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Real Time Estimation in Local Polynomial Regression, with Application to Trend-Cycle Analysis

The paper focuses on the adaptation of local polynomial filters at the end of the sample period. We show that for real time estimation of signals (i.e. exactly at the boundary of the time support) we cannot rely on the automatic adaptation of the local polynomial smoothers, since the direct real time filter turns out to be strongly localised, and thereby yields extremely volatile estimates. As an alternative we evaluate a general family of asymmetric filters that minimises the mean square revision error subject to polynomial reproduction constraints; in the case of the Henderson filter it nests the well known Musgrave’s surrogate filters. The class of filters depends on unknown features of the series such as the slope and the curvature of the underlying signal, which can be estimated from the data. Several empirical examples illustrate the effectiveness of our proposal. We also discuss the merits of using a nearest neighbour bandwidth as opposed to a fixed bandwidth for improving the quality of the approximation.

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Paper provided by Tor Vergata University, CEIS in its series CEIS Research Paper with number 112.

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Length: 26 pages
Date of creation: 14 Jul 2008
Date of revision: 14 Jul 2008
Handle: RePEc:rtv:ceisrp:112
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Order Information: Postal: CEIS - Centre for Economic and International Studies - Faculty of Economics - University of Rome "Tor Vergata" - Via Columbia, 2 00133 Roma
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References listed on IDEAS
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  1. Edward E. Leamer, 2007. "Housing is the business cycle," Proceedings - Economic Policy Symposium - Jackson Hole, Federal Reserve Bank of Kansas City, pages 149-233.
  2. Estela Bee Dagum & Alessandra Luati, 2009. "A Cascade Linear Filter to Reduce Revisions and False Turning Points for Real Time Trend-Cycle Estimation," Econometric Reviews, Taylor & Francis Journals, vol. 28(1-3), pages 40-59.
  3. Proietti, Tommaso, 2007. "Signal extraction and filtering by linear semiparametric methods," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 935-958, October.
  4. repec:cup:cbooks:9780521780506 is not listed on IDEAS
  5. repec:cup:cbooks:9780521785167 is not listed on IDEAS
  6. Quenneville, Benoit & Ladiray, Dominique & Lefrancois, Bernard, 2003. "A note on Musgrave asymmetrical trend-cycle filters," International Journal of Forecasting, Elsevier, vol. 19(4), pages 727-734.
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