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Discussion Paper
Measuring and Managing COVID-19 Model Risk
Jensen, Mark J. (2020-06-18)
One of the many lessons learned from the financial crisis is the increased awareness of model risk. In this article, I apply the best practices of model risk management found in SR 11-7 (which offers regulatory guidance on the best practices for managing model risk) to COVID-19 models. In particular, I investigate the Institute of Health Metrics and Evaluation's (IHME) model to see if it has been effectively challenged with a critical assessment of its conceptual soundness, ongoing monitoring, and outcomes analysis.
Policy Hub* , Paper 2020-07

Discussion Paper
Measuring and Managing COVID-19 Model Risk
Jensen, Mark J. (2020-06-18)
One of the many lessons learned from the financial crisis is the increased awareness of model risk. In this article, I apply the best practices of model risk management found in SR 11-7 (which offers regulatory guidance on the best practices for managing model risk) to COVID-19 models. In particular, I investigate the Institute of Health Metrics and Evaluation’s (IHME) model to see if it has been effectively challenged with a critical assessment of its conceptual soundness, ongoing monitoring, and outcomes analysis.
Policy Hub* , Paper 2020-7

Journal Article
Measuring and Managing COVID-19 Model Risk
Jensen, Mark J. (2020-06-18)
One of the many lessons learned from the financial crisis is the increased awareness of model risk. In this article, I apply the best practices of model risk management found in SR 11-7 (which offers regulatory guidance on the best practices for managing model risk) to COVID-19 models. In particular, I investigate the Institute of Health Metrics and Evaluation's (IHME) model to see if it has been effectively challenged with a critical assessment of its conceptual soundness, ongoing monitoring, and outcomes analysis.
Policy Hub , Volume 2020 , Issue 7 , Pages 12

Working Paper
A Uniformly Valid Test for Instrument Exogeneity
Gospodinov, Nikolay; Dovonon, Prosper (2025-09-25)
This paper studies the limiting behavior of the test for instrument exogeneity in linear models when there is uncertainty about the strength of the identification signal. We consider the test for conditional moment restrictions with an expanding set of constructed instruments. We establish the uniform validity of the standard normal asymptotic approximation, under the null, of this specification test over all possible degrees of model identification. As a result, this allows the researcher to use standard inference for testing instrument exogeneity without the need of any prior knowledge if ...
FRB Atlanta Working Paper , Paper 2025-9

Working Paper
On Model Aggregation and Forecast Combination
Massoumi, Esfandiar; Gospodinov, Nikolay (2025-10-09)
Policy makers express their views and decisions via the lens of a particular model or theory. But since any model is a highly stylized representation of the unknowable object of interest, all these models are inherently misspecified, and the resulting ambiguity injects uncertainty in the decision-making process. We argue that entropy-based aggregation is a convenient device to confront this uncertainty and summarize relevant information from a set of candidate models and forecasts. The proposed aggregation tends to robustify the decision-making process to various sources of risks and ...
FRB Atlanta Working Paper , Paper 2025-12

Working Paper
Misspecification-robust inference in linear asset pricing models with irrelevant risk factors
Robotti, Cesare; Gospodinov, Nikolay; Kan, Raymond (2013-10-01)
We show that in misspecified models with useless factors (for example, factors that are independent of the returns on the test assets), the standard inference procedures tend to erroneously conclude, with high probability, that these irrelevant factors are priced and the restrictions of the model hold. Our proposed model selection procedure, which is robust to useless factors and potential model misspecification, restores the standard inference and proves to be effective in eliminating factors that do not improve the model's pricing ability. The practical relevance of our analysis is ...
FRB Atlanta Working Paper , Paper 2013-09

Working Paper
General Aggregation of Misspecified Asset Pricing Models
Maasoumi, Esfandiar; Gospodinov, Nikolay (2017-11-01)
This paper proposes an entropy-based approach for aggregating information from misspecified asset pricing models. The statistical paradigm is shifted away from parameter estimation of an optimally selected model to stochastic optimization based on a risk function of aggregation across models. The proposed method relaxes the perfect substitutability of the candidate models, which is implicitly embedded in the linear pooling procedures, and ensures that the aggregation weights are selected with a proper (Hellinger) distance measure that satisfies the triangle inequality. The empirical results ...
FRB Atlanta Working Paper , Paper 2017-10

Working Paper
The Anatomy of Out-of-Sample Forecasting Accuracy
Goulet Coulombe, Philippe; Rapach, David E.; Schwenk-Nebbe, Sander; Montes Schütte, Erik Christian; Borup, Daniel (2024-02-21)
We introduce the performance-based Shapley value (PBSV) to measure the contributions of individual predictors to the out-of-sample loss for time-series forecasting models. Our new metric allows a researcher to anatomize out-of-sample forecasting accuracy, thereby providing valuable information for interpreting time-series forecasting models. The PBSV is model agnostic—so it can be applied to any forecasting model, including "black box" models in machine learning, and it can be used for any loss function. We also develop the TS-Shapley-VI, a version of the conventional Shapley value that ...
FRB Atlanta Working Paper , Paper 2022-16b

Working Paper
How Magic a Bullet Is Machine Learning for Credit Analysis? An Exploration with FinTech Lending Data
Wang, J. Christina; Perkins, Charles B. (2019-10-14)
FinTech online lending to consumers has grown rapidly in the post-crisis era. As argued by its advocates, one key advantage of FinTech lending is that lenders can predict loan outcomes more accurately by employing complex analytical tools, such as machine learning (ML) methods. This study applies ML methods, in particular random forests and stochastic gradient boosting, to loan-level data from the largest FinTech lender of personal loans to assess the extent to which those methods can produce more accurate out-of-sample predictions of default on future loans relative to standard regression ...
Working Papers , Paper 19-16

Working Paper
Testing for Differences in Path Forecast Accuracy: Forecast-Error Dynamics Matter
Martinez, Andrew (2017-11-02)
Although the trajectory and path of future outcomes plays an important role in policy decisions, analyses of forecast accuracy typically focus on individual point forecasts. However, it is important to examine the path forecasts errors since they include the forecast dynamics. We use the link between path forecast evaluation methods and the joint predictive density to propose a test for differences in system path forecast accuracy. We also demonstrate how our test relates to and extends existing joint testing approaches. Simulations highlight both the advantages and disadvantages of path ...
Working Papers (Old Series) , Paper 1717

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