameters is zero, the model is given by:where L is the lag number,?t is the error term.Introduction to BPNNArtificial Neural Network (ANN) is a mathematical and computational model which imitates the operation of neural networks of human brain. ANN consists of several layers of neurons. Neurons of contiguous layers are connected with each other. The values of connections between neurons are called “weight”. Back Propagation Neural Network (BPNN) is one of the most widely employed neural network among various types of ANN. BPNN was put forward by Rumelhart and McClelland in 1985. It is a common supervised learning network well suited for prediction. BPNN consists of three parts including one input layer, several hidden layers and one output layer, as is demonstrated in Fig 1. The learning process of BPNN is modifying the weights of connections between neurons based on the deviation between the actual output and the target output until the overall error is in the acceptable range.Fig. 1. Back-propagation Neural Network3.The Proposed MethodThe framework of the dividing-integration modelThe process of forecasting national CPI using the dividing-integration model is demonstrated in Fig 2.Fig. 2.The framework of the dividing-integration modelAs can be seen from Fig. 2, the process of the proposed method can be divided into the following steps: Step1: Data collection. The monthly CPI data including total CPI and eight sub-CPIs are collected from the official website of China’s State Statistics Bureau (/doc/d62de4b46d175f0e7cd184254b35eefdc9d31514.html /).Step2: Dividing the total CPI into eight sub-CPIs. In this step, the respective weight coefficient of eight sub- CPIs in forming the total CPI is decided by consulting authoritative source .(/doc/d62de4b46d175f0e7cd184254b35eefdc9d31514.html /). The eight sub-CPIs are as follows: 1. Food CPI; 2. Articles for Smoking and Drinking CPI; 3. Clothing CPI; 4. Household Facilities, Articles and Maintenance Services CPI; 5. Health Care and Personal Articles CPI; 6. Transportation and Communication CPI;7. Recreation, Education and Culture Articles and Services CPI; 8. Residence CPI. The weight coefficient of each sub-CPI is shown in Table 8.Table 1. 8 sub-CPIs weight coefficient in the total indexNote: The index number stands for the corresponding type of sub-CPI mentioned before. Other indexes appearing in this paper in such form have the same meaning as this one.So the decomposition formula is presented as follows:where TI is the total index; Ii (i 1,2, ,8) are eight sub-CPIs. To verify the formula, we substitute historical numeric CPI and sub-CPI values obtained in Step1 into the formula and find the formula is accurate.Step3: The construction of the GM (1, 1) model, the ARIMA (p, d, q) model and the BPNN model. The three models are established to predict the eight sub-CPIs respectively.Step4: Forecasting the eight sub-CPIs using the three models mentioned in Step3 and choosing the best forecasting result for each sub-CPI based on the errors of the data obtained from the three models.Step5: Making special improvement by adjusting the forecasting methods of sub-CPIs whose predicting results are not satisfying enough and get advanced predicting results of total CPI. Step6: Integrating the best forecasting results of 8 sub-CPIs to form the prediction of total CPI with the decomposition formula in Step2.In this way, the whole process of the prediction by the dividing-integration model is accomplished.3.2. The construction of the GM(1,1) modelThe process of GM (1, 1) model is represented in the following steps:Step1: The original sequence:Step2: Estimate the parameters a and u using the ordinary least square (OLS). Step3: Solve equation as follows.Step4: Test the model using the variance ratio and small error possibility.The construction of the ARIMA modelFirstly, ADF unit root test is used to test the stationarity of the time series. If the initial time series is not stationary, a differencing transformation of the data is necessary to make it stationary. Then the values of p and q are determined by observing the autocorrelation graph, partial correlation graph and the R-squared value.After the model is built, additional judge should be done to guarantee that the residual error is white noise through hypothesis testing. Finally the model is used to forecast the future trend ofthe variable.The construction of the BPNN modelThe first thing is to decide the basic structure of BP neural network. After experiments, we consider 3 input nodes and 1 output nodes to be the best for the BPNN model. This means we use the CPI data of time , ,toforecast the CPI of time .The hidden layer level and the number of hidden neurons should also be defined. Since the single-hidden- layer BPNN are very good at non-liner mapping, the model is adopted in this paper. Based on the Kolmogorov theorem and testing results, we define 5 to be the best number of hidden neurons. Thus the 3-5-1 BPNN structure is determined.As for transferring function and training algorithm, we select ‘tansig’as the transferring function for middle layer, ‘logsig’for input layer and ‘traingd’as training algorithm. The selection is based on the actual performance of these functions, as there are no existing standards to decide which ones are definitely better than others.Eventually, we decide the training times to be 35000 and the goal or the acceptable error to be 0.01.4.Empirical AnalysisCPI data from Jan. 2012 to Mar. 2013 are used to build the three models and the data from Apr. 2013 to Sept. 2013 are used to test the accuracy and stability of these models. What’s more, the MAPE is adopted to evaluate the performance of models. The MAPE is calculated by the equation:Data sourceAn appropriate empirical analysis based on the above discussion can be performed using suitably disaggregated data. We collect the monthly data of sub-CPIs from the website of National Bureau of Statistics of China(/doc/d62de4b46d175f0e7cd184254b35eefdc9d31514.html /).Particularly, sub-CPI data from Jan. 2012 to Mar. 2013 are used to build the three models and the data from Apr. 2013 to Sept. 2013 are used to test the accuracy and stability of these models.Experimental resultsWe use MATLAB to build the GM (1,1) model and the BPNN model, and Eviews 6.0 to build the ARIMA model. The relative predicting errors of sub-CPIs are shown in Table 2.Table 2.Error of Sub-CPIs of the 3 ModelsFrom the table above, we find that the performance of different models varies a lot, because the characteristic of the sub-CPIs are different. Some sub-CPIs like the Food CPI changes drastically with time while some do not have much fluctuation, like the Clothing CPI. We use different models to predict the sub- CPIs and combine them by equation 7.Where Y refers to the predicted rate of the total CPI, is the weight of the sub-CPI which has already been shown in Table1and is the predicted value of the sub-CPI which has the minimum error among the three models mentioned above. The model chosen will be demonstrated in Table 3:Table 3.The model used to forecastAfter calculating, the error of the total CPI forecasting by the dividing-integration model is 0.0034.5.Model Improvement & Error AdjustmentAs we can see from Table 3, the prediction errors of sub-CPIs are mostly below 0.004 except for two sub- CPIs: Food CPI whose error reaches 0.0059 and Transportation & Communication CPI 0.0047.In order to further improve our forecasting results, we modify the prediction errors of the two aforementioned sub-CPIs by adopting other forecasting methods or models to predict them. The specific methods are as follows.Error adjustment of food CPIIn previous prediction, we predict the Food CPI using the BPNN model directly. However, the BPNN model is not sensitive enough to investigate the variation in the values of the data. For instance, although the Food CPI varies a lot from month to month, the forecasting values of it are nearly all around 103.5, which fails to make meaningful prediction.We ascribe this problem to the feature of the training data. As we can see from the original sub-CPI data on the website of National Bureau of Statistics of China, nearly all values of sub-CPIs are around 100. As for Food CPI, although it does have more absolute variations than others, its changes are still very small relative to the large magnitude of the data (100). Thus it will be more difficult for the BPNN model to detect the rules of variations in training data and the forecastingresults are marred.Therefore, we use the first-order difference series of Food CPI instead of the original series to magnify the relative variation of the series forecasted by the BPNN. The training data and testing data are the same as that in previous prediction. The parameters and functions of BPNN are automatically decided by the software, SPSS.We make 100 tests and find the average forecasting error of Food CPI by this method is 0.0028. The part of the forecasting errors in our tests is shown as follows in Table 4:Table 4.The forecasting errors in BPNN testError adjustment of transportation &communication CPIWe use the Moving Average (MA) model to make new prediction of the Transportation and Communication CPI because the curve of the series is quite smooth with only a few fluctuations. We have the following equation(s):where X1, X2…Xn is the time series of the Transportation and Communication CPI, is the value of moving average at time t, is a free parameter which should be decided through experiment.To get the optimal model, we range the value of from 0 to 1. Finally we find that when the value of a is 0.95, the forecasting error is the smallest, which is 0.0039.The predicting outcomes are shown as follows in Table5:Table 5.The Predicting Outcomes of MA modelAdvanced results after adjustment to the modelsAfter making some adjustment to our previous model, we obtain the advanced results as follows in Table 6: Table 6.The model used to forecast and the Relative ErrorAfter calculating, the error of the total CPI forecasting by the dividing-integration model is 0.2359.6.Further DiscussionTo validate the dividing-integration model proposed in this paper, we compare the results of our model with the forecasting results of models that do not adopt the dividing-integration method. For instance, we use the ARIMA model, the GM (1, 1) model, the SARIMA model, the BRF neural network (BRFNN) model, the Verhulst model and the Vector Autoregression (VAR) model respectively to forecast the total CPI directly without the process of decomposition and integration. The forecasting results are shown as follows in Table7.From Table 7, we come to the conclusion that the introduction of dividing-integration method enhances the accuracy of prediction to a great extent. The results of model comparison indicate that the proposed method is not only novel but also valid and effective.The strengths of the proposed forecasting model are obvious. Every sub-CPI time series have different fluctuation characteristics. Some are relatively volatile and have sharp fluctuations such as the Food CPI while others are relatively gentle and quiet such as the Clothing CPI. As a result, by dividing the total CPI into several sub-CPIs, we are able to make use of the characteristics of each sub-CPI series and choose the best forecasting model among several models for every sub-CPI’s prediction. Moreover, the overall prediction error is provided in the following formula:where TE refers to the overall prediction error of the total CPI, is the weight of the sub-CPI shown in table 1 and is the forecasting error of corresponding sub-CPI.In conclusion, the dividing-integration model aims at minimizing the overall prediction errors by minimizing the forecasting errors of sub-CPIs.7.Conclusions and future workThis paper creatively transforms the forecasting of national CPI into the forecasting of 8 sub-CPIs. In the prediction of 8 sub-CPIs, we adopt three widely used models: the GM (1, 1) model, the ARIMA model and the BPNN model. Thus we can obtain the best forecasting results for each sub-CPI. Furthermore, we make special improvement by adjusting the forecasting methods of sub-CPIs whose predicting results are not satisfying enough and get the advanced predicting results of them. Finally, the advanced predicting results of the 8 sub- CPIs are integrated to formthe forecasting results of the total CPI.Furthermore, the proposed method also has several weaknesses and needs improving. Firstly, The proposed model only uses the information of the CPI time series itself. If the model can make use of other information such as the information provided by factors which make great impact on the fluctuation of sub-CPIs, we have every reason to believe that the accuracy and stability of the model can be enhanced. For instance, the price of pork is a major factor in shaping the Food CPI. If this factor is taken into consideration in the prediction of Food CPI, the forecasting results will probably be improved to a great extent. Second, since these models forecast the future by looking at the past, they are not able to sense the sudden or recent change of the environment. So if the model can take web news or quick public reactions with account, it will react much faster to sudden incidence and affairs. Finally, the performance of sub-CPIs prediction can be higher. In this paper we use GM (1, 1), ARIMA and BPNN to forecast sub-CPIs. Some new method for prediction can be used. For instance, besides BPNN, there are other neural networks like genetic algorithm neural network (GANN) and wavelet neural network (WNN), which might have better performance in prediction of sub-CPIs. Other methods such as the VAR model and the SARIMA model should also be taken into consideration so as to enhance the accuracy of prediction.References1.Wang W, Wang T, and Shi Y. Factor analysis on consumer price index rising in China from 2005 to 2008. Management and service science 2009; p. 1-4.2.Qin F, Ma T, and Wang J. The CPI forecast based on GA-SVM. Information networking and automation 2010; p. 142-147.3.George EPB, Gwilym MJ, and Gregory CR. Time series analysis: forecasting and control. 4th ed. Canada: Wiley; 20084.Weng D. The consumer price index forecast based on ARIMA model. WASE International conferenceon information engineering 2010;p. 307-310.5.Jian L, Zhao Y, Zhu YP, Zhang MB, Bertolatti D. An application of ARIMA model to predict submicron particle concentrations from meteorological factors at a busy roadside in Hangzhou, China. Science of total enviroment2012;426:336-345.6.Priya N, Ashoke B, Sumana S, Kamna S. Trend analysis and ARIMA modelling of pre-monsoon rainfall data forwestern India. Comptesrendus geoscience 2013;345:22-27.7.Hwang HB. Insights into neural-network forecasting of time seriescorresponding to ARMA(p; q) structures. Omega2001;29:273-289./doc/d62de4b46d175f0e7cd184254b35eefdc9d31514.html am A. Using a neural network to forecast inflation. Industrial management & data systems 1999;7:296-301.9.Min X, Wong WK. A seasonal discrete grey forecasting model for fashion retailing. Knowledge based systems 2014;57:119-126.11. Weimin M, Xiaoxi Z, Miaomiao W. Forecasting iron ore import and consumption of China using grey model optimized by particleswarm optimization algorithm. Resources policy 2013;38:613-620.12. Zhen D, and Feng S. A novel DGM (1, 1) model for consumer price index forecasting. Greysystems and intelligent services (GSIS)2009; p. 303-307.13. Yu W, and Xu D. Prediction and analysis of Chinese CPI based on RBF neural network. Information technology and applications2009;3:530-533.14. Zhang GP. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 2003;50:159-175.15. Pai PF, Lin CS. A hybrid ARIMA and support vector machines model in stock price forecasting. Omega 2005;33(6):497-505.16. Tseng FM, Yu HC, Tzeng GH. Combining neural network model with seasonal time series ARIMA model. Technological forecastingand social change 2002;69(1):71-87.17.Cho MY, Hwang JC, Chen CS. Customer short term load forecasting by using ARIMA transfer function model. Energy management and power delivery, proceedings of EMPD'95. 1995 international conference on IEEE, 1995;1:317-322.译⽂:⼀种基于ARIMA、灰⾊模型和BPNN对CPI(消费物价指数)进⾏预测的新型分治模型摘要:在本⽂中,利⽤我国现有的消费者价格指数(CPI)的计算⽅法,提出了⼀种新的CPI预测分治模型。

常用广告英语词汇

常用广告英语词汇

常用广告英语词汇常用广告英语词汇汇总告是为了某种特定的需要,通过一定形式的媒体,公开而广泛地向公众传递信息的宣传手段,广告有广义和狭义之分。

以下是小编·整理的常用广告英语词汇汇总,希望大家喜欢。

1.advertising Agency广告公司2.advertising Agent广告代理人3.advertising Research广告调研4.anatomy of a Sale销售分析5.art Director美术指导6.bill Boards路牌广告7.body Copy广告正文8.broadcast Advertising广播广告9.buying motive购买动机10.classified Advertising分类广告mercial Advertising商业广告1 2.consumer Advertising消费者广告13.creative platform创意大纲14.direct Mail邮寄广告15.display Advertising陈列广告1 6.electric Media电子媒介17.industrial Advertising工业广告1 8.international Advertising国际广告19.media Service广告媒介代理20.noncommercial Advertising非商业广告2 I.nonproduct Advertising非产品广告22.outdoor Advertising户外广告23.point.of-Purchase Advertising销售现场广告24.prirne prospect目标消费群25.public relation公共关系26.radio/TV Production广播电视节目制作27.recruitment Advertising招聘广告28.retail Advertising零售广告29.sale Advertising销售广告30.share of Market市场份额3 1.target Market目标市场32.task Budgeting广告预算33.time Spot广告时间P(the Unity Selling Proposition)营销组合35.Cl(Corporatc Identity)企业识别36.MI(Mind Identity)理念识别37.Bl(Behavior Identityl行为识别38.VI(Visual Identity)视觉识别39.advertising广告40.appropriation广告预算分配4 1.audience广告对象42.catalog商品说明书43.consumers消费者44.copy广告正文45.copywriter广告撰稿人46.1ayout广告布局47.media媒介48.motivation动机49.positioning市场定位50.promotion促销51.rebating折扣52.slogan广告口号53.television advertising电视广告54.consumer advertising消费品广告55.film advertising电影广告56.sky advertising空中广告57.postcard advertising明信片广告58.specialty advertising纪念品广告59.product advertising商品广告60.professional advertising商品服务广告61.industrial advertising工业品广告62.corporate advertising公司广告63.one shot报刊上的一次性广告64.display advertisement造型广告65.advertising budget广告支出预算66.advertising media广告传播媒介67.international advertising agency国际广告公司68.full service advertising agency(提供)全套服务的`广告公司69.advertising agency network广告公司网络70.1ay.out ofan advertisement广告设计模型71.buying space购买广告权72.directory advertising在各种手册中刊登广告73.size ofan advertisement广告规格74.advertisement originator]一告编制人75.repeat an advertisement重复广告,重播(重登)广告76.place all advertisement,put up an advertisement登广告77.advertising material,advertising literature广告材料78.Advertising Association广告协会(英国机构,旨在维持广告的水平,保护广告客户及代理商的利益)79.the Advertising Standards Authority广告标准管理局(英国机构,旨在保护公众利益)拓展:广告专业的英语词汇态度 attitude品牌兴趣 brand interest品牌忠诚 brand loyalty企业市场 business markets影响中心 centers of influence有意劝服路径 central route to persuasion认知 cognition消费行为 consumer behavior消费者决策过程 consumer decision making process 消费者市场 consumer markets文化 culture现有顾客 current customers详尽可能性模型 Elaboration Likelihood Model环境因素 environment选择评估 evaluation of alternatives评估标准 evaluative criteria参考组 evoked set交换 exchange政府市场 government markets习惯 habit需要层次 hierachy of needs工业市场 industrial markets信息性动机 informational motives人际影响 interpersonal influences认知 learning市场 market卖主 marketers营销 marketing大脑档案 mental files动机 motivation需要 needs被动生成动机 negatively originated motives 非人员影响 nonpersonal influences。

精细处理可能性模型

精细处理可能性模型

泛化能力相对较弱。
对数据质量要求高
03
精细处理可能性模型对输入数据的准确性和完整性要求较高,
否则可能导致预测结果失真。
改进建议
优化算法
通过改进算法和计算方法,降低模型的计算成本, 提高运行效率。
引入正则化项
在模型中加入正则化项,以防止过拟合,提高模 型的泛化能力。
数据预处理
加强数据预处理工作,确保输入数据的准确性和 完整性,提高模型预测的准确性。
研究意义
精细处理可能性模型为说服性信息传播提供了理论指导, 有助于更好地理解受众的心理机制和行为反应,为传播 实践提供科学依据。
02
精细处理可能性模型概述
定义与概念
定义
精细处理可能性模型(Elaboration Likelihood Model,ELM)是一种解释态度和行为改变的 理论模型,由Petty和Cacioppo提出。
可解释性强
模型中的参数和结构都有明确的 物理意义,这使得模型结果易于 解释,有助于用户更好地理解数 据和问题本质。
缺点分析
计算成本高
01
精细处理可能性模型通常涉及大量的参数和复杂的计算,这导
致了较高的计算成本,尤其是在处理大规模数据集时。
模型泛化能力有待提高
02
由于过度拟合训练数据,该模型在面对新数据时可能表现不佳,
精细处理可能性模型
目录
• 引言 • 精细处理可能性模型概述 • 精细处理可能性模型的应用 • 精细处理可能性模型的优缺点 • 实证研究与案例分析 • 结论与展望
01
引言
背景介绍
01
精细处理可能性模型(Elaboration Likelihood Model,ELM)是由Richard E. Petty和John T. Schumann于1981年提出的理论模型,用于解释和预测说服性信 息传播过程中的态度改变。

多因子量化模型简介

多因子量化模型简介
多因子量化模型简介
量化 vs 非量化 (有非量化吗?)
修正持久期是衡量价格对收益率变化的敏感度的指标。在市场利率水平发生一
定幅度波动时,修正久期越大的金融资产,价格波动越大。
金融资产的现行价格为所有各期未来现金流的现值的加总。

=
修正久期
,
×
− ∙
o 市场中的小市值、价值股表现明显超过市场,
而这一效应不能用CAPM模型解释
o 1981年,大卫·布斯和雷克斯·桑奎菲尔德成立
了维度投资顾问公司(Dimensional Fund
Advisors),买入小市值、估值低的股票,获
得了高额回报
o 1992年,尤金·法玛、肯尼斯·弗伦奇建立了三
因子模型,将资产回报分解为资产在市场风
27
翼丰股票组合与沪深300指数的Beta
在市场“正常状态”下,翼丰股票组合与沪深300指数的Beta为1.03。
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
700
600
500
400
300
200
100
0
-3
-2
-1
-0.5
0
频数
0.5
1
2
3
Beta
选取市场指数日收益率数据进行标准化,标准化后的日收益率描述了市场指数收益率偏离均值的程度。
模型
19
波动率投资:六因子模型
o 低波动率(或低beta)的股票实际回报高于高波动率(高beta)的股票,
这一现象不能用五因子模型解释
实例:杠杆收购交易、货币利差交易、“风险平价”投资

信用交易与投资者行为:对“特质波动率之谜”的再思考

信用交易与投资者行为:对“特质波动率之谜”的再思考

信用交易与投资者行为:对“特质波动率之谜”的再思考李合怡;贝政新【摘要】与经典资产定价理论的假设前提不同,现实世界的投资者往往面临着融资约束,而融资约束将对投资者的投资行为和预期收益产生重要影响.本文延续这一思路,运用Fama-MacBeth回归方法,检验我国股市特质波动率的存在性以及对未来收益的可预测性,并尝试从融资约束下的投资者行为角度来解释特质波动率之谜.本文还对“融资融券”开通前后的市场数据进行了对比.实证结果表明:代表异质信念的换手率的系数显著为负,说明高换手率会导致未来的预期收益率降低,这一现象同时存在于融资融券开通前后的样本区间.放开卖空限制后,异质信念的回归系数仍然显著,说明在部分放开卖空限制的条件下,投资者异质信念对特质波动率之谜仍有一定的解释力.以保证金比率二维分组的结果表明,融资约束对特质波动率之谜也有一定的解释力.【期刊名称】《学海》【年(卷),期】2015(000)006【总页数】6页(P66-71)【关键词】特质波动率;融资约束;信用交易;横截面收益【作者】李合怡;贝政新【作者单位】苏州大学苏州,215006;苏州大学苏州,215006【正文语种】中文引言特质波动率是公司特质风险的衡量指标。

经典的资产定价理论认为,不考虑交易成本等因素,理性投资者会充分分散非系统性风险,公司的特质风险不影响资产均衡定价。

然而现实中大量证据表明投资者的分散化常常是不充分的,许多投资者根本不参与重要的金融市场,例如持有单一股票或不持有债券。

在这一情形下,按照风险收益对等原则,投资者承担了不能充分分散的公司特质风险,将要求更高的风险补偿,特质风险应该对应更高的预期收益。

但Ang A.,Hodrick R.J.和Xing Y.(2006)①研究发现:高特质波动率的股票未来收益率更低,即滞后一期的特质波动率与收益率负相关,这一结论在23个发达国家金融市场普遍存在,这一现象被称为“特质波动率之谜”。

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But this causes a potential problem because you, like other people, have hundreds of little decisions to make each day. For example, a trip to the typical supermarket will confront you with at least 30,000 possible items to be selected. Can you read the labels on all of the products in a given category to find the one that has the best price, combination of ingredients, and so forth? Of course not. Instead, you, like most people, will reserve your effortful thought processes and energy for those tasks that you feel are most deserving and those situations that permit time for reflection. In other instances, you will need to rely on a simpler method of making decisions than effortfully scrutinizing all of the available information. In such situations, you can rely on what might feel like your “gut reaction” or “intuition.” Such reactions might stem from the presence of relatively simple “cues” in the situation such as whether your favorite sports hero is pictured on the cereal box or how many reasons to buy a product are listed on an in-store display. This is the strategy that Laertes followed in forming his attitude about the Great Dane sword. He simply reasoned, “If there are so many arguments for the sword, it must be good!” This counting of reasons can be accomplished with relatively little effort as compared with thinking about all of the reasons individually. If a shopper is willing to devote a small amount of effort to evaluating a product, perhaps only the first few arguments could be assessed. The point is that in any given situation, people can be lined up along a “thinking continuum” where they can devote a certain amount of thinking to the task, ranging from considerable to very little.
I need a sword that kills quickly and decisively. With such a mighty sword, I could rectify wrongs that have been committed. By rectifying the wrongs, there would be one less villainous, adulterous, murderer of kings. After I have rectified the wrongs, I would be free of these thoughts that are driving me mad. Yes, a sword that kills faster and more decisively is precisely what I need.
sword. The ad pictures Fortinbras raising the sword in battle. The ad proclaims, “10 Reasons Why the Great Dane Outperforms Its Competitors!” The ad continues, “Reason #1: Because of its sharper blade, the Great Dane kills faster and more decisively.” Hamlet, who had never heard of the Great Dane before, thinks,
05-Brock.qxd 12/1/2004 3:58 PM Page 81
Chapter 5
TO THINK OR NOT TO THINK
Exploring Two Routes to Persuasion
RICHARD E. PETTY
The Ohio State University
ALAN J. STRATHMAN
University of Missouri–Columbia
JOHN T. CACIOPPO
University of Chicago
JOSEPH R. PRIESTER
University of California, Los Angeles
H amlet is reading a magazine. His eye is caught by an advertisement for the Great Dane
Hamlet continues to read the other 9 reasons, thinking about each in a manner similar to the way in which he thought about the first reason.
Laertes is reading the same magazine, and his eye is also caught by the Great Dane ad. Laertes, who was also unfamiliar with the Great Dane, thinks, “Fortinbras looks very fierce in this picture, and many advantages of the Great Dane are listed. It must be a fine sword.” Laertes merely skims the ad without stopping to think about any of the arguments listed.
To understand the ELM, it is first important to understand an assumption that the model makes about the nature of humans in general. That assumption is that people have neither the ability nor the motivation to evaluate everything carefully. Think about it. You are a busy person with many things to do. Add to this busyness the fact that you live in a complex world. Even if you are the type of person who loves to evaluate (Jarvis & Petty, 1996) and enjoys thinking about most things (Cacioppo & Petty, 1982), you will probably agree that you simply cannot take the time, and do not have the mental energy, to analyze carefully every decision you make and every piece of information you encounter.
Current research on persuasion suggests that, indeed, the amount and nature of the thinking matters greatly. The purpose of this chapter is to describe a theory of persuasion that maintains that not all attitude changes that look the same really are the same. This theory, பைடு நூலகம்alled the Elaboration Likelihood Model (ELM), states that the amount and nature of the thinking that a person does about a persuasive message (e.g., an advertisement) is a very important determinant of the kind of persuasion that occurs (Petty & Cacioppo, 1981, 1986; Petty & Wegener, 1999). By the end of this chapter, you should have a better understanding of why not all ratings of 8 on a 9-point scale are alike, and you should also have a framework for appreciating why certain variables (e.g., a person’s mood, the expertise of the message source) have the impacts on attitude change that they do.
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