Abstract
Bootstrap Aggregating (Bagging) is an ensemble technique for improving the robustness of forecasts. Random Forest is a successful method based on Bagging and Decision Trees. In this chapter, we explore Bagging, Random Forest, and their variants in various aspects of theory and practice. We also discuss applications based on these methods in economic forecasting and inference.
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Audrino, F., & Medeiros, M. C. (2011). Modeling and forecasting short-term interest rates: The benefits of smooth regimes, macroeconomic variables, and Bagging. Journal of Applied Econometrics, 26(6), 999–1022.
Biau, O., & D’Elia, A. (2011). Euro area GDP forecast using large survey dataset - A random forest approach. In EcoMod 2010.
Breiman, L. (1996). Bagging predictors. Machine Learning, 26(2), 123–140.
Breiman, L. (2000). Some infinity theory for predictor ensembles. Berkeley: University of California.
Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32.
Breiman, L., Friedman, J., Stone, C., & Olshen, R. (1984). Classification and regression trees. The Wadsworth and Brooks-Cole Statistics-Probability Series. Oxfordshire: Taylor & Francis.
Bühlmann, P. (2004). Bagging, boosting and ensemble methods (pp. 877–907). Handbook of Computational Statistics: Concepts and Methods. Berlin: Springer.
Bühlmann, P., & Yu, B. (2002). Analyzing bagging. Annals of Statistics, 30(4), 927–961.
Buja, A., & Stuetzle, W. (2000a), Bagging does not always decrease mean squared error definitions (Preprint). Florham Park: AT&T Labs-Research.
Buja, A., & Stuetzle, W. (2000b). Smoothing effects of bagging (Preprint). Florham Park: AT&T Labs-Research.
Fischer, T., Krauss, C., & Treichel, A. (2018). Machine learning for time series forecasting - a simulation study (2018). FAU Discussion Papers in Economics, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
Friedman, J. H., & Hall, P. (2007). On Bagging and nonlinear estimation. Journal of Statistical Planning and Inference, 137(3), 669–683.
Frosst, N., & Hinton, G. (2017). Distilling a neural network into a soft decision tree. In Ceur workshop proceedings.
Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42.
Hillebrand, E., Lee, T.-H., & Medeiros, M. (2014). Bagging constrained equity premium predictors (Chap. 14, pp. 330–356). In Essays in Nonlinear Time Series Econometrics, Festschrift in Honor of Timo Teräsvirta. Oxford: Oxford University Press.
Hirano, K., & Wright, J. H. (2017). Forecasting with model uncertainty: Representations and risk reduction. Econometrica, 85(2), 617–643.
Hothorn, T., & Zeileis, A. (2017). Transformation forests. Technical report. https://arxiv.org/abs/1701.02110.
Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2006). Extreme learning machine: Algorithm, theory and applications. Neurocomputing, 70, 489–501.
Inoue, A., & Kilian, L. (2008). How useful is Bagging in forecasting economic time. Journal of the American Statistical Association, 103(482), 511–522.
Irsoy, O., Yildiz, O. T., & Alpaydin, E. (2012). A soft decision tree. In 21st International Conference on Pattern Recognition (ICPR 2012).
Janitza, S., Celik, E., & Boulesteix, A. L. (2016). A computationally fast variable importance test for Random Forests for high-dimensional data. Advances in Data Analysis and Classification, 185, 1–31.
Jin, S., Su, L., & Ullah, A. (2014). Robustify financial time series forecasting with Bagging. Econometric Reviews, 33(5-6), 575–605.
Jordan, M., & Jacob, R. (1994). Hierarchical Mixtures of Experts and the EM algorithm. Neural Computation, 6, 181–214.
Kontschieder, P., Fiterau, M., Criminisi, A., Bul, S. R., Kessler, F. B., & Bulo’, S. R. (2015). Deep Neural Decision Forests. In The IEEE International Conference on Computer Vision (ICCV) (pp. 1467–1475).
Lee, T.-H., & Yang, Y. (2006). Bagging binary and quantile predictors for time series. Journal of Econometrics, 135(1), 465–497.
Lee, T.-H., Tu, Y., & Ullah, A. (2014). Nonparametric and semiparametric regressions subject to monotonicity constraints: estimation and forecasting. Journal of Econometrics, 182(1), 196–210.
Lee, T.-H., Tu, Y., & Ullah, A. (2015). Forecasting equity premium: Global historical average versus local historical average and constraints. Journal of Business and Economic Statistics, 33(3), 393–402.
Lin, Y., & Jeon, Y. (2006). Random forests and adaptive nearest neighbors. Journal of the American Statistical Association, 101(474), 578–590.
Luong, C., & Dokuchaev, N. (2018). Forecasting of realised volatility with the random forests algorithm. Journal of Risk and Financial Management, 11(4), 61.
Nyman, R., & Ormerod, P. (2016). Predicting economic recessions using machine learning. arXiv:1701.01428.
Panagiotelis, A., Athanasopoulos, G., Hyndman, R. J., Jiang, B., & Vahid, F. (2019). Macroeconomic forecasting for Australia using a large number of predictors. International Journal of Forecasting, 35(2), 616–633.
Quinlan, J. (1986). Induction of decision trees. Machine Learning, 1, 81–106.
Quinlan, J. R. (1994). C4.5: programs for machine learning. Machine Learning, 16(3), 235–240.
Strobl, C., Boulesteix, A.-L., Zeileis, A., & Hothorn, T. (2007). Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC Bioinformatics, 8, 25.
Strobl, C., Boulesteix, A. L., Kneib, T., Augustin, T., & Zeileis, A. (2008). Conditional variable importance for Random Forests. BMC Bioinformatics, 9, 1–11.
Wager, S., & Athey, S. (2018). Estimation and inference of heterogeneous treatment effects using Random Forests. Journal of the American Statistical Association, 113(523), 1228–1242.
Welch, I., & Goyal, A. (2008). A comprehensive look at the empirical performance of equity premium prediction. Review of Financial Studies, 21-4 1455–1508.
Yildiiz, O. T., Írsoy, O., & Alpaydin, E. (2016). Bagging soft decision trees. In Machine Learning for Health Informatics (Vol. 9605, pp. 25–36).
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Lee, TH., Ullah, A., Wang, R. (2020). Bootstrap Aggregating and Random Forest. In: Fuleky, P. (eds) Macroeconomic Forecasting in the Era of Big Data. Advanced Studies in Theoretical and Applied Econometrics, vol 52. Springer, Cham. https://doi.org/10.1007/978-3-030-31150-6_13
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