Hostname: page-component-7f64f4797f-kjzhn Total loading time: 0 Render date: 2025-11-04T04:55:10.286Z Has data issue: false hasContentIssue false
  • English
  • Français

Predictors in Dynamic Nonlinear Models: Large-Sample Behavior

Published online by Cambridge University Press:  18 October 2010

Bryan W. Brown  and
Roberto S. Mariano
Bryan W. Brown
Affiliation:
Rice University
Roberto S. Mariano
Affiliation:
University of Pennsylvania
Get access Rights & Permissions [Opens in a new window]

Abstract

The large-sample behavior of one-period-ahead and multiperiod-ahead predictors for a dynamic nonlinear simultaneous system is examined in this paper. Conditional on final values of the endogenous variables, the asymptotic moments of the deterministic, closed-form, Monte Carlo stochastic, and several variations of the residual-based stochastic predictor are analyzed. For one-period-ahead prediction, the results closely parallel our previous findings for static nonlinear systems. For multiperiod-ahead prediction similar results hold, except that the effective number of sample-period residuals available for use with the residual-based predictor is T/m, where T denotes sample size. In an attempt to avoid the problems associated with sample splitting, the complete enumeration predictor is proposed which is a multiperiod-ahead generalization of the one-period-ahead residual-based predictor. A bootstrap predictor is also introduced which is similar to the multiperiod-ahead Monte Carlo except disturbance proxies are drawn from the empirical distribution of the residuals. The bootstrap predictor is found to be asymptotically inefficient relative to both the complete enumeration and Monte Carlo predictors.

Information

Type
Articles
Information
Econometric Theory , Volume 5 , Issue 3 , December 1989 , pp. 430 - 452
Copyright
Copyright © Cambridge University Press 1989

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

REFERENCES

1.Adelman, E. & Adelman, F.. The dynamic properties of the Klein-Goldberger model. Econometrica 27 (1959): 596–625.CrossRefGoogle Scholar
2.Calzolari, G.Antithetic variates to estimate the simulation bias in nonlinear models. Economic Letters 4 (1979): 323–328.CrossRefGoogle Scholar
3.Brown, B.W. & Mariano, R.S.. Residual-based stochastic prediction and estimation in a nonlinear simultaneous system. Econometrica 52 (1984): 321–343.CrossRefGoogle Scholar
4.Brown, B.W. & Mariano, R.S.. Reduced-variance prediction in nonlinear models. Mimeo, Rice University, February, 1988.Google Scholar
5.Howrey, E.P.Stochastic properties of the Klein-Goldberger model. Econometrica 39 (1971): 73–87.CrossRefGoogle Scholar
6.Mariano, R.S. & Brown, B.W.. Asymptotic behavior of predictors in a nonlinear simultaneous system. International Economic Review 24 (1983): 523–536.CrossRefGoogle Scholar
7.Mariano, R.S. & Brown, B.W.. Prediction in dynamic nonlinear models: finite-sample behavior. Mimeo, University of Pennsylvania, June, 1988.Google Scholar
8.Nagar, A.L.Stochastic simulation of the Brookings model. In Duesenberry, J.S., et al., (eds.), The Brookings Model: Some Further Results. Amsterdam: North-Holland, 1969, pp. 425–426.Google Scholar
9.Phillips, P.C.B.The sampling distribution of forecasts from a first-order autoregression. Journal of Econometrics 9(1979): 241–261.CrossRefGoogle Scholar
10.Schmidt, P.The asymptotic distribution of forecasts in the dynamic simulation of an econometric model. Econometrica 42 (1974): 303–309.CrossRefGoogle Scholar
11.Schmidt, P.Some small-sample evidence on the distribution of dynamic simulation forecasts. Econometrica 45 (1977): 997–1005.CrossRefGoogle Scholar
12.Yamamoto, T.Asymptotic mean-squared prediction error for an autoregressive model with estimated coefficients. Applied Statistics 25 (1976): 123–127.CrossRefGoogle Scholar