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Citations for "Forecasting using a large number of predictors: Is Bayesian shrinkage a valid alternative to principal components?"

by De Mol, Christine & Giannone, Domenico & Reichlin, Lucrezia

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  1. Marine Carrasco & Barbara Rossi, 2016. "In-sample inference and forecasting in misspecified factor models," Economics Working Papers 1530, Department of Economics and Business, Universitat Pompeu Fabra.
  2. Deryugina, Elena & Ponomarenko, Alexey, 2014. "A large Bayesian vector autoregression model for Russia," BOFIT Discussion Papers 22/2014, Bank of Finland, Institute for Economies in Transition.
  3. Sandra Eickmeier & Tim Ng, 2009. "Forecasting national activity using lots of international predictors: an application to New Zealand," Reserve Bank of New Zealand Discussion Paper Series DP2009/04, Reserve Bank of New Zealand.
  4. Joshua Brodie & Ingrid Daubechies & Christine De Mol & Domenico Giannone & Ignace Loris, 2007. "Sparse and stable Markowitz portfolios," Papers 0708.0046, arXiv.org, revised May 2008.
  5. Bańbura, Marta & Giannone, Domenico & Reichlin, Lucrezia, 2008. "Large Bayesian VARs," Working Paper Series 0966, European Central Bank.
  6. Jan J. J. Groen & Richard Paap & Francesco Ravazzolo, 2009. "Real-time inflation forecasting in a changing world," Staff Reports 388, Federal Reserve Bank of New York.
  7. Henzel, Steffen R. & Mayr, Johannes, 2013. "The mechanics of VAR forecast pooling—A DSGE model based Monte Carlo study," The North American Journal of Economics and Finance, Elsevier, vol. 24(C), pages 1-24.
  8. BELMONTE, Miguel A.G. & KOOP, Gary & KOROBILIS, Dimitris, 2011. "Hierarchical shrinkage in time-varying parameter models," CORE Discussion Papers 2011036, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  9. Bai, Jushan & Ng, Serena, 2008. "Forecasting economic time series using targeted predictors," Journal of Econometrics, Elsevier, vol. 146(2), pages 304-317, October.
  10. Berg, Tim O. & Henzel, Steffen R., 2015. "Point and density forecasts for the euro area using Bayesian VARs," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1067-1095.
  11. Fornaro, Paolo, 2015. "Forecasting U.S. Recessions with a Large Set of Predictors," MPRA Paper 62973, University Library of Munich, Germany.
  12. Onatski, Alexei, 2015. "Asymptotic analysis of the squared estimation error in misspecified factor models," Journal of Econometrics, Elsevier, vol. 186(2), pages 388-406.
  13. Exterkate, Peter & Groenen, Patrick J.F. & Heij, Christiaan & van Dijk, Dick, 2016. "Nonlinear forecasting with many predictors using kernel ridge regression," International Journal of Forecasting, Elsevier, vol. 32(3), pages 736-753.
  14. Rachida Ouysse, 2011. "Comparison of Bayesian moving Average and Principal Component Forecast for Large Dimensional Factor Models," Discussion Papers 2012-03, School of Economics, The University of New South Wales.
  15. James H. Stock & Mark W. Watson, 2012. "Generalized Shrinkage Methods for Forecasting Using Many Predictors," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(4), pages 481-493, June.
  16. Giacomini, Raffaella, 2014. "Economic theory and forecasting: lessons from the literature," CEPR Discussion Papers 10201, C.E.P.R. Discussion Papers.
  17. Fady Barsoum, 2013. "The Effects of Monetary Policy Shocks on a Panel of Stock Market Volatilities: A Factor-Augmented Bayesian VAR Approach," Working Paper Series of the Department of Economics, University of Konstanz 2013-15, Department of Economics, University of Konstanz.
  18. Vladimir Kuzin & Massimiliano Marcellino & Christian Schumacher, 2009. "Pooling versus Model Selection for Nowcasting with Many Predictors: An Application to German GDP," Economics Working Papers ECO2009/13, European University Institute.
  19. Karlsson, Sune, 2012. "Forecasting with Bayesian Vector Autoregressions," Working Papers 2012:12, Örebro University, School of Business.
  20. Bańbura, Marta & Giannone, Domenico & Lenza, Michele, 2015. "Conditional forecasts and scenario analysis with vector autoregressions for large cross-sections," International Journal of Forecasting, Elsevier, vol. 31(3), pages 739-756.
  21. Matteo Barigozzi & Christian Brownlees, 2013. "Nets: Network Estimation for Time Series," Working Papers 723, Barcelona Graduate School of Economics.
  22. Götz, T.B. & Hecq, A.W., 2014. "Testing for Granger causality in large mixed-frequency VARs," Research Memorandum 028, Maastricht University, Graduate School of Business and Economics (GSBE).
  23. Giacomini, Raffaella & Ragusa, Giuseppe, 2011. "Incorporating theoretical restrictions into forecasting by projection methods," CEPR Discussion Papers 8604, C.E.P.R. Discussion Papers.
  24. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2016. "Structural analysis with Multivariate Autoregressive Index models," Journal of Econometrics, Elsevier, vol. 192(2), pages 332-348.
  25. Frank Schorfheide & Dongho Song, 2015. "Real-Time Forecasting With a Mixed-Frequency VAR," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 366-380, July.
  26. Alessandro Giovannelli & Tommaso Proietti, 2014. "On the Selection of Common Factors for Macroeconomic Forecasting," CREATES Research Papers 2014-46, Department of Economics and Business Economics, Aarhus University.
  27. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility," Working Paper 1227, Federal Reserve Bank of Cleveland.
  28. Fady Barsoum, 2015. "Point and Density Forecasts Using an Unrestricted Mixed-Frequency VAR Model," Working Paper Series of the Department of Economics, University of Konstanz 2015-19, Department of Economics, University of Konstanz.
  29. Michele Lenza & Huw Pill & Lucrezia Reichlin, 2010. "Monetary policy in exceptional times," Economic Policy, CEPR;CES;MSH, vol. 25, pages 295-339, 04.
  30. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2009. "Forecasting Large Datasets with Bayesian Reduced Rank Multivariate Models," CEPR Discussion Papers 7446, C.E.P.R. Discussion Papers.
  31. Onatski, Alexei, 2012. "Asymptotics of the principal components estimator of large factor models with weakly influential factors," Journal of Econometrics, Elsevier, vol. 168(2), pages 244-258.
  32. Chudik, Alexander & Pesaran, M. Hashem, 2014. "Theory and practice of GVAR modeling," Globalization and Monetary Policy Institute Working Paper 180, Federal Reserve Bank of Dallas.
  33. Gary Koop, 2012. "Using VARs and TVP-VARs with Many Macroeconomic Variables," Central European Journal of Economic Modelling and Econometrics, CEJEME, vol. 4(3), pages 143-167, September.
  34. Cristina Conflitti & Christine De Mol & Domenico Giannone, 2012. "Optimal Combination of Survey Forecasts," Working Papers ECARES ECARES 2012-023, ULB -- Universite Libre de Bruxelles.
  35. Vortelinos, Dimitrios I., 2017. "Forecasting realized volatility: HAR against Principal Components Combining, neural networks and GARCH," Research in International Business and Finance, Elsevier, vol. 39(PB), pages 824-839.
  36. Chris Bloor & Troy Matheson, 2010. "Analysing shock transmission in a data-rich environment: a large BVAR for New Zealand," Empirical Economics, Springer, vol. 39(2), pages 537-558, October.
  37. Gustavo Fruet Dias & George Kapetanios, 2014. "Estimation and Forecasting in Vector Autoregressive Moving Average Models for Rich Datasets," CREATES Research Papers 2014-37, Department of Economics and Business Economics, Aarhus University.
  38. Bicu A.C. & Lieb L.M., 2015. "Cross-border effects of fiscal policy in the Eurozone," Research Memorandum 019, Maastricht University, Graduate School of Business and Economics (GSBE).
  39. Rusnák, Marek, 2016. "Nowcasting Czech GDP in real time," Economic Modelling, Elsevier, vol. 54(C), pages 26-39.
  40. repec:uea:aepppr:2012_56 is not listed on IDEAS
  41. Rangan Gupta & Marius Jurgilas & Alain Kabundi & Stephen M. Miller, 2011. "Monetary policy and housing sector dynamics in a large-scale Bayesian vector autoregressive model," International Journal of Strategic Property Management, Taylor & Francis Journals, vol. 16(1), pages 1-20, August.
  42. Berg, Tim Oliver, 2015. "Multivariate Forecasting with BVARs and DSGE Models," MPRA Paper 62405, University Library of Munich, Germany.
  43. Altavilla, Carlo & Giannone, Domenico & Lenza, Michele, 2014. "The financial and macroeconomic effects of OMT announcements," Working Paper Series 1707, European Central Bank.
  44. Altavilla, Carlo & Darracq Pariès, Matthieu & Nicoletti, Giulio, 2015. "Loan supply, credit markets and the euro area financial crisis," Working Paper Series 1861, European Central Bank.
  45. Bańbura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2013. "Now-Casting and the Real-Time Data Flow," Handbook of Economic Forecasting, Elsevier.
  46. Domenico Giannone & Michèle Lenza & Daphné Momferatu & Luca Onorante, 2010. "Short-term inflation projections: a Bayesian vector autoregressive approach," Working Papers ECARES ECARES 2010-011, ULB -- Universite Libre de Bruxelles.
  47. Carlo Altavilla & Domenico Giannone & Michele Lenza, 2014. "The Financial and Macroeconomic Effects of the OMT Announcements," CSEF Working Papers 352, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
  48. A. Girardi & R. Golinelli & C. Pappalardo, 2014. "The Role of Indicator Selection in Nowcasting Euro Area GDP in Pseudo Real Time," Working Papers wp919, Dipartimento Scienze Economiche, Universita' di Bologna.
  49. Canova, Fabio & Ciccarelli, Matteo, 2013. "Panel Vector Autoregressive Models: A Survey," CEPR Discussion Papers 9380, C.E.P.R. Discussion Papers.
  50. Chudik , A. & Pesaran, M.H., 2007. "Infinite Dimensional VARs and Factor Models," Cambridge Working Papers in Economics 0757, Faculty of Economics, University of Cambridge.
  51. Giannone, Domenico & Lenza, Michele & Pill, Huw & Reichlin, Lucrezia, 2011. "Non-standard monetary policy measures and monetary developments," Working Paper Series 1290, European Central Bank.
  52. Kaabia, Olfa & Abid, Ilyes & Mkaouar, Farid, 2016. "The dark side of the black gold shock onto Europe: One stock's joy is another stock's sorrow," Economic Modelling, Elsevier, vol. 58(C), pages 642-654.
  53. David de Antonio Liedo & Elena Fernández Muñoz, 2010. "Nowcasting Spanish GDP growth in real time: "One and a half months earlier"," Working Papers 1037, Banco de España;Working Papers Homepage.
  54. Chen, Guojin & Hong, Zhiwu & Ren, Yu, 2016. "Durable consumption and asset returns: Cointegration analysis," Economic Modelling, Elsevier, vol. 53(C), pages 231-244.
  55. Raffaella Giacomini & Barbara Rossi, 2014. "Forecasting in Nonstationary Environments: What Works and What Doesn't in Reduced-Form and Structural Models," Working Papers 819, Barcelona Graduate School of Economics.
  56. Julieta Fuentes & Pilar Poncela & Julio Rodríguez, 2015. "Sparse Partial Least Squares in Time Series for Macroeconomic Forecasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(4), pages 576-595, 06.
  57. Marine Carrasco & Guy Tchuente, 2015. "Efficient estimation with many weak instruments using regularization techniques," Studies in Economics 1517, School of Economics, University of Kent.
  58. Giannone, Domenico & Lenza, Michele & Primiceri, Giorgio E., 2012. "Prior selection for vector autoregressions," Working Paper Series 1494, European Central Bank.
  59. Helmut Lütkepohl, 2014. "Structural Vector Autoregressive Analysis in a Data Rich Environment: A Survey," Discussion Papers of DIW Berlin 1351, DIW Berlin, German Institute for Economic Research.
  60. Domenico Giannone & Michèle Lenza & Lucrezia Reichlin, "undated". "Explaining the great moderation: it is not the shocks," ULB Institutional Repository 2013/6413, ULB -- Universite Libre de Bruxelles.
  61. Petre Caraiani, 2014. "Do money and financial variables help forecasting output in emerging European Economies?," Empirical Economics, Springer, vol. 46(2), pages 743-763, March.
  62. Ricco, Giovanni & Callegari, Giovanni & Cimadomo, Jacopo, 2016. "Signals from the government: Policy disagreement and the transmission of fiscal shocks," Journal of Monetary Economics, Elsevier, vol. 82(C), pages 107-118.
  63. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2012. "Forecasting government bond yields with large Bayesian vector autoregressions," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 2026-2047.
  64. Kapetanios, George & Marcellino, Massimiliano & Venditti, Fabrizio, 2016. "Large Time-Varying Parameter VARs: A Non-Parametric Approach," CEPR Discussion Papers 11560, C.E.P.R. Discussion Papers.
  65. Olfa Kaabia & Ilyes Abid & Khaled Guesmi, 2012. "Does Bayesian Shrinkage Help to Better Reflect What Happened during the Subprime Crisis?," EconomiX Working Papers 2012-46, University of Paris West - Nanterre la Défense, EconomiX.
  66. Goodness C. Aye & Rangan Gupta, 2013. "Forecasting Real House Price of the U.S.: An Analysis Covering 1890 to 2012," Working Papers 201362, University of Pretoria, Department of Economics.
  67. Ouysse, Rachida, 2016. "Bayesian model averaging and principal component regression forecasts in a data rich environment," International Journal of Forecasting, Elsevier, vol. 32(3), pages 763-787.
  68. Groen, Jan J.J. & Kapetanios, George, 2016. "Revisiting useful approaches to data-rich macroeconomic forecasting," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 221-239.
  69. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2008. "Forecasting Exchange Rates with a Large Bayesian VAR," CEPR Discussion Papers 7008, C.E.P.R. Discussion Papers.
  70. Chris Bloor & Troy Matheson, 2009. "Real-time conditional forecasts with Bayesian VARs: An application to New Zealand," Reserve Bank of New Zealand Discussion Paper Series DP2009/02, Reserve Bank of New Zealand.
  71. Carrera, Cesar & Ledesma, Alan, 2015. "Proyección de la inflación agregada con modelos de vectores autorregresivos bayesianos," Working Papers 2015-003, Banco Central de Reserva del Perú.
  72. Li, Jiahan & Chen, Weiye, 2014. "Forecasting macroeconomic time series: LASSO-based approaches and their forecast combinations with dynamic factor models," International Journal of Forecasting, Elsevier, vol. 30(4), pages 996-1015.
  73. Domenico Giannone & Michèle Lenza & Huw Pill & Lucrezia Reichlin, 2012. "The ECB and the Interbank Market," Working Papers ECARES ECARES 2012-005, ULB -- Universite Libre de Bruxelles.
  74. Chudik, Alexander & Grossman, Valerie & Pesaran, M. Hashem, 2014. "A multi-country approach to forecasting output growth using PMIs," Globalization and Monetary Policy Institute Working Paper 213, Federal Reserve Bank of Dallas.
  75. Marc Hallin & Marco Lippi, 2013. "Factor Models in High-Dimensional Time Series: A Time-Domain Approach," Working Papers ECARES ECARES 2013-15, ULB -- Universite Libre de Bruxelles.
  76. Marcellino, Massimiliano & Schumacher, Christian, 2007. "Factor-MIDAS for now- and forecasting with ragged-edge data: a model comparison for German GDP," Discussion Paper Series 1: Economic Studies 2007,34, Deutsche Bundesbank, Research Centre.
  77. Tevdovski, Dragan & Petrevski, Goran & Bogoev, Jane, 2016. "The effects of macroeconomic policies under fixed exchange rates: A Bayesian VAR analysis," MPRA Paper 73461, University Library of Munich, Germany, revised 21 Jun 2016.
  78. Christophe Piette, 2016. "Predicting Belgium’s GDP using targeted bridge models," Working Paper Research 290, National Bank of Belgium.
  79. Cesar Carrera & Alan Ledesma, 2015. "Aggregate Inflation Forecast with Bayesian Vector Autoregressive Models," Working Papers 2015-50, Peruvian Economic Association.
  80. Stelios Bekiros & Alessia Paccagnini, 2013. "On the predictability of time-varying VAR and DSGE models," Empirical Economics, Springer, vol. 45(1), pages 635-664, August.
  81. Helmut Lütkepohl, 2012. "Fundamental Problems with Nonfundamental Shocks," Discussion Papers of DIW Berlin 1230, DIW Berlin, German Institute for Economic Research.
  82. Raffaella Giacomini, 2014. "Economic theory and forecasting: lessons from the literature," CeMMAP working papers CWP41/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  83. Castle, Jennifer L. & Clements, Michael P. & Hendry, David F., 2013. "Forecasting by factors, by variables, by both or neither?," Journal of Econometrics, Elsevier, vol. 177(2), pages 305-319.
  84. Ricco, Giovanni, 2015. "A new identification of fiscal shocks based on the information flow," Working Paper Series 1813, European Central Bank.
  85. Gupta, Rangan & Kabundi, Alain & Miller, Stephen M., 2011. "Forecasting the US real house price index: Structural and non-structural models with and without fundamentals," Economic Modelling, Elsevier, vol. 28(4), pages 2013-2021, July.
  86. Giannone, Domenico & Lenza, Michele & Reichlin, Lucrezia, 2012. "Money, credit, monetary policy and the business cycle in the euro area," CEPR Discussion Papers 8944, C.E.P.R. Discussion Papers.
  87. Adina Popescu & Alina Carare, 2011. "Monetary Policy and Risk-Premium Shocks in Hungary; Results from a Large Bayesian VAR," IMF Working Papers 11/259, International Monetary Fund.
  88. Kerstin Bernoth & Andreas Pick, 2009. "Forecasting the Fragility of the Banking and Insurance Sector," Discussion Papers of DIW Berlin 882, DIW Berlin, German Institute for Economic Research.
  89. Tomasz Woźniak, 2016. "Bayesian Vector Autoregressions," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 49(3), pages 365-380, 09.
  90. Eliana González, "undated". "Forecasting With Many Predictors. An Empirical Comparison," Borradores de Economia 643, Banco de la Republica de Colombia.
  91. Roberto Casarin & Daniel Felix Ahelegbey & Monica Billio, 2014. "Sparse Graphical Vector Autoregression: A Bayesian Approach," Working Papers 2014:29, Department of Economics, University of Venice "Ca' Foscari".
  92. Matteo Barigozzi & Marc Hallin, 2015. "Networks, Dynamic Factors, and the Volatility Analysis of High-Dimensional Financial Series," Working Papers ECARES ECARES 2015-34, ULB -- Universite Libre de Bruxelles.
  93. Rangan Gupta & Alain Kabundi, 2008. "Forecasting Macroeconomic Variables in a Small Open Economy: A Comparison between Small- and Large-Scale Models," Working Papers 200830, University of Pretoria, Department of Economics.
  94. Dimitris P. Louzis, 2014. "Macroeconomic and credit forecasts in a small economy during crisis: A large Bayesian VAR approach," Working Papers 184, Bank of Greece.
  95. KOROBILIS, Dimitris, 2011. "Hierarchical shrinkage priors for dynamic regressions with many predictors," CORE Discussion Papers 2011021, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  96. Lombardi, Marco J. & Osbat, Chiara & Schnatz, Bernd, 2010. "Global commodity cycles and linkages a FAVAR approach," Working Paper Series 1170, European Central Bank.
  97. Francisco Dias & Maximiano Pinheiro & António Rua, 2010. "Forecasting using targeted diffusion indexes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(3), pages 341-352.
  98. Sandra Eickmeier & Christina Ziegler, 2008. "How successful are dynamic factor models at forecasting output and inflation? A meta-analytic approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(3), pages 237-265.
  99. Scharnagl, Michael & Schumacher, Christian, 2007. "Reconsidering the role of monetary indicators for euro area inflation from a Bayesian perspective using group inclusion probabilities," Discussion Paper Series 1: Economic Studies 2007,09, Deutsche Bundesbank, Research Centre.
  100. International Monetary Fund., 2014. "Former Yugoslav Republic of Macedonia; Selected Issues," IMF Staff Country Reports 14/232, International Monetary Fund.
  101. Massimiliano Marcellino & Christian Schumacher, 2008. "Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP1," Working Papers 333, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  102. Pirschel, Inske & Wolters, Maik, 2014. "Forecasting German key macroeconomic variables using large dataset methods," Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100587, Verein für Socialpolitik / German Economic Association.
  103. Cubadda, Gianluca & Guardabascio, Barbara, 2012. "A medium-N approach to macroeconomic forecasting," Economic Modelling, Elsevier, vol. 29(4), pages 1099-1105.
  104. Lucrezia Reichlin, 2009. "Comment on "How Has the Euro Changed the Monetary Transmission Mechanism?"," NBER Chapters, in: NBER Macroeconomics Annual 2008, Volume 23, pages 127-139 National Bureau of Economic Research, Inc.
  105. Auer, Simone, 2014. "Monetary policy shocks and foreign investment income: evidence from a large Bayesian VAR," Globalization and Monetary Policy Institute Working Paper 170, Federal Reserve Bank of Dallas.
  106. Sandra Stankiewicz, 2015. "Forecasting Euro Area Macroeconomic Variables with Bayesian Adaptive Elastic Net," Working Paper Series of the Department of Economics, University of Konstanz 2015-12, Department of Economics, University of Konstanz.
  107. Ricco, Giovanni & Ellahie, Atif, 2012. "Government Spending Reloaded: Fundamentalness and Heterogeneity in Fiscal SVARs," MPRA Paper 42105, University Library of Munich, Germany.
  108. Jarociński, Marek, 2010. "Imposing parsimony in cross-country growth regressions," Working Paper Series 1234, European Central Bank.
  109. Luigi Paciello, 2011. "Does Inflation Adjust Faster to Aggregate Technology Shocks than to Monetary Policy Shocks?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 43(8), pages 1663-1684, December.
  110. Ricco, Giovanni & Callegari, Giovanni & Cimadomo, Jacopo, 2014. "Signals from the Government: Policy Uncertainty and the Transmission of Fiscal Shocks," MPRA Paper 56136, University Library of Munich, Germany.
  111. Smeekes, Stephan & Wijler, Etiënne, 2016. "Macroeconomic Forecasting Using Penalized Regression Methods," Research Memorandum 039, Maastricht University, Graduate School of Business and Economics (GSBE).
  112. Mayr, Johannes, 2010. "Forecasting Macroeconomic Aggregates," Munich Dissertations in Economics 11140, University of Munich, Department of Economics.
  113. Liebermann, Joelle, 2012. "Real-time forecasting in a data-rich environment," Research Technical Papers 07/RT/12, Central Bank of Ireland.
  114. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2010. "Forecasting Government Bond Yields with Large Bayesian VARs," CEPR Discussion Papers 7796, C.E.P.R. Discussion Papers.
  115. Robinson Durán & Evelyn Garrido & Carolina Godoy & Juan de Dios Tena, 2012. "Predicción de la inflación en México con modelos desagregados por componente," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, vol. 27(1), pages 133-167.
  116. Haroon Mumtaz & Nitin Kumar, 2012. "An application of data-rich environment for policy analysis of the Indian economy," Joint Research Papers 2, Centre for Central Banking Studies, Bank of England.
  117. Kelly, Bryan & Pruitt, Seth, 2015. "The three-pass regression filter: A new approach to forecasting using many predictors," Journal of Econometrics, Elsevier, vol. 186(2), pages 294-316.
  118. Matteo Luciani, 2011. "Forecasting with Approximate Dynamic Factor Models: the Role of Non-Pervasive Shocks," Working Papers ECARES ECARES 2011‐022, ULB -- Universite Libre de Bruxelles.
  119. Ching Wai (Jeremy) Chiu & Bjørn Eraker & Andrew T. Foerster & Tae Bong Kim & Hernán D. Seoane, 2011. "Estimating VAR's sampled at mixed or irregular spaced frequencies : a Bayesian approach," Research Working Paper RWP 11-11, Federal Reserve Bank of Kansas City.
  120. Petrevski, Goran & Exterkate, Peter & Tevdovski, Dragan & Bogoev, Jane, 2015. "The transmission of foreign shocks to South Eastern European economies: A Bayesian VAR approach," Economic Systems, Elsevier, vol. 39(4), pages 632-643.
  121. Clements, Michael P., 2016. "Real-time factor model forecasting and the effects of instability," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 661-675.
  122. Jiahan Li & Ilias Tsiakas & Wei Wang, 2015. "Predicting Exchange Rates Out of Sample: Can Economic Fundamentals Beat the Random Walk?," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 13(2), pages 293-341.
  123. Xu, Ning & Hong, Jian & Fisher, Timothy, 2016. "Model selection consistency from the perspective of generalization ability and VC theory with an application to Lasso," MPRA Paper 71670, University Library of Munich, Germany.
  124. repec:kie:kieliw:1925 is not listed on IDEAS
  125. Rangan Gupta, 2012. "Forecasting House Prices for the Four Census Regions and the Aggregate US Economy: The Role of a Data-Rich Environment," Working Papers 201214, University of Pretoria, Department of Economics.
  126. Rangan Gupta & Mampho P. Modise & Josine Uwilingiye, 2011. "Out-of-Sample Equity Premium Predictability in South Africa: Evidence from a Large Number of Predictors," Working Papers 201122, University of Pretoria, Department of Economics.
  127. Giuzio, Margherita & Ferrari, Davide & Paterlini, Sandra, 2016. "Sparse and robust normal and t- portfolios by penalized Lq-likelihood minimization," European Journal of Operational Research, Elsevier, vol. 250(1), pages 251-261.
  128. Jennifer Castle & David Hendry, 2013. "Forecasting and Nowcasting Macroeconomic Variables: A Methodological Overview," Economics Series Working Papers 674, University of Oxford, Department of Economics.
  129. Kascha, Christian & Trenkler, Carsten, 2015. "Forecasting VARs, model selection, and shrinkage," Working Papers 15-07, University of Mannheim, Department of Economics.
  130. Demeshev, Boris & Malakhovskaya, Oxana, 2016. "BVAR mapping," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 43, pages 118-141.
  131. Gefang, Deborah, 2014. "Bayesian doubly adaptive elastic-net Lasso for VAR shrinkage," International Journal of Forecasting, Elsevier, vol. 30(1), pages 1-11.
  132. Sydney C. Ludvigson & Serena Ng, 2009. "A Factor Analysis of Bond Risk Premia," NBER Working Papers 15188, National Bureau of Economic Research, Inc.
  133. Pierre Guerin & Danilo Leiva-Leon & Massimiliano Marcellino, 2016. "Markov-Switching Three-Pass Regression Filter," Working Papers 591, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  134. Giacomini, Raffaella & Ragusa, Giuseppe, 2014. "Theory-coherent forecasting," Journal of Econometrics, Elsevier, vol. 182(1), pages 145-155.
  135. Jushan Bai & Serena Ng, 2009. "Boosting diffusion indices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(4), pages 607-629.
  136. Gargano, Antonio & Timmermann, Allan, 2014. "Forecasting commodity price indexes using macroeconomic and financial predictors," International Journal of Forecasting, Elsevier, vol. 30(3), pages 825-843.
  137. Eliana González, 2011. "Forecasting With Many Predictors. An Empirical Comparison," BORRADORES DE ECONOMIA 007996, BANCO DE LA REPÚBLICA.
  138. C. Marsilli, 2014. "Variable Selection in Predictive MIDAS Models," Working papers 520, Banque de France.
  139. Jana Eklund & George Kapetanios, 2008. "A Review of Forecasting Techniques for Large Data Sets," Working Papers 625, Queen Mary University of London, School of Economics and Finance.
  140. Bulligan, Guido & Marcellino, Massimiliano & Venditti, Fabrizio, 2015. "Forecasting economic activity with targeted predictors," International Journal of Forecasting, Elsevier, vol. 31(1), pages 188-206.
  141. Boubaker, Sabri & Gounopoulos, Dimitrios & Nguyen, Duc Khuong & Paltalidis, Nikos, 2015. "Assessing the effects of unconventional monetary policy on pension funds risk incentives," MPRA Paper 73398, University Library of Munich, Germany, revised Aug 2016.
  142. Červená, Marianna & Schneider, Martin, 2014. "Short-term forecasting of GDP with a DSGE model augmented by monthly indicators," International Journal of Forecasting, Elsevier, vol. 30(3), pages 498-516.
This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.