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Recursive estimation in econometrics

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  • Pollock, D. S. G.

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  • Pollock, D. S. G., 2003. "Recursive estimation in econometrics," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 37-75, October.
    • Handle: RePEc:eee:csdana:v:44:y:2003:i:1-2:p:37-75
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    13. Pollock, D. S. G., 2000. "Trend estimation and de-trending via rational square-wave filters," Journal of Econometrics, Elsevier, vol. 99(2), pages 317-334, December.
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    19. Piet De Jong & Singfat Chu‐Chun‐Lin, 2003. "Smoothing With An Unknown Initial Condition," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(2), pages 141-148, March.
    20. Siem Jan Koopman & Neil Shephard & Jurgen A. Doornik, 1999. "Statistical algorithms for models in state space using SsfPack 2.2," Econometrics Journal, Royal Economic Society, vol. 2(1), pages 107-160.
    21. Chu, Chia-Shang James & Hornik, Kurt & Kuan, Chung-Ming, 1995. "The Moving-Estimates Test for Parameter Stability," Econometric Theory, Cambridge University Press, vol. 11(4), pages 699-720, August.
    22. Smith, Richard J. & Robert Taylor, A. M., 2001. "Recursive and rolling regression-based tests of the seasonal unit root hypothesis," Journal of Econometrics, Elsevier, vol. 105(2), pages 309-336, December.
    23. Diebold, Francis X., 1986. "The exact initial covariance matrix of the state vector of a general MA(q) process," Economics Letters, Elsevier, vol. 22(1), pages 27-31.
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    26. Ploberger, Werner & Kramer, Walter & Kontrus, Karl, 1989. "A new test for structural stability in the linear regression model," Journal of Econometrics, Elsevier, vol. 40(2), pages 307-318, February.
    27. Pollock, D. S. G., 2001. "Methodology for trend estimation," Economic Modelling, Elsevier, vol. 18(1), pages 75-96, January.
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    Cited by:

    1. Pollock, D.S.G., 2006. "Econometric methods of signal extraction," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2268-2292, May.
    2. Pollock, D.S.G., 2006. "Introduction to the special issue on statistical signal extraction and filtering," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2137-2145, May.
    3. Bujosa, Marcos & Garcia-Ferrer, Antonio & Young, Peter C., 2007. "Linear dynamic harmonic regression," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 999-1024, October.
    4. Godolphin, E.J. & Triantafyllopoulos, Kostas, 2006. "Decomposition of time series models in state-space form," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2232-2246, May.
    5. Robert A. Hill & Paulo M. M. Rodrigues, 2022. "Forgetting approaches to improve forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1356-1371, November.
    6. Mewael F. Tesfaselassie & Eric Schaling & Sylvester Eijffinger, 2011. "Learning about the Term Structure and Optimal Rules for Inflation Targeting," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 43(8), pages 1685-1706, December.
    7. repec:ejw:journl:v:15:y:2018:i:1:p:51-66 is not listed on IDEAS
    8. O. Olawale Awe & A. Adedayo Adepoju, 2018. "Modified Recursive Bayesian Algorithm For Estimating Time-Varying Parameters In Dynamic Linear Models," Statistics in Transition New Series, Polish Statistical Association, vol. 19(2), pages 239-258, June.
    9. Segarra, Agustí & Teruel, Mercedes, 2012. "An appraisal of firm size distribution: Does sample size matter?," Journal of Economic Behavior & Organization, Elsevier, vol. 82(1), pages 314-328.
    10. Olushina Olawale Awe & Abosede Adedayo Adepoju, 2020. "Change-point detection in CO2 emission-energy consumption nexus using a recursive Bayesian estimation approach," Statistics in Transition New Series, Polish Statistical Association, vol. 21(1), pages 123-136, March.
    11. Lee, Woojoo & Lim, Johan & Lee, Youngjo & del Castillo, Joan, 2011. "The hierarchical-likelihood approach to autoregressive stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 248-260, January.
    12. Mazzocchi, Mario, 2006. "Time patterns in UK demand for alcohol and tobacco: an application of the EM algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2191-2205, May.
    13. Awe Olushina Olawale & Adepoju Abosede Adedayo, 2020. "Change-point detection in CO2 emission-energy consumption nexus using a recursive Bayesian estimation approach," Statistics in Transition New Series, Statistics Poland, vol. 21(1), pages 123-136, March.
    14. Heidorn, Thomas & Van Huellen, Sophie & Ruehl, C. & Woebbeking, F., 2017. "The long- and short-run impact of oil price changes on major global economies," Frankfurt School - Working Paper Series 225, Frankfurt School of Finance and Management.
    15. Calzolari, Giorgio & Neri, Laura, 2002. "Imputation of continuous variables missing at random using the method of simulated scores," MPRA Paper 22986, University Library of Munich, Germany, revised 2002.
    16. Pollock, D.S.G., 2006. "Econometric methods of signal extraction," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2268-2292, May.
    17. Olawale Awe O. & Adedayo Adepoju A., 2018. "Modified Recursive Bayesian Algorithm For Estimating Time-Varying Parameters In Dynamic Linear Models," Statistics in Transition New Series, Statistics Poland, vol. 19(2), pages 258-293, June.
    18. Chiarella, Carl & Hung, Hing & T, Thuy-Duong, 2009. "The volatility structure of the fixed income market under the HJM framework: A nonlinear filtering approach," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2075-2088, April.
    19. K. Triantafyllopoulos, 2007. "Covariance estimation for multivariate conditionally Gaussian dynamic linear models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(8), pages 551-569.
    20. Tesfaselassie, M.F., 2005. "Communication, learning and optimal monetary policy," Other publications TiSEM 33c69063-eed7-4938-9f51-e, Tilburg University, School of Economics and Management.
    21. Izquierdo, Segismundo S. & Hernández, Cesáreo & del Hoyo, Juan, 2006. "Forecasting VARMA processes using VAR models and subspace-based state space models," MPRA Paper 4235, University Library of Munich, Germany.
    22. Freitas, Paulo S.A. & Rodrigues, Antonio J.L., 2006. "Model combination in neural-based forecasting," European Journal of Operational Research, Elsevier, vol. 173(3), pages 801-814, September.

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