

Study with the several resources on Docsity
Earn points by helping other students or get them with a premium plan
Prepare for your exams
Study with the several resources on Docsity
Earn points to download
Earn points by helping other students or get them with a premium plan
Community
Ask the community for help and clear up your study doubts
Discover the best universities in your country according to Docsity users
Free resources
Download our free guides on studying techniques, anxiety management strategies, and thesis advice from Docsity tutors
A problem set for econ 8686 students, focusing on measurement error and its impact on regression analysis. The set includes a simulation exercise to understand the concept of measurement error and its effect on regression coefficients, followed by an analysis of the birth.dta dataset to estimate the relationship between birth rates and afdc benefits, controlling for state and year fixed-effects. Students are asked to calculate the bias of the coefficient, estimate different versions of the state fixed-effects model, and use lagged afdc benefits as instruments in the first-differences model.
Typology: Assignments
1 / 2
This page cannot be seen from the preview
Don't miss anything!
Part I. Simulation
Create a simple measurement error simulation: set obs 100000 gen z=uniform() gen a=uniform() gen b=uniform() gen e1 = invnorm(a) gen e2 =invnorm(b) gen y=3*z+e gen x=z+e
Using the output from the following command: corr z e2, cov Calculate the bias of the coefficient from the regression of y on x (for simplicity, ignore the covariance term).
Confirm your answer to question #2 by regressing y on x
Now: replace b=.5*b replace e2=invnorm(b) replace x=z+e
Repeat questions 2 and 3. What happened? Why?
Part II. Measurement Error and Panel Data
This part of the problem set is based on: McKinnish, Terra. 2008. “Panel Data Models and Transitory Fluctuations in the Explanatory Variable.” Advances in Econometrics Vol.
The data set birth.dta contains data for all 50 states plus D.C. for the years 1972-93. The variables are: births: birth rate, white women ages 20- afdcben: real monthly AFDC Benefit earn: real earnings per capita state: state ID code year
Estimate a first-differences, 3-year long-differences and 5-year long-differences version of the state fixed-effects model in question #1. Compare and discuss the results.
The ideal solution would be to have an instrument, something correlated with the long-term component of AFDC benefits but not the short-term fluctuations. In some literatures, in absence of “external” instruments, researchers will turn to “internal” instruments, usually lagged values of their explanatory variables. Consider the basic differences model:
Yit − Yit − j =β( Xit − Xit − j )+(ε it −ε it − j )
If the measurement error is uncorrelated over time, then any value of X other than Xit and Xit-j , or any function of these values, is a valid instrument for Xit -Xit-j. So, consider the basic first-differences model, and try lagged AFDC benefits, log( afdcbent (^) − 2 ), and the lagged change,log( afdcbent (^) − 2 ) − log( afdcbent − 3 ), as an
instrument for log( afdcbent ) − log( afdcbent − 1 ). Estimate the IV regression, then estimate
and evaluate the first stage. Discuss your findings. Explain why we might expect the first-stage F-stat to be so small for these specifications when there is measurement error.