Modelos FAVAR con factores estáticos y dinámicos para pronosticar la inflación en Costa Rica
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Mandalinci, Zeyyad, 2017.
"Forecasting inflation in emerging markets: An evaluation of alternative models,"
International Journal of Forecasting, Elsevier, vol. 33(4), pages 1082-1104.
- Zeyyad Mandalinci, 2015. "Forecasting Inflation in Emerging Markets: An Evaluation of Alternative Models," CReMFi Discussion Papers 3, CReMFi, School of Economics and Finance, QMUL.
- Winkelried, Diego, 2003. "Hacia una meta explícita de inflación: Anticipando la inflación en el Perú," Revista Estudios Económicos, Banco Central de Reserva del Perú, issue 9, pages 11-40.
- Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2005.
"Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach,"
The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 120(1), pages 387-422.
- Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2004. "Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach," NBER Working Papers 10220, National Bureau of Economic Research, Inc.
- Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2004. "Measuring the effects of monetary policy: a factor-augmented vector autoregressive (FAVAR) approach," Finance and Economics Discussion Series 2004-03, Board of Governors of the Federal Reserve System (U.S.).
- Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
- Doz, Catherine & Giannone, Domenico & Reichlin, Lucrezia, 2011.
"A two-step estimator for large approximate dynamic factor models based on Kalman filtering,"
Journal of Econometrics, Elsevier, vol. 164(1), pages 188-205, September.
- Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2006. "A Two-step estimator for large approximate dynamic factor models based on Kalman filtering," Thema Working Papers 2006-23, THEMA (Théorie Economique, Modélisation et Applications), CY Cergy-Paris University, ESSEC and CNRS.
- Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2011. "A two-step estimator for large approximate dynamic factor models based on Kalman filtering," Post-Print hal-00638009, HAL.
- Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2011. "A two-step estimator for large approximate dynamic factor models based on Kalman filtering," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00638009, HAL.
- Catherine Doz & Lucrezia Reichlin, 2011. "A two-step estimator for large approximate dynamic factor models based on Kalman filtering," Post-Print hal-00844811, HAL.
- Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2011. "A two-step estimator for large approximate dynamic factor models based on Kalman filtering," PSE-Ecole d'économie de Paris (Postprint) hal-00638009, HAL.
- Reichlin, Lucrezia & Doz, Catherine & Giannone, Domenico, 2007. "A Two-Step Estimator for Large Approximate Dynamic Factor Models Based on Kalman Filtering," CEPR Discussion Papers 6043, Centre for Economic Policy Research.
- 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.
- repec:hal:journl:peer-00844811 is not listed on IDEAS
- 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.
- Döhrn, Roland, 2019. "Comparing forecast accuracy in small samples," Ruhr Economic Papers 833, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Carlos Segura-Rodriguez, 2025. "Inflation Forecasting in Costa Rica: The Contribution of Exogenous Variables in Item-Level Disaggregated Models," Documentos de Trabajo 2509, Banco Central de Costa Rica.
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.- 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.
- Branimir, Jovanovic & Magdalena, Petrovska, 2010. "Forecasting Macedonian GDP: Evaluation of different models for short-term forecasting," MPRA Paper 43162, University Library of Munich, Germany.
- Paponpat Taveeapiradeecharoen & Nattapol Aunsri, 2025. "Forecasting in small open emerging economies Evidence from Thailand," Papers 2509.14805, arXiv.org.
- 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.
- Knotek, Edward S. & Zaman, Saeed, 2019.
"Financial nowcasts and their usefulness in macroeconomic forecasting,"
International Journal of Forecasting, Elsevier, vol. 35(4), pages 1708-1724.
- Edward S. Knotek & Saeed Zaman, 2017. "Financial Nowcasts and Their Usefulness in Macroeconomic Forecasting," Working Papers (Old Series) 1702, Federal Reserve Bank of Cleveland.
- Robert Lehmann, 2016. "Economic Growth and Business Cycle Forecasting at the Regional Level," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 65, April.
- Allayioti, Anastasia & Venditti, Fabrizio, 2024. "The role of comovement and time-varying dynamics in forecasting commodity prices," Working Paper Series 2901, European Central Bank.
- Follett, Lendie & Yu, Cindy, 2019. "Achieving parsimony in Bayesian vector autoregressions with the horseshoe prior," Econometrics and Statistics, Elsevier, vol. 11(C), pages 130-144.
- Roberto S. Mariano & Suleyman Ozmucur, 2021. "Predictive Performance of Mixed-Frequency Nowcasting and Forecasting Models (with Application to Philippine Inflation and GDP Growth)," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 383-400, December.
- Aastveit, Knut Are & Trovik, Tørres, 2014.
"Estimating the output gap in real time: A factor model approach,"
The Quarterly Review of Economics and Finance, Elsevier, vol. 54(2), pages 180-193.
- Knut Are Aastveit & Tørres G. Trovik, 2008. "Estimating the output gap in real time: A factor model approach," Working Paper 2008/23, Norges Bank.
- Johanna Posch & Fabio Rumler, 2015. "Semi‐Structural Forecasting of UK Inflation Based on the Hybrid New Keynesian Phillips Curve," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(2), pages 145-162, March.
- Carlos Segura-Rodriguez, 2025. "Inflation Forecasting in Costa Rica: The Contribution of Exogenous Variables in Item-Level Disaggregated Models," Documentos de Trabajo 2509, Banco Central de Costa Rica.
- Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022.
"Forecasting: theory and practice,"
International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
- Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
- Naser, Hanan, 2016. "Estimating and forecasting the real prices of crude oil: A data rich model using a dynamic model averaging (DMA) approach," Energy Economics, Elsevier, vol. 56(C), pages 75-87.
- Li, Mengheng & Koopman, Siem Jan & Lit, Rutger & Petrova, Desislava, 2020. "Long-term forecasting of El Niño events via dynamic factor simulations," Journal of Econometrics, Elsevier, vol. 214(1), pages 46-66.
- Koop, Gary & Korobilis, Dimitris & Pettenuzzo, Davide, 2019. "Bayesian compressed vector autoregressions," Journal of Econometrics, Elsevier, vol. 210(1), pages 135-154.
- De Pooter, Michiel & Ravazzolo, Francesco & van Dijk, Dick, 2006.
"Predicting the term structure of interest rates incorporating parameter uncertainty, model uncertainty and macroeconomic information,"
MPRA Paper
2512, University Library of Munich, Germany, revised 03 Mar 2007.
- Michiel D. de Pooter & Francesco Ravazzolo & Dick van Dijk, 2007. "Predicting the Term Structure of Interest Rates: Incorporating Parameter Uncertainty, Model Uncertainty and Macroeconomic Information," Tinbergen Institute Discussion Papers 07-028/4, Tinbergen Institute.
- Bae, Juhee, 2024. "Factor-augmented forecasting in big data," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1660-1688.
- 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.
- Andrea CARRIERO & Todd E. CLARK & Massimiliano MARCELLINO, 2012. "Common Drifting Volatility in Large Bayesian VARs," Economics Working Papers ECO2012/08, European University Institute.
- Marcellino, Massimiliano & Carriero, Andrea & Clark, Todd, 2012. "Common Drifting Volatility in Large Bayesian VARs," CEPR Discussion Papers 8894, C.E.P.R. Discussion Papers.
- Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Common drifting volatility in large Bayesian VARs," Working Papers (Old Series) 1206, Federal Reserve Bank of Cleveland.
- Elliot Beck & Michael Wolf, 2026. "Forecasting inflation with the hedged random forest," Empirical Economics, Springer, vol. 70(2), pages 1-36, February.
More about this item
Keywords
; ; ; ;JEL classification:
- E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
- R10 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - General
NEP fields
This paper has been announced in the following NEP Reports:- NEP-ETS-2025-07-14 (Econometric Time Series)
- NEP-FOR-2025-07-14 (Forecasting)
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:apk:doctra:2403. 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: Departamento de Investigación Económica (email available below). General contact details of provider: https://edirc.repec.org/data/bccrrcr.html .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.
Printed from https://ideas.repec.org/p/apk/doctra/2403.html