Large stochastic volatility in mean VARs
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