On the real‐time predictive content of financial condition indices for growth
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DOI: 10.1002/jae.2943
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- Aaron Amburgey & Michael W. McCracken, 2022. "On the Real-Time Predictive Content of Financial Conditions Indices for Growth," Working Papers 2022-003, Federal Reserve Bank of St. Louis, revised 03 Jun 2022.
References listed on IDEAS
- Raffaella Giacomini & Halbert White, 2006.
"Tests of Conditional Predictive Ability,"
Econometrica, Econometric Society, vol. 74(6), pages 1545-1578, November.
- Raffaella Giacomini & Halbert White, 2003. "Tests of conditional predictive ability," Boston College Working Papers in Economics 572, Boston College Department of Economics.
- Giacomini, Raffaella & White, Halbert, 2003. "Tests of Conditional Predictive Ability," University of California at San Diego, Economics Working Paper Series qt5jk0j5jh, Department of Economics, UC San Diego.
- Raffaella Giacomini & Halbert White, 2003. "Tests of Conditional Predictive Ability," Econometrics 0308001, University Library of Munich, Germany.
- Piergiorgio Alessandri & Haroon Mumtaz, 2017.
"Financial conditions and density forecasts for US output and inflation,"
Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 24, pages 66-78, March.
- Piergiorgio Alessandri & Haroon Mumtaz, 2013. "Financial conditions and density forecasts for US Output and inflation," Joint Research Papers 4, Centre for Central Banking Studies, Bank of England.
- Piergiorgio Alessandri & Haroon Mumtaz, 2014. "Financial conditions and density forecasts for US output and inflation," CReMFi Discussion Papers 1, CReMFi, School of Economics and Finance, QMUL.
- Piergiorgio Alessandri & Haroon Mumtaz, 2014. "Financial Conditions and Density Forecasts for US Output and Inflation," Working Papers 715, Queen Mary University of London, School of Economics and Finance.
- Clark, Todd E. & McCracken, Michael W., 2001.
"Tests of equal forecast accuracy and encompassing for nested models,"
Journal of Econometrics, Elsevier, vol. 105(1), pages 85-110, November.
- Todd E. Clark & Michael W. McCracken, 1999. "Tests of equal forecast accuracy and encompassing for nested models," Research Working Paper 99-11, Federal Reserve Bank of Kansas City.
- Todd E. Clark & Michael W. McCracken, 2000. "Tests of Equal Forecast Accuracy and Encompassing for Nested Models," Econometric Society World Congress 2000 Contributed Papers 0319, Econometric Society.
- Todd E. Clark & Michael McCracken, 1999. "Tests of Equal Forecast Accuracy and Encompassing for Nested Models," Computing in Economics and Finance 1999 1241, Society for Computational Economics.
- McCracken, Michael W., 2004. "Parameter estimation and tests of equal forecast accuracy between non-nested models," International Journal of Forecasting, Elsevier, vol. 20(3), pages 503-514.
- Lucrezia Reichlin & Giovanni Ricco & Thomas Hasenzagl, 2020.
"Financial Variables as Predictors of Real Growth Vulnerability,"
Documents de Travail de l'OFCE
2020-06, Observatoire Francais des Conjonctures Economiques (OFCE).
- Reichlin, Lucrezia & Ricco, Giovanni & Hasenzagl, Thomas, 2020. "Financial variables as predictors of real growth vulnerability," Discussion Papers 05/2020, Deutsche Bundesbank.
- Lucrezia Reichlin & Giovanni Ricco & Thomas Hasenzagl, 2020. "Financial Variables as Predictors of Real Growth Vulnerability," SciencePo Working papers Main hal-03403077, HAL.
- Reichlin, Lucrezia & Ricco, Giovanni & Hasenzagl, Thomas, 2020. "Financial Variables as Predictors of Real Growth Vulnerability," CEPR Discussion Papers 14322, C.E.P.R. Discussion Papers.
- Lucrezia Reichlin & Giovanni Ricco & Thomas Hasenzagl, 2020. "Financial Variables as Predictors of Real Growth Vulnerability," Working Papers hal-03403077, HAL.
- West, Kenneth D, 1996.
"Asymptotic Inference about Predictive Ability,"
Econometrica, Econometric Society, vol. 64(5), pages 1067-1084, September.
- West, K.D., 1994. "Asymptotic Inference About Predictive Ability," Working papers 9417, Wisconsin Madison - Social Systems.
- Kenneth D. West, 1994. "Asymptotic Inference About Predictive Ability," Macroeconomics 9410002, University Library of Munich, Germany.
- Ferrara, Laurent & Mogliani, Matteo & Sahuc, Jean-Guillaume, 2022.
"High-frequency monitoring of growth at risk,"
International Journal of Forecasting, Elsevier, vol. 38(2), pages 582-595.
- Laurent Ferrara & Matteo Mogliani & Jean-Guillaume Sahuc, 2020. "High-frequency monitoring of growth-at-risk," CAMA Working Papers 2020-97, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
- Jean-Guillaume Sahuc & Matteo Mogliani & Laurent Ferrara, 2022. "High-frequency monitoring of growth at risk," Post-Print hal-03361425, HAL.
- Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2012.
"A Quasi–Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models,"
The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1014-1024, November.
- Doz, Catherine & Giannone, Domenico & Reichlin, Lucrezia, 2006. "A quasi maximum likelihood approach for large approximate dynamic factor models," Working Paper Series 674, European Central Bank.
- Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2008. "A Quasi Maximum Likelihood Approach for Large Approximate Dynamic Factor Models," Working Papers ECARES 2008_034, ULB -- Universite Libre de Bruxelles.
- Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2012. "A Quasi Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models," Post-Print hal-00638440, HAL.
- Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2012. "A Quasi Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00638440, HAL.
- Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2012. "A Quasi Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models," PSE-Ecole d'économie de Paris (Postprint) hal-00638440, HAL.
- Reichlin, Lucrezia & Doz, Catherine & Giannone, Domenico, 2006. "A Quasi Maximum Likelihood Approach for Large Approximate Dynamic Factor Models," CEPR Discussion Papers 5724, C.E.P.R. Discussion Papers.
- Diebold, Francis X & Mariano, Roberto S, 2002.
"Comparing Predictive Accuracy,"
Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
- Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-263, July.
- Francis X. Diebold & Roberto S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
- Tae-Hwan Kim & Halbert White, 2003.
"Estimation, Inference, And Specification Testing For Possibly Misspecified Quantile Regression,"
Advances in Econometrics, in: Maximum Likelihood Estimation of Misspecified Models: Twenty Years Later, pages 107-132,
Emerald Group Publishing Limited.
- White, Halbert & Kim, Tae-Hwan, 2002. "Estimation, Inference, and Specification Testing for Possibly Misspecified Quantile Regression," University of California at San Diego, Economics Working Paper Series qt1s38s0dn, Department of Economics, UC San Diego.
- Andrea Carriero & Todd E. Clark & Marcellino Massimiliano, 2020. "Nowcasting Tail Risks to Economic Activity with Many Indicators," Working Papers 20-13R2, Federal Reserve Bank of Cleveland, revised 22 Sep 2020.
- R. H. Shumway & D. S. Stoffer, 1982. "An Approach To Time Series Smoothing And Forecasting Using The Em Algorithm," Journal of Time Series Analysis, Wiley Blackwell, vol. 3(4), pages 253-264, July.
- Clark, Todd E. & McCracken, Michael W., 2009.
"Tests of Equal Predictive Ability With Real-Time Data,"
Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 441-454.
- Todd E. Clark & Michael W. McCracken, 2007. "Tests of equal predictive ability with real-time data," Research Working Paper RWP 07-06, Federal Reserve Bank of Kansas City.
- Todd E. Clark & Michael W. McCracken, 2008. "Tests of equal predictive ability with real-time data," Working Papers 2008-029, Federal Reserve Bank of St. Louis.
- Rossi, Barbara & Sekhposyan, Tatevik, 2011.
"Understanding models' forecasting performance,"
Journal of Econometrics, Elsevier, vol. 164(1), pages 158-172, September.
- Barbara Rossi & Tatevik Sekhposyan, 2010. "Understanding Models' Forecasting Performance," Working Papers 10-56, Duke University, Department of Economics.
- Davide Delle Monache & Andrea De Polis & Ivan Petrella, 2024.
"Modeling and Forecasting Macroeconomic Downside Risk,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(3), pages 1010-1025, July.
- Delle Monache, Davide & De Polis, Andrea & Petrella, Ivan, 2021. "Modeling and forecasting macroeconomic downside risk," Temi di discussione (Economic working papers) 1324, Bank of Italy, Economic Research and International Relations Area.
- Delle Monache, Davide & De Polis, Andrea & Petrella, Ivan, 2022. "Modeling and Forecasting Macroeconomic Downside Risk," CEPR Discussion Papers 15109, C.E.P.R. Discussion Papers.
- repec:hal:spmain:info:hdl:2441/4nn4ojjkth8qe9ci5b0hpu7ala is not listed on IDEAS
- Travis J. Berge & Òscar Jordà, 2011. "Evaluating the Classification of Economic Activity into Recessions and Expansions," American Economic Journal: Macroeconomics, American Economic Association, vol. 3(2), pages 246-277, April.
- Scott Brave & R. Andrew Butters, 2012. "Diagnosing the Financial System: Financial Conditions and Financial Stress," International Journal of Central Banking, International Journal of Central Banking, vol. 8(2), pages 191-239, June.
- Piergiorgio Alessandri & Haroon Mumtaz, 2017.
"Financial conditions and density forecasts for US output and inflation,"
Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 24, pages 66-78, March.
- Piergiorgio Alessandri & Haroon Mumtaz, 2017. "Online Appendix to "Financial conditions and density forecasts for US output and inflation"," Online Appendices 14-103, Review of Economic Dynamics.
- Raffaella Giacomini & Barbara Rossi, 2010.
"Forecast comparisons in unstable environments,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 595-620.
- Giacomini, Raffaella & Rossi, Barbara, 2008. "Forecast Comparisons in Unstable Environments," Working Papers 08-04, Duke University, Department of Economics.
- Tobias Adrian & Nina Boyarchenko & Domenico Giannone, 2019.
"Vulnerable Growth,"
American Economic Review, American Economic Association, vol. 109(4), pages 1263-1289, April.
- Tobias Adrian & Nina Boyarchenko & Domenico Giannone, 2016. "Vulnerable growth," Staff Reports 794, Federal Reserve Bank of New York.
- Tobias Adrian & Nina Boyarchenko & Domenico Giannone, 2018. "Vulnerable Growth," Liberty Street Economics 20180409, Federal Reserve Bank of New York.
- Nina Boyarchenko & Domenico Giannone & Tobias Adrian, 2017. "Vulnerable Growth," 2017 Meeting Papers 1317, Society for Economic Dynamics.
- Adrian, Tobias & Boyarchenko, Nina & Giannone, Domenico, 2016. "Vulnerable Growth," CEPR Discussion Papers 11583, C.E.P.R. Discussion Papers.
- Mc Cracken, Michael W., 2000. "Robust out-of-sample inference," Journal of Econometrics, Elsevier, vol. 99(2), pages 195-223, December.
- Gianni De Nicolò & Marcella Lucchetta, 2017.
"Forecasting Tail Risks,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(1), pages 159-170, January.
- Gianni De Nicolò & Marcella Lucchetta, 2015. "Forecasting Tail Risks," CESifo Working Paper Series 5286, CESifo.
- Brownlees, Christian & Souza, André B.M., 2021. "Backtesting global Growth-at-Risk," Journal of Monetary Economics, Elsevier, vol. 118(C), pages 312-330.
- Patrick J. Coe & Shaun P. Vahey, 2020. "Financial conditions and the risks to economic growth in the United States since 1875," CAMA Working Papers 2020-36, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
- McCracken, Michael W., 2007. "Asymptotics for out of sample tests of Granger causality," Journal of Econometrics, Elsevier, vol. 140(2), pages 719-752, October.
- Watson, Mark W. & Engle, Robert F., 1983. "Alternative algorithms for the estimation of dynamic factor, mimic and varying coefficient regression models," Journal of Econometrics, Elsevier, vol. 23(3), pages 385-400, December.
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- Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2020. "Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions," Working Papers 20-02R, Federal Reserve Bank of Cleveland, revised 22 Sep 2020.
- Carriero, Andrea & Clark, Todd & Marcellino, Massimiliano, 2022. "Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions," CEPR Discussion Papers 17512, C.E.P.R. Discussion Papers.
- Hilde C. Bjørnland & Roberto Casarin & Marco Lorusso & Francesco Ravazzolo, 2023. "Fiscal Policy Regimes in Resource-Rich Economies," Working Papers No 13/2023, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
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- Efrem Castelnuovo & Lorenzo Mori, 2022. "Uncertainty, Skewness, and the Business Cycle Through the MIDAS Lens," "Marco Fanno" Working Papers 0291, Dipartimento di Scienze Economiche "Marco Fanno".
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- Carriero, Andrea & Clark, Todd & Marcellino, Massimiliano, 2024. "Specification Choices in Quantile Regression for Empirical Macroeconomics," CEPR Discussion Papers 18901, C.E.P.R. Discussion Papers.
- James Mitchell & Aubrey Poon & Dan Zhu, 2024.
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Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(5), pages 790-812, August.
- James Mitchell & Aubrey Poon & Dan Zhu, 2022. "Constructing Density Forecasts from Quantile Regressions: Multimodality in Macro-Financial Dynamics," Working Papers 22-12R, Federal Reserve Bank of Cleveland, revised 11 Apr 2023.
- Philippe Goulet Coulombe & Mikael Frenette & Karin Klieber, 2023. "From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks," Working Papers 23-04, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Nov 2023.
- Philippe Goulet Coulombe & Mikael Frenette & Karin Klieber, 2023. "From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks," Papers 2311.16333, arXiv.org, revised Apr 2024.
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JEL classification:
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
- C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
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