Robust econometric inference with mixed integrated and mildly explosive regressors

DOI10.1016/j.jeconom.2016.02.009zbMath1420.62394OpenAlexW2252586811MaRDI QIDQ281052

Peter C. B. Phillips, Ji Hyung Lee

Publication date: 10 May 2016

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jeconom.2016.02.009

zbMATH Keywords

robustnesslocal to unitypredictive regressionchi-squareinstrumentationIVX methodsmild explosivenessmild integration

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05) Central limit and other weak theorems (60F05)

Related Items (15)

CONSISTENT LOCAL SPECTRUM INFERENCE FOR PREDICTIVE RETURN REGRESSIONS ⋮ On LASSO for predictive regression ⋮ The uniform validity of impulse response inference in autoregressions ⋮ Forecasting vector autoregressions with mixed roots in the vicinity of unity ⋮ Predictive quantile regression with mixed roots and increasing dimensions: the ALQR approach ⋮ Robust inference with stochastic local unit root regressors in predictive regressions ⋮ Low Frequency Cointegrating Regression with Local to Unity Regressors and Unknown Form of Serial Dependence ⋮ A new robust inference for predictive quantile regression ⋮ On the limit theory of mixed to unity VARs: Panel setting with weakly dependent errors ⋮ Predictive regression under various degrees of persistence and robust long-horizon regression ⋮ Testing the Predictability of U.S. Housing Price Index Returns Based on an IVX-AR Model ⋮ Predictive quantile regression with persistent covariates: IVX-QR approach ⋮ Consistent inference for predictive regressions in persistent economic systems ⋮ Moments in Pearson's four-step uniform random walk problem and other applications of very well-poised generalized hypergeometric series ⋮ Predictive quantile regressions under persistence and conditional heteroskedasticity

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