This paper applies a local-linear non-parametric kernel regression technique to examine the effect of macroeconomic …

This paper applies a local-linear non-parametric kernel regression technique to examine the effect of macroeconomic …

This paper applies a local-linear non-parametric kernel regression technique to examine the effect of macroeconomic …

parametric time series analysis with nonparametric local linear kernel regression. The evidence from the two seemingly competing … approaches (parametric versus nonparametric), complemented each other in this paper. However, the nonparametric model provided … growth from both the parametric and nonparametric models. …

In semiparametric models it is a common approach to under-smooth the nonparametric functions in order that estimators … estimator and use them to define an optimal bandwidth for the purposes of index estimation. As a result we obtain a practically …

In semiparametric models it is a common approach to under-smooth the nonparametric functions in order that estimators … estimator and use them to define an optimal bandwidth for the purposes of index estimation. As a result we obtain a practically …

We propose a nonparametric approach to the estimation and testing of structural change in time series regression models …. Under the null of a given set of the coefficients being constant, we develop estimators of both the nonparametric and …

The nonparametric censored regression model, with a fixed, known censoring point (normalized to zero), is y = max[0,m … estimators of m(x) and its derivatives. The convergence rate is the same as for an uncensored nonparametric regression and its … heteroscedasticity. We also extend the estimator to the nonparametric truncated regression model, in which only uncensored data points …

of these estimators to a full nonparametric specication with multiple regressors. In relation to the classic weak …