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1. Time-Varying Parameters as Ridge Regressions. (2024). Goulet Coulombe, Philippe.
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2. Bayesian dynamic variable selection in high dimensions. (2020). Koop, Gary ; Korobilis, Dimitris.
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3. Triple the Gamma—A Unifying Shrinkage Prior for Variance and Variable Selection in Sparse State Space and TVP Models. (2020). Fruhwirth-Schnatter, Sylvia ; Knaus, Peter ; Cadonna, Annalisa.
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4. Relevant parameter changes in structural break models. (2020). Dufays, Arnaud.
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5. Is the slope of the Phillips curve time-varying? Evidence from unobserved components models. (2020). Fu, Bowen.
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6. Bayesian Inference in High-Dimensional Time-varying Parameter Models using Integrated Rotated Gaussian Approximations. (2020). Pfarrhofer, Michael ; Koop, Gary ; Huber, Florian.
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7. Shrinkage in the Time-Varying Parameter Model Framework Using the R Package shrinkTVP. (2020). Fruhwirth-Schnatter, Sylvia ; Knaus, Peter ; Cadonna, Annalisa ; Bitto-Nemling, Angela.
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8. Credit constraints and the propagation of the Great Depression in Germany. (2019). Adam, Marc ; Jansson, Walter.
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9. Steady‐state modeling and macroeconomic forecasting quality. (2019). Louzis, Dimitrios.
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10. Streamlining Time-varying VAR with a Factor Structure in the Parameters. (2019). Beyeler, Simon.
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11. How useful are time-varying parameter models for forecasting economic growth in CESEE?. (2019). Feldkircher, Martin ; Hauzenberger, Nico.
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12. Bagged neural networks for forecasting Polish (low) inflation. (2019). Szafranek, Karol.
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13. Sparse Bayesian time-varying covariance estimation in many dimensions. (2019). Kastner, Gregor.
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14. Achieving shrinkage in a time-varying parameter model framework. (2019). Fruhwirth-Schnatter, Sylvia ; Bitto, Angela.
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15. Dynamic Bayesian predictive synthesis in time series forecasting. (2019). West, Mike ; McAlinn, Kenichiro.
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16. Triple the gamma -- A unifying shrinkage prior for variance and variable selection in sparse state space and TVP models. (2019). Fruhwirth-Schnatter, Sylvia ; Knaus, Peter ; Cadonna, Annalisa.
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17. Measuring international uncertainty using global vector autoregressions with drifting parameters. (2019). Pfarrhofer, Michael.
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18. Bayesian nonparametric graphical models for time-varying parameters VAR. (2019). Rossini, Luca ; Iacopini, Matteo.
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19. Sophisticated and small versus simple and sizeable: When does it pay off to introduce drifting coefficients in Bayesian VARs?. (2018). Kastner, Gregor ; Huber, Florian ; Feldkircher, Martin.
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20. Sophisticated and small versus simple and sizeable: When does it pay off to introduce drifting coefficients in Bayesian VARs?. (2018). Kastner, Gregor ; Huber, Florian ; Feldkircher, Martin.
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21. Forecasting with Bayesian Vector Autoregressions with Time Variation in the Mean. (2018). Banbura, Marta ; van Vlodrop, Andries.
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22. Should I stay or should I go? A latent threshold approach to large-scale mixture innovation models. (2018). Kastner, Gregor ; Huber, Florian ; Feldkircher, Martin.
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23. Variational Bayes inference in high-dimensional time-varying parameter models. (2018). Koop, Gary ; Korobilis, Dimitris.
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24. Variational Bayes inference in high-dimensional time-varying parameter models. (2018). Koop, Gary ; Korobilis, Dimitris.
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25. Sparse Bayesian Variable Selection in Probit Model for Forecasting U.S. Recessions Using a Large Set of Predictors. (2018). Yang, Hongqiang ; Hongqiang, Yang ; Aijun, Yang ; Jinguan, Lin.
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26. Comparing hybrid time-varying parameter VARs. (2018). Eisenstat, Eric ; Chan, Joshua.
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27. Bayesian Vector Autoregressions. (2018). Ricco, Giovanni ; Miranda-Agrippino, Silvia.
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28. FACTOR MODELS AND TIME€ VARYING PARAMETER FRAMEWORK FOR FORECASTING EXCHANGE RATES AND INFLATION: A SURVEY. (2018). Mokhtari, Manouchehr ; Kavtaradze, Lasha.
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29. Predicting crypto-currencies using sparse non-Gaussian state space models. (2018). Zoerner, Thomas ; Huber, Florian ; Zorner, Thomas O ; Hotz-Behofsits, Christian.
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30. Forecasting US inflation using Markov dimension switching. (2017). Pruser, Jan.
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31. An Efficient Bayesian Approach to Multiple Structural Change in Multivariate Time Series. (2017). Song, Yong ; Maheu, John.
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32. Selecting Primal Innovations in DSGE models. (2017). Leon-Ledesma, Miguel ; Grassi, Stefano ; ferroni, filippo ; Benzoni, Luca.
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33. Sparse Bayesian time-varying covariance estimation in many dimensions. (2017). Kastner, Gregor.
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34. Forecasting the Polish Inflation Using Bayesian VAR Models with Seasonality. (2016). Szafrański, Grzegorz ; Stelmasiak, Damian ; Szafraski, Grzegorz.
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35. A note on the identification and transmission of energy demand and supply shocks. (2016). Gilmartin, Michelle ; Michelle, Gilmartin .
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36. Forecasting exchange rates under parameter and model uncertainty. (2016). Beckmann, Joscha ; Schussler, Rainer.
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37. Fundamental shock selection in DSGE models. (2015). Leon-Ledesma, Miguel ; Grassi, Stefano ; ferroni, filippo.
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38. On Flexible Linear Factor Stochastic Volatility Models. (2015). Malefaki, Valia .
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39. Bayesian model comparison for time-varying parameter VARs with stochastic volatility. (2015). Eisenstat, Eric ; Chan, Joshua.
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40. Can the Sharia-based Islamic stock market returns be forecasted using large number of predictors and models?. (2014). Simo-Kengne, Beatrice Desiree ; Hammoudeh, Shawkat ; GUPTA, RANGAN ; Sarafrazi, Soodabeh.
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41. Stochastic Model Specification Search for Time-Varying Parameter VARs. (2014). Strachan, Rodney ; Eisenstat, Eric ; Joshua C. C. Chan, .
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42. An empirical examination of stock market integration in EMU. (2014). Matei, Florin.
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43. Data-based priors for vector autoregressions with drifting coefficients. (2014). Korobilis, Dimitris.
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44. Data-based priors for vector autoregressions with drifting coefficients. (2014). Korobilis, Dimitris.
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45. Stochastic Model Specification Search for Time-Varying Parameter VARs. (2014). Strachan, Rodney ; Eisenstat, Eric ; Joshua C. C. Chan, .
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46. Fast Computation of the Deviance Information Criterion for Latent Variable Models. (2014). Grant, Angelia ; Chan, Joshua ; Joshua C. C. Chan, .
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47. Data-based priors for vector autoregressions with drifting coefficients. (2014). Korobilis, Dimitris.
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48. Forecasting Exchange Rates under Model and Parameter Uncertainty. (2014). Beckmann, Joscha ; Schussler, Rainer.
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49. Can the Sharia-Based Islamic Stock Market Returns be Forecasted Using Large Number of Predictors and Models?. (2013). Simo-Kengne, Beatrice Desiree ; Hammoudeh, Shawkat ; GUPTA, RANGAN ; Sarafrazi, Soodabeh.
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50. Estimation of a sparse group of sparse vectors. (2013). Grinshtein, Vadim ; Abramovich, Felix.
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