Fast and Flexible Bayesian Inference in Time-varying Parameter Regression Models

References

• Allenby, G. M., Arora, N., and Ginter, J. L. (1998), “On the Heterogeneity of Demand,” Journal of Marketing Research, 35, 384–389.
   Web of Science ®Google Scholar
• Ball, L., and Mazumder, S. (2011), “Inflation Dynamics and the Great Recession,” Brookings Papers on Economic Activity, 42, 337–405.
   Google Scholar
• Belmonte, M., Koop, G., and Korobilis, D. (2014), “Hierarchical Shrinkage in Time-Varying Coefficient Models,” Journal of Forecasting, 33, 80–94.
   Web of Science ®Google Scholar
• Bitto, A., and Frühwirth-Schnatter, S. (2019), “Achieving Shrinkage in a Time-Varying Parameter Model Framework,” Journal of Econometrics, 210, 75–97.
   Web of Science ®Google Scholar
• Cadonna, A., Frühwirth-Schnatter, S., and Knaus, P. (2020), “Triple the Gamma—A Unifying Shrinkage Prior for Variance and Variable Selection in Sparse State Space and TVP Models,” Econometrics, 8, 20.
   Web of Science ®Google Scholar
• Carriero, A., Chan, J., Clark, T. E., and Marcellino, M. (2021), “Corrigendum to: Large Bayesian Vector Autoregressions with Stochastic Volatility and Non-Conjugate Priors,” Manuscript.
   Google Scholar
• Carriero, A., Clark, T. E., and Marcellino, M. (2019), “Large Bayesian Vector Autoregressions With Stochastic Volatility and Non-Conjugate Priors,” Journal of Econometrics, 212, 137–154.
   Web of Science ®Google Scholar
• Carter, C., and Kohn, R. (1994), “On Gibbs Sampling for State Space Models,” Biometrika, 81, 541–553.
   Web of Science ®Google Scholar
• Chan, J. C., Eisenstat, E., and Strachan, R. W. (2020), “Reducing the State Space Dimension in a Large TVP-VAR,” Journal of Econometrics, 218, 105–118.
   Web of Science ®Google Scholar
• Chan, J. C., and Jeliazkov, I. (2009), “Efficient Simulation and Integrated Likelihood Estimation in State Space Models,” International Journal of Mathematical Modelling and Numerical Optimisation, 1, 101–120.
   Google Scholar
• Clark, T. (2011), “Real-Time Density Forecasts From BVARs With Stochastic Volatility,” Journal of Business & Economic Statistics, 29, 327–341.
   Web of Science ®Google Scholar
• Cogley, T., and Sargent, T. J. (2005), “Drifts and Volatilities: Monetary Policies and Outcomes in the Post WWII US,” Review of Economic Dynamics, 8, 262–302.
   Web of Science ®Google Scholar
• Coibion, O., and Gorodnichenko, Y. (2015), “Is the Phillips Curve Alive and Well After All? Inflation Expectations and the Missing Disinflation,” American Economic Journal: Macroeconomics 7, 197–232.
   Web of Science ®Google Scholar
• Cong, Y., Chen, B., and Zhou, M. (2017), “Fast Simulation of Hyperplane-Truncated Multivariate Normal Distributions,” Bayesian Analysis, 12, 1017–1037.
   Web of Science ®Google Scholar
• D’Agostino, A., Gambetti, L., and Giannone, D. (2013), “Macroeconomic Forecasting and Structural Change,” Journal of Applied Econometrics, 28, 82–101.
   Web of Science ®Google Scholar
• De Mol, C., Giannone, D., and Reichlin, L. (2008), “Forecasting Using a Large Number of Predictors: Is Bayesian Shrinkage a Valid Alternative to Principal Components?” Journal of Econometrics, 146, 318–328.
   Web of Science ®Google Scholar
• Diebold, F. X., and Mariano, R. S. (1995), “Comparing Predictive Accuracy,” Journal of Business & Economic Statistics, 13, 253–263.
   Web of Science ®Google Scholar
• Doan, T., Litterman, R., and Sims, C. (1984), “Forecasting and Conditional Projection Using Realistic Prior Distributions,” Econometric Reviews, 3, 1–100.
   Google Scholar
• Eddelbuettel, D., François, R., Allaire, J., Ushey, K., Kou, Q., Russel, N., Chambers, J., and Bates, D. (2011), “Rcpp: Seamless R and C++ Integration,” Journal of Statistical Software, 40, 1–18.
   Web of Science ®Google Scholar
• Foster, D. P., George, E. I. (1994), “The Risk Inflation Criterion for Multiple Regression,” The Annals of Statistics, 22, 1947–1975.
   Web of Science ®Google Scholar
• Frühwirth-Schnatter, S. (1994), “Data Augmentation and Dynamic Linear Models,” Journal of Time Series Analysis 15, 183–202.
   Google Scholar
• Frühwirth-Schnatter, S. (2001), “Markov Chain Monte Carlo Estimation of Classical and Dynamic Switching and Mixture Models,” Journal of the American Statistical Association, 96, 194–209.
   Web of Science ®Google Scholar
• Frühwirth-Schnatter, S., and Malsiner-Walli, G. (2019), “From Here to Infinity: Sparse Finite Versus Dirichlet Process Mixtures in Model-Based Clustering,” Advances in Data Analysis and Classification, 13, 33–64.
   PubMed Web of Science ®Google Scholar
• Frühwirth-Schnatter, S., Malsiner-Walli, G., and Grün, B. (2020), “Generalized Mixtures of Finite Mixtures and Telescoping Sampling,” arXiv:2005.09918.
   Google Scholar
• Frühwirth-Schnatter, S., Tüchler, R., and Otter, T. (2004), “Bayesian Analysis of the Heterogeneity Model,” Journal of Business & Economic Statistics, 22, 2–15.
   Web of Science ®Google Scholar
• Frühwirth-Schnatter, S., and Wagner, H. (2010), “Stochastic Model Specification Search for Gaussian and Partial Non-Gaussian State Space Models,” Journal of Econometrics, 154, 85–100.
   Web of Science ®Google Scholar
• Giordani, P., and Kohn, R. (2008), “Efficient Bayesian Inference for Multiple Change-Point and Mixture Innovation Models,” Journal of Business & Economic Statistics, 26, 66–77.
   Web of Science ®Google Scholar
• Greve, J., Grün, B., Malsiner-Walli, G., and Frühwirth-Schnatter, S. (2020), “Spying on the Prior of the Number of Data Clusters and the Partition Distribution in Bayesian Cluster Analysis,” arXiv:2012.12337.
   Google Scholar
• Griffin, J., and Brown, P. (2010), “Inference With Normal-Gamma Prior Distributions in Regression Problems,” Bayesian Analysis, 5, 171–188.
   Web of Science ®Google Scholar
• Griffin, J. E., and Brown, P. J. (2013), “Some Priors for Sparse Regression Modelling,” Bayesian Analysis, 8, 691–702.
   Web of Science ®Google Scholar
• Hamilton, J. (1989), “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle,” Econometrica, 57, 357–384.
   Web of Science ®Google Scholar
• Hassani, H., and Silva, E. S. (2015), “Forecasting with Big Data: A Review,” Annals of Data Science, 2, 5–19.
   Google Scholar
• Huber, F., Koop, G., and Onorante, L. (2021), “Inducing Sparsity and Shrinkage in Time-Varying Parameter Models,” Journal of Business & Economic Statistics, 39, 669–683.
   Web of Science ®Google Scholar
• Kalli, M., and Griffin, J. (2014), “Time-varying Sparsity in Dynamic Regression Models,” Journal of Econometrics, 178, 779–793.
   Web of Science ®Google Scholar
• Kalli, M., and Griffin, J. (2018), “Bayesian Nonparametric Vector Autoregressive Models,” Journal of Econometrics, 203, 267–282.
   Web of Science ®Google Scholar
• Kapetanios, G., Marcellino, M., and Venditti, F. (2019), “Large Time-Varying Parameter VARs: A Nonparametric Approach,” Journal of Applied Econometrics, 34(7), 1027–1049.
   Web of Science ®Google Scholar
• Kastner, G., and Frühwirth-Schnatter, S. (2014), “Ancillarity-Sufficiency Interweaving Strategy (ASIS) for Boosting MCMC Estimation of Stochastic Volatility Models,” Computational Statistics & Data Analysis, 76, 408–423.
   Web of Science ®Google Scholar
• Kastner, G., and Huber, F. (2020), “Sparse Bayesian Vector Autoregressions in Huge Dimensions,” Journal of Forecasting, 39, 1142–1165.
   Web of Science ®Google Scholar
• Knaus, P., Bitto-Nemling, A., Cadonna, A., and Frühwirth-Schnatter, S. (2021), “Shrinkage in the Time-Varying Parameter Model Framework Using the R Package shrinkTVP,” Journal of Statistical Software, forthcoming.
   Web of Science ®Google Scholar
• Koop, G., and Korobilis, D. (2012), “Forecating Inflation Using Dynamic Model Averaging,” International Economic Review, 53, 867–886.
   Web of Science ®Google Scholar
• Koop, G., and Korobilis, D. (2018), “Variational Bayes Inference in High-Dimensional Time-Varying Parameter Models,” SSRN:3246472.
   Google Scholar
• Koop, G., Korobilis, D., and Pettenuzzo, D. (2019), “Bayesian Compressed Vector Autoregressions,” Journal of Econometrics, 210, 135–154.
   Web of Science ®Google Scholar
• Korobilis, D. (2021), “High-Dimensional Macroeconomic Forecasting Using Message Passing Algorithms,” Journal of Business & Economic Statistics, 39, 493–504.
   Web of Science ®Google Scholar
• Lenk, P. J., and DeSarbo, W. S. (2000), “Bayesian Inference for Finite Mixtures of Generalized Linear Models With Random Effects,” Psychometrika, 65, 93–119.
   Web of Science ®Google Scholar
• Litterman, R. (1986), “Forecasting with Bayesian Vector Autoregressions: Five Years of Experience,” Journal of Business & Economic Statistics, 4, 25–38.
   Web of Science ®Google Scholar
• Malsiner-Walli, G., Frühwirth-Schnatter, S., and Grün, B. (2016), “Model-Based Clustering Based on Sparse Finite Gaussian Mixtures,” Statistics and Computing, 26, 303–324.
   PubMed Web of Science ®Google Scholar
• McCausland, W. J., Miller, S., and Pelletier, D. (2011), “Simulation Smoothing for State–Space Models: A Computational Efficiency Analysis,” Computational Statistics & Data Analysis, 55, 199–212.
   Web of Science ®Google Scholar
• Medeiros, M. C., Vasconcelos, G. F., Veiga, Á., and Zilberman, E. (2021), “Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods,” Journal of Business & Economic Statistics, 39, 98–119.
   Web of Science ®Google Scholar
• Moretti, L., Onorante, L., and Zakipour Saber, S. (2019), “Phillips Curves in the Euro Area,” ECB Working Paper No. 2295.
   Google Scholar
• Mukhopadhyay, M., and Dunson, D. B. (2020), “Targeted Random Projection for Prediction From High-Dimensional Features,” Journal of the American Statistical Association, 115, 1998–2010.
   Web of Science ®Google Scholar
• Primiceri, G. (2005), “Time Varying Structural Autoregressions and Monetary Policy,” The Review of Economic Studies, 72, 821–852.
   Web of Science ®Google Scholar
• Raftery, A., Kárný, M., and Ettler, P. (2010), “Online Prediction Under Model Uncertainty Via Dynamic Model Averaging: Application to a Cold Rolling Mill,” Technometrics, 52, 52–66.
   PubMed Web of Science ®Google Scholar
• Rockova, V., and McAlinn, K. (2021), “Dynamic Variable Selection With Spike-and-Slab Process Priors,” Bayesian Analysis, 16, 233–269.
   Web of Science ®Google Scholar
• Stock, J., and Watson, M. (1999), “Forecasting Inflation,” Journal of Monetary Economics, 44, 293–335.
   Web of Science ®Google Scholar
• Stock, J., and Watson, M. (2002), “Macroeconomic Forecasting Using Diffusion Indexes,” Journal of Business & Economic Statistics, 20, 147–162.
   Web of Science ®Google Scholar
• Stock, J., and Watson, M. (2007), “Why Has U.S. Inflation Become Harder to Forecast?” Journal of Money, Credit and Banking, 39, 3–33.
   Web of Science ®Google Scholar
• Stock, J., and Watson, M. (2008), “Phillips Curve Inflation Forecasts,” NBER Working Paper No. 14322.
   Google Scholar
• Stock, J., and Watson, M. (2011), “Dynamic Factor Models,” in The Oxford Handbook of Forecasting, eds. M. Clements and D. Hendry, New York: University Press, pp. 35–60.
   Google Scholar
• Trippe, B., Huggins, J., Agrawal, R., and Broderick, T. (2019), “LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations,” in Proceedings of the 36th International Conference on Machine Learning, Proceedings of Machine Learning Research, pp. 6315–6324, PMLR.
   Google Scholar
• Tsionas, M., Izzeldin, M., and Trapani, L. (2019), “Bayesian Estimation of Large Dimensional Time Varying VARs Using Copulas,” Available at SSRN 3510348.
   Google Scholar
• Zellner, A. (1986), “On Assessing Prior Distributions and Bayesian Regression Analysis With g-Prior Distributions,” in Bayesian Inference and Decision Techniques: Essays in Honor of Bruno de Finetti. Studies in Bayesian Econometrics and Statistics, eds. P. Goel and A. Zellner, Vol. 6, New York: Elsevier. pp. 233–243.
   Google Scholar