Find us on Facebook Find us on LinkedIn Follow us on Twitter Subscribe to our YouTube channel

Operations Management and Econometrics

The effect of misspecification in models for extracting trends and cycles

Dr Davide Delle Monache, Universit?? di Roma-Tor-Vergata, Rome

14th Oct 2011  11:00 am - Room 498, Merewether Building (H04)

This article deals with the specification of trends and cycles in an unobserved components model. We establish a general framework to assess the robustness in misspecified linear time series models based on the MSEs criterion. We show how different criterion can be used for different purposes: forecasting, filtering and smoothing. We generalized the algorithms in Harvey and Delle Monache (2009, HDM hereafter) allowing for all possible misspecifications in linear SSF models. We concentrate on model to extract trends and cycles. We assess the robustness of various sources of possible misspecification. We investigate the discrepancy between the estimated parameters (sample estimation) and the `pseudo-true values'; this yields interesting insights regarding the unknown data generating process (DGP). For example, if the true DGP is a correlated components model, as advocated in the recent literature, then we have that: (i) the calibrate HP filter leads to a big inefficiency for filtering as well as for smoothing; (ii) an uncorrelated components model can still yield a filter with high efficiency. So the extracted cycles on real time are close to each other; (iii) the sample estimates do not match the `pseudo-true values'. Therefore, the differences between the cycles extracted by the alternative specifications is not due to the correlation misspecification and the correlated components model does not match the true DGP.