Economics 472 Problem Set #6 Note: For all of the computer-based exercises, please include the regression output (you don't need to include any graphs) with your completed problem set. (1) Using the b
Economics 472 Problem Set #4 (1) Consider the regression of y on just a constant term : (no x included) yi = 1 + ui .
(1a) Show that the OLS estimate of 1 is just the sample mean of y: ^ 1 = y. (Hint:
Problem Set #1 (1) The following table gives the joint probability distribution p(X, Y ) of random variables X and Y .
Y 1 2 3 4
1 .02 .03 .00 .09
X 2 .04 .18 .02 .18
3 .12 .04 .10 .18
Determine the f
Economics 472 Problem Set #5 (1) Stock and Watson, 4.1 parts (c) and (d). Note: for part (d), you are asked to calculate the p-value associated with the two-sided test that the coecient on class size
Economics 472 Final Problem Set The final problem set for the course asks you to write a small (ideally between 4 and 5 pages) empirical paper. In this paper, you are to perform and correctly interpre
Government Debt1
We take a break from straight theory to look at governmental debt, obviously a relevant issue in
the United States. We begin by looking at the mechanics of decits and debt. The budget
Economics 472 Problem Set #7 (1) Using data from the course website (under the link related to the production function data), run a regression of log output on log labor and log capital (i.e., estimat
Economics 472 Problem Set #8 (1) Load the panel baseball data (panelbb) from the course website. This expanded baseball data set contains a set of fixed effects for each team. In particular, for each
Economics 472 Problem Set #10 (1) Consider a simple model to estimate the eect of personal computer ownership (denoted PC) on college grade point average (GPA): GP Ai = 0 + 1 P Ci + ui . In this model
Economics 472 Problem Set #9 (1) Stock and Watson, 9.1. Note: You will need to look up values of the Normal cumulative density function on pages 642-643 of your book to carry out this exercise. (2) St
Problem Set #2 (1) Stock and Watson, 2.2
(2) Stock and Watson, 2.8
(3) Stock and Watson, 2.10
(4) Show that Cov(aX + b, cY + d) = acCov(X, Y ) for any two random variables X and Y , and constants a, b