Econometrics: Detailed Stata codes with descriptions. Questions :Principals of econometrics 5

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Econometrics: Detailed Stata codes with descriptions. 

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Principals of econometrics 5th edition by R.Carter.Hill

7.24 (diff-in-diff)

9.21 (serial correlation)

12.21, 12.29 (stationarity)

15.25 (panel) 

 

 

 

15.25 Consider the production relationship on Chinese firms used in several chapter examples. We now add another input, MATERIALS. 

Use the data set from the data file chemica13 for this exercise. (The data file chemical includes many more firms.) 

In(SALESit)— + + 

a. Estimate this model using OLS. Compute conventional, heteroskedasticity robust, and cluster-robust standard errors. Using each type 

of standard error construct a 95% interval estimate for the elasticity of SALES with respect to MATERIALS. What do you observe 

about these intervals? 

b. Using each type of standard error in part (a), test at the 5% level the null hypothesis of constant returns to scale, 62 + +64 1 

versus the alternative 62 + + 1. Are the results consistent? 

c. Use the OLS residuals from (a) and carry out the N x R2 test from Chapter 9 to test for AR(I) serial correlation in the errors using the 

2005 and 2006 data. Is there evidence of serial correlation? What factors might be causing it? 

d. Estimate the model using random effects. How do these estimates compare to the OLS estimates? Test the null hypothesis 

+ 63 + 1 versus the alternative + 63 + 1. What do you conclude. Is there evidence of unobserved heterogeneity? 

Carry out the LM test for the presence of random effects at the 5% level of signficance. 

e. Estimate the model using fixed effects. How do the estimates compare to those in (d)? Use the Hausman test for the significance of 

the difference in the coefficients. Is there evidence that the unobserved heterogeneity is correlated with one or more of the explanatory 

variables? Explain. 

f. Obtain the fixed effects residuals, Eit. Using OLS with cluster-robust standard errors estimate the regression — + rit, 

where rit is a random error. As noted in Exercise 15.10, if the idiosyncratic errors eit are uncorrelated we expect p 1/2. Rejecting 

 

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