Identifying and interpreting the factors in factor models via sparsity : Different approaches
Author
Abstract
Suggested Citation
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-03626503v1
Download full text from publisher
References listed on IDEAS
- Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2021.
"Economic Predictions With Big Data: The Illusion of Sparsity,"
Econometrica, Econometric Society, vol. 89(5), pages 2409-2437, September.
- Giannone, Domenico & Lenza, Michele & Primiceri, Giorgio, 2017. "Economic Predictions with Big Data: The Illusion Of Sparsity," CEPR Discussion Papers 12256, C.E.P.R. Discussion Papers.
- Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2018. "Economic predictions with big data: the illusion of sparsity," Staff Reports 847, Federal Reserve Bank of New York.
- Giannone, Domenico & Lenza, Michele & Primiceri, Giorgio E., 2021. "Economic predictions with big data: the illusion of sparsity," Working Paper Series 2542, European Central Bank.
- Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2018. "Economic Predictions with Big Data: The Illusion of Sparsity," Liberty Street Economics 20180521, Federal Reserve Bank of New York.
- Robert C. Feenstra & Robert Inklaar & Marcel P. Timmer, 2015.
"The Next Generation of the Penn World Table,"
American Economic Review, American Economic Association, vol. 105(10), pages 3150-3182, October.
- Robert C. Feenstra & Robert Inklaar & Marcel Timmer, 2013. "The Next Generation of the Penn World Table," NBER Working Papers 19255, National Bureau of Economic Research, Inc.
- Bergmeir, Christoph & Hyndman, Rob J. & Koo, Bonsoo, 2018. "A note on the validity of cross-validation for evaluating autoregressive time series prediction," Computational Statistics & Data Analysis, Elsevier, vol. 120(C), pages 70-83.
- Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2004.
"The generalized dynamic factor model consistency and rates,"
Journal of Econometrics, Elsevier, vol. 119(2), pages 231-255, April.
- Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2004. "The generalised dynamic factor model: consistency and rates," ULB Institutional Repository 2013/10133, ULB -- Universite Libre de Bruxelles.
- 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), Université de Cergy-Pontoise.
- 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.
- 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.
- 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, C.E.P.R. Discussion Papers.
- Jushan Bai & Serena Ng, 2002.
"Determining the Number of Factors in Approximate Factor Models,"
Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
- Jushan Bai & Serena Ng, 2000. "Determining the Number of Factors in Approximate Factor Models," Econometric Society World Congress 2000 Contributed Papers 1504, Econometric Society.
- Jushan Bai & Serena Ng, 2000. "Determining the Number of Factors in Approximate Factor Models," Boston College Working Papers in Economics 440, Boston College Department of Economics.
- 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.
- Robert Jennrich, 2002. "A simple general method for oblique rotation," Psychometrika, Springer;The Psychometric Society, vol. 67(1), pages 7-19, March.
- Schneeweiss, H. & Mathes, H., 1995. "Factor Analysis and Principal Components," Journal of Multivariate Analysis, Elsevier, vol. 55(1), pages 105-124, October.
- Neville Francis & Michael T. Owyang & Ozge Savascin, 2017.
"An endogenously clustered factor approach to international business cycles,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(7), pages 1261-1276, November.
- Neville Francis & Michael T. Owyang & Özge Savascin, 2012. "An endogenously clustered factor approach to international business cycles," Working Papers 2012-014, Federal Reserve Bank of St. Louis.
- Silvia Miranda-Agrippino & Hélène Rey, 2020.
"U.S. Monetary Policy and the Global Financial Cycle,"
The Review of Economic Studies, Review of Economic Studies Ltd, vol. 87(6), pages 2754-2776.
- Silvia Miranda-Agrippino & Hélène Rey, 2015. "US Monetary Policy and the Global Financial Cycle," NBER Working Papers 21722, National Bureau of Economic Research, Inc.
- Johannes Tang Kristensen, 2017. "Diffusion Indexes With Sparse Loadings," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(3), pages 434-451, July.
- Cepni, Oguzhan & Güney, I. Ethem & Swanson, Norman R., 2019. "Nowcasting and forecasting GDP in emerging markets using global financial and macroeconomic diffusion indexes," International Journal of Forecasting, Elsevier, vol. 35(2), pages 555-572.
- Sylvia Kaufmann & Christian Schumacher, 2017. "Identifying relevant and irrelevant variables in sparse factor models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(6), pages 1123-1144, September.
- Christophe Croux & Peter Exterkate, 2011. "Sparse and Robust Factor Modelling," Tinbergen Institute Discussion Papers 11-122/4, Tinbergen Institute.
- Chamberlain, Gary & Rothschild, Michael, 1983.
"Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets,"
Econometrica, Econometric Society, vol. 51(5), pages 1281-1304, September.
- Gary Chamberlain & Michael Rothschild, 1982. "Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets," NBER Working Papers 0996, National Bureau of Economic Research, Inc.
- Chamberlain, Gary & Rothschild, Michael, 1982. "Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets," Scholarly Articles 3230355, Harvard University Department of Economics.
- Refet S Gürkaynak & Brian Sack & Eric Swanson, 2005.
"Do Actions Speak Louder Than Words? The Response of Asset Prices to Monetary Policy Actions and Statements,"
International Journal of Central Banking, International Journal of Central Banking, vol. 1(1), May.
- Refet S. Gürkaynak & Brian P. Sack & Eric T. Swanson, 2004. "Do actions speak louder than words? the response of asset prices to monetary policy actions and statements," Finance and Economics Discussion Series 2004-66, Board of Governors of the Federal Reserve System (U.S.).
- Refet Gurkaynak & Brian Sack & Eric Swanson, 2005. "Do Actions Speak Louder than Words? The Response of Asset Prices to Monetary Policy Actions and Statements," Macroeconomics 0504013, University Library of Munich, Germany.
- Gurkaynak, Refet S & Sack, Brian & Swanson, Eric T, 2005. "Do Actions Speak Louder Than Words? The Response of Asset Prices to Monetary Policy Actions and Statements," MPRA Paper 820, University Library of Munich, Germany.
- Refet Gürkaynak & Brian Sack, 2005. "Do Actions Speak Louder Than Words?The Response of Asset Prices to Monetary Policy Actions and Statements," Computing in Economics and Finance 2005 323, Society for Computational Economics.
- Kaufmann, Sylvia & Schumacher, Christian, 2019. "Bayesian estimation of sparse dynamic factor models with order-independent and ex-post mode identification," Journal of Econometrics, Elsevier, vol. 210(1), pages 116-134.
- M. Ayhan Kose & Christopher Otrok & Charles H. Whiteman, 2003. "International Business Cycles: World, Region, and Country-Specific Factors," American Economic Review, American Economic Association, vol. 93(4), pages 1216-1239, 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).
- Kim, Hyun Hak & Swanson, Norman R., 2018. "Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods," International Journal of Forecasting, Elsevier, vol. 34(2), pages 339-354.
- Henry Kaiser, 1958. "The varimax criterion for analytic rotation in factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 23(3), pages 187-200, September.
- Shen, Haipeng & Huang, Jianhua Z., 2008. "Sparse principal component analysis via regularized low rank matrix approximation," Journal of Multivariate Analysis, Elsevier, vol. 99(6), pages 1015-1034, July.
- Nickolay Trendafilov, 2014. "From simple structure to sparse components: a review," Computational Statistics, Springer, vol. 29(3), pages 431-454, June.
- E. Andreou & P. Gagliardini & E. Ghysels & M. Rubin, 2019. "Inference in Group Factor Models With an Application to Mixed‐Frequency Data," Econometrica, Econometric Society, vol. 87(4), pages 1267-1305, July.
- repec:hal:journl:peer-00844811 is not listed on IDEAS
- Robert Jennrich, 2001. "A simple general procedure for orthogonal rotation," Psychometrika, Springer;The Psychometric Society, vol. 66(2), pages 289-306, June.
- Bai, Jushan & Ng, Serena, 2013. "Principal components estimation and identification of static factors," Journal of Econometrics, Elsevier, vol. 176(1), pages 18-29.
- R. Jennrich & P. Sampson, 1966. "Rotation for simple loadings," Psychometrika, Springer;The Psychometric Society, vol. 31(3), pages 313-323, September.
- John Carroll, 1953. "An analytical solution for approximating simple structure in factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 18(1), pages 23-38, March.
- Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
- Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Jie Wei & Yonghui Zhang, 2023. "Does Principal Component Analysis Preserve the Sparsity in Sparse Weak Factor Models?," Papers 2305.05934, arXiv.org, revised Nov 2024.
- Luke Mosley & Tak-Shing Chan & Alex Gibberd, 2023. "sparseDFM: An R Package to Estimate Dynamic Factor Models with Sparse Loadings," Papers 2303.14125, arXiv.org.
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.- Thomas Despois & Catherine Doz, 2022. "Identifying and interpreting the factors in factor models via sparsity : Different approaches," PSE Working Papers halshs-03626503, HAL.
- Thomas Despois & Catherine Doz, 2023. "Identifying and interpreting the factors in factor models via sparsity: Different approaches," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 533-555, June.
- Thomas Despois & Catherine Doz, 2021. "Identifying and interpreting the factors in factor models via sparsity: Different approaches," Working Papers halshs-02235543, HAL.
- Thomas Despois & Catherine Doz, 2021. "Identifying and interpreting the factors in factor models via sparsity: Different approaches," PSE Working Papers halshs-02235543, HAL.
- 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).
- Simon Freyaldenhoven, 2020. "Identification Through Sparsity in Factor Models," Working Papers 20-25, Federal Reserve Bank of Philadelphia.
- 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.
- Bantis, Evripidis & Clements, Michael P. & Urquhart, Andrew, 2023. "Forecasting GDP growth rates in the United States and Brazil using Google Trends," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1909-1924.
- 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.
- Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
- Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021.
"Factor extraction using Kalman filter and smoothing: This is not just another survey,"
International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
- Poncela Blanco, Maria Pilar, 2020. "Factor extraction using Kalman filter and smoothing: this is not just another survey," DES - Working Papers. Statistics and Econometrics. WS 30644, Universidad Carlos III de Madrid. Departamento de EstadÃstica.
- Luke Mosley & Tak-Shing Chan & Alex Gibberd, 2023. "sparseDFM: An R Package to Estimate Dynamic Factor Models with Sparse Loadings," Papers 2303.14125, arXiv.org.
- Lütkepohl, Helmut, 2014.
"Structural vector autoregressive analysis in a data rich environment: A survey,"
SFB 649 Discussion Papers
2014-004, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
- Helmut Lütkepohl, 2014. "Structural Vector Autoregressive Analysis in a Data Rich Environment: A Survey," Discussion Papers of DIW Berlin 1351, DIW Berlin, German Institute for Economic Research.
- Billio, Monica & Casarin, Roberto & Rossini, Luca, 2019.
"Bayesian nonparametric sparse VAR models,"
Journal of Econometrics, Elsevier, vol. 212(1), pages 97-115.
- Monica Billio & Roberto Casarin & Luca Rossini, 2016. "Bayesian nonparametric sparse VAR models," Papers 1608.02740, arXiv.org, revised Oct 2018.
- Joseph, Andreas & Potjagailo, Galina & Chakraborty, Chiranjit & Kapetanios, George, 2024.
"Forecasting UK inflation bottom up,"
International Journal of Forecasting, Elsevier, vol. 40(4), pages 1521-1538.
- Joseph, Andreas & Kalamara, Eleni & Kapetanios, George & Potjagailo, Galina & Chakraborty, Chiranjit, 2021. "Forecasting UK inflation bottom up," Bank of England working papers 915, Bank of England, revised 27 Sep 2022.
- Jushan Bai & Serena Ng, 2020. "Simpler Proofs for Approximate Factor Models of Large Dimensions," Papers 2008.00254, arXiv.org.
- Simon Beyeler & Sylvia Kaufmann, 2021. "Reduced‐form factor augmented VAR—Exploiting sparsity to include meaningful factors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(7), pages 989-1012, November.
- Hauber, Philipp & Schumacher, Christian, 2021. "Precision-based sampling with missing observations: A factor model application," Discussion Papers 11/2021, Deutsche Bundesbank.
- Thomas Conlon & John Cotter & Iason Kynigakis, 2021.
"Machine Learning and Factor-Based Portfolio Optimization,"
Papers
2107.13866, arXiv.org.
- Thomas Conlon & John Cotter & Iason Kynigakis, 2021. "Machine Learning and Factor-Based Portfolio Optimization," Working Papers 202111, Geary Institute, University College Dublin.
- Mogliani, Matteo & Simoni, Anna, 2021.
"Bayesian MIDAS penalized regressions: Estimation, selection, and prediction,"
Journal of Econometrics, Elsevier, vol. 222(1), pages 833-860.
- Matteo Mogliani & Anna Simoni, 2019. "Bayesian MIDAS Penalized Regressions: Estimation, Selection, and Prediction," Papers 1903.08025, arXiv.org, revised Jun 2020.
- Matteo Mogliani & Anna Simoni, 2020. "Bayesian MIDAS penalized regressions: Estimation, selection, and prediction," Post-Print hal-03089878, HAL.
- Matteo Mogliani, 2019. "Bayesian MIDAS penalized regressions: estimation, selection, and prediction," Working papers 713, Banque de France.
More about this item
Keywords
Identification; Factor interpretation; Sparsity; Sparse PCA; Factor rotation;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:hal:wpaper:halshs-03626503. 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: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.