Measuring forecast uncertainty : A review with evaluation based on a macro model of the French economy

Abstract Five alternative techniques have been applied to measure the degree of uncertainty associated with the forecasts produced by a macro-model of the French economy, the Mini-DMS developed at INSEE. They are bootstrap, analytic simulation on coefficients, Monte Carlo on coefficients, parametric stochastic simulation and re-estimation, a residual-based procedure. Due to the complexity and the size of the model (nonlinear and with more than 200 equations), several associated technical problems had to be solved. The remarkable convergence of results which has been obtained for all the main endogenous variables suggests that forecast confidence intervals are likely to be quite reliable for this model.

[1]  M. D. McCarthy.  A Note on the Forecasting Properties of Two Stage Least Squares Restricted Reduced Forms-The Finite Sample Case , 1972 .

[2]  R. Mariano,et al.  Residual-Based Procedures for Prediction and Estimation in a Nonlinear Simultaneous System , 1984 .

[3]  G. Calzolari,et al.  Analyse et mesure de l'incertitude en prevision d'un modele econometrique. Application au modele mini-DMS , 1984 .

[4]  Lawrence R. Klein,et al.  Estimation on Interdependent Systems in Macroeconometrics , 1969 .

[5]  J. Cooper,et al.  Monetary and Fiscal Policy in the Fully Stochastic St. Louis Econometric Model , 1974 .

[6]  A. Ronald Gallant,et al.  Three-stage least-squares estimation for a system of simultaneous, nonlinear, implicit equations , 1977 .

[7]  Giorgio Calzolari,et al.  Control Variates to Estimate the Reduced Form Variances in Econometric Models , 1986 .

[8]  Giorgio Calzolari,et al.  A NOTE ON THE VARIANCE OF EX-POST FORECASTS IN , 1981 .

[9]  D. Freedman,et al.  Using the bootstrap to evaluate forecasting equations , 1985 .

[10]  P. Fisher,et al.  On Evaluating the Importance of Non-linearity in Large Macroeconometric Models , 1986 .

[11]  George Roland Schink,et al.  Small sample estimates of the variance covariance matrix of forecast error for large econometric models : the stochastic simulation technique , 1980 .

[12]  David H. Nissen.  A Note on the Variance of a Matrix , 1968 .

[13]  Peter Schmidt,et al.  THE ASYMPTOTIC DISTRIBUTION OF FORECASTS IN THE DYNAMIC SIMULATION OF AN ECONOMETRIC MODEL , 1974 .

[14]  Giorgio Calzolari,et al.  Forecast variance in simultaneous equation models: analytic and Monte Carlo methods , 1987 .

[15]  D. Freedman,et al.  Bootstrapping an Econometric Model: Some Empirical Results , 1984 .

[16]  M. Hatanaka.  On the efficient estimation methods for the macro-economic models nonlinear in variables , 1978 .

[17]  Carlo Bianchi,et al.  The One-Period Forecast Errors in Nonlinear Econometric Models , 1980 .

[18]  B. Efron.  Bootstrap Methods: Another Look at the Jackknife , 1979 .

[19]  Ray C. Fair,et al.  Estimating the Expected Predictive Accuracy of Econometric Models , 1980 .

[20]  Giorgio Calzolari,et al.  Evaluating forecast uncertainty due to errors in estimated coefficients: empirical comparison of alternative methods , 1982 .

[21]  Neil Wallace,et al.  A Study of Discretionary and Nondiscretionary Monetary and Fiscal Policies in the Context of Stochastic Macroeconometric Models , 1972 .

[22]  Roberto S. Mariano,et al.  Analytical Small-Sample Distribution Theory in Econometrics: The Simultaneous Equations Case , 1982 .

[23]  Stephen G. Hall,et al.  The importance of non-linearities in large forecasting models with stochastic error processes , 1986 .

[24]  T. Amemiya.  Non-linear regression models , 1983 .

[25]  Dale W. Jorgenson,et al.  Efficient Estimation of Simultaneous Equations by Instrumental Variables , 1971 .

[26]  Calyampudi R. Rao,et al.  Linear Statistical Inference and Its Applications. , 1975 .

[27]  Giorgio Calzolari,et al.  Standard errors of forecasts in dynamic simulation of nonlinear econometric models: some empirical results , 1983 .

[28]  A. Goldberger,et al.  The Covariance Matrices of Reduced-Form Coefficients and of Forecasts for a Structural Econometric Model , 1961 .