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

26 Pages Posted: 3 Apr 2008

See all articles by Tommaso Proietti

Tommaso Proietti

University of Rome II - Department of Economics and Finance

Alessandra Luati

University of Bologna - Department of Statistics

Date Written: February 1, 2008

Abstract

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.

Keywords: Henderson filter, Trend estimation, Nearest Neighbour Bandwidth, Musgrave asymmetric

Suggested Citation

Proietti, Tommaso and Luati, Alessandra, Real Time Estimation in Local Polynomial Regression, with Application to Trend-Cycle Analysis (February 1, 2008). CEIS Working Paper No. 112. Available at SSRN: https://ssrn.com/abstract=1114883 or http://dx.doi.org/10.2139/ssrn.1114883

Tommaso Proietti (Contact Author)

University of Rome II - Department of Economics and Finance ( email )

Via Columbia, 2
Rome, 00133
Italy

Alessandra Luati

University of Bologna - Department of Statistics ( email )

Bologna, 40126
Italy

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