No abstract

To capture location shifts in the context of model selection, we propose selecting significant step indicators from a saturating set added to the union of all of the candidate variables. The null retention frequency and approximate non-centrality of a selection test are derived using a 'split-half' analysis, the simplest specialization of a multiple-path block-search algorithm. Monte Carlo simulations, extended to sequential reduction, confirm the accuracy of nominal significance levels under the null and show retentions when location shifts occur, improving the non-null retention frequency compared to the corresponding impulse-indicator saturation (IIS)-based method and the lasso.

a b s t r a c tWe consider model selection facing uncertainty over the choice of variables and the occurrence and timing of multiple location shifts. General-to-simple selection is extended by adding an impulse indicator for every observation to the set of candidate regressors: see Johansen and Nielsen (2009). We apply that approach to a fat-tailed distribution, and to processes with breaks: Monte Carlo experiments show its capability of detecting up to 20 shifts in 100 observations, while jointly selecting variables. An illustration to US real interest rates compares impulse-indicator saturation with the procedure in Bai and Perron (1998).

We outline a range of criteria for evaluating model selection approaches that have been used in the literature. Focusing on three key criteria, we evaluate automatically selecting the relevant variables in an econometric model from a large candidate set. General-tospecific selection is outlined for a regression model in orthogonal variables, where only one decision is required to select, irrespective of the number of regressors. Comparisons with an automated model selection algorithm, Autometrics (Doornik, 2009), show similar properties, but not restricted to orthogonal cases. Monte Carlo experiments examine the roles of post-selection bias corrections and diagnostic testing, and evaluate selection in dynamic models by costs of search versus costs of inference.JEL classifications: C51, C22.