Functional Coefficient Nonstationary Regression with Non- and Semi-Parametric Cointegration

Jiti Gao and Peter Phillips

No 16/13, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: This paper studies a general class of nonlinear varying coefficient time series models with possible nonstationarity in both the regressors and the varying coefficient components. The model accommodates a cointegrating structure and allows for endo-geneity with contemporaneous correlation among the regressors, the varying coefficient drivers, and the residuals. This framework allows for a mixture of stationary and non-stationary data and is well suited to a variety of models that are commonly used in applied econometric work. Nonparametric and semiparametric estimation methods are proposed to estimate the varying coefficient functions. The analytical findings reveal some important differences, including convergence rates, that can arise in the conduct of semiparametric regression with nonstationary data. The results include some new asymptotic theory for nonlinear functionals of nonstationary and stationary time series that are of wider interest and applicability and subsume much earlier research on such systems. The finite sample properties of the proposed econometric methods are analyzed in simulations. An empirical illustration examines nonlinear dependencies in aggregate consumption function behavior in the US over the period 1960 - 2009.

Keywords: fractional Aggregate consumption; Asymptotic theory; cointegration; density; local time; nonlinear functional; nonparametric estimation; semiparametric; time series; varying coefficient model. (search for similar items in EconPapers)
Date: 2013
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (9)

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