Bianchi, Carlo and Calzolari, Giorgio and Weihs, Claus (1986): Parametric and nonparametric Monte Carlo estimates of standard errors of forecasts in econometric models.

PDF
MPRA_paper_29120.pdf Download (524Kb)  Preview 
Abstract
In the econometric literature simulation techniques are suggested for estimating standard errors of forecasts, especially in case of nonlinear models, where explicit analytic formulae are not available. For this purpose analytic simulation on coefficients, Monte Carlo on coefficients, Monte Carlo simulation based on parametric estimate of the underlying error distribution have been proposed, and more recently a nonparametric procedure which uses the bootstrap technique is also suggested. Main purpose of this paper is to compare, in empirical applications for real world models, parametric and nonparametrlc estimates. Furthermore, in case of linear models, the same comparisons are performed with respect to the results obtained via analytic formulae. Additional results are obtained from an errorinvariables approach.
Item Type:  MPRA Paper 

Original Title:  Parametric and nonparametric Monte Carlo estimates of standard errors of forecasts in econometric models 
Language:  English 
Keywords:  Standard errors of forecasts; econometric models; parametric and nonparametric simulations 
Subjects:  C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C53  Forecasting and Prediction Methods; Simulation Methods C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C52  Model Evaluation, Validation, and Selection C  Mathematical and Quantitative Methods > C3  Multiple or Simultaneous Equation Models; Multiple Variables > C30  General 
Item ID:  29120 
Depositing User:  Giorgio Calzolari 
Date Deposited:  09. Mar 2011 20:32 
Last Modified:  11. Feb 2013 16:30 
References:  AMEMIYA, T. (1977). The maximum likelihood and the nonlinear threestage least squares estimator in the general nonlinear simultaneous equation model. Econometrica 45(4): 955968. BlANCHI, C. and CALZOLARI, G. (1980). The oneperiod forecast errors in nonlinear econometric models. International Economic Review 21(1): 201208. BIANCHI,C. and CALZOLARI,G. (1982). Evaluating forecast uncertainty due to errors in estimated coefficients: empirical comparison of alternative methods. In Evaluating the ReliabiIity of MacroEconomic Models. Eds: G.C.Chow and P.Corsi, John Wiley, New York, 251277. BIANCHI,C. and CALZOLARI,G. (1983). Standard errors of forecasts in dynamic simulation of nonlinear econometric models: some empirical results. In Time Series Analysis Theory and Practice 3. Ed: O. D. Anderson, North Holland, Amsterdam, 177198. BIANCHI,C., CALZOLARI,G. and CORSI,P. (1981). Standard errors of multipliers and forecasts from structural coefficients with blockdiagonal covariance matrix. In Dynamic Modelling and Control of National Economies (IFAC). Eds: J.M.L.Janssen, L.F.Pau and A.Straszak, Pergamon Press, Oxford, 311316. BROWN,B.W. and MARIANO,R.S. (1984). Residualbased procedures for prediction and estimation in a nonlinear simultaneous system. Econometrica 52(2): 321 343. BRUNDY,J.M. and JORGENSON,D.W. (1971). Efficient estimation of simultaneous equations by instrumental variables. The Review of Economics and Statistics 53(3): 207224. CALZOLARI, G . (1981). A note on the variance of expost forecasts In econometric models. Econometrica 49(6): 15931595. CALZOLARI, G. and STERBENZ, F. P. (1986). Control variates to estimate the reduced form variances in econometric models. Econometrica, to appear. COOPER,J.P. and FISCHER,S. (1974). Monetary and fiscal policy in the fully stochastic St. Louis econometric model. Journal of Money, Credit, and Banking 6(1): 122. DEL MONTE, C. (1981). Un quadro macroeconomico di riferimento (PCDMOD78). Universita' di Perugia, Annali della Facolta' di Scienze Politiche, Quaderni dell'lstituto di Studi Economici No.5, 950. DHRYMES,P.J. (1970). Econometrics: Statistical Foundations and Applications. Harper & Row, New York. EFRON, B. (1979). Bootstrap methods: another look to Jackknife. Annals of Statistics 7(1): 116. FAIR,R.C. (1980). Estimating the expected predictive accuracy of econometric models. International Economic Review 21(2): 355378. FREEDMAN,A. D. and PETERS, S. C. (1984). Bootstrapping an econometric model: some empirical results. Journal of Business and Economics Statistics 2(2): 150158. GALLANT,A. R. (1977). Threestage leastsquares estimation for a system of simuItaneous, non linear, implicit equations. Journal of Econometrics 5(1): 7188. GOLDBERGER,A.S., NAGAR,A.L. and ODEH,H.S. (1961). The covariance matrices of reducedform coefficients and of forecasts for a structural econometric model. Econometrica 29(4): 556573. HAITOVSKV,Y. and WALLACE,N. (1972). A study of discretionary and nondiscretionary monetary and fiscal policies in the context of stochastic macroeconometric models. In The Business Cycle Today. Ed: V.Zarnowitz, NBER, New York, 261309. HOWREY,E.P. and KELEJIAN,H.H. (1971). Simulation versus analytical solutions: the case of econometric models. In Computer Simulation Experiments with Models of Economic Systems. Ed: T. H. Naylor, John Wiley, New York. 299319. KLEIN, L. R. (1950). Economic Fluctuations in the United States, 19211941. John Wiley, New York, Cowles Commission Monograph 11. MARIANO,R.S. (1982). Analytical small sample distribution theory in econometrics: the simultaneous equations case. International Economic Review 23 (3): 503533. MARIANO,R.S. and BROWN,B.W. (1983). Asymptotic behavior of predictors in a nonlinear simultaneous system. International Economic Review 24(3): 523536. MC CARTHY, M. D. (1972,a). Some notes on the generation of pseudostructural errors for use in stochastic simulation studies. In Econometric Models of Cyclical Behavior. Ed: B.G.Hickman. NBER, Studies in Income and Wealth No.36, New York, 185191. MC CARTHY,M.D. (1972,b). A note on the forecasting properties of twostage least squares restricted reduced forms: the finite sample case. International Economic Review 13(3): 757761. NAGAR, A. L. (1969). Stochastic simulation of the Brookings econometric model. In The Brookings Model: Some Further Results. Ed: J.S.Duesenberry, G.Fromm, L.R.Klein and E.Kuh, North Holland, Amsterdam, 425456. RAO,C. R. (1965). Linear Statistical Inference and its Applications. John Wiley, New York. SARGAN,J.D. (1976). The existence of the moments of estimated reduced form coefficients. London School of Economics & Political Science, discussion paper A6. SCHINK,G. R. (1971). Small sample estimates of the variance covariance matrix of forecast error for large econometric models: the stochastic simulation technique. University of Pennsylvania, Ph.D. dissertation. SCHMIDT,P. (1974). The asymptotic distribution of forecasts in the dynamic simulation of an econometric model. Econometrica 42(2): 303309. SCHMIDT,P. (1976). Econometrics. Marcel Dekker, New York. SHEININ,Y. (1982). Wharton Mini Growth Model of the U.S. Economy. Wharton Econometric Forecasting Associates, Philadelphia. SOWEY, E. R. (1973). Stochastic simulation of macroeconometric models: methodology and interpretation. In Econometric Studies of Macro and Monetary Relations. Ed: A.A.Powell and R.A.Williams, North Holland, Amsterdam, 195230. SYLOS LABINI,P. (1967). Prezzi, distribuzione e investimenti dal 1951 al 1966: uno schema interpretativo. Moneta e Credito 20(3): 264344. WEIHS,C. (1986). Auswirkungen von Fehlern in den Daten auf Parameterschaetzungen und Prognosen. University of Trier (FRG), dissertation, to appear. 
URI:  http://mpra.ub.unimuenchen.de/id/eprint/29120 