Generalized least squares estimation for cointegration parameters under conditional heteroskedasticity
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- Helmut Herwartz & Helmut Luetkepohl, 2009. "Generalized Least Squares Estimation for Cointegration Parameters Under Conditional Heteroskedasticity," Economics Working Papers ECO2009/42, European University Institute.
References listed on IDEAS
- Christian M. Hafner & Helmut Herwartz, 2009. "Testing for linear vector autoregressive dynamics under multivariate generalized autoregressive heteroskedasticity," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 63(3), pages 294-323.
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- Seo, Byeongseon, 2007. "Asymptotic distribution of the cointegrating vector estimator in error correction models with conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 137(1), pages 68-111, March.
CitationsCitations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
- Westerlund, Joakim, 2014. "On the choice of test for a unit root when the errors are conditionally heteroskedastic," Computational Statistics & Data Analysis, Elsevier, vol. 69(C), pages 40-53.
- López Cabrera, Brenda & Schulz, Franziska, 2016.
"Volatility linkages between energy and agricultural commodity prices,"
Elsevier, vol. 54(C), pages 190-203.
- Brenda López Cabrera, & Franziska Schulz,, 2013. "Volatility linkages between energy and agricultural commodity prices," SFB 649 Discussion Papers SFB649DP2013-042, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
- Joakim Westerlund & Paresh Narayan, 2013. "Testing the Efficient Market Hypothesis in Conditionally Heteroskedastic Futures Markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 33(11), pages 1024-1045, November.
- Joakim Westerlund, 2013. "A computationally convenient unit root test with covariates, conditional heteroskedasticity and efficient detrending," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(4), pages 477-495, July.
More about this item
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
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