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

of a regression equation.

b. The irrelevant variable is the variable included in an equation that doesn’t belong there.

c. The specification bias is the bias caused by leaving a variable out of an equation.

d. sequential specification search 6.4.2

e. The specification error results from choosing the incorrect independent variables, the incorrect functional form and the incorrect form of the stochastic error term.

f. The four valid criteria are to help decide whether a given variable belongs in the equation:

g. expected bias 6.1.3

CP7

1.

a. Elasticity of Y with respect to X, the percentage change in the dependent variable caused by a 1 percent increase in the independent variable, holding the other varia bles in the equation constant can be calculated.

b. In a doublelog functional form, the natural log of Y is the dependent variables an d the natural log of X is the independent variable.

c. The semiology functional form is a variant of double-log equation in which some but not all of the variables are expressed in terms of their natural logs

d. Polynomial functional forms express Y as a function of independent variables, so me of which are raised to powers other than on

e.

e. The inverse functional form expresses Y as a function of the reciprocal of one or more of the independent variables.

f. A slope dummy is a dummy variable that is multiplied by an independent variabl e to allow the slope of the relationship between the dependent variable and the pa rticular independent variable to change, depending on whether or not a particular condition is met.

g. The natural log of a number is its logarithm to the base of the mathematical cons tant e, where e is an irrational and transcendental number approximately equal to

2.718.

h. The omitted condition, forms the basis against which the included conditions are compared.

i. An interaction term is an independent variable in a regression equation that is th e multiple of two or more other independent variables.

j. A form that is linear in the variables should be used unless a specific hypothesis s uggestions otherwise.

k. An equation is linear in the coefficients only if the coefficients appear in their si mplest form, they are not raised to any powers and not multiplied or divided by ot her coefficients, and do not themselves include some sort of function.