BOSTON COLLEGE

Department of Economics

EC 761

Fall 1999

Prof. Baum

PROBLEM SET ONE

due Friday 17 September 1999 at classtime

For the following empirical exercises, hand in both a printout of the results and your evaluation of findings from the results.

1. Use dataset TBL5-1 from Greene 2000 (see below). Variable definitions are on p.953 of Greene under "Table A5.1" and may be viewed here.
  1. Regress income on age. Is this a significant relationship?
  2. Test the hypothesis that distinguishing between the incomes of self-employed workers and wage slaves improves the relationship.
  3. Fit the income-age relationship in semilog form. How do you interpret the slope coefficient in this relationship? How would you test that this form of the relationship is more appropriate than the linear form of part (a)? [You need not do the test, but you should lay out the method].
  4. Fit the log(income)-age relationship separately for self-employed workers and wage slaves, and comment on the results.
  5. Test the hypothesis that in the context of the log(income)-age relationship, homeowners have higher incomes than renters, cet.par.
2. Use dataset TBL6-1 from Greene 2000 (see below). Variable definitions are on p.953 of Greene under "Table A6.1" and may be viewed here. (Hint: you may find the —tsmktim- command useful to place the dataset into timeseries form. This command is installed on fmrisc, and can be installed on any machine from the SSC-IDEAS archive. The command tsmktim time,start(1960) is appropriate for this dataset).
  1. Estimate the annual growth rate of per capita disposable income.
  2. Regress gasoline consumption per capita on the price index for gasoline. Explain your results.
  3. Include a time trend in the model. Explain your results.
  4. Run the regression excluding years in which the price index of gasoline changed by more than 10%. What effect do these outliers have on the results?
  5. Use instrumental variables (two-stage least squares) to estimate the relationship between gasoline consumption per capita, the price index for gasoline, and a time trend, specifying that population and the lagged price index for gasoline are instruments. How does this affect the results?
  6. Use Stata’s hausman procedure to test for consistency of the OLS estimates from part (c) versus the 2SLS results of part (e). Explain your results.

Notes re Stata and UNIX usage:

• The datasets are accessible on both fmrisc.bc.edu, in directory

/res0/resdata/baum/gr2000/

and on Sleek, in directory

Sleek4:[SleekShare]:[Greene2000]

You cannot write in either of those directories; you may save the file, with any changes, to your own directory on either system (i.e. into /u/username on UNIX, or Sleek4:[PersonalStorage]:Yourname-EC on Sleek).

• UNIX systems are case-sensitive with respect to usernames, commands, filenames, etc. Stata, stata and STATA are three different programs (only one of which exists). When in doubt, emulate e.e. cummings.

• You will not be able to save the dataset to this account (nor

do you need to save it), so you might want to end your Stata program with exit, clear.

• If you are not familar with Stata’s capabilities, you might want to run

Stata and use the tutorial command to see examples of the syntax. help command will give you extensive assistance, but you must know the command name. lookup keyword will provide assistance on that keyword, whether or not it is a command.

• The regress command runs OLS regressions; the vce command displays the estimated variance-covariance matrix of the estimated parameters. You might also want to experiment with the test command.

• The gen command (generate) allows you to compute new variables. The display command allows you to operate on scalar quantities.