Estimation in Semiparametric Time Series Regression
In this paper, we consider a semiparametric time series regression model and establish a set of identi cation conditions such that the model under discussion is both identi able and estimable. We then discuss how to estimate a sequence of local alternative functions nonparametrically when the null hypothesis does not hold. An asymptotic theory is established in each case. An empirical application is also included.
|Date of creation:||Oct 2010|
|Date of revision:|
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