1. Exploring The Fama-French Five Factor Model Within a Time-Varying Parameters Framework. (2024). Budris, Cristian Andrei ; Dima, Bogdan.
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2. The Tourism Industry’s Performance During the Years of the COVID-19 Pandemic. (2024). Daglis, Theodoros.
   In: Computational Economics.
   RePEc:kap:compec:v:63:y:2024:i:3:d:10.1007_s10614-023-10442-y.

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3. Multi-scale impacts of oil shocks on travel and leisure stocks: A MODWT-Bayesian TVP model with shrinkage approach. (2024). Cao, Yan ; Cheng, Sheng ; Liang, Ruibin.
   In: Technological Forecasting and Social Change.
   RePEc:eee:tefoso:v:200:y:2024:i:c:s0040162523008764.

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4. Evaluating the nonlinear association between PM10 and emergency department visits. (2024). Golinelli, Davide ; Santi, Luca ; Sanmarchi, Francesco ; Bucci, Andrea.
   In: Socio-Economic Planning Sciences.
   RePEc:eee:soceps:v:93:y:2024:i:c:s0038012124000867.

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5. Partial Identification of Heteroskedastic Structural VARs: Theory and Bayesian Inference. (2024). Lütkepohl, Helmut ; Lutkepohl, Helmut ; Wo, Tomasz ; Uzeda, Luis ; Shang, Fei.
   In: Papers.
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6. Bayesian Bi-level Sparse Group Regressions for Macroeconomic Forecasting. (2024). Mogliani, Matteo ; Simoni, Anna.
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   RePEc:arx:papers:2404.02671.

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7. Bayesian Modelling of TVP-VARs Using Regression Trees. (2023). Mitchell, James ; Koop, Gary ; Huber, Florian ; Hauzenberger, Niko.
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8. Semiparametric finite mixture of regression models with Bayesian P-splines. (2023). Ranciati, Saverio ; Galimberti, Giuliano ; Berrettini, Marco.
   In: Advances in Data Analysis and Classification.
   RePEc:spr:advdac:v:17:y:2023:i:3:d:10.1007_s11634-022-00523-5.

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9. Dynamic Effects of Climate Policy Uncertainty on Green Bond Volatility: An Empirical Investigation Based on TVP-VAR Models. (2023). Wang, Guodong ; Liu, Ruoyu ; Zhang, Maojun ; Yu, Jiasheng.
   In: Sustainability.
   RePEc:gam:jsusta:v:15:y:2023:i:2:p:1692-:d:1037260.

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10. The time-varying impact of geopolitical risk on natural resource prices: The post-COVID era evidence. (2023). Cui, Tianxiang ; Wang, Kaihao ; Ding, Shusheng ; Du, Min.
    In: Resources Policy.
    RePEc:eee:jrpoli:v:86:y:2023:i:pb:s0301420723008723.

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11. Nowcasting growth using Google Trends data: A Bayesian Structural Time Series model. (2023). Bhattacharjee, Arnab ; Kohns, David.
    In: International Journal of Forecasting.
    RePEc:eee:intfor:v:39:y:2023:i:3:p:1384-1412.

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12. Implicit Copulas: An Overview. (2023). Smith, Michael Stanley.
    In: Econometrics and Statistics.
    RePEc:eee:ecosta:v:28:y:2023:i:c:p:81-104.

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13. Specification tests for time-varying coefficient models. (2023). Su, Liangjun ; Hong, Yongmiao ; Wang, Xia ; Fu, Zhonghao.
    In: Journal of Econometrics.
    RePEc:eee:econom:v:235:y:2023:i:2:p:720-744.

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14. Modeling realized covariance measures with heterogeneous liquidity: A generalized matrix-variate Wishart state-space model. (2023). Hartkopf, Jan Patrick ; Gribisch, Bastian.
    In: Journal of Econometrics.
    RePEc:eee:econom:v:235:y:2023:i:1:p:43-64.

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15. Fast estimation of a large TVP-VAR model with score-driven volatilities. (2023). Hong, Yongmiao ; Ye, Shiqi ; Zheng, Tingguo.
    In: Journal of Economic Dynamics and Control.
    RePEc:eee:dyncon:v:157:y:2023:i:c:s0165188923001689.

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16. Shared Bayesian variable shrinkage in multinomial logistic regression. (2023). Gaskins, Jeremy T ; Uddin, Md Nazir.
    In: Computational Statistics & Data Analysis.
    RePEc:eee:csdana:v:177:y:2023:i:c:s0167947322001487.

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17. Combining Large Numbers of Density Predictions with Bayesian Predictive Synthesis. (2023). Chernis, Tony.
    In: Staff Working Papers.
    RePEc:bca:bocawp:23-45.

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18. Bayesian Nonlinear Regression using Sums of Simple Functions. (2023). Huber, Florian.
    In: Papers.
    RePEc:arx:papers:2312.01881.

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19. Stochastic volatility models with skewness selection. (2023). Lopes, Hedibert Freitas ; Batista, Igor Ferreira.
    In: Papers.
    RePEc:arx:papers:2312.00282.

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20. Bayesian Approaches to Shrinkage and Sparse Estimation. (2022). Shimizu, Kenichi ; Korobilis, Dimitris.
    In: Working Paper series.
    RePEc:rim:rimwps:22-02.

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21. Nowcasting Growth using Google Trends Data: A Bayesian Structural Time Series Model. (2022). Kohns, David ; Bhattacharjee, Arnab.
    In: National Institute of Economic and Social Research (NIESR) Discussion Papers.
    RePEc:nsr:niesrd:538.

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22. What Drives Long-Term Interest Rates? Evidence from the Entire Swiss Franc History 1852-2020. (2022). Kaufmann, Daniel ; Tille, Cedric ; Stuart, Rebecca ; Hauzenberger, Niko.
    In: IRENE Working Papers.
    RePEc:irn:wpaper:22-03.

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23. Modeling Industrial Energy Demand in Relation to Subsector Manufacturing Output and Climate Change: Artificial Neural Network Insights. (2022). Adha, Rishan ; Yang, Su-Fen ; Shiau, Yuo-Hsien ; Muzayyanah, Syamsiyatul.
    In: Sustainability.
    RePEc:gam:jsusta:v:14:y:2022:i:5:p:2896-:d:762295.

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24. Monetary policy surprises and interest rates under Chinas evolving monetary policy framework. (2022). Ho, Chun-Yu ; Fu, Liang.
    In: Emerging Markets Review.
    RePEc:eee:ememar:v:52:y:2022:i:c:s1566014122000127.

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25. Estimation of large dimensional time varying VARs using copulas. (2022). Tsionas, Mike ; Trapani, Lorenzo ; Izzeldin, Marwan.
    In: European Economic Review.
    RePEc:eee:eecrev:v:141:y:2022:i:c:s0014292121002439.

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26. Parsimony inducing priors for large scale state–space models. (2022). Tsay, Ruey S ; McCulloch, Robert E ; Lopes, Hedibert F.
    In: Journal of Econometrics.
    RePEc:eee:econom:v:230:y:2022:i:1:p:39-61.

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27. Bayesian Modeling of Time-varying Parameters Using Regression Trees. (2022). Mitchell, James ; Koop, Gary ; Huber, Florian ; Hauzenberger, Niko.
    In: Papers.
    RePEc:arx:papers:2209.11970.

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28. Sparse Bayesian State-Space and Time-Varying Parameter Models. (2022). Knaus, Peter ; Fruhwirth-Schnatter, Sylvia.
    In: Papers.
    RePEc:arx:papers:2207.12147.

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29. Forecasting macroeconomic data with Bayesian VARs: Sparse or dense? It depends!. (2022). Kastner, Gregor ; Gruber, Luis.
    In: Papers.
    RePEc:arx:papers:2206.04902.

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30. Sparse multivariate modeling for stock returns predictability. (2022). Bernardi, Mauro ; Bianco, Nicolas ; Bianchi, Daniele.
    In: Papers.
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31. On the empirical estimator of the boundary in inverse first-exit problems. (2021). Potzelberger, Klaus ; Gur, Sercan.
    In: Computational Statistics.
    RePEc:spr:compst:v:36:y:2021:i:3:d:10.1007_s00180-020-00989-x.

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32. Bayesian Approaches to Shrinkage and Sparse Estimation. (2021). Korobilis, Dimitris ; Shimizu, Kenichi.
    In: MPRA Paper.
    RePEc:pra:mprapa:111631.

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33. The Condemned Live Longer – New Evidence of the New Keynesian Phillips Curve in Central and Eastern Europe. (2021). Ertl, Martin ; Zobl, Franz Xaver.
    In: Open Economies Review.
    RePEc:kap:openec:v:32:y:2021:i:4:d:10.1007_s11079-020-09604-4.

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34. Bayesian Approaches to Shrinkage and Sparse Estimation. (2021). Shimizu, Kenichi ; Korobilis, Dimitris.
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35. Macroeconomic Forecasting in a Multi-country Context. (2021). Marcellino, Massimiliano ; Clark, Todd ; Carriero, Andrea ; Bai, Yu.
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36. Flexible Mixture Priors for Large Time-varying Parameter Models. (2021). Hauzenberger, Niko.
    In: Econometrics and Statistics.
    RePEc:eee:ecosta:v:20:y:2021:i:c:p:87-108.

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37. Spatially varying sparsity in dynamic regression models. (2021). Hu, Guanyu.
    In: Econometrics and Statistics.
    RePEc:eee:ecosta:v:17:y:2021:i:c:p:23-34.

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38. Bayesian TVP-VARX models with time invariant long-run multipliers. (2021). Polbin, Andrey ; Belomestny, Denis ; Krymova, Ekaterina.
    In: Economic Modelling.
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39. Time-Varying Dynamics of the German Business Cycle: A Comprehensive Investigation. (2021). Reif, Magnus.
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40. Bayesian Approaches to Shrinkage and Sparse Estimation. (2021). Korobilis, Dimitris ; Shimizu, Kenichi.
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41. Implicit Copulas: An Overview. (2021). Smith, Michael Stanley.
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42. Financial Stress and Basis in Energy Markets. (2021). Manera, Matteo ; Niaz, Maryam Ahmadi.
    In: The Energy Journal.
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43. .

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44. Reply to Discussion of “Bayesian forecasting of multivariate time series: scalability, structure uncertainty and decisions”. (2020). West, Mike.
    In: Annals of the Institute of Statistical Mathematics.
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45. Bayesian dynamic variable selection in high dimensions. (2020). Korobilis, Dimitris ; Koop, Gary.
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46. Triple the Gamma—A Unifying Shrinkage Prior for Variance and Variable Selection in Sparse State Space and TVP Models. (2020). Knaus, Peter ; Fruhwirth-Schnatter, Sylvia ; Cadonna, Annalisa.
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47. Bayesian state space models in macroeconometrics. (2020). Strachan, Rodney.
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48. Relevant parameter changes in structural break models. (2020). Dufays, Arnaud.
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49. Large-scale minimum variance portfolio allocation using double regularization. (2020). Liao, Yin ; Bian, Zhicun ; Zhang, Xueyong ; Shi, Jing ; Oneill, Michael.
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50. Developments on the Bayesian Structural Time Series Model: Trending Growth. (2020). Kohns, David ; Bhattacharjee, Arnab.
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51. Deep Distributional Time Series Models and the Probabilistic Forecasting of Intraday Electricity Prices. (2020). Nott, David J ; Smith, Michael Stanley ; Klein, Nadja.
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52. Inference in Bayesian Additive Vector Autoregressive Tree Models. (2020). Huber, Florian ; Rossini, Luca.
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53. Flexible Mixture Priors for Large Time-varying Parameter Models. (2020). Hauzenberger, Niko.
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54. 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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55. Markov Switching. (2020). Wo, Tomasz ; Song, Yong.
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56. A multi-country dynamic factor model with stochastic volatility for euro area business cycle analysis. (2020). Huber, Florian ; Piribauer, Philipp ; Pfarrhofer, Michael.
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57. Measuring international uncertainty using global vector autoregressions with drifting parameters. (2019). Pfarrhofer, Michael.
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58. Bayesian Analysis of Coefficient Instability in Dynamic Regressions. (2019). Taboga, Marco ; Ciapanna, Emanuela.
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59. Triple the gamma -- A unifying shrinkage prior for variance and variable selection in sparse state space and TVP models. (2019). Knaus, Peter ; Fruhwirth-Schnatter, Sylvia ; Cadonna, Annalisa.
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60. Measuring international uncertainty using global vector autoregressions with drifting parameters. (2019). Pfarrhofer, Michael.
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61. Shrinkage in the Time-Varying Parameter Model Framework Using the R Package shrinkTVP. (2019). Knaus, Peter ; Fruhwirth-Schnatter, Sylvia ; Cadonna, Annalisa ; Bitto-Nemling, Angela.
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62. Bayesian nonparametric graphical models for time-varying parameters VAR. (2019). Rossini, Luca ; Iacopini, Matteo.
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1. Bayesian dynamic variable selection in high dimensions. (2020). Korobilis, Dimitris ; Koop, Gary.
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2. Triple the Gamma—A Unifying Shrinkage Prior for Variance and Variable Selection in Sparse State Space and TVP Models. (2020). Knaus, Peter ; Fruhwirth-Schnatter, Sylvia ; Cadonna, Annalisa.
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4. Is the slope of the Phillips curve time-varying? Evidence from unobserved components models. (2020). Fu, Bowen.
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5. Time-Varying Parameters as Ridge Regressions. (2020). Coulombe, Philippe Goulet.
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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. Credit constraints and the propagation of the Great Depression in Germany. (2019). Adam, Marc ; Jansson, Walter.
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8. Steady‐state modeling and macroeconomic forecasting quality. (2019). Louzis, Dimitrios.
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9. Streamlining Time-varying VAR with a Factor Structure in the Parameters. (2019). Beyeler, Simon.
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10. How useful are time-varying parameter models for forecasting economic growth in CESEE?. (2019). Feldkircher, Martin ; Hauzenberger, Nico.
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11. Bagged neural networks for forecasting Polish (low) inflation. (2019). Szafranek, Karol.
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12. Sparse Bayesian time-varying covariance estimation in many dimensions. (2019). Kastner, Gregor.
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13. Achieving shrinkage in a time-varying parameter model framework. (2019). Fruhwirth-Schnatter, Sylvia ; Bitto, Angela.
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14. Dynamic Bayesian predictive synthesis in time series forecasting. (2019). West, Mike ; McAlinn, Kenichiro.
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15. Triple the gamma -- A unifying shrinkage prior for variance and variable selection in sparse state space and TVP models. (2019). Knaus, Peter ; Fruhwirth-Schnatter, Sylvia ; Cadonna, Annalisa.
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16. Measuring international uncertainty using global vector autoregressions with drifting parameters. (2019). Pfarrhofer, Michael.
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17. Shrinkage in the Time-Varying Parameter Model Framework Using the R Package shrinkTVP. (2019). Knaus, Peter ; Fruhwirth-Schnatter, Sylvia ; Cadonna, Annalisa ; Bitto-Nemling, Angela.
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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). Korobilis, Dimitris ; Koop, Gary.
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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 ; Ju, Xiang ; Aijun, Yang ; Jinguan, Lin.
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26. Comparing hybrid time-varying parameter VARs. (2018). Chan, Joshua ; Eisenstat, Eric.
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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). 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). Chan, Joshua ; Eisenstat, Eric.
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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 ; Simo -Kengne, Beatrice D. ; 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 ; Simo -Kengne, Beatrice D. ; Sarafrazi, Soodabeh.
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50. Estimation of a sparse group of sparse vectors. (2013). Abramovich, Felix ; Grinshtein, Vadim .
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