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# Handbook of Research Methods and Applications in Empirical Macroeconomics

## Edited by Nigar Hashimzade and Michael A. Thornton

This comprehensive Handbook presents the current state of art in the theory and methodology of macroeconomic data analysis. It is intended as a reference for graduate students and researchers interested in exploring new methodologies, but can also be employed as a graduate text. The Handbook concentrates on the most important issues, models and techniques for research in macroeconomics, and highlights the core methodologies and their empirical application in an accessible manner. Each chapter is largely self-contained, whilst the comprehensive introduction provides an overview of the key statistical concepts and methods. All of the chapters include the essential references for each topic and provide a sound guide for further reading.
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# Chapter 11: Factor models

## Extract

Factor models are becoming increasingly popular in economics because they can utilize large data sets in an effective manner. Factor models have been used for various purposes. First, they have been used to construct economic indicators. Monthly coincident business cycle indicators such as the Chicago Fed National Activity index (CFNAI) for the US and EuroCOIN for the Euro area (cf. Altissimo et al., 2001) are related examples. Second, factor models have widely been used in order to forecast real and nominal economic variables. They often provide more accurate forecasts than autoregressive and vector autoregressive models (see Eickmeier and Ziegler, 2008 and the literature cited therein). Third, factor models have been used for monetary policy analysis in combination with a vector autoregressive (VAR) system as in Bernanke et al. (2005). In many cases only five to ten factors are sufficient to capture more than a half of the total variation within a data set of more than three hundred macroeconomic variables. Therefore, adding a few common factors to a macroeconomic VAR system is supposed to control for a variety of omitted variables within a typical low-dimensional VAR analysis. Fourth, factor models are used for instrumental variables estimation. Bai and Ng (2010) assume that endogenous regressors are driven by a small number of unobserved, exogenous factors and suggest using the estimated factors as instruments. Fifth, factor models have been used in panel regressions as a way of modelling cross-sectional correlation.

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