Econometrics Problem Set Using Stata Questions
Order Number
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636738393092 |
Type of Project
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ESSAY
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Writer Levela
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PHD VERIFIED
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Format
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APA
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Academic Sources
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10
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Page Count
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3-12 PAGES
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Instructions/Descriptions
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1) Use data from PRMINWAGE.DTA, which is described in Examples 10.3, 10.9, and 12.2. Use the –tsset- command to declare –year- as a time variable. Run the OLS regression of the log of the Puerto Rican employment/population ratio (lprepop) against:
- log of the effective minimum wage (lmincov)
- log of US GNP (lusgnp)
- log of PR GNP (lprgnp)
- a time trend term (year)
- a) Interpret these results
- b) Use equation 12.14 to test for serial correlation in the error terms of this equation. What do you conclude?
- c) Use Durbin’s alternative estimator (In Stata, it is –estat durbinalt-) to test for serial correlation. (Note: you must re-run the regression in a) just prior to running -estat durbinalt-). What do you conclude?
- d) Run a Dickey Fuller test, with a trend term, to determine if –lprepop- has a unit root. Interpret the results.
- e) Run another Dickey Fuller test, with a trend term, to determine if –lmincov- has unit root. Interpret the results; what does this tell us about the results in a)?
- f) Now rerun the regression in a) but this time using first differences of all variables. Drop the time trend (“year”), since differencing takes care of linear trends. Interpret the results for –lmincov- and compare them to a). Which results are more likely to be correct?
- g) Use Durbin’s alternative test to test for serial correlation in the error terms of the differenced equation that you just ran. Do we have a problem with serial correlation? Explain.
- h) Whether or not you see serial correlation, run the regression in differences again, using Newey-West standard errors. (In Stata the command is –newey-.) Specify that you want –newey- to consider 2 lags of the error term, using the lag(2) option. Does the use of robust standard errors change your conclusions about the effects of the minimum wage?
- i) Now use FGLS Cochran-Orcutt method to run the model (still using first differences for all variables). (In Stata, this is the –prais- command with the ,corc option.) Does this change your conclusions?
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