IDEAS home Printed from https://ideas.repec.org/r/anr/reveco/v8y2016p53-80.html
   My bibliography  Save this item

Econometric Analysis of Large Factor Models

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Maldonado, Javier & Ruiz Ortega, Esther, 2017. "Accurate Subsampling Intervals of Principal Components Factors," DES - Working Papers. Statistics and Econometrics. WS 23974, Universidad Carlos III de Madrid. Departamento de Estadística.
  2. Ferrari, Davide & Ravazzolo, Francesco & Vespignani, Joaquin, 2021. "Forecasting energy commodity prices: A large global dataset sparse approach," Energy Economics, Elsevier, vol. 98(C).
  3. Carlos Trucíos & João H. G. Mazzeu & Marc Hallin & Luiz K. Hotta & Pedro L. Valls Pereira & Mauricio Zevallos, 2022. "Forecasting Conditional Covariance Matrices in High-Dimensional Time Series: A General Dynamic Factor Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(1), pages 40-52, December.
  4. Milda Norkuté & Vasilis Sarafidis & Takashi Yamagata, 2018. "Instrumental Variable Estimation of Dynamic Linear Panel Data Models with Defactored Regressors and a Multifactor Error Structure," ISER Discussion Paper 1019, Institute of Social and Economic Research, Osaka University.
  5. Chen, Mingli & Fernández-Val, Iván & Weidner, Martin, 2021. "Nonlinear factor models for network and panel data," Journal of Econometrics, Elsevier, vol. 220(2), pages 296-324.
  6. 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.
  7. Carlos Cesar Trucios-Maza & João H. G Mazzeu & Luis K. Hotta & Pedro L. Valls Pereira & Marc Hallin, 2019. "On the robustness of the general dynamic factor model with infinite-dimensional space: identification, estimation, and forecasting," Working Papers ECARES 2019-32, ULB -- Universite Libre de Bruxelles.
  8. Jad Beyhum & Eric Gautier, 2020. "Factor and factor loading augmented estimators for panel regression," Working Papers hal-02957008, HAL.
  9. Francisco Corona & Pilar Poncela & Esther Ruiz, 2020. "Estimating Non-stationary Common Factors: Implications for Risk Sharing," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 37-60, January.
  10. Ivan Fernandez-Val & Martin Weidner, 2017. "Fixed effect estimation of large T panel data models," CeMMAP working papers 42/17, Institute for Fiscal Studies.
  11. Iván Fernández-Val & Martin Weidner, 2018. "Fixed Effects Estimation of Large-TPanel Data Models," Annual Review of Economics, Annual Reviews, vol. 10(1), pages 109-138, August.
  12. Christian Glocker & Serguei Kaniovski, 2022. "Macroeconometric forecasting using a cluster of dynamic factor models," Empirical Economics, Springer, vol. 63(1), pages 43-91, July.
  13. Vasilis Sarafidis & Tom Wansbeek, 2020. "Celebrating 40 Years of Panel Data Analysis: Past, Present and Future," Monash Econometrics and Business Statistics Working Papers 6/20, Monash University, Department of Econometrics and Business Statistics.
  14. Norkutė, Milda & Sarafidis, Vasilis & Yamagata, Takashi & Cui, Guowei, 2021. "Instrumental variable estimation of dynamic linear panel data models with defactored regressors and a multifactor error structure," Journal of Econometrics, Elsevier, vol. 220(2), pages 416-446.
  15. Thomaidis, Nikolaos S. & Christodoulou, Theodoros & Santos-Alamillos, Francisco J., 2023. "Handling the risk dimensions of wind energy generation," Applied Energy, Elsevier, vol. 339(C).
  16. Anil Özdemir & Helmut Dietl & Giambattista Rossi & Robert Simmons, 2020. "Are Workers Rewarded for Inconsistent Performance?," Working Papers 386, University of Zurich, Department of Business Administration (IBW).
  17. Vidal-Llana, Xenxo & Uribe, Jorge M. & Guillén, Montserrat, 2023. "European stock market volatility connectedness: The role of country and sector membership," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 82(C).
  18. Sokbae Lee & Yuan Liao & Myung Hwan Seo & Youngki Shin, 2018. "Factor-Driven Two-Regime Regression," Department of Economics Working Papers 2018-14, McMaster University.
  19. Steven Campbell & Ting-Kam Leonard Wong, 2022. "Efficient Convex PCA with applications to Wasserstein geodesic PCA and ranked data," Papers 2211.02990, arXiv.org, revised Aug 2023.
  20. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers halshs-02262202, HAL.
  21. Yuki Takara & Shingo Takagi, 2023. "An empirical approach to measure unobserved cultural relations using music trade data," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 47(2), pages 205-245, June.
  22. Wang,Dieter, 2021. "Natural Capital and Sovereign Bonds," Policy Research Working Paper Series 9606, The World Bank.
  23. Hugo Freeman & Martin Weidner, 2021. "Linear panel regressions with two-way unobserved heterogeneity," CeMMAP working papers CWP39/21, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  24. Ekvall, Karl Oskar, 2022. "Targeted principal components regression," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
  25. Philipp Gersing & Christoph Rust & Manfred Deistler, 2023. "Weak Factors are Everywhere," Papers 2307.10067, arXiv.org, revised Jan 2024.
  26. Xiao Huang, 2023. "Composite Quantile Factor Models," Papers 2308.02450, arXiv.org.
  27. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," PSE Working Papers halshs-02262202, HAL.
  28. Jianqing Fan & Kunpeng Li & Yuan Liao, 2020. "Recent Developments on Factor Models and its Applications in Econometric Learning," Papers 2009.10103, arXiv.org.
  29. Daniel Wochner, 2020. "Dynamic Factor Trees and Forests – A Theory-led Machine Learning Framework for Non-Linear and State-Dependent Short-Term U.S. GDP Growth Predictions," KOF Working papers 20-472, KOF Swiss Economic Institute, ETH Zurich.
  30. Trucíos, Carlos & Mazzeu, João H.G. & Hotta, Luiz K. & Valls Pereira, Pedro L. & Hallin, Marc, 2021. "Robustness and the general dynamic factor model with infinite-dimensional space: Identification, estimation, and forecasting," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1520-1534.
  31. He, Yong & Kong, Xinbing & Trapani, Lorenzo & Yu, Long, 2023. "One-way or two-way factor model for matrix sequences?," Journal of Econometrics, Elsevier, vol. 235(2), pages 1981-2004.
  32. Francisco Corona & Graciela Gonz'alez-Far'ias & Jes'us L'opez-P'erez, 2021. "A nowcasting approach to generate timely estimates of Mexican economic activity: An application to the period of COVID-19," Papers 2101.10383, arXiv.org.
  33. Jia Chen Author-Name-First: Jia & Yongcheol Shin & Chaowen Zheng, 2023. "Dynamic Quantile Panel Data Models with Interactive Effects," Economics Discussion Papers em-dp2023-06, Department of Economics, University of Reading.
  34. Matthew Harding & Carlos Lamarche & Chris Muris, 2022. "Estimation of a Factor-Augmented Linear Model with Applications Using Student Achievement Data," Papers 2203.03051, arXiv.org.
  35. Li, Yan & Gao, Zhigen & Huang, Wei & Guo, Jianhua, 2023. "Matrix-variate data analysis by two-way factor model with replicated observations," Statistics & Probability Letters, Elsevier, vol. 202(C).
  36. Lukoianove, Tatiana & Agarwal, James & Osiyevskyy, Oleksiy, 2022. "Modeling a country's political environment using dynamic factor analysis (DFA): A new methodology for IB research," Journal of World Business, Elsevier, vol. 57(5).
  37. Hugo Freeman & Martin Weidner, 2021. "Linear Panel Regressions with Two-Way Unobserved Heterogeneity," Papers 2109.11911, arXiv.org, revised Aug 2022.
  38. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers 2019-4, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
  39. Dimitar EFTIMOSKI, 2019. "Improving Short-Term Forecasting of Macedonian GDP: Comparing the Factor Model with the Macroeconomic Structural Equation Model," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 32-53, June.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.