The analysis of Klein`s model „ The economic fluctuations in the United States ” (1950) Prepared by: Aleksander Rzewuski Roman Gąsowski

Few words about Lawrence R. Klein  born in 1920 in Omaha, Nebraska in Jewish family  1947 the book The Keynesian Revolution established him as one of the foremost scholars on Keynesian economics  In 1980 he was awarded the Nobel Memorial Prize in Economic Sciences

The goal of our presentation Introduction of Klein’s model Estimation for data covering American economy in the period of Analysis of the results Estimation of the model for contemporary data ( ) Comparison and final conclusions

Background for Klein’s model One of the first models to explain the economy as a whole Published in 1950 It includes years of Great Depression till the beginning of American participation in World War II All variables are measured in billions of dollars

Structural form of the model  Endogenous variables of the model are: Consumption – C t Wages in private sector – W p Investments – I t Capital stock – K t GNP – X t Profits in private sector – P t

Structural form (cont’d)  Exogenous variables are: Government expenditure – G t Wages in public sector – W g Taxes – T t Lagged variables: P t-1, K t-1, X t-1

Equations of the model C t = α 1 + α 2 P t + α 3 P t-1 + α 4 (W p +W g ) + u 1t W p = γ 1 + γ 2 X t + γ 3 X t-1 + γ 4 t + u 3t I t = β 1 + β 2 P t + β 3 P t-1 + β 4 K t-1 + u 2t K t = I t + K t-1 X t = C t + I t + G t P t = X t – W p – T t

Three-stage least squares regression Equation Obs Parms RMSE "R-sq" chi2 P c wp i | Coef. Std. Err. z P>|z| [95% Conf. Interval] c | p | L1 | | wp_plus_wg | _cons | wp | x | -- | L1 | year | _cons | i | p | -- | L1 | k1 | _cons | Endogenous variables: c wp i wp_plus_wg x p Exogenous variables: L.p L.x year k1 g wg t

Interpretation Estimates of all parameters look very reasonable The point estimates are not sufficient The signs of the parameter seem to be expectable in the context of economic theory Most of the coefficients are significant] Coefficient of determination (R 2 ) for all equations is very high

Observed level of GNP (solid line), simulated level of GNP (dotted line) As many economists pointed out the model does not track the historical data well However it can be used to simulate various policies

Possible way of policy simulation Change the value of government expenditure in one year holding all other levels of g and all other variables fixed Run the estimation with altered data and compare new „under shock” values of GNP with those obtained before Observe the difference in GNP and conclude policy recommendation

Analogous analysis of contemporary data Data set covers the period of 1970 – 2000 The data were gathered from World Development Indicators database and from Bureau of Economic Analysis of U.S. Department of Commerce All variables are measured in billions of dollars

Three-stage least squares regression Equation Obs Parms RMSE "R-sq" chi2 P c wp i | Coef. Std. Err. z P>|z| [95% Conf. Interval] c | p | L1 | | wp_plus_wg | _cons | wp | x | -- | L1 | year | _cons | i | p | -- | L1 | k1 | _cons | Endogenous variables: c wp i wp_plus_wg x p Exogenous variables: L.p L.x year k1 g wg t

Interpretation Coefficient of determination (R2) is even higher Most of the parameters are significant at the level of 1% Model tracks the economy indicators well

Final conclusions Both models follow the reality well Do they give any predictions? Only policy simulation Pioneer work and the cornerstone in the evolution of econometrics

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