2023年机器学习危机预测模型：大数据与可解释替代方法

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本文档《2023替代数据模型：解释大规模机器学习危机预测模型》主要探讨了在经济危机预测领域，机器学习技术的应用以及面临的挑战。随着大数据和复杂算法的发展，大规模机器学习模型在预测经济危机方面展现出强大的潜力。然而，这些模型的黑箱特性使得其决策过程难以理解和解释，这在高度依赖透明度和可解释性的金融监管环境中成为一个问题。作者们分析了经济危机对长期增长和生产力的影响，引用了Barnichon et al. (2018) 和 Romer and Romer (2017) 的研究，指出金融危机后的潜在产出通常会显著低于危机趋势线，这强调了准确预测危机的重要性。传统的经济模型可能无法捕捉到现代经济中的所有动态，而机器学习模型，如深度神经网络和集成学习方法，能够从海量数据中提取复杂的模式。然而，如何理解和解释这些大规模机器学习模型的预测结果是关键。"Surrogate Data Models"（替代数据模型）这一概念应运而生，它旨在通过构建可解释的简化模型来辅助理解复杂机器学习模型的决策过程。通过这种方法，研究人员可以将难以解读的模型决策映射到更为直观和经济学基础的框架内，提高模型的可信度和接受度。本文的核心议题包括以下几个方面： 1. **模型解释性**：作者关注如何提升机器学习模型的可解释性，以便政策制定者、金融机构和公众能够理解模型在预测危机时所依据的因果关系，从而增强决策信心。 2. **替代数据模型的构建**：通过设计和应用替代数据模型，如基于统计学方法或规则集的模型，能够在保留预测性能的同时提供更清晰的因果路径。 3. **数据驱动的危机预警系统**：利用机器学习技术挖掘历史数据中的危机信号，构建实时或定期更新的预警系统，以提前识别潜在的经济风险。 4. **监管与透明度**：讨论在大数据驱动的金融市场中，如何平衡数据隐私、模型透明度和风险管理的需求，以确保模型在满足监管要求的同时保持有效性。 5. **研究方法和未来方向**：论文可能还探讨了评估模型性能的指标、模型验证的挑战，以及如何持续改进模型以适应不断变化的经济环境。《2023替代数据模型》这篇论文提出了一种解决大规模机器学习危机预测模型黑箱问题的新途径，强调了在推动科技进步的同时，保持模型的可解释性和监管符合性的重要性。通过深入研究和实证分析，论文希望能促进经济学界和金融科技领域的进一步讨论和实践应用。

IMF WORKING PAPERS

Surrogate Data Models

INTERNATIONAL MONETARY FUND

2. A short survey of ML crisis prediction models

Early crisis prediction studies made heavy use of probit and/or logit models (Eichengreen et al., 1995; Frankel

and Rose, 1996), and non-parametric signal extraction models (Kaminsky et al. 1998). Recent applied work on

crisis prediction and early warning systems has moved beyond these traditional approaches by incorporating

machine learning methods. These methods, which tend to emphasize predictive ability rather than casual

inference, can handle a large number of features (explanatory variables) and can capture nonlinear effects

better than generalized linear models such as logistic regression and multinomial regression. A non-exhaustive

list of recent work is reviewed below.

Holopainen and Sarlin (2017) undertook a comparison of conventional statistical methods and machine

learning methods in early-warning systems of banking crises in 15 European countries. They found machine

learning methods, such as k-nearest neighbors, neural networks, and ensemble learning models outperform

logistic regression in out-of-sample forecasting exercise.

Bluwstein et al. (2021) compared the performance of different early warning models for financial crisis

prediction for a sample of 17 advanced economies over the period 1870–2016. The models included 16

features (explanatory variables) aimed at capturing the domestic and global economic and credit cycles. In

addition to logistic regression, they implemented a variety of machine learning models, including decision trees,

random forests, extremely randomized trees, support vector machines, and artificial neural networks. Except

for decision trees, all machine learning models outperformed the logistic regression. The limited number of

features allows the application of Shapley regressions (Joseph, 2020), which identify credit growth and the

slope of the yield curve as the main predictors of financial crises.

Fouliard et al. (2021) showed it was possible to predict systemic financial stress episodes in European Union

countries and the United States three years ahead by using a set of different machine learning models. Their

approach incorporates information from economic data sequentially as soon as the data become available, a

sequential process known in the ML literature as online learning. The models used 244 features observed on a

quarterly frequency, of which about half are available for online estimation.

Hellwig (2021) showed that traditional econometric models were unable to outperform simple heuristic “rules of

thumb” in the prediction of fiscal crises in advanced economies, emerging markets countries, and low

income/developing countries. On the other hand, machine learning techniques such as elastic net, random

forests, and gradient-boosted trees, delivered superior performance when the number of predictors is large.

The models are based on extensive set of predictors comprising economic, financial, demographic, and

institutional variables, as well as various feature engineering of the raw variables, including lags, temporal

changes, and averages.

IMF (2021) described a set of different ML prediction models, each tailored to predict a crisis affecting different

sectors of the economy. Examples included: financial crisis, fiscal crisis, external sector crisis (balance of

payment crisis), and real sector crisis. Compared to other studies reviewed here, the dataset covered more

countries (all 190 IMF member countries), and each sectoral crisis model includes a substantial number of

features, including several data transformations. The crisis events definitions used reflected the needs policy

IMF WORKING PAPERS

Surrogate Data Models

INTERNATIONAL MONETARY FUND

the IMF faces.

Due to the wide country coverage, data imputation techniques were used to address missing

data problems. Synthetic oversampling methods were used to deal with potential issues arising from the

imbalance between few crisis observations. Horse-race comparisons showed that ML models outperform

conventional approaches except for external sector crises, for which the signal extraction approach remains the

most appropriate modeling technique. Feature contributions (importance) to crisis prediction were assessed

using the SHAP method (see next section on feature importance).

Hacibedel and Qu (2022) study systemic non-financial corporate sector distress as identified by the prevalent

elevated probability of default across firms in an economy. An ensemble of ML models is constructed by

optimally pooling gradient-boosted trees, linear discriminant, and logit Lasso to predict the onset of distress in

one year’s time. The model leverages a total of 40 predictors covering domestic and international

macroeconomic variables, firm balance sheet variables, and model-based PD from the Credit Research

Initiative of NUS (2019). The paper shows the ensemble model has a better and more robust performance

against its individual model constituents across different time blocks and country groups.

Notwithstanding the results above showing that ML models outperform traditional crisis prediction models,

some caution is warranted as results may depend on the crisis definition and data sample used. Beutel et al.

(2019) found that machine learning models might not necessarily outperform standard econometric techniques

in all instances. The researchers, using a sample of advanced economies covering a 45-year data sample from

1971–2016 and a set of crisis events collected across different banking crisis event studies, found that machine

learning models, while often delivering a good in-sample fit, failed to meet the performance of a logit model in

recursive out-of-sample predictions of systemic banking crises, including the 2007-8 global financial crisis.

These results suggest that the performance of ML crisis prediction models is highly dependent on the crisis

definition used, the degree of imbalance between crisis and non-crisis observations, and the availability of the

dataset features. The results also indicate that a trial-and-error approach based on the task at hand is

appropriate.

3. Enhancing ML crisis prediction model

interpretability using surrogate data models

Surrogate models and feature importance

The large number of features most crisis prediction models use hampers their interpretability. Without a clear

understanding of the feature importance to evaluate the crisis likelihood or the input-output relationship, it is

difficult to reconcile the lessons derived from theoretical crisis models, empirical work, and past policy decisions

with the output of high-dimensional complex models. Surrogate models address the lack of intuition problems

present in the latter models. Surrogate models, widely used in engineering design optimization and emulation,

are simple approximation models that mimic the behavior of complex systems and models at a lower

computational cost and provide a clearer understanding of the systems’ dynamics (Forrester et al. 2008).

See annexes.

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