Enhancing forecast accuracy through frequencydomain combination: Applications to financial and economic indicators
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- Ivo Welch & Amit Goyal, 2008.
"A Comprehensive Look at The Empirical Performance of Equity Premium Prediction,"
The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
- Amit Goyal & Ivo Welch, 2004. "A Comprehensive Look at the Empirical Performance of Equity Premium Prediction," Yale School of Management Working Papers amz2412, Yale School of Management, revised 01 Jan 2006.
- Amit Goyal & Ivo Welch & Athanasse Zafirov, 2021. "A Comprehensive Look at the Empirical Performance of Equity Premium Prediction II," Swiss Finance Institute Research Paper Series 21-85, Swiss Finance Institute.
- Amit Goval & Ivo Welch, 2004. "A Comprehensive Look at the Empirical Performance of Equity Premium Prediction," NBER Working Papers 10483, National Bureau of Economic Research, Inc.
- Bańbura, Marta & Bobeica, Elena, 2023.
"Does the Phillips curve help to forecast euro area inflation?,"
International Journal of Forecasting, Elsevier, vol. 39(1), pages 364-390.
- Bańbura, Marta & Bobeica, Elena, 2020. "Does the Phillips curve help to forecast euro area inflation?," Working Paper Series 2471, European Central Bank.
- Daniele Bianchi & Matthias Büchner & Tobias Hoogteijling & Andrea Tamoni, 2021. "Corrigendum: Bond Risk Premiums with Machine Learning [Bond risk premiums with machine learning]," The Review of Financial Studies, Society for Financial Studies, vol. 34(2), pages 1090-1103.
- Leippold, Markus & Wang, Qian & Zhou, Wenyu, 2022. "Machine learning in the Chinese stock market," Journal of Financial Economics, Elsevier, vol. 145(2), pages 64-82.
- Michael W. McCracken & Serena Ng, 2016.
"FRED-MD: A Monthly Database for Macroeconomic Research,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 574-589, October.
- Michael W. McCracken & Serena Ng, 2015. "FRED-MD: A Monthly Database for Macroeconomic Research," Working Papers 2015-12, Federal Reserve Bank of St. Louis.
- Antonio Gargano & Davide Pettenuzzo & Allan Timmermann, 2019.
"Bond Return Predictability: Economic Value and Links to the Macroeconomy,"
Management Science, INFORMS, vol. 65(2), pages 508-540, February.
- Davide Pettenuzzo & Antonio Gargano & Allan Timmermann, 2014. "Bond Return Predictability: Economic Value and Links to the Macroeconomy," Working Papers 75, Brandeis University, Department of Economics and International Business School.
- Davide Pettenuzzo & Antonio Gargano & Allan Timmermann, 2014. "Bond Return Predictability: Economic Value and Links to the Macroeconomy," Working Papers 75R, Brandeis University, Department of Economics and International Business School, revised Jul 2016.
- Timmermann, Allan & Pettenuzzo, Davide & Gargano, Antonio, 2014. "Bond Return Predictability: Economic Value and Links to the Macroeconomy," CEPR Discussion Papers 10104, C.E.P.R. Discussion Papers.
- Hodrick, Robert J & Prescott, Edward C, 1997.
"Postwar U.S. Business Cycles: An Empirical Investigation,"
Journal of Money, Credit and Banking, Blackwell Publishing, vol. 29(1), pages 1-16, February.
- Robert J. Hodrick & Edward Prescott, 1981. "Post-War U.S. Business Cycles: An Empirical Investigation," Discussion Papers 451, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
- Guidolin, Massimo & Timmermann, Allan, 2007.
"Asset allocation under multivariate regime switching,"
Journal of Economic Dynamics and Control, Elsevier, vol. 31(11), pages 3503-3544, November.
- Massimo Guidolin & Allan Timmerman, 2006. "Asset allocation under multivariate regime switching," Working Papers 2005-002, Federal Reserve Bank of St. Louis.
- Andrea A. Naghi & Eoghan O'Neill & Martina Danielova Zaharieva, 2024. "The benefits of forecasting inflation with machine learning: New evidence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(7), pages 1321-1331, November.
- Daniele Bianchi & Matthias Büchner & Andrea Tamoni, 2021. "Bond Risk Premiums with Machine Learning [Quadratic term structure models: Theory and evidence]," The Review of Financial Studies, Society for Financial Studies, vol. 34(2), pages 1046-1089.
- Martins, Manuel M.F. & Verona, Fabio, 2023. "Inflation dynamics in the frequency domain," Economics Letters, Elsevier, vol. 231(C).
- Davide Pettenuzzo & Francesco Ravazzolo, 2016.
"Optimal Portfolio Choice Under Decision‐Based Model Combinations,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1312-1332, November.
- Davide Pettenuzzo & Francesco Ravazzolo, 2014. "Optimal portfolio choice under decision-based model combinations," Working Paper 2014/15, Norges Bank.
- Davide Pettenuzzo & Francesco Ravazzolo, 2015. "Optimal Portfolio Choice under Decision-Based Model Combinations," Working Papers No 9/2015, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
- Davide Pettenuzzo & Francesco Ravazzolo, 2014. "Optimal Portfolio Choice under Decision-Based Model Combinations," Working Papers 80, Brandeis University, Department of Economics and International Business School.
- Clark, Todd E. & West, Kenneth D., 2007.
"Approximately normal tests for equal predictive accuracy in nested models,"
Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
- Todd E. Clark & Kenneth D. West, 2005. "Approximately normal tests for equal predictive accuracy in nested models," Research Working Paper RWP 05-05, Federal Reserve Bank of Kansas City.
- Kenneth D. West & Todd Clark, 2006. "Approximately Normal Tests for Equal Predictive Accuracy in Nested Models," NBER Technical Working Papers 0326, National Bureau of Economic Research, Inc.
- Julien Cujean & Michael Hasler, 2017. "Why Does Return Predictability Concentrate in Bad Times?," Journal of Finance, American Finance Association, vol. 72(6), pages 2717-2758, December.
- Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2014.
"Forecasting the Equity Risk Premium: The Role of Technical Indicators,"
Management Science, INFORMS, vol. 60(7), pages 1772-1791, July.
- Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2010. "Out-of-sample equity premium prediction: economic fundamentals vs. moving-average rules," Working Papers 2010-008, Federal Reserve Bank of St. Louis.
- Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2011. "Forecasting the Equity Risk Premium: The Role of Technical Indicators," Working Papers CoFie-02-2011, Singapore Management University, Sim Kee Boon Institute for Financial Economics.
- Fulvio Ortu & Andrea Tamoni & Claudio Tebaldi, 2013. "Long-Run Risk and the Persistence of Consumption Shocks," The Review of Financial Studies, Society for Financial Studies, vol. 26(11), pages 2876-2915.
- Berge, Travis J., 2018.
"Understanding survey-based inflation expectations,"
International Journal of Forecasting, Elsevier, vol. 34(4), pages 788-801.
- Travis J. Berge, 2017. "Understanding Survey Based Inflation Expectations," Finance and Economics Discussion Series 2017-046, Board of Governors of the Federal Reserve System (U.S.).
- Aruoba, S. BoraÄŸan & Diebold, Francis X. & Scotti, Chiara, 2009.
"Real-Time Measurement of Business Conditions,"
Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 417-427.
- Chiara Scotti & S.Boragan Aruoba & Francis X. Diebold & University of Maryland, 2006. "Real-Time Measurement of Business Conditions," Computing in Economics and Finance 2006 387, Society for Computational Economics.
- S. Boragan Aruoba & Francis X. Diebold & Chiara Scotti, 2008. "Real-Time Measurement of Business Conditions," NBER Working Papers 14349, National Bureau of Economic Research, Inc.
- S. Boragan Aruoba & Francis X. Diebold & Chiara Scotti, 2007. "Real-Time Measurement of Business Conditions," PIER Working Paper Archive 07-028, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
- S. Boragan Aruoba & Francis X. Diebold & Chiara Scotti, 2008. "Real-time measurement of business conditions," Working Papers 08-19, Federal Reserve Bank of Philadelphia.
- S. Boragan Aruoba & Francis X. Diebold & Chiara Scotti, 2007. "Real-time measurement of business conditions," International Finance Discussion Papers 901, Board of Governors of the Federal Reserve System (U.S.).
- Beveridge, Stephen & Nelson, Charles R., 1981. "A new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the `business cycle'," Journal of Monetary Economics, Elsevier, vol. 7(2), pages 151-174.
- Faria, Gonçalo & Verona, Fabio, 2018.
"Forecasting stock market returns by summing the frequency-decomposed parts,"
Journal of Empirical Finance, Elsevier, vol. 45(C), pages 228-242.
- Faria, Gonçalo & Verona, Fabio, 2016. "Forecasting stock market returns by summing the frequency-decomposed parts," Bank of Finland Research Discussion Papers 29/2016, Bank of Finland.
- Gonçalo Faria & Fabio Verona, 2017. "Forecasting stock market returns by summing the frequency-decomposed parts," CEF.UP Working Papers 1702, Universidade do Porto, Faculdade de Economia do Porto.
- Gonçalo Faria & Fabio Verona, 2016. "Forecasting stock market returns by summing the frequency-decomposed parts," Working Papers de Economia (Economics Working Papers) 05, Católica Porto Business School, Universidade Católica Portuguesa.
- Inske Pirschel & Maik H. Wolters, 2018. "Forecasting with large datasets: compressing information before, during or after the estimation?," Empirical Economics, Springer, vol. 55(2), pages 573-596, September.
- Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020.
"Empirical Asset Pricing via Machine Learning,"
The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
- Shihao Gu & Bryan T. Kelly & Dacheng Xiu, 2018. "Empirical Asset Pricing via Machine Learning," Swiss Finance Institute Research Paper Series 18-71, Swiss Finance Institute.
- Shihao Gu & Bryan Kelly & Dacheng Xiu, 2018. "Empirical Asset Pricing via Machine Learning," NBER Working Papers 25398, National Bureau of Economic Research, Inc.
- 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.
- James H. Stock & Mark W. Watson, 1994. "Evidence on Structural Instability in Macroeconomic Time Series Relations," NBER Technical Working Papers 0164, National Bureau of Economic Research, Inc.
- James H. Stock & Mark W. Watson, 1994. "Evidence on structural instability in macroeconomic times series relations," Working Paper Series, Macroeconomic Issues 94-13, Federal Reserve Bank of Chicago.
- Estrella, Arturo & Hardouvelis, Gikas A, 1991.
"The Term Structure as a Predictor of Real Economic Activity,"
Journal of Finance, American Finance Association, vol. 46(2), pages 555-576, June.
- Arturo Estrella & Gikas A. Hardouvelis, 1989. "The term structure as a predictor of real economic activity," Research Paper 8907, Federal Reserve Bank of New York.
- John Y. Campbell & Samuel B. Thompson, 2008.
"Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?,"
The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
- Campbell, John & Thompson, Samuel P., 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," Scholarly Articles 2622619, Harvard University Department of Economics.
- Henkel, Sam James & Martin, J. Spencer & Nardari, Federico, 2011. "Time-varying short-horizon predictability," Journal of Financial Economics, Elsevier, vol. 99(3), pages 560-580, March.
- Ferroni, Filippo & Fisher, Jonas D.M. & Melosi, Leonardo, 2024.
"Unusual shocks in our usual models,"
Journal of Monetary Economics, Elsevier, vol. 147(C).
- Filippo Ferroni & Jonas D. M. Fisher & Leonardo Melosi, 2022. "Usual Shocks in our Usual Models," Working Paper Series WP 2022-39, Federal Reserve Bank of Chicago.
- Ferroni, Filippo & Fisher, Jonas & Melosi, Leonardo, 2023. "Unusual Shocks in our Usual Models," CEPR Discussion Papers 17830, C.E.P.R. Discussion Papers.
- Bryan Kelly & Semyon Malamud & Kangying Zhou, 2024. "The Virtue of Complexity in Return Prediction," Journal of Finance, American Finance Association, vol. 79(1), pages 459-503, February.
- G. Elliott & C. Granger & A. Timmermann (ed.), 2006. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 1, number 1.
- Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.
- Risse, Marian, 2019. "Combining wavelet decomposition with machine learning to forecast gold returns," International Journal of Forecasting, Elsevier, vol. 35(2), pages 601-615.
- Stein, Tobias, 2024. "Forecasting the equity premium with frequency-decomposed technical indicators," International Journal of Forecasting, Elsevier, vol. 40(1), pages 6-28.
- Kilponen, Juha & Verona, Fabio, 2022. "Investment dynamics and forecast: Mind the frequency," Finance Research Letters, Elsevier, vol. 49(C).
- Ilan Cooper, 2009. "Time-Varying Risk Premiums and the Output Gap," The Review of Financial Studies, Society for Financial Studies, vol. 22(7), pages 2601-2633, July.
- Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
- 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.
- Tatevik Sekhposyan & Barbara Rossi, 2008.
"Has modelsí forecasting performance for US output growth and inflation changed over time, and when?,"
Working Papers
09-02, Duke University, Department of Economics.
- Barbara Rossi & Tatevik Sekhposyan, 2010. "Has Models' Forecasting Performance for US Output Growth and Inflation Changed over Time, and When?," Working Papers 10-16, Duke University, Department of Economics.
- Dangl, Thomas & Halling, Michael, 2012. "Predictive regressions with time-varying coefficients," Journal of Financial Economics, Elsevier, vol. 106(1), pages 157-181.
- Marcelo C. Medeiros & Gabriel F. R. Vasconcelos & Álvaro Veiga & Eduardo Zilberman, 2021.
"Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 98-119, January.
- Marcelo Madeiros & Gabriel Vasconcelos & Álvaro Veiga & Eduardo Zilberman, 2019. "Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods," Working Papers Central Bank of Chile 834, Central Bank of Chile.
- G. Elliott & C. Granger & A. Timmermann (ed.), 2013. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 2, number 2.
- Gallegati, Marco & Ramsey, James B., 2013. "Bond vs stock market's Q: Testing for stability across frequencies and over time," Journal of Empirical Finance, Elsevier, vol. 24(C), pages 138-150.
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More about this item
Keywords
forecast combination; frequency domain; equity premium; GDP growth; Haar filter;All these keywords.
JEL classification:
- C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
- G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
- G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
NEP fields
This paper has been announced in the following NEP Reports:- NEP-ETS-2025-01-13 (Econometric Time Series)
- NEP-FOR-2025-01-13 (Forecasting)
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