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Measurement Error Problem Set: Simulation and Panel Data Analysis - Prof. Jeffrey S. Zax, Assignments of Economics

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.

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Pre 2010

Uploaded on 02/13/2009

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ECON 8686 Problem Set #3- Measurement Error
Part I. Simulation
1) 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+e1
gen x=z+e2
2) 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).
3) Confirm your answer to question #2 by regressing y on x
4) Now:
replace b=.5*b
replace e2=invnorm(b)
replace x=z+e2
5) 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.21
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 20-24
afdcben: real monthly AFDC Benefit
earn: real earnings per capita
state: state ID code
year
1) Regress the logged birth rate on logged AFDC benefits, logged per capita earnings,
and state and year fixed-effects.
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ECON 8686 Problem Set #3- Measurement Error

Part I. Simulation

  1. 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

  2. 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).

  3. Confirm your answer to question #2 by regressing y on x

  4. Now: replace b=.5*b replace e2=invnorm(b) replace x=z+e

  5. 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

  1. Regress the logged birth rate on logged AFDC benefits, logged per capita earnings, and state and year fixed-effects.
  1. 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.

  2. 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:

YitYitj =β( XitXitj )+(ε it −ε itj )

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.

  1. BONUS: Gold star and extra credit to anyone that explains why we can’t use lagged values of afdc benefits as instruments in the state fixed-effects specification.