Large stochastic volatility in mean VARs

Journal of Econometrics - Tập 236 - Trang 105469 - 2023
Jamie L. Cross1, Chenghan Hou2, Gary Koop3, Aubrey Poon4
1Melbourne Business School, University of Melbourne, Australia
2Hunan University, China
3University of Strathclyde, United Kingdom
4Örebro University, Sweden

Tài liệu tham khảo

Baker, 2016, Measuring economic policy uncertainty, Q. J. Econ., 131, 1593, 10.1093/qje/qjw024 Bańbura, 2010, Large Bayesian vector autoregressions, J. Appl. Econometrics, 25, 71, 10.1002/jae.1137 Beckmann, 2023, Cross-country uncertainty spillovers: Evidence from international survey data, J. Int. Money Finance, 130, 10.1016/j.jimonfin.2022.102760 Bernanke, 2005, Measuring the effects of monetary policy: a factor-augmented vector autoregressive (FAVAR) approach, Q. J. Econ., 120, 387 Bloom, 2009, The impact of uncertainty shocks, Econometrica, 77, 623, 10.3982/ECTA6248 Carriero, 2021, Corrigendum to “large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors”[J. Econometrics 212 (1)(2019) 137–154], J. Econometrics Carriero, 2015, Bayesian VARs: specification choices and forecast accuracy, J. Appl. Econometrics, 30, 46, 10.1002/jae.2315 Carriero, 2016, Common drifting volatility in large Bayesian VARs, J. Bus. Econom. Statist., 34, 375, 10.1080/07350015.2015.1040116 Carriero, 2018, Measuring uncertainty and its impact on the economy, Rev. Econ. Stat., 100, 799, 10.1162/rest_a_00693 Carriero, 2019, Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors, J. Econometrics, 212, 137, 10.1016/j.jeconom.2019.04.024 Carriero, 2020, Assessing international commonality in macroeconomic uncertainty and its effects, J. Appl. Econometrics, 35, 273, 10.1002/jae.2750 Carriero, A., Clark, T.E., Marcellino, M.G., Mertens, E., 2021b. Measuring uncertainty and its effects in the COVID-19 era. CEPR Discussion Paper No. DP15965. Carriero, 2021, Addressing COVID-19 outliers in BVARs with stochastic volatility, Rev. Econ. Stat., 1 Carriero, 2011, Forecasting large datasets with Bayesian reduced rank multivariate models, J. Appl. Econometrics, 26, 735, 10.1002/jae.1150 Castelnuovo, E., 2021. Domestic and Global Uncertainty in Normal Times and Extreme Events: A Survey. CAMA Working Paper. Chan, 2017, The stochastic volatility in mean model with time-varying parameters: An application to inflation modeling, J. Bus. Econom. Statist., 35, 17, 10.1080/07350015.2015.1052459 Chan, 2021, Minnesota-type adaptive hierarchical priors for large Bayesian VARs, Int. J. Forecast., 37, 1212, 10.1016/j.ijforecast.2021.01.002 Chan, 2022, Asymmetric conjugate priors for large Bayesian VARs, Quant. Econ., 13, 1145, 10.3982/QE1381 Chan, 2022, Comparing stochastic volatility specifications for large Bayesian VARs, J. Econometrics Chan, 2018 Chan, 2009, Efficient simulation and integrated likelihood estimation in state space models, Int. J. Math. Model. Numer. Optim., 1, 101 Chib, 1995, Understanding the Metropolis-Hastings algorithm, Amer. Statist., 49, 327 Chib, 2010, Tailored randomized block MCMC methods with application to DSGE models, J. Econometrics, 155, 19, 10.1016/j.jeconom.2009.08.003 Clark, 2011, Real-time density forecasts from Bayesian vector autoregressions with stochastic volatility, J. Bus. Econom. Statist., 29, 327, 10.1198/jbes.2010.09248 Clark, 2015, Macroeconomic forecasting performance under alternative specifications of time-varying volatility, J. Appl. Econometrics, 30, 551, 10.1002/jae.2379 Creal, 2017, Monetary policy uncertainty and economic fluctuations, Internat. Econom. Rev., 58, 1317, 10.1111/iere.12253 Cross, 2020, Macroeconomic forecasting with large Bayesian VARs: Global-local priors and the illusion of sparsity, Int. J. Forecast., 36, 899, 10.1016/j.ijforecast.2019.10.002 Giannone, 2015, Prior selection for vector autoregressions, Rev. Econ. Stat., 27, 436, 10.1162/REST_a_00483 Herbst, 2010, Gradient and hessian-based MCMC for DSGE models, Mimeo Hou, 2020, Time-varying relationship between inflation and inflation uncertainty, Oxf. Bull. Econ. Stat., 82, 83, 10.1111/obes.12327 Husted, 2020, Monetary policy uncertainty, J. Monetary Econ., 115, 20, 10.1016/j.jmoneco.2019.07.009 Jacquier, 2002, Bayesian analysis of stochastic volatility models, J. Bus. Econom. Statist., 20, 69, 10.1198/073500102753410408 Jurado, 2015, Measuring uncertainty, Amer. Econ. Rev., 105, 1177, 10.1257/aer.20131193 Kim, 1998, Stochastic volatility: likelihood inference and comparison with ARCH models, Rev. Econom. Stud., 65, 361, 10.1111/1467-937X.00050 Koop, 2013, Forecasting with medium and large Bayesian VARs, J. Appl. Econometrics, 28, 177, 10.1002/jae.1270 Lenza, 2020 Lindsten, 2014, Particle gibbs with ancestor sampling, J. Mach. Learn. Res., 15, 2145 Ludvigson, 2021, Uncertainty and business cycles: exogenous impulse or endogenous response?, Am. Econ. J.: Macroecon., 13, 369 McCausland, 2012, The HESSIAN method: Highly efficient simulation smoothing, in a nutshell, J. Econometrics, 168, 189, 10.1016/j.jeconom.2011.12.003 Mumtaz, 2015, The international transmission of volatility shocks: An empirical analysis, J. Eur. Econom. Assoc., 13, 512, 10.1111/jeea.12120 Mumtaz, 2017, Common and country specific economic uncertainty, J. Int. Econ., 105, 205, 10.1016/j.jinteco.2017.01.007 Mumtaz, 2018, The changing transmission of uncertainty shocks in the US, J. Bus. Econom. Statist., 36, 239, 10.1080/07350015.2016.1147357 Mumtaz, 2013, The impact of the volatility of monetary policy shocks, J. Money Credit Bank., 45, 535, 10.1111/jmcb.12015 Qi, Y., Minka, T.P., 2002. Hessian-based Markov chain Monte Carlo algorithms. In: First Cape Cod Workshop on Monte Carlo Methods. Rossi, 2015, Macroeconomic uncertainty indices based on nowcast and forecast error distributions, Amer. Econ. Rev., 105, 650, 10.1257/aer.p20151124 Rue, 2009, Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations, J. R. Stat. Soc. Ser. B Stat. Methodol., 71, 319, 10.1111/j.1467-9868.2008.00700.x Schorfheide, 2021 Shephard, 1997, Likelihood analysis of non-Gaussian measurement time series, Biometrika, 84, 653, 10.1093/biomet/84.3.653 Stock, 2016, Core inflation and trend inflation, Rev. Econ. Stat., 98, 770, 10.1162/REST_a_00608