Bibliografía

[Ame81]

Takeshi Amemiya. Qualitative response models: a survey. Journal of economic literature, 19(4):1483–1536, 1981.

[AP09]

Joshua D Angrist and Jörn-Steffen Pischke. Mostly harmless econometrics: An empiricist's companion. Princeton university press, 2009.

[AP14]

Joshua D Angrist and Jörn-Steffen Pischke. Mastering'metrics: The path from cause to effect. Princeton university press, 2014.

[B+09]

Christopher F Baum and others. An introduction to Stata programming. Volume 2. Stata Press College Station, 2009.

[CT05]

A Colin Cameron and Pravin K Trivedi. Microeconometrics: methods and applications. Cambridge university press, 2005. doi:https://doi.org/10.1017/CBO9780511811241.

[Cun21]

Scott Cunningham. Causal inference: The mixtape. Yale university press, 2021.

[DM04]

Russell Davidson and James MacKinnon. Econometric theory and methods. Oxford University Press New York, 2004. ISBN 0-19-512372-7. URL: http://qed.econ.queensu.ca/ETM/.

[Elw13]

Felix Elwert. Graphical causal models. In Handbook of causal analysis for social research, pages 245–273. Springer, 2013.

[GPS06]

William Gould, Jeffrey Pitblado, and William Sribney. Maximum likelihood estimation with Stata. Stata press, 2006.

[Ham94]

James D. Hamilton. Time series analysis. Princeton university press, 1994. ISBN 9780691042893. URL: https://press.princeton.edu/books/hardcover/9780691042893/time-series-analysis.

[Han22]

Bruce Hansen. Econometric. Princeton University Press, 2022. ISBN 9780691235899. URL: https://www.ssc.wisc.edu/~bhansen/econometrics/.

[Han82]

Lars Peter Hansen. Large sample properties of generalized method of moments estimators. Econometrica: Journal of the econometric society, pages 1029–1054, 1982. doi:https://doi.org/10.2307/1912775.

[HK21]

Nick Huntington-Klein. The effect: An introduction to research design and causality. CRC Press, 2021.

[IR15]

Guido W Imbens and Donald B Rubin. Causal inference in statistics, social, and biomedical sciences. Cambridge University Press, 2015.

[McE18]

Richard McElreath. Statistical rethinking: A Bayesian course with examples in R and Stan. Chapman and Hall/CRC, 2018.

[Julia21]

Julia. The Julia programming language. MIT, 2021. URL: https://julialang.org/.

[RCTeam24]

R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2024. URL: https://www.R-project.org/.

[StataCorp23]

StataCorp. Stata Statistical Software: Release 16. College Station, TX: StataCorp LLC, 2023. URL: https://www.stata.com/.