Hostname: page-component-cd9895bd7-7cvxr Total loading time: 0 Render date: 2024-12-27T18:13:26.683Z Has data issue: false hasContentIssue false
  • English
  • Français

A Unified Theory of Estimation and Inference for Nonlinear Dynamic ModelsA.R. Gallant and H. White

Published online by Cambridge University Press:  18 October 2010

Donald W.K. Andrews
Donald W.K. Andrews
Affiliation:
Cowles Foundation, Yale University
Get access Rights & Permissions [Opens in a new window]

Abstract

An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Book Reviews
Information
Econometric Theory , Volume 5 , Issue 1 , April 1989 , pp. 166 - 171
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.)

References

REFERENCES

1.Andrews, D.W.K. Heteroskedasticity and autocorrelation consistent covariance matrix estimation. Cowles Foundation Discussion Paper No. 877, Yale University, 1988.Google Scholar
2.Andrews, D.W.K. Inference in semiparametric models: time series and cross section. Unpublished manuscript, Cowles Foundation, Yale University, 1988.Google Scholar
3.Bates, C. Nonlinear parametric models with dependent observations. Ph.D. Dissertation, Department of Economics, University of Rochester, 1984.Google Scholar
4.Bates, C. & White, H.. A unified theory of consistent estimation for parametric models. Econometric Theory 1 (1985): 151–178.CrossRefGoogle Scholar
5.Billingsley, P.Convergence of probability measures. New York: J. Wiley & Sons, 1968.Google Scholar
6.Burguete, J., Gallant, A.R. & Souza, G.. On the unification of the asymptotic theory of nonlinear econometric models. Econometric Reviews 1 (1982): 151–212.CrossRefGoogle Scholar
7.Domowitz, I. & White, H.. Misspecified models with dependent observations. Journal of Econometrics 20 (1982): 35–58.CrossRefGoogle Scholar
8.Gallant, A.R.Nonlinear statistical models. New York: J. Wiley & Sons, 1987.CrossRefGoogle Scholar
9.Gourieroux, C., Maurel, F. & Monfort, A.. Regression and nonstationarity. Working Paper No. 8708, INSEE, Paris, France, 1987.Google Scholar
10.Huber, P.J.The behaviour of maximum-likelihood estimates under nonstandard conditions. In Proceedings of the Fifth Berkeley Symposium in Mathematical Statistics and Probability 1 (1967): 221–233.Google Scholar
11.Huber, P.J.Robust statistics. New York: J. Wiley & Sons, 1982.Google Scholar
12.Kramer, W.On the consequences of trend for simultaneous equation estimation. Economics Letters 14 (1984): 23–30.CrossRefGoogle Scholar
13.McFadden, D. A method of simulated moments for estimation of multinomial probits without numerical integration. Working paper, Department of Economics, Massachusetts Institute of Technology, 1986.Google Scholar
14.McLeish, D.L.Invariance principles for dependent variables. Zeitschrift fur Wahrscheinlichkeitstheorie and Verwandete Gebiete 32 (1975): 165–178.CrossRefGoogle Scholar
15.Newey, W.K. & West, K.D.. A simple, positive semidefinite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica 55 (1987): 703–708.CrossRefGoogle Scholar
16.Pakes, A. & Pollard, D.. The asymptotics of simulation estimators. Working paper, Department of Statistics, Yale University, 1986.Google Scholar
17.Park, J.Y. & Phillips, P.C.B.. Statistical inference in regressions with integrated processes: Part 1. Cowles Foundation Discussion Paper No. 811, Yale University, 1986.Google Scholar
18.Park, J.Y. & Phillips, P.C.B.. Statistical inference in regressions with integrated processes: Part 2. Cowles Foundation Discussion Paper No. 819, Yale University, 1987.Google Scholar
19.Phillips, P.C.B. Partially identified econometric models. Cowles Foundation Discussion Paper No. 845, Yale University, 1987.Google Scholar
20.Pollard, D.New ways to prove central limit theorems. Econometric Theory 1 (1985): 295–313.CrossRefGoogle Scholar
21.Pollard, D. Bracketing methods in statistics and econometrics. Paper presented at the Fifth International Symposium in Economic Theory and Econometrics, Durham, North Carolina, 1988.Google Scholar
22.Powell, J.L.Least absolute deviations estimation for the censored regression model. Journal of Econometrics 25 (1984): 303–325.CrossRefGoogle Scholar
23.Robinson, P.M. Automatic generalized least squares. Working Paper, London School of Economics, 1988.Google Scholar
24.Wooldridge, J. Asymptotic properties of econometric estimators. Ph.D. Dissertation, Department of Economics, University of California, San Diego, 1986.Google Scholar
25.Wooldridge, J. & White, H.. Consistency of optimization estimators. Discussion Paper 85–29, Department of Economics, University of California, San Diego, 1985.Google Scholar
26.Wu, C.-F.Asymptotic theory of nonlinear least-squares estimation. Annals of Statistics 9 (1981): 501–513.CrossRefGoogle Scholar