计量经济学概念题

Cp1

1.

a:Stochastic error term: Stochastic error term is a term that is added to a regression equation to introduce all of the variation in Y that cannot be explained by the included Xs.

B:Linear : An equation is linear if plotting the function in terms of X and Y generates a straight line,

c: Slope coefficient(β)shows the response of Y to a one-unit increase in X.

d:: a linear regression model which has more than one independent variables .

e:. Expected value：the expression β0+β1x is called the deterministic component of the regression equation ,this deterministic component can also be thought of as the expected value of Y given X.

f:Residual: the difference between the estimated value of the dependent variable and the actual value of the dependent variable is defined as the residual

CP2

P43:

a: Ordinary Least Squares is a regression estimation technique that calculates theβs so as to minimize the sum of the squared residuals.

b:multivariate regression coefficient indicates the change in the dependent variable associated with a one-unit increase in the independent variable in question.

c: total sum of squares is the squared variations of Y around its mean as a measure of the amount of variation to be explained by the regression. The explained sum of squares is the amount if the squared deviation of Yi from its mean that is explained by the regression line. Residual sum of squares is the unexplained in an empirical sense by the estimated regression equation.

d: coefficient of determination is the ratio of the explained sum of squares to the total sum of squares.

e: degrees of freedom is the excess of the number of observations(N) over the number of coefficients(including the intercept estimated (K+1).

f: R^2 is the coefficient of determination.

Chapter 3

1.

a. Review the literature and develop the theoretical model.

Specify the model.

Hypothesize the expected signs of coefficient.

Collect data.

Estimate and evaluate the equation.

Document the results.

b. Apply other researchers’ model to my data set.

c. Any mistakes in the specification of a model.

d. The excess of the number of observations over the number of coefficient s to be estimated.

Chapter 4

1.

a. The regression model is linear.

The error term has a zero population mean.

All explanatory variables are uncorrelated with the err term

Observations of the err term are uncorrelated with each other.

The error term has a constant variance

No explanatory variable is a perfect linear function of any other explanatory variables.

The error term is normally distributed.

b. An error term satisfying Assumption through one to five.

c. A normal distribution with a mean equal to zero and a variance equal to one.

d. SE is the square root of the estimated variance of β s.

e. An estimate β is an unbiased estimator if its sampling distribution has as its expected value the true value of β.

f. Best Linear Unbiased Estimator.

g. The probability distribution of these β values across different samples. Chapter 5

1.

a. A statement of the values that the researcher does not expect.

b. A statement of the values that the researcher expects.

c. We reject a true null hypothesis

d. Indicate the probability of observing an estimated t-value greater than the critical t-value if the null hypothesis were correct.

e. In which the alternative hypothesis has values on both sides if null hypothesis.

f. When testing a hypothesis, you have to calculate a sample statistic and compare it with a critical value selected in advance.

g. A value that divides the acceptance region from the rejection region when testing a null hypothesis.

h. It is a ratio of departure of an estimated parameter from its notional value and its standard error.

i. A range which contains the true value of an item a specified percentage of the time.

j. A p-value for a t-score is the probability of observing a t score that big or bigger if the null hypothesis were true.

CHAPTER 6

1.

a. The omitted variable is an important explanatory variable that has been left out