Uniform Asymptotic Normality In Stationary And Unit Root Autoregression
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- Chirok Han & Peter C.B. Phillips & Donggyu Sul, 2010. "Uniform Asymptotic Normality in Stationary and Unit Root Autoregression," Cowles Foundation Discussion Papers 1746, Cowles Foundation for Research in Economics, Yale University.
References listed on IDEAS
- Phillips, Peter C B, 1995.
"Fully Modified Least Squares and Vector Autoregression,"
Econometric Society, vol. 63(5), pages 1023-1078, September.
- Peter C.B. Phillips, 1993. "Fully Modified Least Squares and Vector Autoregression," Cowles Foundation Discussion Papers 1047, Cowles Foundation for Research in Economics, Yale University.
- Kim, Yangseon & Qian, Hailong & Schmidt, Peter, 1999. "Efficient GMM and MD estimation of autoregressive models," Economics Letters, Elsevier, vol. 62(3), pages 265-270, March.
- Peter C.B. Phillips & Chin Chin Lee, 1996. "Efficiency Gains from Quasi-Differencing Under Nonstationarity," Cowles Foundation Discussion Papers 1134, Cowles Foundation for Research in Economics, Yale University.
- Phillips, Peter C.B. & Han, Chirok, 2008.
"Gaussian Inference In Ar(1) Time Series With Or Without A Unit Root,"
Cambridge University Press, vol. 24(03), pages 631-650, June.
- Peter C. B. Phillips & Chirok Han, 2006. "Gaussian Inference in AR(1) Time Series with or without a Unit Root," Cowles Foundation Discussion Papers 1546, Cowles Foundation for Research in Economics, Yale University.
- Roy, Anindya & Fuller, Wayne A, 2001. "Estimation for Autoregressive Time Series with a Root Near 1," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 482-493, October.
- Phillips, Peter C.B. & Magdalinos, Tassos, 2009.
"Unit Root And Cointegrating Limit Theory When Initialization Is In The Infinite Past,"
Cambridge University Press, vol. 25(06), pages 1682-1715, December.
- Peter C.B. Phillips & Tassos Magdalinos, 2008. "Unit Root and Cointegrating Limit Theory When Initialization Is in the Infinite Past," Cowles Foundation Discussion Papers 1655, Cowles Foundation for Research in Economics, Yale University.
- Andrews, Donald W.K., 1988.
"Laws of Large Numbers for Dependent Non-Identically Distributed Random Variables,"
Cambridge University Press, vol. 4(03), pages 458-467, December.
- Andrews, Donald W. K., 1987. "Laws of Large Numbers for Dependent Non-Identically Distributed Random Variables," Working Papers 645, California Institute of Technology, Division of the Humanities and Social Sciences.
- Andrews, Donald W K, 1993. "Exactly Median-Unbiased Estimation of First Order Autoregressive/Unit Root Models," Econometrica, Econometric Society, vol. 61(1), pages 139-165, January.
CitationsCitations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
- Gorodnichenko, Yuriy & Mikusheva, Anna & Ng, Serena, 2012.
"Estimators For Persistent And Possibly Nonstationary Data With Classical Properties,"
Cambridge University Press, vol. 28(05), pages 1003-1036, October.
- Yuriy Gorodnichenko & Anna Mikusheva & Serena Ng, 2011. "Estimators for Persistent and Possibly Non-Stationary Data with Classical Properties," NBER Working Papers 17424, National Bureau of Economic Research, Inc.
- Han, Chirok & Phillips, Peter C.B., 2013. "First difference maximum likelihood and dynamic panel estimation," Journal of Econometrics, Elsevier, vol. 175(1), pages 35-45.
- Jhih-Gang Chen & Biing-Shen Kuo, 2013. "Gaussian inference in general AR(1) models based on difference," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(4), pages 447-453, July.
- Joakim Westerlund & Mehdi Hosseinkouchack, 2016. "Modified CADF and CIPS Panel Unit Root Statistics with Standard Chi-squared and Normal Limiting Distributions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(3), pages 347-364, June.
More about this item
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
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