Empirical Economics

, Volume 51, Issue 2, pp 621–658 | Cite as

Empirical identification of factor models

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Abstract

In the conventional factor-augmented vector autoregression (FAVAR), the extracted factors cannot be used in structural analysis because the factors do not retain a clear economic interpretation. This paper proposes a new method to identify macroeconomic factors, which is associated with better economic interpretations. Using an empirical-based search algorithm, we select variables that are individually caused by a single factor. These variables are then used to impose restrictions on the factor loading matrix, and we obtain an economic interpretation for each factor. We apply our method to time-series data in the USA and further conduct a monetary policy analysis. Our method yields stronger responses of price variables and muted responses of output variables than what the literature has found.

Keywords

Monetary policy Causal search FAVAR PC algorithm 

JEL Classification

C30 C32 C51 E58 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Sasin Graduate Institute of Business Administration of Chulalongkorn UniversityBangkokThailand
  2. 2.Department of Economics, Strome College of BusinessOld Dominion UniversityNorfolkUSA

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