Dynamic misspecification in nonparametric cointegrating regression

Autor: Kasparis, Ioannis, Phillips, Peter C. B.
Přispěvatelé: Kasparis, Ioannis [0000-0002-9792-4183]
Rok vydání: 2012
Předmět:
Distributed lag
Local time
Statistics::Theory
Economics and Econometrics
Functional regression
jel:C22
Dynamic misspecification
Functional regression
Integrable function
Integrated process
Local time
Misspecification
Mixed normality
Nonlinear cointegration
Nonparametric regression
Specifications
Statistical tests
Nonlinear cointegration
Dynamic misspecification
Integrable functions
Econometrics
Statistics::Methodology
Integrable function
Limit (mathematics)
Divergence (statistics)
Statistical hypothesis testing
Mathematics
Polynomial regression
Non-parametric regression
Cointegration
Integrated process
Applied Mathematics
Nonparametric statistics
Estimator
jel:C32
Nonparametric regression
Regression
Statistics::Computation
Linearity test
Misspecification
Kernel regression
Regression analysis
Estimation
Mixed normality
Zdroj: Journal of Econometrics
ISSN: 0304-4076
DOI: 10.1016/j.jeconom.2012.01.037
Popis: Linear cointegration is known to have the important property of invariance under temporal translation. The same property is shown not to apply for nonlinear cointegration. The requisite limit theory involves sample covariances of integrable transformations of non-stationary sequences and time translated sequences, allowing for the presence of a bandwidth parameter so as to accommodate kernel regression. The theory is an extension of Wang and Phillips (2008) and is useful for the analysis of nonparametric regression models with a misspecified lag structure and in situations where temporal aggregation issues arise. The limit properties of the Nadaraya-Watson (NW) estimator for cointegrating regression under misspecified lag structure are derived, showing the NW estimator to be inconsistent with a 'pseudo-true function' limit that is a local average of the true regression function. In this respect nonlinear cointegrating regression differs importantly from conventional linear cointegration which is invariant to time translation. When centered on the pseudo-function and appropriately scaled, the NW estimator still has a mixed Gaussian limit distribution. The convergence rates are the same as those obtained under correct specification but the variance of the limit distribution is larger. Some applications of the limit theory to non-linear distributed lag cointegrating regression are given and the practical import of the results for index models, functional regression models, and temporal aggregation are discussed.
Databáze: OpenAIRE
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