The large scale econometric models
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The large scale econometric models l.jpg
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 modelwent through various iterations and evolved into the MIT-FR-Wharton model


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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.


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A simple macro model

Consider the following economy:

Yt = Ct + It + Gt + Xt – Mt [5.5]

Equation [5.5] can be read as follows: Total output in period t is equal to total spending for new goods and services in period t , or consumption plus investment plus government expenditure plus imports minus exports.

Equation [5.5] is an identity—that is, it is true by definition


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The behavioral equations

Ct = a + b(Yt – Tt) + dPt – 1 [5.6]

Tt = e + fYt [5.7]

It = h + jYt – 1 + kRt [5.8]

Mt = n + qYt [5.9]

Pt = s + uYt + vPt – 1 [5.10]

We call these behavioral equations because they describe the way the way the spending category has behaved in the past as a function of the explanatory variables.


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Finding the reduced form

First, we substitute the behavioral equations [5.6] through [5.10] into [5.5] to obtain the following (we have dropped the t subscripts to economize on notation):

Y = [a + b(Y – e – fY) + dPt – 1] + (h + jYt-1 + kR) + G + X – (n + qY)

By rearranging this equation, we obtain the following reduced form equation

[5.11]


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Exogenous and endogenous variables

  • Exogenous variables are determined outside the model. They may be know by forecasters—or forecasters may have to forecast them In our model: X, G, and R

  • Endogenous variables are determined within the model—specifically, by equation [5.11] In our model: Y, C, I, T, M, and P


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Forecasting using the reduced form

  • The forecaster can estimate the values of a, b, d, e, f, h, j, k, n, q, s, u, and v with time series regression analysis.

  • Pt – 1 and Yt – 1 are known

  • That leaves the exogenous variables Gt, Xt, and Rt. Perhaps Gt is known. The forecaster will have to estimate (forecast) the values of the other exogenous variables


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The Suits modela

The article is noteworthybecause is educatedeconomists on the newapplications of econometrics made possible by advances in computer technology

Y = C + I + G (1)

C = 20 + 0.7(Y - T) (2)

I = 2 + .01Yt - 1 (3)

T = 0.2Y (4)

  • The unkown variables are Y, C, I, and T

  • The known variables are G and Yt - 1.

aDaniel Suits.” Forecasting and Analysis with an Econometric Model,” American Economic Review, March 1962: 104-132.


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A simple national econometric model a

Consider a closed economy with government

GDP = C + I + G

GDP is the dependent variable.Hence, to get solution for GDP, we mustfirst specify and estimate models for C, I, and G

a The following is based on A. Migliario. “The National Econometric Model: A Layman’s Guide,” Graceway Publishing, 1987.


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The aggregate level specifications

GDP t + 1 = C t + 1 + I t + 1 + G t + 1 (2)

C t + 1 = 1 + 2DYt + et (3)

I t + 1 = 3 + 4it + et (4)

G t + 1 = 5 + 6Gtb (5)

  • 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 - 0.419it

b Migliaro used the trend component to forecast G.


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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 + . . .


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A trucks specification

Trucks t + 1 = 1 + 2DYt + 3AGEt + 4PRICEt + et

  • 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.


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A lot of

equations

  • 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.


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