A CONVERGENT t-STATISTIC IN SPURIOUS REGRESSIONS

DOI10.1017/S0266466604205072zbMath1071.62061MaRDI QIDQ5696356

Yixiao Sun

Publication date: 18 October 2005

Published in: Econometric Theory (Search for Journal in Brave)

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Linear regression; mixed models (62J05) Functional limit theorems; invariance principles (60F17)

Related Items (12)

Multivariate trend function testing with mixed stationary and integrated disturbances ⋮ Long run variance estimation and robust regression testing using sharp origin kernels with no truncation ⋮ A FIXED-bTEST FOR A BREAK IN LEVEL AT AN UNKNOWN TIME UNDER FRACTIONAL INTEGRATION ⋮ Spurious regression ⋮ Spurious regressions between stationary generalized long memory processes ⋮ Asymptotic F test in regressions with observations collected at high frequency over long span ⋮ Real-time monitoring test for realized volatility ⋮ High-dimensional IV cointegration estimation and inference ⋮ Robust testing for explosive behavior with strongly dependent errors ⋮ Distribution theory for the Studentized mean for long, short, and negative memory time series ⋮ Weak \(\sigma\)-convergence: theory and applications ⋮ Spurious regressions in technical trading

Cites Work

• Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation
• Understanding spurious regressions in econometrics
• Spurious regressions in econometrics
• Spurios regression theory with nonstationary fractionally integrated processes
• Alternative forms of fractional Brownian motion
• Semiparametric analysis of long-memory time series
• Weak convergence of multivariate fractional processes
• Trend stationarity versus long-range dependence in time series analysis
• The spurious regression of fractionally integrated processes
• THE FUNCTIONAL CENTRAL LIMIT THEOREM AND WEAK CONVERGENCE TO STOCHASTIC INTEGRALS II
• Trends versus Random Walks in Time Series Analysis
• Inference-Without-Smoothing in the Presence of Nonparametric Autocorrelation
• New Tools for Understanding Spurious Regressions
• SAMPLE MEANS, SAMPLE AUTOCOVARIANCES, AND LINEAR REGRESSION OF STATIONARY MULTIVARIATE LONG MEMORY PROCESSES
• Heteroskedasticity-Autocorrelation Robust Standard Errors Using The Bartlett Kernel Without Truncation

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