Real-Time Density Forecasts From Bayesian Vector Autoregressions With Stochastic Volatility

By Todd E. Clark. 2011

375 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/93925348e0f7841ea6ef719cb283e9183e1b4507

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In Management Science and Operations Research

Citations in the corpus

Bayesian forecasting in economics and finance: A modern review (2022, background): “, Clark, T. E., and Marcellino, M. (2019). Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors.”
Forecasting with Bayesian Vector Autoregression (2013, positive): “Carriero, Clark and Marcellino (2011) conducts an impressive study of the many specification choices needed when formulating a BVAR for forecasting purposes.”
Forecasting Interest Rates with Shifting Endpoints (2012, neutral): “…be modeled as the sum of a transitory component and a slowly-varying trend or permanent component; see, among others, Kozicki and Tinsley (2001, 2005), Cogley and Sargent (2005), Stock and Watson (2007, 2010), Clark (2011), Orphanides and Wei (2012), Wright (2012) and Faust and Wright (2012).”
Common Drifting Volatility in Large Bayesian VARs (2012, neutral): “…such studies feature homoscedastic disturbances, while there is convincing evidence that instead macroeconomic fluctuations are characterized by time-varying volatilities (e.g., Cogley and Sargent 2005; Primiceri 2005; Clark 2011; D’Agostino, Gambetti, and Giannone 2013; Clark and Ravazzolo 2015).”
Advances in Forecasting Under Instability (2012, positive): “For example, Clark (2011) focuses on density forecasts of U.S. GDP growth, unemployment, in ation and the interest rate in a Bayesian VAR with stochastic volatility, to better capture the decrease in volatility during the Great Moderation period.”
Short-Term GDP Forecasting With a Mixed-Frequency Dynamic Factor Model With Stochastic Volatility (2016, positive): “Second, following Clark (2011), we extend the model to allow for random shifts in the volatility of the underlying shocks.2 After showing how to estimate the model we turn to an empirical application in which we use a small number of monthly indicators to predict quarterly GDP growth in the euro…”
The Stochastic Volatility in Mean Model With Time-Varying Parameters: An Application to Inflation Modeling (2017, background): “…we also compute density forecasts—it is generally acknowledged that when assessing the quality of competing models, both the ability to accurately predict the central tendency and the uncertainty around the forecasts should be taken into account (see, e.g., Amisano and Giacomini 2007; Clark 2011).”
Real-Time Forecasting With a Mixed-Frequency VAR (2015, positive): “Recent work by Clark (2011) suggests that real-time VAR density forecasts can be improved by adding stochastic volatility to the VAR.”
Alternative tests for correct specification of conditional predictive densities (2019, neutral): “There are several cases in the literature where a model is estimated with Bayesian methods and yet inference is based on PITs, e.g. Clark (2011).”
Real-Time Inflation Forecasting in a Changing World (2009, neutral): “Jore, Mitchell, and Vahey (2010) and Clark (2011) assessed inflation density forecasts from, respectively, forecast combinations and fourvariable VAR models based on real-time data that allow for a limited degree of instability through rolling windows and the use of stochastic volatility…”
Macroeconomic Uncertainty Through the Lens of Professional Forecasters (2019, neutral): “Similar in spirit to Clark (2012) and Clements (2017), we also examine the impact of data revisions on the estimation of economic uncertainty.”
Large Order-Invariant Bayesian VARs with Stochastic Volatility (2021, background): “1 Second, there is an increasing recognition, in most macroeconomic datasets, of the need to allow for time variation in VAR error variances, see, among many others, Clark (2011).”
Using Entropic Tilting to Combine BVAR Forecasts With External Nowcasts (2014, methodology): “A range of studies have considered similar variable sets (e.g., Clark 2011; D’Agostino, Gambetti, and Giannone 2013).”
Bayesian VARs : specification choices and forecast accuracy (2011, neutral): “However, while stationary, some variables can still be very persistent, and some studies (e.g. Clark, 2011) set the prior means of persistent variables to something like 0.8.”
The Evolution of Forecast Density Combinations in Economics (2018, positive): “As a result, models with time-varying parameters and stochastic volatility are popular, with time-varying parameter (Bayesian) Vector Autoregressive models with stochastic volatility increasingly a popular workhorse forecasting model (e.g. see Clark (2011)).”
Adaptive Shrinkage in Bayesian Vector Autoregressive Models (2019, background): “A plethora of recent studies (see, among others, Giordani and Villani 2010; Clark 2011; Groen, Paap, and Ravazzolo 2013; Clark and Ravazzolo 2015; Carriero, Clark, and Marcellino 2016) emphasize the importance of allowing for heteroscedasticity in a VAR framework to improve inference and obtain…”