Forecasting inflation in post-oil boom years: A case for regime switches?
Author
Abstract
Suggested Citation
DOI: 10.1007/s12197-017-9410-1
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
- Hendry, David F. & Mizon, Grayham E., 2014.
"Unpredictability in economic analysis, econometric modeling and forecasting,"
Journal of Econometrics, Elsevier, vol. 182(1), pages 186-195.
- David Hendry, 2011. "Unpredictability in Economic Analyis, Econometric Modelling and Forecasting," Economics Series Working Papers 551, University of Oxford, Department of Economics.
- David F. Hendry & Grayham E. Mizon, 2013. "Unpredictability in Economic Analysis, Econometric Modeling and Forecasting," Economics Papers 2013-W04, Economics Group, Nuffield College, University of Oxford.
- Andrew Blake & Haroon Mumtaz, 2015.
"Applied Bayesian Econometrics for central bankers,"
Handbooks,
Centre for Central Banking Studies, Bank of England, number 36, April.
- Andrew P Blake & Haroon Mumtaz, 2012. "Applied Bayesian econometrics for central bankers," Technical Books, Centre for Central Banking Studies, Bank of England, edition 1, number 4, April.
- Barnett, Alina & Mumtaz, Haroon & Theodoridis, Konstantinos, 2014.
"Forecasting UK GDP growth and inflation under structural change. A comparison of models with time-varying parameters,"
International Journal of Forecasting, Elsevier, vol. 30(1), pages 129-143.
- Barnett, Alina & Mumtaz, Haroon & Theodoridis, Konstantinos, 2012. "Forecasting UK GDP growth, inflation and interest rates under structural change: a comparison of models with time-varying parameters," Bank of England working papers 450, Bank of England.
- Antonello D'Agostino & Domenico Giannone & Paolo Surico, 2005.
"(Un)Predictability and Macroeconomic Stability,"
Macroeconomics
0510024, University Library of Munich, Germany.
- D'Agostino, Antonello & Domenico, Giannone & Surico, Paolo, 2006. "(Un)Predictability and Macroeconomic Stability," Research Technical Papers 5/RT/06, Central Bank of Ireland.
- Surico, Paolo & Giannone, Domenico & D'Agostino, Antonello, 2006. "(Un)Predictability and macroeconomic stability," Working Paper Series 605, European Central Bank.
- Giannone, Domenico & D’Agostino, Antonello & Surico, Paolo, 2007. "(Un)Predictability and Macroeconomic Stability," CEPR Discussion Papers 6594, C.E.P.R. Discussion Papers.
- Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
- Groen, Jan J J & Mumtaz, Haroon, 2008. "Investigating the structural stability of the Phillips curve relationship," Bank of England working papers 350, Bank of England.
- J. B. Taylor & Harald Uhlig (ed.), 2016. "Handbook of Macroeconomics," Handbook of Macroeconomics, Elsevier, edition 1, volume 2, number 2.
- 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.
- McCracken, Michael W., 2007. "Asymptotics for out of sample tests of Granger causality," Journal of Econometrics, Elsevier, vol. 140(2), pages 719-752, October.
- 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, 2017. "Online Appendix to "Financial conditions and density forecasts for US output and inflation"," Online Appendices 14-103, Review of Economic Dynamics.
- 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.
- Piergiorgio Alessandri & Haroon Mumtaz, 2017. "Code and data files for "Financial conditions and density forecasts for US output and inflation"," Computer Codes 14-103, Review of Economic Dynamics.
- Koop, Gary & Korobilis, Dimitris, 2013.
"Large time-varying parameter VARs,"
Journal of Econometrics, Elsevier, vol. 177(2), pages 185-198.
- Koop, Gary & Korobilis, Dimitris, 2012. "Large time-varying parameter VARs," MPRA Paper 38591, University Library of Munich, Germany.
- Koop, Gary & Korobilis, Dimitris, 2012. "Large Time-Varying Parameter VARs," SIRE Discussion Papers 2012-14, Scottish Institute for Research in Economics (SIRE).
- Gary Koop & Dimitris Korobilis, 2012. "Large Time-Varying Parameter VARs," Working Paper series 11_12, Rimini Centre for Economic Analysis.
- Gary Koop & Dimitris Korobilis, 2012. "Large time-varying parameter VARs," Working Papers 2012_04, Business School - Economics, University of Glasgow.
- Baumeister, Christiane & Liu, Philip & Mumtaz, Haroon, 2010. "Changes in the transmission of monetary policy: evidence from a time-varying factor-augmented VAR," Bank of England working papers 401, Bank of England.
- 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.
- 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.
- Cathy W. S. Chen & Jack C. Lee, 1995. "Bayesian Inference Of Threshold Autoregressive Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 16(5), pages 483-492, September.
- Chen, Cathy W. S., 1998. "A Bayesian analysis of generalized threshold autoregressive models," Statistics & Probability Letters, Elsevier, vol. 40(1), pages 15-22, September.
- Rochelle M. Edge & Refet S. Gurkaynak, 2010.
"How Useful Are Estimated DSGE Model Forecasts for Central Bankers?,"
Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 41(2 (Fall)), pages 209-259.
- Gürkaynak, Refet & Edge, Rochelle, 2010. "How Useful Are Estimated DSGE Model Forecasts for Central Bankers?," CEPR Discussion Papers 8158, C.E.P.R. Discussion Papers.
- Andrew Atkeson & Lee E. Ohanian, 2001. "Are Phillips curves useful for forecasting inflation?," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 25(Win), pages 2-11.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Krzysztof DRACHAL, 2020. "Forecasting the Inflation Rate in Poland and U.S. Using Dynamic Model Averaging (DMA) and Google Queries," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 18-34, July.
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.- Vugar Ahmadov & Shaig Adigozalov & Salman Huseynov & Fuad Mammadov & Vugar Rahimov, 2016. "Forecasting inflation in post-oil boom years: A case for non-linear models?," Working Papers 1601, Central Bank of Azerbaijan Republic.
- Rossi, Barbara, 2013.
"Advances in Forecasting under Instability,"
Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1203-1324,
Elsevier.
- Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.
- Barnett, Alina & Mumtaz, Haroon & Theodoridis, Konstantinos, 2014.
"Forecasting UK GDP growth and inflation under structural change. A comparison of models with time-varying parameters,"
International Journal of Forecasting, Elsevier, vol. 30(1), pages 129-143.
- Barnett, Alina & Mumtaz, Haroon & Theodoridis, Konstantinos, 2012. "Forecasting UK GDP growth, inflation and interest rates under structural change: a comparison of models with time-varying parameters," Bank of England working papers 450, Bank of England.
- Raffaella Giacomini & Barbara Rossi, 2013. "Forecasting in macroeconomics," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 17, pages 381-408, Edward Elgar Publishing.
- Dur, Ayşe & Martínez García, Enrique, 2020.
"Mind the gap!—A monetarist view of the open-economy Phillips curve,"
Journal of Economic Dynamics and Control, Elsevier, vol. 117(C).
- Ayse Dur & Enrique Martínez García, 2020. "Mind the Gap!—A Monetarist View of the Open-Economy Phillips Curve," Globalization Institute Working Papers 392, Federal Reserve Bank of Dallas.
- Tallman, Ellis W. & Zaman, Saeed, 2020.
"Combining survey long-run forecasts and nowcasts with BVAR forecasts using relative entropy,"
International Journal of Forecasting, Elsevier, vol. 36(2), pages 373-398.
- Ellis W. Tallman & Saeed Zaman, 2018. "Combining Survey Long-Run Forecasts and Nowcasts with BVAR Forecasts Using Relative Entropy," Working Papers (Old Series) 1809, Federal Reserve Bank of Cleveland.
- 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.
- Kirstin Hubrich & Kenneth D. West, 2010.
"Forecast evaluation of small nested model sets,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 574-594.
- Kirstin Hubrich & Kenneth D. West, 2008. "Forecast Evaluation of Small Nested Model Sets," NBER Working Papers 14601, National Bureau of Economic Research, Inc.
- Hubrich, Kirstin & West, Kenneth D., 2009. "Forecast evaluation of small nested model sets," Working Paper Series 1030, European Central Bank.
- Huseynov, Salman & Ahmadov, Vugar & Adigozalov, Shaig, 2014. "Beating a Random Walk: “Hard Times” for Forecasting Inflation in Post-Oil Boom Years?," MPRA Paper 63515, University Library of Munich, Germany.
- Lasha Kavtaradze & Manouchehr Mokhtari, 2018. "Factor Models And Time†Varying Parameter Framework For Forecasting Exchange Rates And Inflation: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 32(2), pages 302-334, April.
- Gary Cornwall & Jeffrey A. Mills & Beau A. Sauley & Huibin Weng, 2018. "Predictive Testing for Granger Causality via Posterior Simulation and Cross Validation," BEA Working Papers 0156, Bureau of Economic Analysis.
- Ayse Kabukcuoglu & Enrique Martínez-García, 2016. "What Helps Forecast U.S. Inflation?—Mind the Gap!," Koç University-TUSIAD Economic Research Forum Working Papers 1615, Koc University-TUSIAD Economic Research Forum.
- Szafranek, Karol, 2019.
"Bagged neural networks for forecasting Polish (low) inflation,"
International Journal of Forecasting, Elsevier, vol. 35(3), pages 1042-1059.
- Karol Szafranek, 2017. "Bagged artificial neural networks in forecasting inflation: An extensive comparison with current modelling frameworks," NBP Working Papers 262, Narodowy Bank Polski.
- Mayer, Walter J. & Liu, Feng & Dang, Xin, 2017. "Improving the power of the Diebold–Mariano–West test for least squares predictions," International Journal of Forecasting, Elsevier, vol. 33(3), pages 618-626.
- Clark, Todd E. & McCracken, Michael W., 2015.
"Nested forecast model comparisons: A new approach to testing equal accuracy,"
Journal of Econometrics, Elsevier, vol. 186(1), pages 160-177.
- Todd E. Clark & Michael W. McCracken, 2009. "Nested forecast model comparisons: a new approach to testing equal accuracy," Working Papers 2009-050, Federal Reserve Bank of St. Louis.
- Todd E. Clark & Michael W. McCracken, 2009. "Nested forecast model comparisons: a new approach to testing equal accuracy," Research Working Paper RWP 09-11, Federal Reserve Bank of Kansas City.
- Michael McLeay & Silvana Tenreyro, 2020.
"Optimal Inflation and the Identification of the Phillips Curve,"
NBER Macroeconomics Annual, University of Chicago Press, vol. 34(1), pages 199-255.
- Michael McLeay & Silvana Tenreyro, 2019. "Optimal Inflation and the Identification of the Phillips Curve," NBER Chapters, in: NBER Macroeconomics Annual 2019, volume 34, pages 199-255, National Bureau of Economic Research, Inc.
- McLeay, Michael & Tenreyro, Silvana, 2018. "Optimal inflation and the identification of the Phillips Curve," LSE Research Online Documents on Economics 90373, London School of Economics and Political Science, LSE Library.
- Tenreyro, Silvana & McLeay, Michael, 2018. "Optimal Inflation and the Identification of the Phillips Curve," CEPR Discussion Papers 12981, C.E.P.R. Discussion Papers.
- Michael McLeay & Silvana Tenreyro, 2018. "Optimal Inflation and the Identification of the Phillips Curve," Discussion Papers 1815, Centre for Macroeconomics (CFM).
- Michael McLeay & Silvana Tenreyro, 2019. "Optimal Inflation and the Identification of the Phillips Curve," NBER Working Papers 25892, National Bureau of Economic Research, Inc.
- McLeay, Michael & Tenreyro, Silvana, 2020. "Optimal inflation and the identification of the Phillips curve," LSE Research Online Documents on Economics 103080, London School of Economics and Political Science, LSE Library.
- Jan Prüser, 2021. "Forecasting US inflation using Markov dimension switching," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(3), pages 481-499, April.
- Shintani, Mototsugu, 2005.
"Nonlinear Forecasting Analysis Using Diffusion Indexes: An Application to Japan,"
Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 517-538, June.
- Mototsugu Shintani, 2003. "Nonlinear Forecasting Analysis Using Diffusion Indexes: An Application to Japan," Vanderbilt University Department of Economics Working Papers 0322, Vanderbilt University Department of Economics, revised Apr 2004.
- Mototsugu Shintani, 2010. "Nonlinear Forecasting Analysis Using Diffusion Indexes: An Application to Japan," Levine's Working Paper Archive 506439000000000168, David K. Levine.
- Marta Banbura & Andries van Vlodrop, 2018. "Forecasting with Bayesian Vector Autoregressions with Time Variation in the Mean," Tinbergen Institute Discussion Papers 18-025/IV, Tinbergen Institute.
- Barbara Rossi, 2019.
"Forecasting in the presence of instabilities: How do we know whether models predict well and how to improve them,"
Economics Working Papers
1711, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2021.
- Barbara Rossi, 2019. "Forecasting in the Presence of Instabilities: How Do We Know Whether Models Predict Well and How to Improve Them," Working Papers 1162, Barcelona School of Economics.
- Rossi, Barbara, 2020. "Forecasting in the Presence of Instabilities: How Do We Know Whether Models Predict Well and How to Improve Them," CEPR Discussion Papers 14472, C.E.P.R. Discussion Papers.
More about this item
Keywords
Inflation; Forecasting; Bayesian methods; Regime switching models;All these keywords.
JEL classification:
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: 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
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
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:spr:jecfin:v:42:y:2018:i:2:d:10.1007_s12197-017-9410-1. See general information about how to correct material in RePEc.
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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.