Lecture 10

# Lecture 10 - Introduction to Econometrics Lecture 10 Model...

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Introduction to Econometrics 10-1 Technical Notes Introduction to Econometrics Lecture 10: Model design and specification Introduction This lecture is an illustration of the process that an investigator might go through in trying to specify and estimate a simple econometric model using multiple regression techniques to analyse cross-section data. It is important to stress that there is no single set of rules for carrying out this process which will produce the right answer in every case. However it is important to design any investigation in a systematic way, to use economic theory and common sense to suggest the hypotheses which you wish to test, and to make sure that you have a complete record of the regressions which you ran and of their results. That means that if you later have a bright idea about how to extend your original investigation, or if you get some additional data, you do not need to go back to the beginning. You can keep this record in a notebook (as a scientist would record details of a series of experiments), but it’s often easier just to ensure that any results which you generate are written to a computer file before you go on to the next regression. Eviews makes it easy to do this, since you can save the specification of any regression (as an Equation object) or the results (as a Table object) in your Eviews workfile. The example used in the lecture is the problem of explaining individual earnings, using a dataset on a random sample of 1000 US workers in 1988 1 . Although this is the same problem as illustrated in earlier lectures, I have used a different dataset which includes older workers, and has more information on exogenous variables. All the workers in this new dataset are male, so obviously we cannot use it to investigate the differences between male and female wage levels. Instead the illustration focusses on a more detailed investigation of the role of schooling and educational attainment on wage determination. The variables available in the dataset are AGE Individual’s age WAGE Hourly wage (in \$) LNWAGE Log of wage OCC1 Categorical variable for occupational category (see below) IND1 Categorical variable for industrial category (see below) UNION 1 if union member, 0 otherwise GRADE highest educational grade completed (1-12 are high school grades) MARRIED 1 if married, 0 otherwise PARTT 1 if part-time worker, 0 otherwise POTEXP Years of potential experience EXPSQ POTEXP squared WEIGHT Sampling weight GRADSESQ GRADE squared FINHIGH 1 if graduated from high school, 0 otherwise (implies GRADE >= 12) STCOL 1 if started college, 0 otherwise (implies GRADE > 12) FINCOL 1 if completed college, 0 otherwise (implies GRADE >= 16) DROPOUT 1 if dropped out of college, 0 otherwise (implies STCOL = 1, FINCOL = 0) HSGRADE GRADE for those who did not complete high school, 0 otherwise HPOCC 1 if in high-paid occupation, 0 otherwise (see below) LPIND 1 if in low-paid industry, 0 otherwise (see below) The categories for the occupational variable, OCC1 are:

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Lecture 10 - Introduction to Econometrics Lecture 10 Model...

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