Based on a classical study of investment demand (Greene [2008], pp.250-252, Grunfeld and Griliches [1960], Boot and deWitt [1960]), the model is:
Iit = αit + βitFit + γitCit + εit
where
i = 10 firms: GM, CH, GE, WE, US, AF, DM, GY, UN, IBM.
t = 20 years: 1935-1954.
Iit = Gross investment.
Fit = Market value.
Cit = Value of the stock of plant and equipment.
eit = Error term.
Data of above 3 variables for 10 companies are available:
General Motors (GM) | Chrysler (CH) |
General Electric (GE) | Westinghouse (WE) |
U. S. Steel (US) | Atlantic Refining (AF) |
Diamond Match (DM) | Goodyear (GY) |
Union Oil (UN) | IBM |
Formulate the panel data model as:
Iit = αi + γt + βFit + γCit + εit
That is, with common slopes, only the intercept parameters vary across firms and over time. To study the individual (i.e., firm) effects alone, we further assume γt = 0. Similarly, to study the time effects alone, we assume αi = 0.
The regression model under consideration is
ln Wageit = | αi + β1 Educationit + β2 Expeienceit + β3 Experienceit2 |
+ β4 Broken_Homei + β5 Siblingsi + εit |
Using all of the 17,919 observations of the data provided, estimate (1) fixed effects panel data model; (2) random effects panel data model; and (3) compare and test to select either fixed effects or random effects model.