ASYMPTOTIC THEORY FOR LOCAL TIME DENSITY ESTIMATION AND NONPARAMETRIC COINTEGRATING REGRESSION

Asymptotic theory is developed for local time density estimation for a general class of functionals of integrated and fractionally integrated time series. The main result provides a convenient basis for developing a limit theory for nonparametric cointegrating regression and nonstationary autoregres...

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Vydáno v:Econometric theory Ročník 25; číslo 3; s. 710 - 738
Hlavní autoři: Wang, Qiying, Phillips, Peter C.B.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York, USA Cambridge University Press 01.06.2009
Témata:
Alternative approaches
Asymptotic methods
Asymptotic theory
Bandwidths
Brownian motion
Cointegration
Density
Density estimation
Econometrics
Economic theory
Estimation
Estimation theory
Markov analysis
Martingales
Mathematical methods
Model testing
Nonparametric models
Probabilities
Random variables
Regression analysis
Stochastic models
Studies
Theory
Time
Time series
ISSN:0266-4666, 1469-4360
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Shrnutí:Asymptotic theory is developed for local time density estimation for a general class of functionals of integrated and fractionally integrated time series. The main result provides a convenient basis for developing a limit theory for nonparametric cointegrating regression and nonstationary autoregression. The treatment directly involves local time estimation and the density function of the processes under consideration, providing an alternative approach to the Markov chain and Fourier integral methods that have been used in other recent work on these problems.
Bibliografie:ark:/67375/6GQ-9ST1KJQT-H
PII:S0266466608090269
ArticleID:09026
The authors thank the co-editor and two referees for helpful comments on the original version. Wang acknowledges partial research support from the Australian Research Council. Phillips acknowledges partial research support from a Kelly Fellowship and the NSF under grant, SES 04-142254 and SES 06-47086. Wang can be contacted at qiying@maths.usyd.edu.au.
istex:197207267B9D8D70FEFFEE050CCF73F64E22B2FA
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ISSN:0266-4666
1469-4360
DOI:10.1017/S0266466608090269