Density Forecasts in Panel Models: A semiparametric Bayesian Perspective
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- Omiros Papaspiliopoulos & Gareth O. Roberts, 2008. "Retrospective Markov chain Monte Carlo methods for Dirichlet process hierarchical models," Biometrika, Biometrika Trust, vol. 95(1), pages 169-186.
- Canova, Fabio & Ciccarelli, Matteo, 2013. "Panel Vector Autoregressive Models: A Survey," CEPR Discussion Papers 9380, C.E.P.R. Discussion Papers.
- Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006.
"A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series,"
Journal of Econometrics,
Elsevier, vol. 135(1-2), pages 499-526.
- Marcellino, Massimiliano & Stock, James H & Watson, Mark W, 2005. "A Comparison of Direct and Iterated Multistep AR Methods for Forecasting Macroeconomic Time Series," CEPR Discussion Papers 4976, C.E.P.R. Discussion Papers.
- Massimiliano Marcellino & James Stock & Mark Watson, 2005. "A Comparison of Direct and Iterated Multistep AR Methods for Forecasting Macroeconomic Time Series," Working Papers 285, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
- Geweke, John & Amisano, Gianni, 2010. "Comparing and evaluating Bayesian predictive distributions of asset returns," International Journal of Forecasting, Elsevier, vol. 26(2), pages 216-230, April.
- Ufuk Akcigit & William R. Kerr, 2010.
"Growth through Heterogeneous Innovations,"
PIER Working Paper Archive
10-035, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
- Akcigit, Ufuk & Kerr, William R., 2016. "Growth through Heterogeneous Innovations," CEPR Discussion Papers 11660, C.E.P.R. Discussion Papers.
- Ufuk Akcigit & William R. Kerr, 2010. "Growth Through Heterogeneous Innovations," NBER Working Papers 16443, National Bureau of Economic Research, Inc.
- Akcigit, Ufuk & Kerr, William R., 2013. "Growth through heterogeneous innovations," Research Discussion Papers 28/2013, Bank of Finland.
- Ufuk Akcigit & William R. Kerr, 2012. "Growth Through Heterogeneous Innovations," Working Papers 12-08, Center for Economic Studies, U.S. Census Bureau.
- Keisuke Hirano, 2002. "Semiparametric Bayesian Inference in Autoregressive Panel Data Models," Econometrica, Econometric Society, vol. 70(2), pages 781-799, March.
- Diebold, Francis X & Gunther, Todd A & Tay, Anthony S, 1998. "Evaluating Density Forecasts with Applications to Financial Risk Management," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 863-883, November.
- Bronwyn H. Hall & Nathan Rosenberg (ed.), 2010. "Handbook of the Economics of Innovation," Handbook of the Economics of Innovation, Elsevier, edition 1, volume 1, number 1, 00.
- Laura Liu & Hyungsik Moon & Frank Schorfheide, 2016. "Forecasting with Dynamic Panel Data Models," PIER Working Paper Archive 16-022, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 21 Dec 2016.
- Fern ndez, Carmen & Steel, Mark F.J., 2000.
"Bayesian Regression Analysis With Scale Mixtures Of Normals,"
Cambridge University Press, vol. 16(01), pages 80-101, February.
- Carmen Fernandez & Mark F J Steel, 1999. "Bayesian Regression Analysis with scale mixtures of normals," ESE Discussion Papers 27, Edinburgh School of Economics, University of Edinburgh.
- C. Yau & O. Papaspiliopoulos & G. O. Roberts & C. Holmes, 2011. "Bayesian non‐parametric hidden Markov models with applications in genomics," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(1), pages 37-57, January.
- David B. Dunson, 2009. "Nonparametric Bayes local partition models for random effects," Biometrika, Biometrika Trust, vol. 96(2), pages 249-262.
- Youngki Lee & Luis A. N. Amaral & David Canning & Martin Meyer & H. Eugene Stanley, 1998. "Universal features in the growth dynamics of complex organizations," Papers cond-mat/9804100, arXiv.org.
- Peter E. Rossi, 2014. "Bayesian Non- and Semi-parametric Methods and Applications," Economics Books, Princeton University Press, edition 1, number 10259, November.
- Pati, Debdeep & Dunson, David B. & Tokdar, Surya T., 2013. "Posterior consistency in conditional distribution estimation," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 456-472.
- Pelenis, Justinas, 2014. "Bayesian regression with heteroscedastic error density and parametric mean function," Journal of Econometrics, Elsevier, vol. 178(P3), pages 624-638.
- Liverani, Silvia & Hastie, David I. & Azizi, Lamiae & Papathomas, Michail & Richardson, Sylvia, 2015. "PReMiuM: An R Package for Profile Regression Mixture Models Using Dirichlet Processes," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i07).
More about this item
KeywordsBayesian; Semiparametric Methods; Panel Data; Density Forecasts; Posterior Consistency; Young Firms Dynamics;
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- L25 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Firm Performance
NEP fieldsThis paper has been announced in the following NEP Reports:
- NEP-ALL-2018-09-03 (All new papers)
- NEP-ECM-2018-09-03 (Econometrics)
- NEP-FOR-2018-09-03 (Forecasting)
- NEP-ORE-2018-09-03 (Operations Research)
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