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.
0000-0002-0741-2599 (Piyachart Phiromswad), 0000-0001-9916-6008 (Takeshi Yagihashi)
Original Publication Citation
Phiromswad, P., & Yagihashi, T. (2015). Empirical identification of factor models. Empirical Economics, 51(2), 621-658. doi: 10.1007/s00181-015-1025-9
Phiromswad, Piyachart and Yagihashi, Takeshi, "Empirical Identification of Factor Models" (2015). Economics Faculty Publications. 23.