El problema de la reducción dimensional. Análisis de Componentes Principales (PCA)

Resumen

En este trabajo de investigación se presenta la técnica de Principal Component Analysis (PCA), y su aplicación práctica al aprendizaje automático (machine learning). La intención es abordar la problemática de la reducción dimensional o compresión de datos. A partir de un análisis intuitivo, se espera acercar a los economistas y otros profesionales de las ciencias sociales estas ideas que, generalmente, resultan ajenas a sus discusiones.

Palabras clave:

análisis de componentes principales aprendizaje no supervisado reducción dimensional ingeniería

Citas

Athey-Imbens. (2019).Susan Athey and GuidoW. Imbens, “Machine Learning Methods That Economists Should Know About”, Annual Review of Economics 2019 11:1, 685-725. https://www.annualreviews.org/doi/abs/10.1146/annurev-economics-080217-053433.

Bai-Ng, Bai, J. and Ng, S. (2004). “A PANIC Attack on Unit Roots and Cointegration”. Econometrica, 72: 1127-1177. https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1468-0262.2004.00528.x.

Bai-Ng. (2007). Bai, Jushan, and Serena Ng. “Determining the Number of Primitive Shocks in Factor Models.” Journal of Business & Economic Statistics 25, no. 1 (2007): 52–60. http://www.jstor.org/stable/27638906.

Bai-Ng. (2017). Bai, J. and Ng, S.. “Principal components and regularized estimation of factor models”, https://arxiv.org/abs/1708.08137.

Bates, B. J., M. PLAGBORG-MoLLER-Stock-Watson, J. H. Stock, AND M.W.Watson. (2013). “Consistent Factor Estimation in Dynamic Factor Models with Structural Instability,” Journal of Econometrics, V. 177 (2), p. 289-304, 2013. https://www.
sciencedirect.com/science/article/abs/pii/S0304407613000912?via%3Dihub.

Ben S. Bernanke, Jean Boivin, Piotr Eliasz. (2005). “Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach”, The Quarterly Journal of Economics, Volume 120, Issue 1, February 2005, Pages 387–422. https://doi.org/10.1162/0033553053327452

Cheng, X, Z. Liao, F. Schorfheide. (2016). “Shrinkage Estimation of High-
Dimensional Factor Models with Structural Instabilities”, The Review of Economic Studies, Volume
83, Issue 4, October 2016, Pages 1511–1543, https://doi.org/10.1093/restud/rdw005.

Chen, L., Dolado, J.J. and Gonzalo, J. (2021). “Quantile Factor Models”. Econometrica, 89: 875-910. https://doi.org/10.3982/ECTA15746.

Lettau, M., Markus Pelger. (2020). “Estimating latent asset-pricing factors”, Journal of Econometrics, ISSN: 0304-4076, Vol: 218, Issue: 1, Page: 1-31, 2020. https://doi.org/10.1016/j.jeconom.2019.08.012.

Mol, C., Giannone, D., Reichlin, L. (2007). “Forecasting Using a Large Number of Predictors: Is Bayesian Regression a Valid Alternative to Principal Components?”. Journal of Econometrics, Volume 146, Issue 2, Pages 318-328. https://www.sciencedirect.com/science/article/abs/pii/S0304407608001103?via%3Dihub.

Stock, J. H., and M. W. Watson. (2002). “Forecasting Using Principal Components from a Large Number of Predictors.” Journal of the American Statistical Association 97, no. 460 (2002): 1167–79. http://www.jstor.org/stable/3085839.

Stock, J. H., and M. W. Watson. (2012). “Disentangling the Channels of the 2007-09 Recession,” Brookings Papers on Economic Activity, pp. 81-156. https://www.brookings.edu/bpea-articles/disentangling-the-channels-of-the-2007-2009-recession/20

Vyas, S., L. Kumaranayake. (2006). “Constructing socio-economic status indices: how to use principal components analysis”, Health Policy and Planning, Volume 21, Issue 6, November 2006, Pages 459–468. https://doi.org/10.1093/heapol/czl029.

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