References on Econometrics and Machine Learning

In our series of posts on the history and foundations of econometric and machine learning models, a lot of references where given. Here they are.

Ahamada, I. & E. Flachaire (2011). Non-Parametric Econometrics. Oxford University Press.

Aigner, D., Lovell, C.A.J & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6, 21–37.

Aldrich, J. (2010). The Econometricians’ Statisticians, 1895-1945. History of Political Economy, 42 111–154.

Altman, E., Marco, G. & Varetto, F. (1994). Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience). Journal of Banking & Finance 18, 505–529.

Angrist, J.D. & Lavy, V. (1999). Using Maimonides’ Rule to Estimate the Effect of Class Size on Scholastic Achievement. Quarterly Journal of Economics, 114, 533–575.

Angrist, J.D. & Pischke, J.S. (2010). The Credibility Revolution in Empirical Economics: How Better Research Design Is Taking the Con out of Econometrics. Journal of Economic Perspective, 24, 3–30.

Angrist, J.D. & Pischke, J.S. (2015). Mastering Metrics. Princeton University Press.

Angrist, J.D. & Krueger, A.B. (1991). Does Compulsory School Attendance Affect Schooling and Earnings? Quarterly Journal of Economics, 106, 979–1014.

Bottou, L. (2010) Large-Scale Machine Learning with Stochastic Gradient Descent Proceedings of the 19th International Conference on Computational Statistics (COMPSTAT’2010), 177–187.

Bajari, P., Nekipelov, D., Ryan, S.P. & Yang, M. 2015. Machine learning methods for demand estimation. American Economic Review, 105 481–485.

Bazen, S. & K. Charni (2015). Do earnings really decline for older workers? AMSE 2015-11 Discussion Paper, Aix-Marseille University.

Bellman, R.E. (1957). Dynamic programming. Princeton University Press.

Belloni, A., Chernozhukov, V. & Hansen, C. (2010). Inference Methods for High-Dimensional Sparse Econometric Models. Advances in Economics and Econometrics, 245–295

Belloni, A., Chen, D., Chernozhukov, V. & Hansen, C. (2012). Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain. Econometrica, 80, 2369–2429.

Benjamini, Y. & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B, 57:289–300.

Berger, J.O. (1985). Statistical decision theory and Bayesian Analysis (2nd ed.). Springer-Verlag.

Berk, R.A. (2008). Statistical Learning from a Regression Perspective. Springer Verlag.

Berkson, J. (1944). Applications of the logistic function to bioassay. Journal of the American Statistical Association, 9, 357–365.

Berkson, J. (1951). Why I prefer logits to probits. Biometrics, 7 (4), 327–339.

Bernardo, J.M. & Smith, A.F.M. (2000). Bayesian Theory. John Wiley.

Berndt, E. R. (1990). The Practice of Econometrics: Classic and Contemporary. Addison Wesley.

Bickel, P.J., Gotze, F. & van Zwet, W. (1997). Resampling fewer than  observations: gains, losses and remedies for losses. Statistica Sinica, 7, 1-31.

Bishop, C. (2006). Pattern Recognition and Machine Learning. Springer Verlag.

Blanco, A. Pino-Mejias, M., Lara, J. & Rayo, S. (2013). Credit scoring models for the microfinance industry using neural networks: Evidence from peru. Expert Systems with Applications, 40, 356–364.

Bliss, C.I. (1934). The method of probits. Science, 79, 38–39.

Blumer, A., Ehrenfeucht, A., Haussler, D. & Warmuth, M.K. (1989). Learnability and the Vapnik-Chervonenkis dimension. Journal of the ACM, 36:4, 929–965.

Breiman, L. Fiedman, J., Olshen, R.A. & Stone, C.J. (1984). Classification And Regression Trees. Chapman & Hall/CRC.

Breiman, L. (1995). Better Subset Regression Using the Nonnegative Garrote. Technometrics, 37:4,  373–384.

Breiman, L. (2001a). Statistical Modeling: The Two Cultures. Statistical Science, 16:3, 199–231.

Breiman, L. (2001b). Random forests. Machine learning, 45:1, 5–32.

Brown, L.D. (1986) Fundamentals of statistical exponential families: with applications in statistical decision theory. Institute of Mathematical Statistics, Hayworth, CA, USA.

Bühlmann, P. & van de Geer, S. (2011). Statistics for High Dimensional Data: Methods, Theory and Applications. Springer Verlag.

Candès, E. & Plan, Y. (2009). Near-ideal model selection by  minimization. The Annals of Statistics, 37:5, 2145–2177.

Clarke, B.S., Fokoué, E. & Zhang, H.H. (2009). Principles and Theory for Data Mining and Machine Learning. Springer Verlag.

Cortes, C. & Vapnik, V. (1995). Support-vector networks. Machine Learning 20 273–297.

Cover, T.M. (1965). Geometrical and Statistical Properties of Systems of Linear Inequalities with Applications in Pattern Recognition. IEEE Transactions on Electronic Computers, 14:3, 326–334.

Cover, T.M. & Hart, P. (1965). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13:1, 21 – 27.

Cover, T.M. & Thomas, J. (1991). Elements of Information Theory. Wiley.

Cybenko, G. (1989). Approximation by Superpositions of a Sigmoidal Function. Mathematics of Control, Signals, and Systems, 2, 303–314.

Darmois, G. (1935). Sur les lois de probabilites a estimation exhaustive. Comptes Rendus de l’Académie des Sciences, Paris, 200 1265–1266.

Daubechies, I., Defrise, M. & De Mol, C. (2004). An iterative thresholding algorithm for linear inverse problems with sparsity constraint. Communications on Pure and Applied Mathematics, 57:11, 1413–1457

Davison, A.C. (1997). Bootstrap. Cambridge University Press.

Davidson, R. & MacKinnon, J.G. (1993). Estimation and Inference in Econometrics. Oxford University Press.

Davidson, R. & MacKinnon, J.G. (2003). Econometric Theory and Methods. Oxford University Press.

Duo, Q. (1993). The Formation of Econometrics. Oxford University Press.

Debreu, G. (1986). Theoretic Models: Mathematical Form and Economic Content. Econometrica, 54, 1259–1270.

Dhillon, P., Lu, Y. Foster, D.P. & Ungar, L.H. (2014). New Subsampling Algorithms for Fast Least Squares Regression. in Advances in Neural Information Processing Systems 26, Burges, Bottou, Welling, Ghahramani & Weinberger Eds., Curran Associates.

Efron, B. & Tibshirani, R. (1993). Bootstrap. Chapman Hall CRC.

Engel, E. (1857). Die Productions- und Consumtionsverhältnisse des Königreichs Sachsen. Statistisches Bureau des Königlich Sächsischen Ministeriums des Innern.

Feldstein, M. & Horioka, C. (1980). Domestic Saving and International Capital Flows. Economic Journal, 90, 314–329.

Flach, P. (2012). Machine Learning. Cambridge University Press.

Foster, D.P. & George, E.I. (1994). The Risk Inflation Criterion for Multiple Regression. The Annals of Statistics, 22:4, 1947–1975.

Friedman, J.H. (1997). Data Mining and Statistics: What’s the Connection. Proceedings of the 29th Symposium on the Interface Between Computer Science and Statistics.

Frisch, R. & Waugh, F.V. (1933). Partial Time Regressions as Compared with Individual Trends. Econometrica. 1, 387–401.

Gneiting, T. (2011). Making and Evaluating Point Forecasts. Journal of the American Statistical Association, 106, 746–762.

Givord, P. (2010). Méthodes économétriques pour l’évaluation de politiques publiques. INSEE Document de Travail, 08

Grandvalet, Y., Mariéthoz, J., & Bengio, S. 2005. A probabilistic interpretation of SVMs with an application to unbalanced classification. Advances in Neural Information Processing Systems 18.

Groves, T. & Rothenberg, T. (1969). A note on the expected value of an inverse matrix. Biometrika, 56:3, 690–691.

Haavelmo, T. (1944). The probability approach in econometrics, Econometrica, 12:iii-vi and 1–115.

Hastie, T. & Tibshirani, R. (1990). Generalized Additive Models. Chapman & Hall/CRC.

Hastie, T., Tibshirani, R. & Friedman, J. (2009). The Elements of Statistical Learning. Springer Verlag.

Hastie, T., Tibshirani, W. & Wainwright, M. (2015). Statistical Learning with Sparsity. Chapman CRC.

Hastie, T., Tibshiriani, R. & Tibshiriani, R.J. (2016). Extended comparisons of best subset selection, forward stepwise selection and the Lasso. ArXiV,  1707.08692.

d’Haultefœuille, X. & Givord, P. (2014) La régression quantile en pratique. économie & Statistiques, 471, 85–111.

Hebb, D.O. (1949). The organization of behavior, New York, Wiley.

Heckman, J.J. (1979). Sample selection bias as a specification error. Econometrica, 47, 153–161.

Heckman, J.J., Tobias, J.L. & Vytlacil, E. (2003). Simple Estimators for Treatment Parameters in a Latent-Variable Framework. The Review of Economics and Statistics, 85, 748–755.

Hendry, D F. & Krolzig, H.-M. (2001). Automatic Econometric Model Selection. Timberlake Press.

Herbrich, R., Keilbach, M., Graepel, T. Bollmann-Sdorra, P. & Obermayer, K. (1999). Neural Networks in Economics. in Computational Techniques for Modelling in Economics, T. Brenner Eds. Springer Verlag, 169–196.

Hoerl, A.E. (1962). Applications of ridge analysis to regression problems. Chemical Engineering Progress, 58:3, 54–59.

Hoerl, A.E. & Kennard, R.W. (1981). Ridge regression: biased estimation for nonorthogonal problems This Week’s Citation Classic, ISI.

Holland, P. (1986). Statistics and causal inference. Journal of the American Statistical Association, 81, 945–960.

Hyndman, R. , Koehler, A.B., Ord, J.K. & Snyder, R.D. (2009). Forecasting with Exponential Smoothing. Springer Verlag.

James, G., D. Witten, T. Hastie, & R. Tibshirani (2013). An introduction to Statistical Learning. Springer Series in Statistics.

Khashman, A. (2011). Credit risk evaluation using neural networks: Emotional versus conventional models. Applied Soft Computing, 11, 5477–5484.

Kean, M.P. (2010). Structural vs. atheoretic approaches to econometrics. Journal of Econometrics, 156, 3–20.

Kleiner, A., Talwalkar, A., Sarkar , P. & Jordan, M. (2012). The Big Data Bootstrap. arXiv:1206.6415 .

Koch, I. (2013). Analysis of Multivariate and High-Dimensional Data. Cambridge University Press.

Koenker, R. (1998). Galton, Edgeworth, Frish, and prospects for quantile regression in Econometrics. Conference on Principles of Econometrics, Madison.

Koenker, R. (2003). Quantile Regression. Cambridge University Press.

Koenker, R. & Machado, J. (1999). Goodness of fit and related inference processes for quantile regression Journal of the American Statistical Association, 94, 1296-1309.

Kolda, T. G. & Bader, B. W. (2009). Tensor decompositions and applications. SIAM Review 51, 455–500.

Koopmans, T.C. (1957). Three Essays on the State of Economic Science. McGraw-Hill.

Kuhn, M. & Johnson, K. (2013). Applied Predictive Modeling. Springer Verlag.

Landis, J.R. & Koch, G.G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33, 159–174.

LeCun, Y., Bengio, Y. & Hinton, G. (2015). Deep learning. Nature 521 436–444.

Leeb, H. (2008). Evaluation and selection of models for out-of-sample prediction when the sample size is small relative to the complexity of the data-generating process. Bernoulli 14:3, 661–690.

Lemieux, T. (2006). The « Mincer Equation » Thirty Years After Schooling, Experience, and Earnings. in  Jacob Mincer A Pioneer of Modern Labor Economics, Grossbard Eds, 127–145, Springer Verlag.

Li, J. & J. S. Racine (2006). Nonparametric Econometrics. Princeton University Press.

Li, C., Li, Q., Racine, J. & Zhang, D. (2017). Optimal Model Averaging Of Varying Coefficient Models. Department of Economics Working Papers 2017-01, McMaster University.

Lin, H.W., Tegmark, M. & Rolnick, D. (2016). Why does deep and cheap learning work so well? ArXiv 1608.08225.

Lucas, R.E. (1976). Econometric Policy Evaluation: A Critique. Carnegie-Rochester Conference Series on Public Policy, 19–46.

Mallows, C.L. (1973). Some Comments on Cp . Technometrics, 15, 661–675.

McCullogh, W.S. & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5:4, 115–133.

Mincer, J. (1974). Schooling, experience and earnings. Columbia University Press.

Mitchell, T. (1997). Machine Learning. McGraw-Hill.

Morgan, J.N. & Sonquist, J.A. (1963). Problems in the analysis of survey data, and a proposal. Journal of the American Statistcal Association, 58, 415–434.

Morgan, M.S. (1990). The history of econometric ideas. Cambridge University Press.

Mohri, M., Rostamizadeh, A. & Talwalker, A. (2012) Foundations of Machine Learning. MIT Press.

Mullainathan, S. & Spiess, J. (2017). Machine learning: An applied econometric approach. Journal of Economic Perspectives, 31 87–106.

Müller, M. (2011). Generalized Linear Models in  Handbook of Computational Statistics, J.E Gentle, W.K. Härdle & Y. Mori Eds. Springer Verlag.

Murphy, K.R. (2012). Machine Learning: a Probabilistic Perspective. MIT Press.

Murphy, K. M. & F. Welch (1990). Empirical age-earnings profiles. Journal of Labor Economics 8, 202–229.

Nadaraya, E. A. (1964). On Estimating Regression. Theory of Probability and its Applications, 9:1, 141–2.

Natarajan, B. K. (1995). Sparse approximate solutions to linear systems. SIAM Journal on Computing (SICOMP), 24 227–-234.

Nevo, A. & Whinston, M.D. (2010). Taking the Dogma out of Econometrics: Structural Modeling and Credible Inference. Journal of Economic Perspective, 24, 69–82.

Neyman, J. (1923). Sur les applications de la théorie des probabilités aux expériences agricoles : Essai des principes. Mémoire de master, republibé dans Statistical Science, 5, 463–472.

Nisbet, R., Elder, J. & Miner, G. (2011). Handbook of Statistical Analysis and Data Mining Applications. Academic Press, New York.

Okun, A. (1962). Potential GNP: Its measurement and significance. Proceedings of the Business and Economics Section of the American Statistical Association, 98–103.

Orcutt, G.H. (1952). Toward a partial redirection of econometrics. Review of Economics and Statistics, 34 195–213.

Pagan, A. & A. Ullah (1999). Nonparametric Econometrics. Themes in Modern Econometrics. Cambridge: Cambridge University Press.

Pearson, K. (1901). On lines and planes of closest fit to systems of points in space. Philosophical Magazine, 2, 559-–572.

Platt, J. (1999). Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in Large Margin Classifiers. 10, 61–74.

Portnoy, S. (1988). Asymptotic Behavior of Likelihood Methods for Exponential Families when the Number of Parameters Tends to Infinity. Annals of Statistics, 16:356–366.

Quenouille, M. H. (1949). Problems in Plane Sampling. The Annals of Mathematical Statistics 20(3):355–375.

Quenouille, M. H. (1956). Notes on Bias in Estimation. Biometrika 43(3-4), 353–360.

Quinlan, J.R. (1986). Induction of decision trees. Machine Learning  1 81–106.

Reiersøl, O. (1945). Confluence analysis of means of instrumental sets of variables. Arkiv. for Mathematik, Astronomi Och Fysik, 32.

Rosenbaum, P. & Rubin, D. (1983). The Central Role of the Propensity Score in Observational Studies for Causal Effects. Biometrika, 70, 41–55.

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Shao, J. (1993). Linear Model Selection by Cross-Validation. Journal of the American Statistical Association 88:(422), 486–494.

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Shapire, R.E. & Freund, Y. (2012). Boosting. MIT Press.

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Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, Series B., 58, 267–288.

Tibshirani, R. & Wasserman, L. (2016). A Closer Look at Sparse Regression.

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Cite this blog post
Arthur Charpentier (2019, February 5). References on Econometrics and Machine Learning. Freakonometrics. Retrieved April 15, 2024, from

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