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A similarity‐based approach to time‐varying coefficient non‐stationary autoregression


  • Offer Lieberman


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  • Offer Lieberman, 2012. "A similarity‐based approach to time‐varying coefficient non‐stationary autoregression," Journal of Time Series Analysis, Wiley Blackwell, vol. 33(3), pages 484-502, May.
  • Handle: RePEc:bla:jtsera:v:33:y:2012:i:3:p:484-502 DOI: j.1467-9892.2012.00783.x

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    References listed on IDEAS

    1. Weiss, Andrew A., 1984. "Systematic sampling and temporal aggregation in time series models," Journal of Econometrics, Elsevier, vol. 26(3), pages 271-281, December.
    2. Ricardo J. Caballero & Eduardo M. R. A. Engel, 1993. "Microeconomic Adjustment Hazards and Aggregate Dynamics," The Quarterly Journal of Economics, Oxford University Press, vol. 108(2), pages 359-383.
    3. Stock, James H., 1987. "Measuring Business Cycle Time," Scholarly Articles 3425950, Harvard University Department of Economics.
    4. Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
    5. Arthur F. Burns & Wesley C. Mitchell, 1946. "Measuring Business Cycles," NBER Books, National Bureau of Economic Research, Inc, number burn46-1, January.
    6. Marcellino, M., 1997. "Temporal Disaggregation, Missing Observations, Outliers, and Forecasting: A Unifying Non-Model Based Procedures," Economics Working Papers eco97/30, European University Institute.
    7. Jorda, Oscar, 1999. "Random-Time Aggregation in Partial Adjustment Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(3), pages 382-395, July.
    8. Marcellino, Massimiliano, 1999. "Some Consequences of Temporal Aggregation in Empirical Analysis," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 129-136, January.
    9. Brewer, K. R. W., 1973. "Some consequences of temporal aggregation and systematic sampling for ARMA and ARMAX models," Journal of Econometrics, Elsevier, vol. 1(2), pages 133-154, June.
    10. Durland, J Michael & McCurdy, Thomas H, 1994. "Duration-Dependent Transitions in a Markov Model of U.S. GNP Growth," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 279-288, July.
    11. White,Halbert, 1996. "Estimation, Inference and Specification Analysis," Cambridge Books, Cambridge University Press, number 9780521574464, March.
    12. Hinich, Melvin J., 1999. "Sampling Dynamical Systems," Macroeconomic Dynamics, Cambridge University Press, vol. 3(04), pages 602-609, December.
    13. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    14. Stock, James H, 1987. "Measuring Business Cycle Time," Journal of Political Economy, University of Chicago Press, vol. 95(6), pages 1240-1261, December.
    15. Hannan, E J, 1971. "The Identification Problem for Multiple Equation Systems with Moving Average Errors," Econometrica, Econometric Society, vol. 39(5), pages 751-765, September.
    16. Rothenberg, Thomas J, 1971. "Identification in Parametric Models," Econometrica, Econometric Society, vol. 39(3), pages 577-591, May.
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    Cited by:

    1. Hamid, Alain & Heiden, Moritz, 2015. "Forecasting volatility with empirical similarity and Google Trends," Journal of Economic Behavior & Organization, Elsevier, vol. 117(C), pages 62-81.
    2. Kapetanios, George & Mitchell, James & Shin, Yongcheol, 2014. "A nonlinear panel data model of cross-sectional dependence," Journal of Econometrics, Elsevier, vol. 179(2), pages 134-157.
    3. Lieberman, Offer & Phillips, Peter C.B., 2017. "A multivariate stochastic unit root model with an application to derivative pricing," Journal of Econometrics, Elsevier, vol. 196(1), pages 99-110.
    4. Offer Lieberman & Peter C. B. Phillips, 2014. "Norming Rates And Limit Theory For Some Time-Varying Coefficient Autoregressions," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(6), pages 592-623, November.
    5. Teitelbaum, Joshua C., 2013. "Asymmetric empirical similarity," Mathematical Social Sciences, Elsevier, vol. 66(3), pages 346-351.
    6. Kinjo Keita & Sugawara Shinya, 2016. "Predicting Empirical Patterns in Viewing Japanese TV Dramas Using Case-Based Decision Theory," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 16(2), pages 679-709, June.
    7. Golosnoy, Vasyl & Hamid, Alain & Okhrin, Yarema, 2014. "The empirical similarity approach for volatility prediction," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 321-329.

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