منابع مشابه
Quantile Cointegrating Regression
Quantile regression has important applications in risk management, portfolio optimization, and asset pricing. The current paper studies estimation, inference and nancial applications of quantile regression with cointegrated time series. In addition, a new cointegration model with varying coe¢ cients is proposed. In the proposed model, the value of cointegrating coe¢ cients may be a¤ected by th...
متن کاملStructural Nonparametric Cointegrating Regression
Nonparametric estimation of a structural cointegrating regression model is studied. As in the standard linear cointegrating regression model, the regressor and the dependent variable are jointly dependent and contemporaneously correlated. In nonparametric estimation problems, joint dependence is known to be a major complication that affects identification, induces bias in conventional kernel es...
متن کاملNonparametric LAD cointegrating regression
We deal with nonparametric estimation in a nonlinear cointegration model whose regressor and dependent variable can be contemporaneously correlated. The asymptotic properties of the Nadaraya-Watson estimator are already examined in the literature. In this paper, we consider nonparametric least absolute deviation (LAD) regression and derive the asymptotic distributions of the local constant and ...
متن کاملEXTREMAL QUANTILE REGRESSION 3 quantile regression
Quantile regression is an important tool for estimation of conditional quantiles of a response Y given a vector of covariates X. It can be used to measure the effect of covariates not only in the center of a distribution, but also in the upper and lower tails. This paper develops a theory of quantile regression in the tails. Specifically , it obtains the large sample properties of extremal (ext...
متن کاملSTRUCTURAL NONPARAMETRIC COINTEGRATING REGRESSION By
Nonparametric estimation of a structural cointegrating regression model is studied. As in the standard linear cointegrating regression model, the regressor and the dependent variable are jointly dependent and contemporaneously correlated. In nonparametric estimation problems, joint dependence is known to be a major complication that affects identification, induces bias in conventional kernel es...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Econometrics
سال: 2009
ISSN: 0304-4076
DOI: 10.1016/j.jeconom.2008.12.005