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- W2152116741 abstract "This paper studies a general class of nonlinear varying coecient time series models with possible nonstationarity in both the regressors and the varying coecient components. The model accommodates a cointegrating structure and allows for endogeneity with contemporaneous correlation among the regressors, the varying coecient drivers, and the residuals. This framework allows for a mixture of stationary and nonstationary 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 coecient functions. The analytical ndings reveal some important dierences, 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 nite 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." @default.
- W2152116741 created "2016-06-24" @default.
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- W2152116741 date "2013-01-01" @default.
- W2152116741 modified "2023-09-27" @default.
- W2152116741 title "Functional Coecient Nonstationary Regression with Non{ and Semi{Parametric Cointegration" @default.
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