One of the earlier textbooks in econometrics, first published in 1971, compared "estimation of a parameter to shooting at a target with a rifle. The bull's-eye can be taken to represent the true value of the parameter, the rifle the estimator, and each shot a particular estimate." Use this analogy to discuss small and large sample properties of estimators. How do you think the author approached the n → ∞ condition?

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"I am an applied econometrician and therefore should not have to deal with econometric theory. There will be others who I leave that to. I am more interested in interpreting the estimation results." Evaluate. What will be an ideal response?

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(Requires Appendix material) If X and Y are jointly normally distributed and are uncorrelated,

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The advantage of using heteroskedasticity-robust standard errors is that

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The extended least squares assumptions in the multiple regression model include four assumptions from Chapter 6 (ui has conditional mean zero; (Xi,Yi), i = 1,…, n are i.i.d. draws from their joint distribution;

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The multiple regression model can be written in matrix form as follows:

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The linear multiple regression model can be represented in matrix notation as Y= Xβ + U, where X is of order n×(k+1). k represents the number of

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Let there be q joint hypothesis to be tested. Then the dimension of r in the expression Rβ = r is

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The formulation Rβ= r to test a hypotheses

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To prove that the OLS estimator is BLUE requires the following assumption

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You have re-estimated the two variable VAR model of the change in the inflation rate and the unemployment rate presented in your textbook using the sample period 1982:I (first quarter) to 2009:IV. To see if the conclusions regarding Granger causality of changed, you conduct an F-test for this new sample period. The results are as follows: The F-statistic testing the null hypothesis that the coefficients on Unempt-1, Unempt-2, Unempt-3, and Unemplt-4 are zero in the inflation equation (Equation 16.5 in your textbook) is 6.04. The F-statistic testing the hypothesis that the coefficients on the four lags of ΔInft are zero in the unemployment equation (Equation 16.6 in your textbook) is 0.80. a. What is the critical value of the F-statistic in both cases?

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The interpretation of the coefficients in a distributed lag regression as causal dynamic effects hinges on

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You have obtained data on test scores and student-teacher ratios in region A and region B of your state. Region B, on average, has lower student-teacher ratios than region A. You decide to run the following regression. Yi = β0+ β1X1i + β2X2i + β3X3i+ui where X1 is the class size in region A, X2 is the difference between the class size between region A and B, and X3 is the class size in region B. Your regression package shows a message indicating that it cannot estimate the above equation. What is the problem here and how can it be fixed?

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All of the following are good reasons for an applied econometrician to learn some econometric theory, with the exception of

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When using panel data and in the presence of endogenous regressors

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Binary variables

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The accompanying table lists the height (STUDHGHT) in inches and weight (WEIGHT) in pounds of five college students. Calculate the correlation coefficient. STUDHGHTWEIGHT 74165 73165 72145 68155 66140 What will be an ideal response?

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(Requires Calculus) In the multiple regression model you estimate the effect on Yi of a unit change in one of the Xi while holding all other regressors constant. This

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Using 143 observations, assume that you had estimated a simple regression function and that your estimate for the slope was 0.04, with a standard error of 0.01.

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The t-statistic has the following distribution:

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