Nonlinear time series models can exhibit components such as long range trends and seasonalities that may be modeled in a flexible fashion. The resulting unconstrained maximum likelihood estimator can be too heavily parameterized and suboptimal for forecasting purposes. The paper proposes the use of a class of shrinkage estimators that includes the Ridge estimator for forecasting time series, with a special attention to GARCH and ACD models. The local large sample properties of this class of shrinkage estimators is investigated. Moreover, we propose symmetric and asymmetric focused selection criteria of shrinkage estimators. The focused information criterion selection strategy consists of picking up the shrinkage estimator that minimizes the estimated risk (e.g. MSE) of a given smooth function of the parameters of interest to the forecaster. The usefulness of such shrinkage techniques is illustrated by means of a simulation exercise and an intra-daily financial durations forecasting application. The empirical application shows that an appropriate shrinkage forecasting methodology can significantly outperform the unconstrained ML forecasts of rich flexible specifications.
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Paper provided by Universita' degli Studi di Firenze, Dipartimento di Statistica "G. Parenti" in its series Econometrics Working Papers Archive with number
wp2007_02.
Find related papers by JEL classification: C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
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