Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-26T06:44:28.661Z Has data issue: false hasContentIssue false

LOCAL LIMIT THEORY AND SPURIOUS NONPARAMETRIC REGRESSION

Published online by Cambridge University Press:  01 December 2009

Peter C.B. Phillips*
Affiliation:
Yale University, University of Auckland, University of York and Singapore Management University
*
*Address correspondence to Peter C.B. Phillips, Department of Economics, Yale University, P.O. Box 208268, New Haven, CT 06520–8268, USA; e-mail: [email protected].

Abstract

A local limit theorem is proved for sample covariances of nonstationary time series and integrable functions of such time series that involve a bandwidth sequence. The resulting theory enables an asymptotic development of nonparametric regression with integrated or fractionally integrated processes that includes the important practical case of spurious regressions. Some local regression diagnostics are suggested for forensic analysis of such regresssions, including a local R2 and a local Durbin–Watson (DW) ratio, and their asymptotic behavior is investigated. The most immediate findings extend the earlier work on linear spurious regression (Phillips, 1986, Journal of Econometrics 33, 311–340) showing that the key behavioral characteristics of statistical significance, low DW ratios and moderate to high R2 continue to apply locally in nonparametric spurious regression. Some further applications of the limit theory to models of nonlinear functional relations and cointegrating regressions are given. The methods are also shown to be applicable in partial linear semiparametric nonstationary regression.

Type
ARTICLES
Copyright
Copyright © Cambridge University Press 2009

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

Akonom, J. (1993) Comportement asymptotique du temps d’occupation du processus des sommes partielles. Annals of the Institute of Henri Poincaré 29, 5781.Google Scholar
Berkes, I. & Horváth, L. (2006) Convergence of integral functionals of stochastic processes. Econometric Theory 20, 627635.Google Scholar
Borodin, A.N. & Ibragimov, I.A. (1995) Limit Theorems for Functionals of Random Walks. Proceedings of the Steklov Institute of Mathematics, vol. 195, no. 2. American Mathematical Society.Google Scholar
Box, G.E.P. & Jenkins, G.M. (1970) Time Series Analysis, Forecasting and Control. Holden-Day.Google Scholar
Box, G.E.P. & Pierce, D.A. (1970) The distribution of residual autocorrelations in autoregressive-integrated moving average time series models. Journal of the American Statistical Association 65, 15091526.CrossRefGoogle Scholar
de Jong, R.M. (2004) Addendum to: “Asymptotics for nonlinear transformations of integrated time series.” Econometric Theory 20, 627635.Google Scholar
de Jong, R.M. & Wang, C.-H. (2005) Further results on the asymptotics for nonlinear transformations of integrated time series. Econometric Theory 21, 413430.Google Scholar
Fisher, I. (1907) The Rate of Interest. Macmillan.Google Scholar
Fisher, I. (1930) The Theory of Interest. Macmillan.Google Scholar
Gao, J. (2007) Nonlinear Time Series: Semiparametric and Nonparametric Methods. Chapman and Hall.Google Scholar
Geman, D. & Horowitz, J. (1980) Occupation densities. Annals of Probability 8, 167.Google Scholar
Granger, C.W.J. & Newbold, P. (1974) Spurious regressions in econometrics. Journal of Econometrics 74, 111120.Google Scholar
Härdle, W. & Linton, O. (1994) Applied nonparametric methods. In McFadden, D.F. & Engle, R.F. III, eds., The Handbook of Econometrics, vol. 4, pp. 22952339. North–Holland.Google Scholar
Hong, S.-H., & Phillips, P.C.B. (2006) Testing Linearity in Cointegrating Relations with an Application to Purchasing Power Parity. Working paper, Yale University.Google Scholar
Hooker, R.H. (1901) Correlation of the marriage rate with trade. Journal of the Royal Statistical Society 64, 485492.Google Scholar
Hooker, R.H. (1905) On the correlation of successive observations. Journal of the Royal Statistical Society 68, 696703.Google Scholar
Horowitz, J.L. (1998) Semiparametric Methods in Econometrics. Springer-Verlag.Google Scholar
Huang, L.-S. & Chen, J. (2008) Analysis of variance, coefficient of determination, and F-test for local polynomial regression. Annals of Statistics 36, 20852109.Google Scholar
Jeganathan, P. (2004) Convergence of functionals of sums of r.v.s to local times of fractional stable motions. Annals of Probability 32, 17711795.Google Scholar
Karatzas, I. & Shreve, S.E. (1991) Brownian Motion and Stochastic Calculus, 2nd ed.Springer-Verlag.Google Scholar
Karlsen, H.A., Myklebust, T., & Tjøstheim, D. (2007) Nonparametric estimation in a nonlinear cointegration model. Annals of Statistics 35, 252299.Google Scholar
Kasparis, I. (2006) Detection of Functional Form Misspecification in Cointegrating Relations: Working paper, University of Nottingham.Google Scholar
Li, Q. & Racine, J.S. (2007) Nonparametric Econometrics: Theory and Practice. Princeton University Press.Google Scholar
Pagan, A. & Ullah, A. (1999) Nonparametric Econometrics. Cambridge University Press.CrossRefGoogle Scholar
Park, J. (2006) Nonstationary nonlinearity: An outlook for new opportunities. In Corbae, D., Durlauf, S.N., & Hansen, B.E., eds., Econometric Theory and Practice, pp. 178211. Cambridge University Press.CrossRefGoogle Scholar
Park, J. & Phillips, P.C.B. (1999) Asymptotics for nonlinear transformations of integrated time series. Econometric Theory 15, 269298.Google Scholar
Park, J.Y. & Phillips, P.C.B. (2000) Nonstationary binary choice. Econometrica 68, 12491280.Google Scholar
Park, J. & Phillips, P.C.B. (2001) Nonlinear regression with integrated time series. Econometrica 69, 117161.CrossRefGoogle Scholar
Persons, W.M. (1910) The correlation of economic statistics. Journal of the American Statistical Association 12, 287333.Google Scholar
Phillips, P.C.B. (1986) Understanding spurious regressions in econometrics. Journal of Econometrics 33, 311340.Google Scholar
Phillips, P.C.B. (1988) Multiple regression with integrated processes. In Prabhu, N.U., ed., Statistical Inference from Stochastic Processes. Contemporary Mathematics 80, pp. 79106.CrossRefGoogle Scholar
Phillips, P.C.B. (1998) New tools for understanding spurious regressions. Econometrica 66, 12991326.CrossRefGoogle Scholar
Phillips, P.C.B. (2001) Descriptive econometrics for nonstationary time series with empirical illustrations. Journal of Applied Econometrics 16, 389413.Google Scholar
Phillips, P.C.B. (2005a) Challenges of trending time series econometrics. Mathematics and Computers in Simulation 68, 401416.Google Scholar
Phillips, P.C.B. (2005b) Econometric analysis of Fisher’s equation. American Journal of Economics and Sociology 64, 125168.Google Scholar
Phillips, P.C.B. & Hansen, B.E. (1990) Standard inference in instrumental variable regressions with I(1) processes. Review of Economic Studies 57, 99125.Google Scholar
Phillips, P.C.B. & Park, J.Y. (1998) Nonstationary Density Estimation and Kernel Autoregression, Cowles Foundation Discussion paper 1182, Yale University.Google Scholar
Phillips, P.C.B. & Solo, V. (1992) Asymptotics for linear processes. Annals of Statistics 20, 9711001.Google Scholar
Pötscher, B.M. (2004) Nonlinear functions and convergence to Brownian motion: Beyond the continuous mapping theorem. Econometric Theory 20, 122.Google Scholar
Revuz, D. & Yor, M. (1999) Continuous Martingale and Brownian Motion, 3rd ed.Springer-Verlag.Google Scholar
“Student.” (1914) The elimination of spurious correlation due to position in time or space. Biometrika 10, 179180.Google Scholar
Wang, Q. & Phillips, P.C.B. (2009a) Asymptotic theory for local time density estimation and nonparametric cointegrating regression. Econometric Theory 25, 710738.Google Scholar
Wang, Q. & Phillips, P.C.B. (2009b) Structural nonparametric cointegrating regression. Econometrica, forthcoming.Google Scholar
Yatchew, A. (2003) Semiparametric Regression for the Applied Econometrician. Cambridge University Press.Google Scholar
Yule, G.U. (1921) On the time-correlation problem, with especial reference to the variate-difference correlation method. Journal of the Royal Statistical Society 84, 497537.Google Scholar
Yule, G.U. (1926) Why do we sometimes get nonsense-correlations between time series? A study in sampling and the nature of time series. Journal of the Royal Statistical Society 89, 169.CrossRefGoogle Scholar