IDEAS home Printed from https://ideas.repec.org/r/nbr/nberch/7192.html
   My bibliography  Save this item

A Nine-Variable Probabilistic Macroeconomic Forecasting Model

In: Business Cycles, Indicators, and Forecasting

Citations

RePEc Biblio mentions

As found on the RePEc Biblio, the curated bibliography for Economics:
  1. > Econometrics > Time Series Models > VAR Models > Bayesian Vector autoregressions (BVARs)

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Amos Golan & Jeffrey M. Perloff, 2004. "Superior Forecasts of the U.S. Unemployment Rate Using a Nonparametric Method," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 433-438, February.
  2. Marek Jarociński & Bartosz Maćkowiak, 2017. "Granger Causal Priority and Choice of Variables in Vector Autoregressions," The Review of Economics and Statistics, MIT Press, vol. 99(2), pages 319-329, May.
  3. Matthieu Darracq Paries, 2018. "Financial frictions and monetary policy conduct," Erudite Ph.D Dissertations, Erudite, number ph18-01 edited by Ferhat Mihoubi, September.
  4. De Santis, Roberto A. & Tornese, Tommaso, 2023. "Energy supply shocks’ nonlinearities on output and prices," Working Paper Series 2834, European Central Bank.
  5. Gossé, Jean-Baptiste & Guillaumin, Cyriac, 2013. "L’apport de la représentation VAR de Christopher A. Sims à la science économique," L'Actualité Economique, Société Canadienne de Science Economique, vol. 89(4), pages 309-319, Décembre.
  6. Karlsson, Sune, 2013. "Forecasting with Bayesian Vector Autoregression," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 791-897, Elsevier.
  7. 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.
  8. Tsionas, Mike G. & Izzeldin, Marwan & Trapani, Lorenzo, 2022. "Estimation of large dimensional time varying VARs using copulas," European Economic Review, Elsevier, vol. 141(C).
  9. Miao, Ke & Phillips, Peter C.B. & Su, Liangjun, 2023. "High-dimensional VARs with common factors," Journal of Econometrics, Elsevier, vol. 233(1), pages 155-183.
  10. 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.
  11. Altavilla, Carlo & Boucinha, Miguel & Peydró, José-Luis, 2018. "Monetary policy and bank profitability in a low interest rate environment," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 33(96), pages 531-586.
  12. Giannone, Domenico & Lenza, Michele & Momferatou, Daphne & Onorante, Luca, 2014. "Short-term inflation projections: A Bayesian vector autoregressive approach," International Journal of Forecasting, Elsevier, vol. 30(3), pages 635-644.
  13. Philippe Goulet Coulombe, 2020. "The Macroeconomy as a Random Forest," Papers 2006.12724, arXiv.org, revised Mar 2021.
  14. Duo Qin, 2010. "Econometric Studies of Business Cycles in the History of Econometrics," Working Papers 669, Queen Mary University of London, School of Economics and Finance.
  15. Anttonen, Jetro, 2018. "Nowcasting the Unemployment Rate in the EU with Seasonal BVAR and Google Search Data," ETLA Working Papers 62, The Research Institute of the Finnish Economy.
  16. Branimir, Jovanovic & Magdalena, Petrovska, 2010. "Forecasting Macedonian GDP: Evaluation of different models for short-term forecasting," MPRA Paper 43162, University Library of Munich, Germany.
  17. John C. Robertson & Ellis W. Tallman, 1999. "Prior parameter uncertainty: Some implications for forecasting and policy analysis with VAR models," FRB Atlanta Working Paper 99-13, Federal Reserve Bank of Atlanta.
  18. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2021. "No‐arbitrage priors, drifting volatilities, and the term structure of interest rates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(5), pages 495-516, August.
  19. Antonio M. Conti & Andrea Nobili & Federico M. Signoretti, 2018. "Bank capital constraints, lending supply and economic activity," Temi di discussione (Economic working papers) 1199, Bank of Italy, Economic Research and International Relations Area.
  20. Koop, Gary & Korobilis, Dimitris, 2010. "Bayesian Multivariate Time Series Methods for Empirical Macroeconomics," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(4), pages 267-358, July.
  21. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Large Vector Autoregressions with Asymmetric Priors," Working Papers 759, Queen Mary University of London, School of Economics and Finance.
  22. Matkovskyy, Roman, 2012. "Прогнозування розвитку економіки України на основі баєсівських авторегресійних (BVAR) моделей з різними priors [Forecasting Economic Development of Ukraine based on BVAR models with different prior," MPRA Paper 44725, University Library of Munich, Germany, revised Nov 2012.
  23. repec:wrk:wrkemf:10 is not listed on IDEAS
  24. John C. Robertson & Ellis W. Tallman, 1999. "Vector autoregressions: forecasting and reality," Economic Review, Federal Reserve Bank of Atlanta, vol. 84(Q1), pages 4-18.
  25. Ching-Wai (Jeremy) Chiu & Haroon Mumtaz & Gabor Pinter, 2016. "Bayesian Vector Autoregressions with Non-Gaussian Shocks," CReMFi Discussion Papers 5, CReMFi, School of Economics and Finance, QMUL.
  26. Faust, Jon & Wright, Jonathan H., 2013. "Forecasting Inflation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 2-56, Elsevier.
  27. 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.
  28. Antolín-Díaz, Juan & Petrella, Ivan & Rubio-Ramírez, Juan F., 2021. "Structural scenario analysis with SVARs," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 798-815.
  29. Carlos Garcia & Pablo Gonzalez & Antonio Moncado, 2010. "Proyecciones Macroeconómicas en Chile: Una Aproximación Bayesiana," ILADES-UAH Working Papers inv262, Universidad Alberto Hurtado/School of Economics and Business.
  30. Bloor, Chris & Matheson, Troy, 2011. "Real-time conditional forecasts with Bayesian VARs: An application to New Zealand," The North American Journal of Economics and Finance, Elsevier, vol. 22(1), pages 26-42, January.
  31. repec:wrk:wrkemf:32 is not listed on IDEAS
  32. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87.
  33. John C. Robertson & Ellis W. Tallman, 1999. "Improving forecasts of the federal funds rate in a policy model," FRB Atlanta Working Paper 99-3, Federal Reserve Bank of Atlanta.
  34. Roberto A. De Santis, 2024. "Supply Chain Disruption and Energy Supply Shocks: Impact on Euro-Area Output and Prices," International Journal of Central Banking, International Journal of Central Banking, vol. 20(2), pages 193-235, April.
  35. Giordani, Paolo & Kohn, Robert, 2008. "Efficient Bayesian Inference for Multiple Change-Point and Mixture Innovation Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 66-77, January.
  36. Halberstadt, Arne, 2015. "The term structure of interest rates and the macroeconomy: Learning about economic dynamics from a FAVAR," Discussion Papers 02/2015, Deutsche Bundesbank.
  37. Ciccarelli, Matteo & Kuik, Friderike & Martínez Hernández, Catalina, 2023. "The asymmetric effects of weather shocks on euro area inflation," Working Paper Series 2798, European Central Bank.
  38. Knut Are Aastveit & Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2017. "Have Standard VARS Remained Stable Since the Crisis?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(5), pages 931-951, August.
  39. 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, September.
  40. Lukas Boer & Mr. Andrea Pescatori & Martin Stuermer, 2021. "Energy Transition Metals," IMF Working Papers 2021/243, International Monetary Fund.
  41. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2015. "Prior Selection for Vector Autoregressions," The Review of Economics and Statistics, MIT Press, vol. 97(2), pages 436-451, May.
  42. De Santis, Roberto A. & Tornese, Tommaso, 2024. "US monetary policy is more powerful in low economic growth regimes," Working Paper Series 2919, European Central Bank.
  43. Angelini, Elena & Lalik, Magdalena & Lenza, Michele & Paredes, Joan, 2019. "Mind the gap: A multi-country BVAR benchmark for the Eurosystem projections," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1658-1668.
  44. Giovanni Nicolo, 2020. "Monetary Policy, Self-Fulfilling Expectations and the U.S. Business Cycle," Finance and Economics Discussion Series 2020-035, Board of Governors of the Federal Reserve System (U.S.).
  45. Gupta, Rangan & Kotzé, Kevin, 2017. "The role of oil prices in the forecasts of South African interest rates: A Bayesian approach," Energy Economics, Elsevier, vol. 61(C), pages 270-278.
  46. Francis Neville & Owyang Michael T. & Sekhposyan Tatevik, 2012. "The Local Effects of Monetary Policy," The B.E. Journal of Macroeconomics, De Gruyter, vol. 12(2), pages 1-38, March.
  47. James H. Stock & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," NBER Working Papers 11467, National Bureau of Economic Research, Inc.
  48. Korobilis, D & Yilmaz, K, 2018. "Measuring Dynamic Connectedness with Large Bayesian VAR Models," Essex Finance Centre Working Papers 20937, University of Essex, Essex Business School.
  49. Branimir Jovanovic & Magdalena Petrovska, 2010. "Forecasting Macedonian GDP: Evaluation of different models for short-term forecasting," Working Papers 2010-02, National Bank of the Republic of North Macedonia, revised Aug 2010.
  50. Ahmed Ibrahim & Rasha Kashef & Menglu Li & Esteban Valencia & Eric Huang, 2020. "Bitcoin Network Mechanics: Forecasting the BTC Closing Price Using Vector Auto-Regression Models Based on Endogenous and Exogenous Feature Variables," JRFM, MDPI, vol. 13(9), pages 1-21, August.
  51. Apostolos Ampountolas, 2019. "Forecasting hotel demand uncertainty using time series Bayesian VAR models," Tourism Economics, , vol. 25(5), pages 734-756, August.
  52. Ali-Kazim Zaidi & Iqbal Zaidi, 2011. "Rethinking the Monetary Policy Framework of the State Bank of Pakistan," SBP Research Bulletin, State Bank of Pakistan, Research Department, vol. 7, pages 87-123.
  53. Crompton, Paul & Wu, Yanrui, 2005. "Energy consumption in China: past trends and future directions," Energy Economics, Elsevier, vol. 27(1), pages 195-208, January.
  54. Demeshev, Boris & Malakhovskaya, Oxana, 2016. "BVAR mapping," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 43, pages 118-141.
  55. Kansho Piotr Otsubo, 2018. "The Effects of Fiscal and Monetary Policies in Japan: What Combination of Policies Should Be Used?," Journal of International Commerce, Economics and Policy (JICEP), World Scientific Publishing Co. Pte. Ltd., vol. 9(01n02), pages 1-25, February.
  56. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2016. "Common Drifting Volatility in Large Bayesian VARs," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(3), pages 375-390, July.
  57. repec:zbw:bofitp:2014_022 is not listed on IDEAS
  58. Mestiri, Sami, 2019. "Bayesian Structural VAR Approach to Tunisian Monetary Policy Framework," MPRA Paper 91357, University Library of Munich, Germany.
  59. Enrique M. Quilis(1), "undated". "Modelos Bvar: Especificación, Estimación E Inferencia," Working Papers 8-02 Classification-JEL :, Instituto de Estudios Fiscales.
  60. Deryugina, Elena & Ponomarenko, Alexey, 2014. "A large Bayesian vector autoregression model for Russia," BOFIT Discussion Papers 22/2014, Bank of Finland Institute for Emerging Economies (BOFIT).
  61. Rostagno, Massimo & Altavilla, Carlo & Carboni, Giacomo & Lemke, Wolfgang & Motto, Roberto & Saint Guilhem, Arthur, 2021. "Combining negative rates, forward guidance and asset purchases: identification and impacts of the ECB’s unconventional policies," Working Paper Series 2564, European Central Bank.
  62. Reif Magnus, 2021. "Macroeconomic uncertainty and forecasting macroeconomic aggregates," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 25(2), pages 1-20, April.
  63. Ho, Paul, 2023. "Global robust Bayesian analysis in large models," Journal of Econometrics, Elsevier, vol. 235(2), pages 608-642.
  64. 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.
  65. Mike Tsionas & Marwan Izzeldin & Lorenzo Trapani, 2019. "Bayesian estimation of large dimensional time varying VARs using copulas," Papers 1912.12527, arXiv.org.
  66. Matkovskyy, Roman, 2012. "The Index of the Financial Safety (IFS) of South Africa and Bayesian Estimates for IFS Vector-Autoregressive Model," MPRA Paper 42173, University Library of Munich, Germany.
  67. Guney, Selin, 2015. "An evaluation of price forecasts of the cattle market under structural changes," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205109, Agricultural and Applied Economics Association.
  68. Allen, P. Geoffrey & Morzuch, Bernard J., 2006. "Twenty-five years of progress, problems, and conflicting evidence in econometric forecasting. What about the next 25 years?," International Journal of Forecasting, Elsevier, vol. 22(3), pages 475-492.
  69. Ibrahim Ayoade Adekunle & Sheriffdeen Adewale Tella & Oluwaseyi Adedayo Adelowokan, 2021. "Macroeconomic policy volatility and household consumption in Africa," SN Business & Economics, Springer, vol. 1(3), pages 1-22, March.
  70. Rubio-Ramírez, Juan Francisco & Petrella, Ivan & Antolin-Diaz, Juan, 2021. "Dividend Momentum and Stock Return Predictability: A Bayesian Approach," CEPR Discussion Papers 16613, C.E.P.R. Discussion Papers.
  71. Francis, Brian M. & Moseley, Leo & Iyare, Sunday Osaretin, 2007. "Energy consumption and projected growth in selected Caribbean countries," Energy Economics, Elsevier, vol. 29(6), pages 1224-1232, November.
  72. Stock, James H & Watson, Mark W, 1996. "Evidence on Structural Instability in Macroeconomic Time Series Relations," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(1), pages 11-30, January.
  73. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Large Vector Autoregressions with Asymmetric Priors," Working Papers 759, Queen Mary University of London, School of Economics and Finance.
  74. Tomasz Wozniak, 2016. "Rare Events and Risk Perception: Evidence from Fukushima Accident," Department of Economics - Working Papers Series 2021, The University of Melbourne.
  75. Kenichiro McAlinn & Knut Are Aastveit & Jouchi Nakajima & Mike West, 2020. "Multivariate Bayesian Predictive Synthesis in Macroeconomic Forecasting," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(531), pages 1092-1110, July.
  76. Altavilla, Carlo & Pariès, Matthieu Darracq & Nicoletti, Giulio, 2019. "Loan supply, credit markets and the euro area financial crisis," Journal of Banking & Finance, Elsevier, vol. 109(C).
  77. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2016. "Large Vector Autoregressions with Stochastic Volatility and Flexible Priors," Working Papers (Old Series) 1617, Federal Reserve Bank of Cleveland.
  78. Sims, Christopher A., 2000. "Using a likelihood perspective to sharpen econometric discourse: Three examples," Journal of Econometrics, Elsevier, vol. 95(2), pages 443-462, April.
  79. Sokol, Andrej, 2021. "Fan charts 2.0: flexible forecast distributions with expert judgement," Working Paper Series 2624, European Central Bank.
  80. Korobilis, Dimitris, 2014. "Data-based priors for vector autoregressions with drifting coefficients," SIRE Discussion Papers 2014-022, Scottish Institute for Research in Economics (SIRE).
  81. Itkonen, Juha & Juvonen, Petteri, 2017. "Nowcasting the Finnish economy with a large Bayesian vector autoregressive model," BoF Economics Review 6/2017, Bank of Finland.
  82. Silvia Miranda-Agrippino & Hélène Rey, 2020. "U.S. Monetary Policy and the Global Financial Cycle," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 87(6), pages 2754-2776.
  83. James H. Stock & Mark W. Watson, 2001. "Vector Autoregressions," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 101-115, Fall.
  84. Carlos J. García & Pablo González M. & Antonio Moncado S., 2013. "Macroeconomic Forecasting in Chile: a Structural Bayesian Approach," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 16(1), pages 24-63, April.
  85. Cesar Carrera & Alan Ledesma, 2015. "Aggregate Inflation Forecast with Bayesian Vector Autoregressive Models," Working Papers 50, Peruvian Economic Association.
  86. Carriero, Andrea & Clark, Todd E. & Marcellino, Massimiliano, 2019. "Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors," Journal of Econometrics, Elsevier, vol. 212(1), pages 137-154.
  87. Rey, Hélène & Miranda-Agrippino, Silvia, 2015. "World Asset Markets and the Global Financial Cycle," CEPR Discussion Papers 10936, C.E.P.R. Discussion Papers.
  88. Amarasekara, Chandranath, 2008. "The Impact of Monetary Policy on Economic Growth and Inflation in Sri Lanka," MPRA Paper 64866, University Library of Munich, Germany.
  89. Dimitrios P. Louzis, 2017. "Macroeconomic and credit forecasts during the Greek crisis using Bayesian VARs," Empirical Economics, Springer, vol. 53(2), pages 569-598, September.
  90. Aloui, Donia & Benkraiem, Ramzi & Guesmi, Khaled & Mzoughi, Hela, 2023. "Managing natural resource prices in a geopolitical risk environment," Resources Policy, Elsevier, vol. 83(C).
  91. Peter McAdam & Anders Warne, 2024. "Density forecast combinations: The real‐time dimension," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1153-1172, August.
  92. Carriero, Andrea & Galvão, Ana Beatriz & Kapetanios, George, 2019. "A comprehensive evaluation of macroeconomic forecasting methods," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1226-1239.
  93. Jushan Bai & Kunpeng Li & Lina Lu, 2016. "Estimation and Inference of FAVAR Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 620-641, October.
  94. Dieppe, Alistair & van Roye, Björn & Legrand, Romain, 2016. "The BEAR toolbox," Working Paper Series 1934, European Central Bank.
  95. Paul Viefers, 2011. "Bayesian Inference for the Mixed-Frequency VAR Model," Discussion Papers of DIW Berlin 1172, DIW Berlin, German Institute for Economic Research.
  96. Aloui, Donia & Zouaoui, Riadh & Rachdi, Houssem & Guesmi, Khaled & Yarovaya, Larisa, 2024. "The impact of ECB’s Quantitative Easing on cryptocurrency markets during times of crisis," Research in International Business and Finance, Elsevier, vol. 69(C).
  97. Manfred Deistler & Klaus Neusser, 2004. "Prognose uni- und multivariater Zeitreihen," Diskussionsschriften dp0401, Universitaet Bern, Departement Volkswirtschaft.
  98. Rostagno, Massimo & Altavilla, Carlo & Carboni, Giacomo & Lemke, Wolfgang & Motto, Roberto & Saint Guilhem, Arthur & Yiangou, Jonathan, 2019. "A tale of two decades: the ECB’s monetary policy at 20," Working Paper Series 2346, European Central Bank.
  99. James H. James & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," Working Papers 2005-2, Princeton University. Economics Department..
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.