Diffusion Index Model Specification and Estimation Using Mixed Frequency Datasets

Kihwan Kim () and Norman Swanson ()
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Kihwan Kim: Rutgers University

Departmental Working Papers from Rutgers University, Department of Economics

Abstract: In this chapter, we discuss the use of mixed frequency models and diffusion index approximation methods in the context of prediction. In particular, select recent specification and estimation methods are outlined, and an empirical illustration is provided wherein U.S. unemployment forecasts are constructed using both classical principal components based diffusion indexes as well as using a combination of diffusion indexes and factors formed using small mixed frequency datasets. Preliminary evidence that mixed frequency based forecasting models yield improvements over standard fixed frequency models is presented.

Keywords: forecasting; diffusion index; mixed frequency; recursive estimation; Kalman filter (search for similar items in EconPapers)
JEL-codes: C22 C51 (search for similar items in EconPapers)
Pages: 20 pages
Date: 2013-07-16
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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Persistent link: https://EconPapers.repec.org/RePEc:rut:rutres:201315

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