Forecasting U.S. money growth using economic uncertainty measures and regularisation techniques
Author
Abstract
Suggested Citation
DOI: 10.1016/j.ijforecast.2018.09.012
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- 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.
- repec:taf:jnlbes:v:30:y:2012:i:1:p:53-66 is not listed on IDEAS
- Franz Seitz & Julian von Landesberger, 2014.
"Household Money Holdings in the Euro Area: An Explorative Investigation,"
Journal of Banking and Financial Economics, University of Warsaw, Faculty of Management, vol. 2(2), pages 83-115, November.
- Seitz, Franz & von Landesberger, Julian, 2010. "Household money holdings in the euro area: An explorative investigation," Working Paper Series 1238, European Central Bank.
- Guenter W. Beck & Volker Wieland, 2007.
"Money in Monetary Policy Design: A Formal Characterization of ECB-Style Cross-Checking,"
Journal of the European Economic Association, MIT Press, vol. 5(2-3), pages 524-533, 04-05.
- Beck, Günter & Wieland, Volker, 2007. "Money in Monetary Policy Design: A Formal Characterization of ECB-Style Cross-Checking," CEPR Discussion Papers 6097, C.E.P.R. Discussion Papers.
- Fama, Eugene F. & French, Kenneth R., 1988. "Dividend yields and expected stock returns," Journal of Financial Economics, Elsevier, vol. 22(1), pages 3-25, October.
- Kyle Jurado & Sydney C. Ludvigson & Serena Ng, 2015.
"Measuring Uncertainty,"
American Economic Review, American Economic Association, vol. 105(3), pages 1177-1216, March.
- Kyle Jurado & Sydney C. Ludvigson & Serena Ng, 2013. "Measuring Uncertainty," NBER Working Papers 19456, National Bureau of Economic Research, Inc.
- 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.
- Lemke, Wolfgang & Greiber, Claus, 2005. "Money demand and macroeconomic uncertainty," Discussion Paper Series 1: Economic Studies 2005,26, Deutsche Bundesbank.
- Song Song & Peter J. Bickel, 2011.
"Large Vector Auto Regressions,"
Papers
1106.3915, arXiv.org.
- Song Song & Peter J. Bickel, 2011. "Large Vector Auto Regressions," SFB 649 Discussion Papers SFB649DP2011-048, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
- Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
- Smeekes, Stephan & Wijler, Etienne, 2018.
"Macroeconomic forecasting using penalized regression methods,"
International Journal of Forecasting, Elsevier, vol. 34(3), pages 408-430.
- Smeekes, Stephan & Wijler, Etiënne, 2016. "Macroeconomic Forecasting Using Penalized Regression Methods," Research Memorandum 039, Maastricht University, Graduate School of Business and Economics (GSBE).
- Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016.
"Measuring Economic Policy Uncertainty,"
The Quarterly Journal of Economics, Oxford University Press, vol. 131(4), pages 1593-1636.
- Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2015. "Measuring Economic Policy Uncertainty," NBER Working Papers 21633, National Bureau of Economic Research, Inc.
- Baker, Scott R. & Bloom, Nicholas & Davis, Steven J, 2015. "Measuring Economic Policy Uncertainty," CEPR Discussion Papers 10900, C.E.P.R. Discussion Papers.
- Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2015. "Measuring Economic Policy Uncertainty," CEP Discussion Papers dp1379, Centre for Economic Performance, LSE.
- Baker, Scott R. & Bloom, Nicholas & Davis, Steven J., 2015. "Measuring economic policy uncertainty," LSE Research Online Documents on Economics 64986, London School of Economics and Political Science, LSE Library.
- Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2015. "Measuring Economic Policy Uncertainty," Economics Working Papers 15111, Hoover Institution, Stanford University.
- Medeiros, Marcelo C. & Mendes, Eduardo F., 2016. "ℓ1-regularization of high-dimensional time-series models with non-Gaussian and heteroskedastic errors," Journal of Econometrics, Elsevier, vol. 191(1), pages 255-271.
- 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.
- Nicholas Bloom, 2009.
"The Impact of Uncertainty Shocks,"
Econometrica, Econometric Society, vol. 77(3), pages 623-685, May.
- Nicholas Bloom, 2007. "The Impact of Uncertainty Shocks," NBER Working Papers 13385, National Bureau of Economic Research, Inc.
- William Barnett & Jia Liu & Ryan Mattson & Jeff Noort, 2013.
"The New CFS Divisia Monetary Aggregates: Design, Construction, and Data Sources,"
Open Economies Review, Springer, vol. 24(1), pages 101-124, February.
- Barnett, William A. & Liu, Jia & Mattson, Ryan S. & van den Noort, Jeff, 2012. "The new CFS Divisia monetary aggregates: design, construction, and data sources," MPRA Paper 38905, University Library of Munich, Germany.
- William Barnett & Jia Liu & Ryan Mattson & Jeff van den Noort, 2012. "The New CFS Divisia Monetary Aggregates: Design, Construction, and Data Sources," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201208, University of Kansas, Department of Economics, revised May 2012.
- 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.
- Tom Doan, "undated". "DMARIANO: RATS procedure to compute Diebold-Mariano Forecast Comparison Test," Statistical Software Components RTS00055, Boston College Department of Economics.
- Sydney C. Ludvigson & Sai Ma & Serena Ng, 2021.
"Uncertainty and Business Cycles: Exogenous Impulse or Endogenous Response?,"
American Economic Journal: Macroeconomics, American Economic Association, vol. 13(4), pages 369-410, October.
- Sydney C. Ludvigson & Sai Ma & Serena Ng, 2015. "Uncertainty and Business Cycles: Exogenous Impulse or Endogenous Response?," NBER Working Papers 21803, National Bureau of Economic Research, Inc.
- Michael T. Belongia & Peter N. Ireland, 2015.
"Interest Rates and Money in the Measurement of Monetary Policy,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(2), pages 255-269, April.
- Michael T. Belongia & Peter N. Ireland, 2014. "Interest Rates and Money in the Measurement of Monetary Policy," NBER Working Papers 20134, National Bureau of Economic Research, Inc.
- Ingrid Groessl & Artur Tarassow, 2015.
"A Microfounded Model of Money Demand Under Uncertainty, and some Empirical Evidence,"
Macroeconomics and Finance Series
201504, University of Hamburg, Department of Socioeconomics, revised Jan 2018.
- Ingrid Groessl & Artur Tarassow, 2018. "A Microfounded Model of Money Demand Under Uncertainty, and some Empirical Evidence," Macroeconomics and Finance Series 201802, University of Hamburg, Department of Socioeconomics.
- James H. Stock & Mark W. Watson, 2007.
"Why Has U.S. Inflation Become Harder to Forecast?,"
Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
- James H. Stock & Mark W. Watson, 2006. "Why Has U.S. Inflation Become Harder to Forecast?," NBER Working Papers 12324, National Bureau of Economic Research, Inc.
- Belongia, Michael T. & Ireland, Peter N., 2017.
"Circumventing the zero lower bound with monetary policy rules based on money,"
Journal of Macroeconomics, Elsevier, vol. 54(PA), pages 42-58.
- Michael T. Belongia & Peter N. Ireland, 2016. "Circumventing the Zero Lower Bound with Monetary Policy Rules Based on Money," Boston College Working Papers in Economics 911, Boston College Department of Economics.
- Michael T. Belongia & Peter N. Ireland, 2017. "Circumventing the Zero Lower Bound with Monetary Policy Rules Based on Money," NBER Working Papers 23157, National Bureau of Economic Research, Inc.
- Peter R. Hansen & Asger Lunde & James M. Nason, 2011.
"The Model Confidence Set,"
Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
- Peter R. Hansen & Asger Lunde & James M. Nason, 2010. "The Model Confidence Set," CREATES Research Papers 2010-76, Department of Economics and Business Economics, Aarhus University.
- Kascha, Christian & Trenkler, Carsten, 2015. "Forecasting VARs, model selection, and shrinkage," Working Papers 15-07, University of Mannheim, Department of Economics.
- Garcia, Márcio G.P. & Medeiros, Marcelo C. & Vasconcelos, Gabriel F.R., 2017. "Real-time inflation forecasting with high-dimensional models: The case of Brazil," International Journal of Forecasting, Elsevier, vol. 33(3), pages 679-693.
- Pesaran, M. Hashem & Timmermann, Allan, 2007. "Selection of estimation window in the presence of breaks," Journal of Econometrics, Elsevier, vol. 137(1), pages 134-161, March.
- Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
- Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Cui Jinxin & Zou Huiwen, 2020. "Connectedness Among Economic Policy Uncertainties: Evidence from the Time and Frequency Domain Perspectives," Journal of Systems Science and Information, De Gruyter, vol. 8(5), pages 401-433, October.
- Cho, Dooyeon & Kim, Husang, 2023. "Macroeconomic effects of uncertainty shocks: Evidence from Korea," Journal of Asian Economics, Elsevier, vol. 84(C).
- Simmons, Richard & Dini, Paolo & Culkin, Nigel & Littera, Giuseppe, 2021. "Crisis and the role of money in the real and financial economies: an innovative approach to monetary stimulus," LSE Research Online Documents on Economics 110904, London School of Economics and Political Science, LSE Library.
- Richard Simmons & Paolo Dini & Nigel Culkin & Giuseppe Littera, 2021. "Crisis and the Role of Money in the Real and Financial Economies—An Innovative Approach to Monetary Stimulus," JRFM, MDPI, vol. 14(3), pages 1-28, March.
- Gillmann, Niels & Kim, Alisa, 2021. "Quantification of Economic Uncertainty: a deep learning approach," VfS Annual Conference 2021 (Virtual Conference): Climate Economics 242421, Verein für Socialpolitik / German Economic Association.
- Karanasos, M. & Yfanti, S., 2021. "On the Economic fundamentals behind the Dynamic Equicorrelations among Asset classes: Global evidence from Equities, Real estate, and Commodities," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
- M. Karanasos & S. Yfanti & J. Hunter, 2022. "Emerging stock market volatility and economic fundamentals: the importance of US uncertainty spillovers, financial and health crises," Annals of Operations Research, Springer, vol. 313(2), pages 1077-1116, June.
- Guglielmo Maria Caporale & Menelaos Karanasos & Stavroula Yfanti, 2019. "Macro-Financial Linkages in the High-Frequency Domain: The Effects of Uncertainty on Realized Volatility," CESifo Working Paper Series 8000, CESifo.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Artur Tarassow, 2017. "Forecasting growth of U.S. aggregate and household-sector M2 after 2000 using economic uncertainty measures," Macroeconomics and Finance Series 201702, University of Hamburg, Department of Socioeconomics.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022.
"How is machine learning useful for macroeconomic forecasting?,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2019. "How is Machine Learning Useful for Macroeconomic Forecasting?," CIRANO Working Papers 2019s-22, CIRANO.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stephane Surprenant, 2020. "How is Machine Learning Useful for Macroeconomic Forecasting?," Working Papers 20-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Aug 2020.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & St'ephane Surprenant, 2020. "How is Machine Learning Useful for Macroeconomic Forecasting?," Papers 2008.12477, arXiv.org.
- Smeekes, Stephan & Wijler, Etienne, 2018.
"Macroeconomic forecasting using penalized regression methods,"
International Journal of Forecasting, Elsevier, vol. 34(3), pages 408-430.
- Smeekes, Stephan & Wijler, Etiënne, 2016. "Macroeconomic Forecasting Using Penalized Regression Methods," Research Memorandum 039, Maastricht University, Graduate School of Business and Economics (GSBE).
- Granziera, Eleonora & Sekhposyan, Tatevik, 2019.
"Predicting relative forecasting performance: An empirical investigation,"
International Journal of Forecasting, Elsevier, vol. 35(4), pages 1636-1657.
- Granziera, Eleonora & Sekhposyan, Tatevik, 2018. "Predicting relative forecasting performance: An empirical investigation," Bank of Finland Research Discussion Papers 23/2018, Bank of Finland.
- Nonejad, Nima, 2021. "The price of crude oil and (conditional) out-of-sample predictability of world industrial production," Journal of Commodity Markets, Elsevier, vol. 23(C).
- Ingrid Groessl & Artur Tarassow, 2015.
"A Microfounded Model of Money Demand Under Uncertainty, and some Empirical Evidence,"
Macroeconomics and Finance Series
201504, University of Hamburg, Department of Socioeconomics, revised Jan 2018.
- Ingrid Groessl & Artur Tarassow, 2018. "A Microfounded Model of Money Demand Under Uncertainty, and some Empirical Evidence," Macroeconomics and Finance Series 201802, University of Hamburg, Department of Socioeconomics.
- Daniel Borup & Jonas N. Eriksen & Mads M. Kjær & Martin Thyrsgaard, 2020. "Predicting bond return predictability," CREATES Research Papers 2020-09, Department of Economics and Business Economics, Aarhus University.
- Mawuli Segnon & Rangan Gupta & Stelios Bekiros & Mark E. Wohar, 2018.
"Forecasting US GNP growth: The role of uncertainty,"
Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(5), pages 541-559, August.
- Mawuli Segnon & Rangan Gupta & Stelios Bekiros & Mark E. Wohar, 2016. "Forecasting US GNP Growth: The Role of Uncertainty," Working Papers 201667, University of Pretoria, Department of Economics.
- Nonejad, Nima, 2022. "Understanding the conditional out-of-sample predictive impact of the price of crude oil on aggregate equity return volatility," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
- Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2020. "Machine Learning Advances for Time Series Forecasting," Papers 2012.12802, arXiv.org, revised Apr 2021.
- Nonejad, Nima, 2023. "Conditional out-of-sample predictability of aggregate equity returns and aggregate equity return volatility using economic variables," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 91-122.
- Nonejad, Nima, 2022. "Predicting equity premium out-of-sample by conditioning on newspaper-based uncertainty measures: A comparative study," International Review of Financial Analysis, Elsevier, vol. 83(C).
- Ardia, David & Bluteau, Keven & Boudt, Kris, 2019. "Questioning the news about economic growth: Sparse forecasting using thousands of news-based sentiment values," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1370-1386.
- Nonejad, Nima, 2022. "Equity premium prediction using the price of crude oil: Uncovering the nonlinear predictive impact," Energy Economics, Elsevier, vol. 115(C).
- Nonejad, Nima, 2021. "Predicting equity premium using news-based economic policy uncertainty: Not all uncertainty changes are equally important," International Review of Financial Analysis, Elsevier, vol. 77(C).
- 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.
- Wilms, Ines & Rombouts, Jeroen & Croux, Christophe, 2021. "Multivariate volatility forecasts for stock market indices," International Journal of Forecasting, Elsevier, vol. 37(2), pages 484-499.
- Dew-Becker, Ian & Giglio, Stefano & Kelly, Bryan, 2021.
"Hedging macroeconomic and financial uncertainty and volatility,"
Journal of Financial Economics, Elsevier, vol. 142(1), pages 23-45.
- Ian Dew-Becker & Stefano Giglio & Bryan T. Kelly, 2019. "Hedging Macroeconomic and Financial Uncertainty and Volatility," NBER Working Papers 26323, National Bureau of Economic Research, Inc.
- Dew-Becker, Ian & Kelly, Bryan, 2020. "Hedging macroeconomic and financial uncertainty and volatility," CEPR Discussion Papers 15239, C.E.P.R. Discussion Papers.
- Stavros Degiannakis & George Filis, 2019.
"Forecasting European economic policy uncertainty,"
Scottish Journal of Political Economy, Scottish Economic Society, vol. 66(1), pages 94-114, February.
- Stavros Degiannakis & George Filis, 2018. "Forecasting European Economic Policy Uncertainty," BAFES Working Papers BAFES15, Department of Accounting, Finance & Economic, Bournemouth University.
- Degiannakis, Stavros & Filis, George, 2019. "Forecasting European Economic Policy Uncertainty," MPRA Paper 96268, University Library of Munich, Germany.
- Garcia, Márcio G.P. & Medeiros, Marcelo C. & Vasconcelos, Gabriel F.R., 2017. "Real-time inflation forecasting with high-dimensional models: The case of Brazil," International Journal of Forecasting, Elsevier, vol. 33(3), pages 679-693.
More about this item
Keywords
Divisia money; Risk; Model confidence set; VAR; Forecast comparison; Shrinkage; Lasso; Machine learning;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:intfor:v:35:y:2019:i:2:p:443-457. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: http://www.elsevier.com/locate/ijforecast .
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijforecast .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.