Time Varying Structural Vector Autoregressions and Monetary Policy

By Giorgio E. Primiceri. 2005

1,164 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/305c9167b63e6f7b7d8175288d434a0e178e7d0d

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In Economics and Econometrics

Citations in the corpus

Using Entropic Tilting to Combine BVAR Forecasts With External Nowcasts (2014, positive): “Stochastic volatility is estimated with the algorithm of Kim, Shephard, and Chib (1998), as detailed by Primiceri (2005), and correcting the ordering of sampling steps as proposed by Del Negro and Primiceri (2015).”
Escaping the Great Recession (2017, neutral): “A large recession can be avoided by abandoning fiscal discipline, but this results in a sharp increase in macroeconomic instability once the economy is out of the recession.”
Bayesian forecasting using stochastic search variable selection in a VAR subject to breaks (2010, positive): “This set of variables (possibly transformed) has been used by, among many others, Cogley and Sargent (2005), Koop, Leon-Gonzalez and Strachan (2007) and Primiceri (2005).”
Large Order-Invariant Bayesian VARs with Stochastic Volatility (2021, positive): “In Online Appendix C we provide another illustration using a stochastic volatility specification with a time-varying impact matrix similar to that in Primiceri (2005).”
Bayesian analysis of time-varying parameter vector autoregressive model for the Japanese economy and monetary policy (2011, positive): “This method has been widely used in the financial and macroeconomics literature (Omori et al. (2007), Primiceri (2005)).”
Forecasting and Estimating Multiple Change-Point Models with an Unknown Number of Change Points (2004, neutral): “Stock (2001) nds little evidence for variation in the conditional mean of ination using classical methods and Primiceri (2005) nds some evidence for variation in the conditional variance but little in the conditional mean.”
Loan Supply Shocks and the Business Cycle (2017, positive): “To draw from the joint posterior distribution of model parameters we use a Gibbs sampling algorithm along the lines described in Primiceri (2005). The basic idea of the algorithm is to draw sets of coefficients from known conditional posterior distributions.”
Bayesian Model Comparison for Time-Varying Parameter VARs with Stochastic Volatility (2018, positive): “This representation is used in Eisenstat, Chan, and Strachan (2016) to improve the efficiency of the sampler by drawing 𝜷 t and 𝜸t jointly—instead of the conventional approach in Primiceri (2005) that samples 𝜷 t given 𝜸t followed by sampling 𝜸t given 𝜷 t.”
Explaining the great moderation: it is not the shocks (2008, positive): “This approach has been extensively used in the literature (Stock and Watson 2002, 2003; Ahmed, Levin and Wilson 2004; Primiceri 2005; Justiniano and Primiceri 2006; Smets and Wouters 2007). Our exercise is based on four model specifications: a small VAR with GDP, GDP deflator, federal funds rate, an”
No-Arbitrage Taylor Rules (2007, neutral): “However, other authors like Bernanke and Mihov (1998), Sims (1999 and 2001), Sims and Zha (2002), and Primiceri (2003) find either little or no evidence for time-varying policy rules, or negligible effect on the impulse responses of macro variables from time-varying policy rules.”
Assessing the Economy-Wide Effects of Quantitative Easing (2012, positive): “The seminal work by Sims (1980) introduced the use of vector autoregressions (VAR) into macroeconometric modelling and VAR models continue to occupy centre stage.”
Time Varying Dimension Models (2012, positive): “Furthermore, TVP-VARs (see among many others, Canova 1993 ; Cogley and Sargent 2005 ; Primiceri 2005 ; D’Agostino, Gambetti, and Giannone 2009 ) are obtained by letting Z t contain deterministic terms and appropriate lags of the dependent variables, setting Q t = Q and giving H t a m”
Short-Term GDP Forecasting With a Mixed-Frequency Dynamic Factor Model With Stochastic Volatility (2016, neutral): “In particular, policy makers have become more and more interested in having not only point forecasts, but also a model-based assessment of the uncertainty surrounding the outlook.”
Transmission of Government Spending Shocks in the Euro Area: Time Variation and Driving Forces (2010, positive): “Like the coefficient states, the covariance and volatility states are modeled as (geometric) random walks:9 αt = αt−1 + νt log ht = log ht−1 + ωt (5) 6Our specification of the TVP-VAR follows Cogley and Sargent (2001, 2005) and Primiceri (2005).”