Implementing Residual-Based KPSS Tests for Cointegration with Data Subject to Temporal Aggregation and Mixed Sampling Frequencies

DOI10.1111/JTSA.12188zbMath1403.62167OpenAlexW2297892436MaRDI QIDQ2830681

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Publication date: 28 October 2016

Published in: Journal of Time Series Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/jtsa.12188

zbMATH Keywords

cointegrationtemporal aggregationMIDASKPSS testdynamic OLSmixed sampling frequenciesresidual-based cointegration test

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Related Items (3)

Reverse restricted MIDAS model with application to US interest rate forecasts ⋮ Frequency domain estimation of cointegrating vectors with mixed frequency and mixed sample data ⋮ Testing Cointegrating Relationships Using Irregular and Non‐Contemporaneous Series with an Application to Paleoclimate Data

Cites Work

• Predicting volatility: getting the most out of return data sampled at different frequencies
• Testing for cointegration: Power versus frequency of observation--another view
• Testing the null hypothesis of stationarity against the alternative of a unit root. How sure are we that economic time series have a unit root?
• Regression models with mixed sampling frequencies
• Time aggregation and skip sampling in cointegration tests.
• Testing for cointegration: power versus frequency of observation -- further Monte Carlo results
• Cointegration and sampling frequency
• Note on the Correlation of First Differences of Averages in a Random Chain
• STATIONARITY TESTS FOR IRREGULARLY SPACED OBSERVATIONS AND THE EFFECTS OF SAMPLING FREQUENCY ON POWER
• Testing for Cointegration with Temporally Aggregated and Mixed‐Frequency Time Series
• THE ASYMPTOTIC EFFICIENCY OF COINTEGRATION ESTIMATORS UNDER TEMPORAL AGGREGATION
• Forecasting Mixed‐Frequency Time Series with ECM‐MIDAS Models
• Error Correction and Long-Run Equilibrium in Continuous Time

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