- Adrian, T. ; Boyarchenko, N. ; Giannone, D. Vulnerable growth. 2019 American Economic Review. 109 1263-1289
Paper not yet in RePEc: Add citation now
- Athey, S. ; Tibshirani, J. ; Wager, S. Generalized random forests. 2019 :
Paper not yet in RePEc: Add citation now
Azzalini, A. ; Capitanio, A. Distributions generated by perturbation of symmetry with emphasis on a multivariate skew t-distribution. 2003 Journal of the Royal Statistical Society. Series B. Statistical Methodology. 65 367-389
- Barth, M. ; Emrich, E. ; Güllich, A. A machine learning approach to “revisit” specialization and sampling in institutionalized practice. 2019 Sage Open. 9 -
Paper not yet in RePEc: Add citation now
- Bishop, C.M. ; Nasrabadi, N.M. Pattern recognition and machine learning. 2006 Springer:
Paper not yet in RePEc: Add citation now
- Burrell, J. How the machine ‘thinks’: Understanding opacity in machine learning algorithms. 2016 Big Data & Society. 3 -
Paper not yet in RePEc: Add citation now
Chevillon, G. ; Hendry, D.F. Non-parametric direct multi-step estimation for forecasting economic processes. 2005 International Journal of Forecasting. 21 201-218
Cleary, S. ; Hebb, G. An efficient and functional model for predicting bank distress: In and out of sample evidence. 2016 Journal of Banking & Finance. 64 101-111
Covas, F.B. ; Rump, B. ; Zakrajšek, E. Stress-testing US bank holding companies: A dynamic panel quantile regression approach. 2014 International Journal of Forecasting. 30 691-713
Danielsson, J. ; James, K.R. ; Valenzuela, M. ; Zer, I. Can we prove a bank guilty of creating systemic risk? A minority report. 2016 Journal of Money, Credit and Banking. 48 795-812
Danielsson, J. ; James, K.R. ; Valenzuela, M. ; Zer, I. Model risk of risk models. 2016 Journal of Financial Stability. 23 79-91
- De Prado, M.L. Advances in financial machine learning. 2018 John Wiley & Sons:
Paper not yet in RePEc: Add citation now
- Dhar, V. Data science and prediction. 2013 Communications of the ACM. 56 64-73
Paper not yet in RePEc: Add citation now
Diebold, F.X. ; Mariano, R.S. Comparing predictive accuracy. 2002 Journal of Business & Economic Statistics. 20 134-144
Engle, R.F. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. 1982 Econometrica: Journal of the Econometric Society. 987-1007
Galvao, A.F. Quantile regression for dynamic panel data with fixed effects. 2011 Journal of Econometrics. 164 142-157
Gneiting, T. ; Raftery, A.E. Strictly proper scoring rules, prediction, and estimation. 2007 Journal of the American Statistical Association. 102 359-378
Gogas, P. ; Papadimitriou, T. ; Agrapetidou, A. Forecasting bank failures and stress testing: A machine learning approach. 2018 International Journal of Forecasting. 34 440-455
Goulet Coulombe, P. ; Leroux, M. ; Stevanovic, D. ; Surprenant, S. How is machine learning useful for macroeconomic forecasting?. 2022 Journal of Applied Econometrics. 37 920-964
Guerrieri, L. ; Welch, M. Can macro variables used in stress testing forecast the performance of banks?. 2012 :
Hirtle, B. ; Kovner, A. ; Vickery, J. ; Bhanot, M. Assessing financial stability: The capital and loss assessment under stress scenarios (CLASS) model. 2016 Journal of Banking & Finance. 69 S35-S55
- Hull, J. Risk management and financial institutions,+ web site. 2012 John Wiley & Sons:
Paper not yet in RePEc: Add citation now
Iacopini, M. ; Ravazzolo, F. ; Rossini, L. Proper scoring rules for evaluating density forecasts with asymmetric loss functions. 2023 Journal of Business & Economic Statistics. 41 482-496
- Iturriaga, F.J.L. ; Sanz, I.P. Bankruptcy visualization and prediction using neural networks: A study of US commercial banks. 2015 Expert Systems with Applications. 42 2857-2869
Paper not yet in RePEc: Add citation now
- James, G. ; Witten, D. ; Hastie, T. ; Tibshirani, R. An introduction to statistical learning. 2013 Springer:
Paper not yet in RePEc: Add citation now
- Jordan, M.I. ; Mitchell, T.M. Machine learning: Trends, perspectives, and prospects. 2015 Science. 349 255-260
Paper not yet in RePEc: Add citation now
- Jorion, P. Value at risk: the new benchmark for managing financial risk. 2007 McGraw-Hill:
Paper not yet in RePEc: Add citation now
Kapinos, P. ; Mitnik, O.A. A top-down approach to stress-testing banks. 2016 Journal of Financial Services Research. 49 229-264
Khandani, A.E. ; Kim, A.J. ; Lo, A.W. Consumer credit-risk models via machine-learning algorithms. 2010 Journal of Banking & Finance. 34 2767-2787
Liu, J. ; Li, C. ; Ouyang, P. ; Liu, J. ; Wu, C. Interpreting the prediction results of the tree-based gradient boosting models for financial distress prediction with an explainable machine learning approach. 2023 Journal of Forecasting. 42 1112-1137
Liu, L. ; Moon, H.R. ; Schorfheide, F. Forecasting with dynamic panel data models. 2020 Econometrica. 88 171-201
Marcellino, M. ; Stock, J.H. ; Watson, M.W. A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series. 2006 Journal of Econometrics. 135 499-526
- Molnar, C. Interpretable machine learning. 2020 Lulu. com:
Paper not yet in RePEc: Add citation now
- Murphy, K.P. Machine learning: a probabilistic perspective. 2012 MIT Press:
Paper not yet in RePEc: Add citation now
Plagborg-Møller, M. ; Reichlin, L. ; Ricco, G. ; Hasenzagl, T. When is growth at risk?. 2020 Brookings Papers on Economic Activity. 2020 167-229
Schorfheide, F. VAR forecasting under misspecification. 2005 Journal of Econometrics. 128 99-136
- Simon, H.A. Models of bounded rationality: Empirically grounded economic reason. 1997 MIT Press:
Paper not yet in RePEc: Add citation now
Su, Z. ; Cai, X. ; Wu, Y. Exchange rates forecasting and trend analysis after the COVID-19 outbreak: new evidence from interpretable machine learning. 2023 Applied Economics Letters. 30 2052-2059
Varian, H.R. Big data: New tricks for econometrics. 2014 Journal of Economic Perspectives. 28 3-28
- Winkler, R.L. ; Murphy, A.H. Use of probabilities in forecasts of maximum and minimum temperatures. 1979 Meteorological Magazine. 108 317-329
Paper not yet in RePEc: Add citation now
Zhang, H. ; Shi, Y. ; Yang, X. ; Zhou, R. A firefly algorithm modified support vector machine for the credit risk assessment of supply chain finance. 2021 Research in International Business and Finance. 58 -