作者：HANSEN, BE
摘要：A new asymptotic theory of regression is introduced for possibly nonstationary time series. The regressors are assumed to be generated by a linear process with martingale difference innovations. The conditional variances of these martingale differences are specified as autoregressive stochastic volatility processes, with autoregressive roots which are local to unity. We find conditions under which the least squares estimates are consistent and asymptotically normal. A simple adaptive estimator...
作者：LINTON, O
摘要：We examine the second order properties of various quantities of interest in the partially linear regression model. We obtain a stochastic expansion with remainder o(p)(n(-2 mu)), where mu < 1/2, for the standardized semiparametric least squares estimator, a standard error estimator, and a studentized statistic. We use the second order expansions to correct the standard error estimates for second order effects, and to define a method of bandwidth choice. A Monte Carlo experiment provides favora...
