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CaseBasedReasoning - Case Based Reasoning

Case-based reasoning is a problem-solving methodology that involves solving a new problem by referring to the solution of a similar problem in a large set of previously solved problems. The key aspect of Case Based Reasoning is to determine the problem that "most closely" matches the new problem at hand. This is achieved by defining a family of distance functions and using these distance functions as parameters for local averaging regression estimates of the final result. The optimal distance function is chosen based on a specific error measure used in regression estimation. This approach allows for efficient problem-solving by leveraging past experiences and adapting solutions from similar cases. The underlying concept is inspired by the work of Dippon J. et al. (2002) <doi:10.1016/S0167-9473(02)00058-0>.

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case-based-reasoningcox-regressiondistance-matrixmachine-learningnearest-neighborsrandom-forestsimilaritycpp

5.23 score 2 stars 14 scripts 461 downloads

EventStudy - Event Study Analysis

Financial event study analysis. Implements the classical event study methodology of MacKinlay (1997) <https://www.jstor.org/stable/2729691> with multiple return models: market model, market adjusted, comparison period mean adjusted, Fama and French three-factor (1993) <doi:10.1016/0304-405X(93)90023-5> and five-factor (2015) <doi:10.1016/j.jfineco.2014.10.010>, Carhart (1997) four-factor <doi:10.1111/j.1540-6261.1997.tb03808.x>, generalized autoregressive conditional heteroskedasticity (GARCH; Bollerslev, 1986) <doi:10.1016/0304-4076(86)90063-1>, rolling window, and dynamic conditional correlation GARCH (DCC-GARCH; Engle, 2002) <doi:10.1198/073500102288618487>. Provides parametric and non-parametric test statistics, including the standardized cross-sectional test of Boehmer, Musumeci and Poulsen (1991) <doi:10.1016/0304-405X(91)90032-F> and its cross-correlation adjustment by Kolari and Pynnonen (2010) <doi:10.1093/rfs/hhq072>; buy-and-hold abnormal returns (Barber and Lyon, 1997) <doi:10.1016/S0304-405X(96)00890-2>; volume and volatility event studies; cross-sectional regression; intraday event studies; panel difference-in-differences event studies with the estimators of Callaway and Sant'Anna (2021) <doi:10.1016/j.jeconom.2020.12.001>, de Chaisemartin and D'Haultfoeuille (2020) <doi:10.1257/aer.20181169>, and Borusyak, Jaravel and Spiess (2024) <doi:10.1093/restud/rdae007>; synthetic control methods (Abadie, Diamond and Hainmueller, 2010) <doi:10.1198/jasa.2009.ap08746>; wild bootstrap inference; multiple testing corrections; power analysis via simulation; data download helpers; automated reporting; and visualization tools. Results can be exported to comma-separated values (CSV), 'Excel', or 'LaTeX'.

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4.98 score 4 stars 119 scripts 127 downloads