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Shrinkage estimation of semiparametric multiplicative error models

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  • Brownlees, Christian T.
  • Gallo, Giampiero M.

Abstract

Within models for nonnegative time series, it is common to encounter deterministic components (trends, seasonalities) which can be specified in a flexible form. This work proposes the use of shrinkage type estimation for the parameters of such components. The amount of smoothing to be imposed on the estimates can be chosen using different methodologies: Cross-Validation for dependent data or the recently proposed Focused Information Criterion. We illustrate such a methodology using a semiparametric autoregressive conditional duration model that decomposes the conditional expectations of durations into their dynamic (parametric) and diurnal (flexible) components. We use a shrinkage estimator that jointly estimates the parameters of the two components and controls the smoothness of the estimated flexible component. The results show that, from the forecasting perspective, an appropriate shrinkage strategy can significantly improve on the baseline maximum likelihood estimation.

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Bibliographic Info

Article provided by Elsevier in its journal International Journal of Forecasting.

Volume (Year): 27 (2011)
Issue (Month): 2 (April)
Pages: 365-378

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Handle: RePEc:eee:intfor:v:27:y::i:2:p:365-378

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Web page: http://www.elsevier.com/locate/ijforecast

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Keywords: Shrinkage estimation Forecasting ACD MEM CV FIC;

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Cited by:
  1. Yuanhua Feng & Sarah Forstinger & Christian Peitz, 2013. "On the iterative plug-in algorithm for estimating diurnal patterns of financial trade durations," Working Papers CIE 66, University of Paderborn, CIE Center for International Economics.
  2. repec:pdn:wpaper:66 is not listed on IDEAS

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