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Research Articles

Time-dependent shrinkage of time-varying parameter regression models

References

  • Abramowitz, M., Stegun, I. (1973). Handbook of Mathematical Functions, New York: Dover Publications.
  • Barndorff-Nielsen, O., Kent, J., Sørensen, M., Sorensen, M. (1982). Normal variance-mean mixtures and z distributions. International Statistical Review/Revue Internationale De Statistique 50(2):145. doi:10.2307/1402598
  • Belmonte, M. A., Koop, G., Korobilis, D. (2014). Hierarchical shrinkage in time-varying parameter models. Journal of Forecasting 33(1):80–94. doi:10.1002/for.2276
  • Bhadra, A., Datta, J., Polson, N., Willard, B. (2019). Lasso meets horseshoe: A survey. Statistical Science 34(3):405–427. 10.1214/19-STS700
  • Bhattacharya, A., Chakraborty, A., Mallick, B. K. (2016). Fast sampling with gaussian scale-mixture priors in high-dimensional regression. Biometrika 103(4):985–991. doi:10.1093/biomet/asw042 28435166
  • Bitto, A., Frühwirth-Schnatter, S. (2019). Achieving shrinkage in a time-varying parameter model framework. Journal of Econometrics 210(1):75–97. doi:10.1016/j.jeconom.2018.11.006
  • Cadonna, A., Frühwirth-Schnatter, S., Knaus, P. (2020). Triple the gamma-a unifying shrinkage prior for variance and variable selection in sparse state space and TVP models. Econometrics 8(2):20. doi:10.3390/econometrics8020020
  • Carriero, A., Clark, T. E., Marcellino, M. (2019). Large bayesian vector autoregressions with stochastic volatility and non-conjugate priors. Journal of Econometrics 212(1):137–154. doi:10.1016/j.jeconom.2019.04.024
  • Carvalho, C. M., Polson, N. G., Scott, J. G. (2010). The horseshoe estimator for sparse signals. Biometrika 97(2):465–480. doi:10.1093/biomet/asq017
  • Chan, J. C., Eisenstat, E., Strachan, R. W. (2020). Reducing the state space dimension in a large TVP-VAR. Journal of Econometrics 218(1):105–118. doi:10.1016/j.jeconom.2019.11.006
  • Chan, J. C., Koop, G., Leon-Gonzalez, R., Strachan, R. W. (2012). Time varying dimension models. Journal of Business & Economic Statistics 30(3):358–367. doi:10.1080/07350015.2012.663258
  • Chib, S. (1998). Estimation and comparison of multiple change-point models. Journal of Econometrics 86(2):221–241. doi:10.1016/S0304-4076(97)00115-2
  • Cogley, T., Sargent, T. J. (2005). Drifts and volatilities: monetary policies and outcomes in the post WWII US. Review of Economic Dynamics 8(2):262–302. doi:10.1016/j.red.2004.10.009
  • Dangl, T., Halling, M. (2012). Predictive regressions with time-varying coefficients. Journal of Financial Economics 106(1):157–181. doi:10.1016/j.jfineco.2012.04.003
  • Devroye, L. (2014). Random variate generation for the generalized inverse gaussian distribution. Statistics and Computing 24(2):239–246. doi:10.1007/s11222-012-9367-z
  • Diebold, F. X., Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business & Economic Statistics 13(3): 253–263. doi:10.1080/07350015.1995.10524599
  • Dufays, A., Rombouts, J. V. (2020). Relevant parameter changes in structural break models. Journal of Econometrics 217(1):46–78. doi:10.1016/j.jeconom.2019.10.008
  • Durbin, J., Koopman, S. J. (2002). A simple and efficient simulation smoother for state space time series analysis. Biometrika 89(3):603–616. doi:10.1093/biomet/89.3.603
  • Fruhwirth-Schnatter, S. (1994). Data augmentation and dynamic linear models. Journal of Time Series Analysis 15: 183–202. 10.1111/j.1467-9892.1994.tb00184.x
  • Fruhwirth-Schnatter, S. (2004). Efficient Bayesian parameter estimation. In: Harvey, A., Koopman, S., Shephard, N., eds., State Space and Unobserved Component Models: Theory and Applications. Cambridge: Cambridge University Press, pp. 123–151.
  • Frühwirth-Schnatter, S., Wagner, H. (2010). Stochastic model specification search for gaussian and partial non-gaussian state space models. Journal of Econometrics 154(1):85–100. doi:10.1016/j.jeconom.2009.07.003
  • George, E. I., McCulloch, R. E. (1993). Variable selection via gibbs sampling. Journal of the American Statistical Association 88(423):881–889. doi:10.1080/01621459.1993.10476353
  • Geweke, J., Amisano, G. (2010). Comparing and evaluating bayesian predictive distributions of asset returns. International Journal of Forecasting 26(2):216–230. doi:10.1016/j.ijforecast.2009.10.007
  • Geyer, C. (1992). Practical Markov chain Monte Carlo. Statistical Science 7:473–483. 10.1214/ss/1177011137
  • Giordani, P., Kohn, R. (2008). Efficient bayesian inference for multiple change-point and mixture innovation models. Journal of Business & Economic Statistics 26(1):66–77. doi:10.1198/073500107000000241
  • Granger, C. (2008). Non-linear models: Where do we go next- time varying parameter models?. Studies in Nonlinear Dynamics & Econometrics 12:1–11.
  • Griffin, J., Brown, P. (2010). Inference with normal-gamma prior distributions in regression problems. Bayesian Analysis 5(1):171–188.
  • Harvey, C. R. (1989). Forecasts of economic growth from the bond and stock markets. Financial Analysts Journal 45(5): 38–45. doi:10.2469/faj.v45.n5.38
  • Hauzenberger, N. (2021). Flexible mixture priors for large time-varying parameter models. Econometrics and Statistics 20:87–108. doi:10.1016/j.ecosta.2021.06.001
  • Hauzenberger, N., Huber, F., Koop, G. (2020). Dynamic shrinkage priors for large time-varying parameter regressions using scalable Markov chain Monte Carlo methods. econ.EM. arXiv:2005.03906v1.
  • Huber, F., Pfarrhofer, M. (2021). Dynamic shrinkage in time-varying parameter stochastic volatility in mean models. Journal of Applied Econometrics (Chichester, England) 36(2):262–270. doi:10.1002/jae.2804 33867657
  • Ishwaran, H., Rao, J. (2005). Spike and slab variable selection: Frequentist and bayesian strategies. Annals of Statistics 33:730–773.
  • Johndrow, J., Orenstein, P., Bhattacharya, A. (2020). Scalable approximate MCMC algorithms for the horseshoe prior. Journal of Machine Learning Research 21(73):1–61.
  • Kalli, M., Griffin, J. E. (2014). Time-varying sparsity in dynamic regression models. Journal of Econometrics 178(2): 779–793. doi:10.1016/j.jeconom.2013.10.012
  • Kastner, G., Frühwirth-Schnatter, S. (2014). Ancillarity-sufficiency interweaving strategy (ASIS) FOR boosting MCMC estimation of stochastic volatility models. Computational Statistics & Data Analysis 76:408–423. doi:10.1016/j.csda.2013.01.002
  • Kim, S., Shepherd, N., Chib, S. (1998). Stochastic volatility: Likelihood inference and comparison with ARCH models. Review of Economic Studies 65(3):361–393. doi:10.1111/1467-937X.00050
  • Kowal, D. R., Matteson, D. S., Ruppert, D. (2019). Dynamic shrinkage processes. Journal of the Royal Statistical Society Series B: Statistical Methodology 81(4):781–804. doi:10.1111/rssb.12325
  • Lopes, H. F., McCulloch, R. E., Tsay, R. S. (2022). Parsimony inducing priors for large scale state–space models. Journal of Econometrics 230(1):39–61. doi:10.1016/j.jeconom.2021.11.005
  • Makalic, E., Schmidt, D. F. A simple sampler for the horseshoe estimator. IEEE Signal Processing Letters 23(1):179–182. doi:10.1109/LSP.2015.2503725
  • McCausland, W. J., Miller, S., Pelletier, D. (2011). Simulation smoothing for state–space models: A computational efficiency analysis. Computational Statistics & Data Analysis 55(1):199–212. doi:10.1016/j.csda.2010.07.009
  • Nakajima, J., West, M. (2013). Bayesian analysis of latent threshold dynamic models. Journal of Business & Economic Statistics 31(2):151–164. doi:10.1080/07350015.2012.747847
  • Omori, Y., Chib, S., Shephard, N., Nakajima, J. (2007). Stochastic volatility with leverage: Fast and efficient likelihood inference. Journal of Econometrics 140(2):425–449. doi:10.1016/j.jeconom.2006.07.008
  • Polson, N., Scott, J. (2012). On the half-cauchy prior for a global scale parameter. Bayesian Analysis 7(4):887–902. 10.1214/12-BA730
  • Polson, N. G., Scott, J. G., Windle, J. (2013). Bayesian inference for logistic models using pólya–gamma latent variables. Journal of the American Statistical Association 108(504):1339–1349. doi:10.1080/01621459.2013.829001
  • Primiceri, G. E. (2005). Time varying structural vector autoregressions and monetary policy. The Review of Economic Studies 72(3):821–852. doi:10.1111/j.1467-937X.2005.00353.x
  • Rockova, V., Mcalinn, K. (2021). Dynamic variable selection with spike-AND-slab process priors. Bayesian Analysis 16(1):233–269. 10.1214/20-BA1199
  • Rue, H. (2001). Fast sampling of gaussian markov random fields. Journal of the Royal Statistical Society Series B: Statistical Methodology 63(2):325–338. doi:10.1111/1467-9868.00288
  • Simpson, M., Niemi, J., Roy, V. (2017). Interweaving markov chain monte carlo strategies for efficient estimation of dynamic linear models. Journal of Computational and Graphical Statistics 26(1):152–159. doi:10.1080/10618600.2015.1105748
  • Windle, J. (2013). Forecasting high-dimensional, time-varying variance-covariance matrices with high-frequency data and sampling polya-gamma random variates for posterior distributions derived from logistic likelihoods, PhD Thesis. Austin, TX: University of Texas at Austin.
  • Yu, Y., Meng, X.-L. (2011). To center or not to center: That is not the question - an ancillarity–sufficiency interweaving strategy (ASIS) FOR boosting MCMC efficiency. Journal of Computational and Graphical Statistics 20(3):531–570. doi:10.1198/jcgs.2011.203main

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