Structural Break Detection in Quantile Predictive Regression Models with Persistent Covariates
We propose an econometric environment for structural break detection in nonstationary quantile predictive regressions. We establish the limit distributions for a class of Wald and fluctuation type statistics based on both the ordinary least squares estimator and the endogenous instrumental regression estimator proposed by Phillips and Magdalinos (2009a, Econometric Inference in the Vicinity of Unity. Working paper, Singapore Management University). Although the asymptotic distribution of these test statistics appears to depend on the chosen estimator, the IVX based tests are shown to be asymptotically nuisance parameter-free regardless of the degree of persistence and consistent under local alternatives. The finite-sample performance of both tests is evaluated via simulation experiments. An empirical application to house pricing index returns demonstrates the practicality of the proposed break tests for regression quantiles of nonstationary time series data.
Code (0)
등록된 구현이 없습니다.
Tasks
ManagementregressionTime SeriesTime Series AnalysisUnityMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Unified Inference for Dynamic Quantile Predictive Regression
This paper develops unified asymptotic distribution theory for dynamic quantile predictive regressions which is useful when examining quantile predictability in stock returns under possible presence of nonstationarity.
regressionBreak-Point Date Estimation for Nonstationary Autoregressive and Predictive Regression Models
In this article, we study the statistical and asymptotic properties of break-point estimators in nonstationary autoregressive and predictive regression models for testing the presence of a single structural break at an u…
regressionInference in Predictive Quantile Regressions
This paper studies inference in predictive quantile regressions when the predictive regressor has a near-unit root. We derive asymptotic distributions for the quantile regression estimator and its heteroskedasticity and …
quantile regressionregressionUnityEstimating Conditional Value-at-Risk with Nonstationary Quantile Predictive Regression Models
This paper develops an asymptotic distribution theory for an endogenous instrumentation approach in quantile predictive regressions when both generated covariates and persistent predictors are used. The generated covaria…
regressionDeep Huber quantile regression networks
Typical machine learning regression applications aim to report the mean or the median of the predictive probability distribution, via training with a squared or an absolute error scoring function. The importance of issui…
quantile regressionregression