Large Dimensional Latent Factor Modeling with Missing Observations and Applications to Causal Inference
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- Kelly, Bryan T. & Pruitt, Seth & Su, Yinan, 2019.
"Characteristics are covariances: A unified model of risk and return,"
Journal of Financial Economics, Elsevier, vol. 134(3), pages 501-524.
- Bryan Kelly & Seth Pruitt & Yinan Su, 2018. "Characteristics Are Covariances: A Unified Model of Risk and Return," NBER Working Papers 24540, National Bureau of Economic Research, Inc.
- Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008.
"Nowcasting: The real-time informational content of macroeconomic data,"
Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
- Domenico Giannone & Lucrezia Reichlin & David H. Small, 2005. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Finance and Economics Discussion Series 2005-42, Board of Governors of the Federal Reserve System (U.S.).
- Domenico Giannone & Lucrezia Reichlin & David H Small, 2007. "Nowcasting GDP and Inflation: The Real-Time Informational Content of Macroeconomic Data Releases," Money Macro and Finance (MMF) Research Group Conference 2006 164, Money Macro and Finance Research Group.
- Reichlin, Lucrezia & Giannone, Domenico & Small, David, 2005. "Nowcasting GDP and Inflation: The Real Time Informational Content of Macroeconomic Data Releases," CEPR Discussion Papers 5178, C.E.P.R. Discussion Papers.
- 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.
- Gagliardini, Patrick & Ossola, Elisa & Scaillet, Olivier, 2019.
"A diagnostic criterion for approximate factor structure,"
Journal of Econometrics, Elsevier, vol. 212(2), pages 503-521.
- Patrick Gagliardini & Elisa Ossola & O. Scaillet, 2016. "A Diagnostic Criterion for Approximate Factor Structure," Swiss Finance Institute Research Paper Series 16-51, Swiss Finance Institute, revised Dec 2016.
- Patrick Gagliardini & Elisa Ossola & Olivier Scaillet, 2016. "A diagnostic criterion for approximate factor structure," Papers 1612.04990, arXiv.org, revised Aug 2017.
- Kozak, Serhiy & Nagel, Stefan & Santosh, Shrihari, 2020.
"Shrinking the cross-section,"
Journal of Financial Economics, Elsevier, vol. 135(2), pages 271-292.
- Serhiy Kozak & Stefan Nagel & Shrihari Santosh, 2017. "Shrinking the Cross Section," NBER Working Papers 24070, National Bureau of Economic Research, Inc.
- Nagel, Stefan & Santosh, Shrihari & Kozak, Serhiy, 2017. "Shrinking the Cross Section," CEPR Discussion Papers 12463, C.E.P.R. Discussion Papers.
- R. David Mclean & Jeffrey Pontiff, 2016. "Does Academic Research Destroy Stock Return Predictability?," Journal of Finance, American Finance Association, vol. 71(1), pages 5-32, February.
- 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.
- Markus Pelger & Ruoxuan Xiong, 2022.
"State-Varying Factor Models of Large Dimensions,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1315-1333, June.
- Markus Pelger & Ruoxuan Xiong, 2018. "State-Varying Factor Models of Large Dimensions," Papers 1807.02248, arXiv.org, revised Oct 2020.
- Susan Athey & Mohsen Bayati & Nikolay Doudchenko & Guido Imbens & Khashayar Khosravi, 2021.
"Matrix Completion Methods for Causal Panel Data Models,"
Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1716-1730, October.
- Susan Athey & Mohsen Bayati & Nikolay Doudchenko & Guido Imbens & Khashayar Khosravi, 2017. "Matrix Completion Methods for Causal Panel Data Models," Papers 1710.10251, arXiv.org, revised Apr 2022.
- Susan Athey & Mohsen Bayati & Nikolay Doudchenko & Guido Imbens & Khashayar Khosravi, 2018. "Matrix Completion Methods for Causal Panel Data Models," NBER Working Papers 25132, National Bureau of Economic Research, Inc.
- Schwert, G. William, 2003.
"Anomalies and market efficiency,"
Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, edition 1, volume 1, chapter 15, pages 939-974,
Elsevier.
- G. William Schwert, 2002. "Anomalies and Market Efficiency," NBER Working Papers 9277, National Bureau of Economic Research, Inc.
- Pelger, Markus, 2019. "Large-dimensional factor modeling based on high-frequency observations," Journal of Econometrics, Elsevier, vol. 208(1), pages 23-42.
- Martin Lettau & Markus Pelger & Stijn Van Nieuwerburgh, 2020.
"Factors That Fit the Time Series and Cross-Section of Stock Returns,"
The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2274-2325.
- Lettau, Martin & Pelger, Markus, 2018. "Factors that Fit the Time Series and Cross-Section of Stock Returns," CEPR Discussion Papers 13049, C.E.P.R. Discussion Papers.
- Martin Lettau & Markus Pelger, 2018. "Factors that Fit the Time Series and Cross-Section of Stock Returns," NBER Working Papers 24858, National Bureau of Economic Research, Inc.
- Athey, Susan & Imbens, Guido W., 2022.
"Design-based analysis in Difference-In-Differences settings with staggered adoption,"
Journal of Econometrics, Elsevier, vol. 226(1), pages 62-79.
- Athey, Susan & Imbens, Guido W., 2018. "Design-based Analysis in Difference-In-Differences Settings with Staggered Adoption," Research Papers 3712, Stanford University, Graduate School of Business.
- Susan Athey & Guido Imbens, 2018. "Design-based Analysis in Difference-In-Differences Settings with Staggered Adoption," Papers 1808.05293, arXiv.org, revised Sep 2018.
- Susan Athey & Guido W. Imbens, 2018. "Design-based Analysis in Difference-In-Differences Settings with Staggered Adoption," NBER Working Papers 24963, National Bureau of Economic Research, Inc.
- Andrew Y Chen & Tom Zimmermann & Jeffrey Pontiff, 2020.
"Publication Bias and the Cross-Section of Stock Returns,"
The Review of Asset Pricing Studies, Society for Financial Studies, vol. 10(2), pages 249-289.
- Andrew Y. Chen & Tom Zimmermann, 2018. "Publication Bias and the Cross-Section of Stock Returns," Finance and Economics Discussion Series 2018-033, Board of Governors of the Federal Reserve System (U.S.).
- Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003.
"Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score,"
Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, July.
- Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2000. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," NBER Technical Working Papers 0251, National Bureau of Economic Research, Inc.
- Guido Imbens, 2000. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometric Society World Congress 2000 Contributed Papers 1166, Econometric Society.
- Li, Kathleen T. & Bell, David R., 2017. "Estimation of average treatment effects with panel data: Asymptotic theory and implementation," Journal of Econometrics, Elsevier, vol. 197(1), pages 65-75.
- Jushan Bai & Serena Ng, 2021.
"Matrix Completion, Counterfactuals, and Factor Analysis of Missing Data,"
Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1746-1763, October.
- Jushan Bai & Serena Ng, 2019. "Matrix Completion, Counterfactuals, and Factor Analysis of Missing Data," Papers 1910.06677, arXiv.org, revised Aug 2021.
- Alberto Abadie & Alexis Diamond & Jens Hainmueller, 2015. "Comparative Politics and the Synthetic Control Method," American Journal of Political Science, John Wiley & Sons, vol. 59(2), pages 495-510, February.
- Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
- Jungbacker, B. & Koopman, S.J. & van der Wel, M., 2011.
"Maximum likelihood estimation for dynamic factor models with missing data,"
Journal of Economic Dynamics and Control, Elsevier, vol. 35(8), pages 1358-1368, August.
- B. Jungbacker & S.J. Koopman & M. van Der Wel, 2011. "Maximum likelihood estimation for dynamic factor models with missing data," Post-Print hal-00828980, HAL.
- repec:hal:journl:peer-00844811 is not listed on IDEAS
- Nikolay Doudchenko & Guido W. Imbens, 2016. "Balancing, Regression, Difference-In-Differences and Synthetic Control Methods: A Synthesis," NBER Working Papers 22791, National Bureau of Economic Research, Inc.
- Cheng Hsiao & H. Steve Ching & Shui Ki Wan, 2012. "A Panel Data Approach For Program Evaluation: Measuring The Benefits Of Political And Economic Integration Of Hong Kong With Mainland China," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(5), pages 705-740, August.
- Carvalho, Carlos & Masini, Ricardo & Medeiros, Marcelo C., 2018.
"ArCo: An artificial counterfactual approach for high-dimensional panel time-series data,"
Journal of Econometrics, Elsevier, vol. 207(2), pages 352-380.
- Carlos Viana de Carvalho & Ricardo Masini & Marcelo Cunha Medeiros, 2016. "ARCO: an artificial counterfactual approach for high-dimensional panel time-series data," Textos para discussão 653, Department of Economics PUC-Rio (Brazil).
- Carvalho, Carlos Viana de & Masini, Ricardo Pereira & Medeiros, Marcelo C., 2017. "Arco: an artificial counterfactual approach for high-dimensional panel time-series data," Textos para discussão 454, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
- Marta Bańbura & Michele Modugno, 2014.
"Maximum Likelihood Estimation Of Factor Models On Datasets With Arbitrary Pattern Of Missing Data,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(1), pages 133-160, January.
- Bańbura, Marta & Modugno, Michele, 2010. "Maximum likelihood estimation of factor models on data sets with arbitrary pattern of missing data," Working Paper Series 1189, European Central Bank.
- Jin, Sainan & Miao, Ke & Su, Liangjun, 2021.
"On factor models with random missing: EM estimation, inference, and cross validation,"
Journal of Econometrics, Elsevier, vol. 222(1), pages 745-777.
- Su, Liangjun & Miao, Ke & Jin, Sainan, 2019. "On Factor Models with Random Missing: EM Estimation, Inference, and Cross Validation," Economics and Statistics Working Papers 4-2019, Singapore Management University, School of Economics.
- Jushan Bai, 2009. "Panel Data Models With Interactive Fixed Effects," Econometrica, Econometric Society, vol. 77(4), pages 1229-1279, July.
- Domenico Giannone & Lucrezia Reichlin & David Small, 2008. "Nowcasting: the real time informational content of macroeconomic data releases," ULB Institutional Repository 2013/6409, ULB -- Universite Libre de Bruxelles.
- Jinyong Hahn, 1998. "On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects," Econometrica, Econometric Society, vol. 66(2), pages 315-332, March.
- Abadie, Alberto & Diamond, Alexis & Hainmueller, Jens, 2010. "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 493-505.
- 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.
- Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
- Domenico Giannone & Lucrezia Reichlin & David H. Small, 2005.
"Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases,"
Finance and Economics Discussion Series
2005-42, Board of Governors of the Federal Reserve System (U.S.).
- Giannone, Domenico & Reichlin, Lucrezia & Small, David H., 2006. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Working Paper Series 633, European Central Bank.
- Domenico Giannone & Lucrezia Reichlin & David H Small, 2007. "Nowcasting GDP and Inflation: The Real-Time Informational Content of Macroeconomic Data Releases," Money Macro and Finance (MMF) Research Group Conference 2006 164, Money Macro and Finance Research Group.
- Reichlin, Lucrezia & Giannone, Domenico & Small, David, 2005. "Nowcasting GDP and Inflation: The Real Time Informational Content of Macroeconomic Data Releases," CEPR Discussion Papers 5178, C.E.P.R. Discussion Papers.
- Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
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"Low-rank approximations of nonseparable panel models,"
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- Ivan Fernandez-Val & Hugo Freeman & Martin Weidner, 2020. "Low-rank approximations of nonseparable panel models," CeMMAP working papers CWP52/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Ivan Fernandez-Val & Hugo Freeman & Martin Weidner, 2021. "Low-rank approximations of nonseparable panel models," CeMMAP working papers CWP10/21, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Cahan, Ercument & Bai, Jushan & Ng, Serena, 2023.
"Factor-based imputation of missing values and covariances in panel data of large dimensions,"
Journal of Econometrics, Elsevier, vol. 233(1), pages 113-131.
- Ercument Cahan & Jushan Bai & Serena Ng, 2021. "Factor-Based Imputation of Missing Values and Covariances in Panel Data of Large Dimensions," Papers 2103.03045, arXiv.org, revised Feb 2022.
- Guido Imbens & Nathan Kallus & Xiaojie Mao, 2021. "Controlling for Unmeasured Confounding in Panel Data Using Minimal Bridge Functions: From Two-Way Fixed Effects to Factor Models," Papers 2108.03849, arXiv.org.
- Liu, Wei & Luo, Lan & Zhou, Ling, 2023. "Online missing value imputation for high-dimensional mixed-type data via generalized factor models," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
- Yinchu Zhu, 2019. "How well can we learn large factor models without assuming strong factors?," Papers 1910.10382, arXiv.org, revised Nov 2019.
- Jianqing Fan & Kunpeng Li & Yuan Liao, 2020. "Recent Developments on Factor Models and its Applications in Econometric Learning," Papers 2009.10103, arXiv.org.
- Vivek F. Farias & Andrew A. Li & Tianyi Peng, 2021. "Learning Treatment Effects in Panels with General Intervention Patterns," Papers 2106.02780, arXiv.org, revised Mar 2023.
- Iv'an Fern'andez-Val & Hugo Freeman & Martin Weidner, 2020. "Low-Rank Approximations of Nonseparable Panel Models," Papers 2010.12439, arXiv.org, revised Mar 2021.
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