Assessing the Transmission of Monetary Policy Using Time‐varying Parameter Dynamic Factor Models*

By Dimitris Korobilis. 2013

152 citations recorded by the source corpus.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/0b21a1821e93fa8fb14cb126ce03ffe0a25cb8a2

Citations in the corpus

• A new index of financial conditions (2014, unknown): “We use a standard Cholesky factorization of Vt to identify the structural shocks (see., e.g., Primiceri, 2005, Castelnuovo, 2012 and Korobilis, 2013).”
• Time-Varying Effect of Oil Market Shocks on the Stock Market (2015, unknown): “Priors In the empirical Bayesian analysis we follow Primiceri (2005), Koop et al. (2009) and Korobilis (2013) to use a training sample, the first 5 years of data, 1968:01-1972:12, to calibrate the key prior hyperparameters: 0 0 ˆˆ( , ( 1) ),N m p V 0 0ˆln( ) (ln( ), )mN I and 0 0 ˆˆ( , ( 1) ),aa N…”
• Short-Term GDP Forecasting With a Mixed-Frequency Dynamic Factor Model With Stochastic Volatility (2016, unknown): “(1) 2See Baumeister, Liu, and Mumtaz (2013), Del Negro and Otrok (2008), and Korobilis (2013) for examples of Bayesian dynamic factor models with stochastic volatility.”