1. Econometric Methodology
Chapter 3

2. Step 1 Review the literature and develop a theoretical model
Keyword Search Databases (EBSCO, EconLit)
Journal of Economic Literature (JEL)

3. Sample Model Y = Erie Mfg Employment X1 = Total Employment
X2 = Exchange Rate
X3 = Economic Activity
X4 = Stock Market Activity

4. Step 2 Specify the model’s variables and functional form
Measurement issues for Y and X
Dummy variables
Determine appropriate functional form

5. Example Choosing Y Measuring Erie Manufacturing Employment (Y)
Number of Manufacturing Employees
Manufacturing/Total Employment
Change in Manufacturing Employment
Change in Manufacturing/Total Employment

6. Options for the Dependent Variable

7. Options for the X Variables

8. Total Employment – X1

9. Expressed as Differences

10. Exchange Rate – X2 U.S. versus a particular currency?
Broad exchange rate index?
Major currencies exchange rate index?
Values in levels or differences?

11. Economic Activity – X3 Level or Difference in:
PA Manufacturing Employment
U.S. Manufacturing Employment
Regional or National Unemployment Rate
Index of Industrial Production
Capacity Utilization Rate
Real GDP (quarterly)

12. Stock Market – X4 Level or Difference in: S&P 500 Stock Index
Dow Jones 30 Stock Index
Price/Earnings Ratio (SP500)
NASDAQ Stock Index

13. Dummy Variables
Measure qualitative characteristics or events

14. Dummy Example
To measure the impact of the 9/11/2001 attacks, use a dummy for September through November of 2001.

15. Determining if there is a Relationship between Y and X
Scatter Plots

16. Determining if there is a Relationship between Y and X
Correlation Coefficient

17. Correlation Coefficient
r = 0 (no relationship between Y and X)
r > 0 (positive relationship)
r < 0 (negative relationship)
|r| → 1.0 (strong relationship)

18. Example from EViews ERMFG SP500 USTOT XCHBRD 1.000000 -0.562928

19. Functional Form of Equation
Linear:
Quadratic

20. Step 3 Hypothesize the expected signs of the variable relationships
Base the decision on theory
Assists in validating the model

21. Example Y = Erie Mfg Employment X1 = Total Employment
X2 = Exchange Rate
X3 = Economic Activity
X4 = Stock Market Activity

22. Step 4
Collect the data
Use sufficient data to maximize degrees of freedom (d.f.) for the model
d.f. = n-k-1
Larger data sets allow + and (-) errors to offset – maximizing model accuracy

23. Special Considerations with Time-Series Data
“More data the better” not necessarily true for T-S data
Data far in the past may no longer be relevant
The issue of “spurious regression”
Two variables may “trend” together over time because they are both affected by a third variable
Consider use of “real” instead of “nominal” variables when possible

24. Step 5 Estimate & evaluate the regression model
Estimate β values using OLS or other method
Validate the model to determine usefulness

25. Forms of Validation Testing sign of slope coefficients
“Goodness-of-Fit” – (sy,x, R2, Adj-R2)
Testing for significance of relationship (t)
Testing model (OLS) assumptions
Testing for correct functional form

26. Step 6 Documenting the results
Make results clear to the non-technical reader
Include sufficient statistical evidence of model usefulness
Thoroughly document variable definitions and data sources