Nonparametric Regression Estimation for Multivariate Null Recurrent Processes

Econometrics

Biqing Cai²  Dag Tjøstheim¹ 

[1] Department of Mathematics, University of Bergen, 5020 Bergen, Norway; E-Mail

关键词: β-null recurrent;    cointegration;    conditional heteroscedasticity;    Markov chain;    nonparametric regression;   

DOI  :  10.3390/econometrics3020265

来源: mdpi

【 摘 要 】

This paper discusses nonparametric kernel regression with the regressor being a d-dimensional β-null recurrent process in presence of conditional heteroscedasticity. We show that the mean function estimator is consistent with convergence rateis the number of regenerations for a β-null recurrent process and the limiting distribution (with proper normalization) is normal. Furthermore, we show that the two-step estimator for the volatility function is consistent. The finite sample performance of the estimate is quite reasonable when the leave-one-out cross validation method is used for bandwidth selection. We apply the proposed method to study the relationship of Federal funds rate with 3-month and 5-year T-bill rates and discover the existence of nonlinearity of the relationship. Furthermore, the in-sample and out-of-sample performance of the nonparametric model is far better than the linear model.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.