2. The large scale econometric models The first large-scale econometric model was built by Professor Lawrence Klein in the 1950s. The equations which formed the model represented a “synthetic” or artificial economy.The model went through various iterations and evolved into the MIT-FR-Wharton model

3. Uses of the model Using this model, it was possible to simulate the effects of proposed fiscal policy measures such as increased military spending and tax cuts on a wide array of aggregate (Y, I, C, S,...) and disaggregate level variables (truck sales, employment in construction trades, cement prices). For example, The people who ran the model were asked to simulate the impact of the proposed Kennedy-Johnson tax cuts in the early 60s (took effect in 1964) on a broad array of economic variables.

4. The Suits model a a Daniel Suits.” Forecasting and Analysis with an Econometric Model,” American Economic Review, March 1962: 104-132. Y = C + I + G (1) C = 20 + 0.7(Y - T) (2) I = 2 +.01Y t - 1 (3) T = 0.2Y (4) The unkown variables are Y, C, I, and T The known variables are G and Y t - 1. The article is noteworthy because is educated economists on the new applications of econometrics made possible by advances in computer technology

5. A simple national econometric model a a The following is based on A. Migliario. “The National Econometric Model: A Layman’s Guide,” Graceway Publishing, 1987. Consider a closed economy with government GDP = C + I + G GDP is the dependent variable. Hence, to get solution for GDP, we must first specify and estimate models for C, I, and G

6. The aggregate level specifications GDP t + 1 = C t + 1 + I t + 1 + G t + 1 (2) C t + 1 =  1 +  2 DY t + e t (3) I t + 1 =  3 +  4 i t + e t (4) G t + 1 =  5 +  6 G t b (5) b Migliaro used the trend component to forecast G. Migliaro used OLS to estimated  1,  2,  3,  4,  5, and  6 Having accomplished that, he substituted estimated equations (3), (4), and (5) back into (2) to get a forecasted value of G t + 1. An example: I t + 1 = 11.567 - 0.419i t

7. Extending (disaggregating) the model Let: C t + 1 = DUR t + 1 + NONDUR t + 1 + SERVICES t + 1 Now let: DUR t + 1 = AUTOS t + 1 + FURNITURE t + 1 + APPLIANCES t + 1 +... Now let: AUTOS t + 1 = Passenger Cars t + 1 + Vans t + 1 + Trucks t + 1 +...

8. Trucks t + 1 =  1 +  2 DY t +  3 AGE t +  4 PRICE t + e t As we increase the level of disaggregation, we increase the number of equations.That is, we could have equations for different classes of trucks--midsize, etc. It is the disaggregate level forecasts which are most valuable tobusiness decision-makers. Entities such as DRI-McGraw Hill and Chase econometrics sell disaggregate-level forecasts to a high-powered client base.

9. The DRI-McGraw Hill Model has approximately 450 equations. The FRB-MIT-Wharton model has 669 equations. The Chase Econometrics modle has 350 equations The Kent model has 44,400 equations.