Diffusion index model specification and estimation using mixed frequency datasets

Kihwan Kim; Norman Rasmus Swanson

doi:10.1007/978-1-4614-8060-0_2

Diffusion index model specification and estimation using mixed frequency datasets

Kihwan Kim, Norman Rasmus Swanson

School of Arts and Sciences, Economics

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

In this chapter, we discuss the use of mixed frequency models and diffusion index approximation methods in the context of prediction. In particular, 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 and 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.

Original language	English (US)
Title of host publication	Recent Advances in Estimating Nonlinear Models
Subtitle of host publication	With Applications in Economics and Finance
Publisher	Springer New York
Pages	15-31
Number of pages	17
ISBN (Electronic)	9781461480600
ISBN (Print)	1461480590, 9781461480594
DOIs	https://doi.org/10.1007/978-1-4614-8060-0_2
State	Published - May 1 2013

All Science Journal Classification (ASJC) codes

Economics, Econometrics and Finance(all)
Business, Management and Accounting(all)

Keywords

Diffusion index
Forecasting
Kalman filter
Mixed frequency
Recursive estimation

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10.1007/978-1-4614-8060-0_2

Cite this

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KW - Forecasting

KW - Kalman filter

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KW - Recursive estimation

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Diffusion index model specification and estimation using mixed frequency datasets

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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