Referências:
1. Revisão MQO matricial, variáveis instrumentais e efeitos de tratamento exógeno
Revisão álgebra linear
Livro online: https://web.stanford.edu/~boyd/vmls/
playlist impa: https://www.youtube.com/playlist?list=PLo4jXE-LdDTSE0DFoq4es_iMvjlCeG8pP
Playlist ime: https://www.youtube.com/watch?v=-JcQJFNVjaA&list=PLIEzh1OveCVczEZAjhVIVd7Qs-X8ILgnI&index=1
Revisão ime: https://www.ime.usp.br/~jeancb/A01MAT0074.pdf
Econometria matricial
Goldberger,A. A Course in Econometrics. Harvard Univ. Press; July 1991.
Greene, W. H. Econometric Analysis, 7th Edion, Prence Hall, 2011.
Hayashi, F. Econometrics, Princeton University Press, 2000
Davidson, R. e J. G. Mackinon, Econometric Theory and Methods, Oxford University Press,2004.
Jonhston, J. e J. DiNARDO, Econometric Methods, 4th Edition, McGraw Hill, 1997.
PINDYCK, Robert S.; RUBINFELD, Daniel L. Econometric models and economic forecasts. 4. ed. Boston: Irwin/McGraw-Hill, 1998.
IV com muitos instrumentos
Imbens, G. W., & Wooldridge, J. M. (2007, August). Weak instruments and many instruments [What’s New in Econometrics, Lecture Notes 13]. National Bureau of Economic Research.
https://users.nber.org/~confer/2007/si2007/WNE/lect_13_weakmany_iv.pdf
2. Modelos de dados em Painel
Efeitos fixos e aleatórios
Wooldridge, J. M. Econometric Analysis of Cross-Section and Panel Data, The MIT Press, 2002.
Cameron, A. C., & Trivedi, P. K. (2005). Microeconometrics: methods and applications. Cambridge University Press.
Painel dinâmico
Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277–297. https://doi.org/10.2307/2297968
Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277–297. https://doi.org/10.2307/2297968
Bond, S. R. (2002). Dynamic panel data models: A guide to micro data methods and practice. Portuguese Economic Journal, 1(2), 141–162. https://doi.org/10.1007/s10258-002-0009-9
Hausman, J. A., & Taylor, W. E. (1981). Panel data and unobservable individual effects. Econometrica, 49(6), 1377–1398. https://doi.org/10.2307/1911406
3. Modelos não lineares para variáveis qualitativas e variáveis censuradas
Heckman, James J. — “Sample Selection Bias as a Specification Error.” Econometrica, 1979.
4. Métodos não-paramétricos
Pagan, A., & Ullah, A. (1999). Nonparametric econometrics. Cambridge University Press.
Horowitz, J. L. (2001). The bootstrap (Chapter 5). In J. J. Heckman & E. E. Leamer (Eds.), Handbook of Econometrics (Vol. 5, pp. 3159–3228). Elsevier.
LI, Q.; RACINE, J. S. Nonparametric econometrics: theory and practice. Princeton: Princeton University Press, 2007.
5. Tópicos adicionais
Machine learning
James, G.; Witten, D.; Hastie, T.; Tibshirani, R. An Introduction to Statistical Learning with Applications in R. Editora Springer. 2013. https://www.statlearning.com/
Mullainathan, S., & Spiess, J. (2017). Machine learning: An applied econometric approach. Journal of Economic Perspectives, 31(2), 87–106. https://doi.org/10.1257/jep.31.2.87
Belloni, A., Chernozhukov, V., & Hansen, C. (2014). High-dimensional methods and inference on structural and treatment effects. Journal of Economic Perspectives, 28(2), 29–50. https://doi.org/10.1257/jep.28.2.29
Korinek, A. (2024). Generative AI for economic research: LLMs learn to collaborate and reason (NBER Working Paper No. 33198). National Bureau of Economic Research. https://doi.org/10.3386/w33198
Ferrara, A. (2026). A practitioner’s guide to using large language models in economic history (NBER Working Paper No. 35374). National Bureau of Economic Research. https://doi.org/10.3386/w35374
Athey, S., & Imbens, G. W. (2019). Machine learning methods that economists should know about. Annual Review of Economics, 11, 685–725. https://doi.org/10.1146/annurev-economics-080217-053433
Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1–C68. https://doi.org/10.1111/ectj.12097
CHARPENTIER, Arthur; ELIE, Romuald; REMLINGER, Carl. Reinforcement learning in economics and finance. Computational Economics, p. 1-38, 2020 https://arxiv.org/pdf/2003.10014
Machine learning em saúde: vídeo-aulas USP
Métodos bayesianos
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian data analysis (3rd ed.). CRC Press.
Koop, G. (2003). Bayesian econometrics. Wiley.
Dados de contagem e análise de sobrevivência
Cameron, A. C., & Trivedi, P. K. (2013). Regression analysis of count data (2nd ed.). Cambridge University Press.
Lancaster, T. (1990). The econometric analysis of transition data. Cambridge University Press.
Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–220.
Regressores gerados
Pagan, A. (1984). Econometric issues in the analysis of regressions with generated regressors. International Economic Review, 25(1), 221–247.
Murphy, K. M., & Topel, R. H. (1985). Estimation and inference in two-step econometric models. Journal of Business & Economic Statistics, 3(4), 370–379.
Missing data
Little, R. J. A., & Rubin, D. B. (2019). Statistical analysis with missing data (3rd ed.). Wiley.
Van Buuren, S. (2018). Flexible imputation of missing data (2nd ed.). CRC Press.
Wooldridge, J. M. (2007, August 1). 2007 methods lecture: Missing data [Video]. National Bureau of Economic Research.
https://www.nber.org/research/videos/2007-methods-lecture-jeffrey-wooldridge-missing-data
Wooldridge, J. M. (2008, August). A course in applied econometrics: Lecture 18: Missing data [Lecture slides]. Institute for Research on Poverty, University of Wisconsin–Madison:
https://www.irp.wisc.edu/newsevents/workshops/appliedmicroeconometrics/participants/slides/Slides_18.pdf
Links diversos:
https://www.refine.ink/
high frequency data in macroeconomics: https://sites.socsci.uci.edu/~swanson2/papers/are.pdf
https://www.rodrigopinto.net/_files/ugd/95d94d_1432e11a1469451ba9bac50df89e16e5.pdf
https://blog.oup.com/2025/07/why-economists-should-learn-machine-learning/