Showing 1 - 10 of 8,383
This paper proposes an analytic representation of sequence-space Jacobians in heterogeneous agent models with aggregate shocks in continuous time. Our approach is based on a pen-and-paper perturbation of individual policy functions with respect to price changes, rather than numerical or...
Persistent link: https://www.econbiz.de/10015326518
Despite the clear success of forecast combination in many economic environments, several important issues remain incompletely resolved. The issues relate to selection of the set of forecasts to combine, and whether some form of additional regularization (e.g., shrinkage) is desirable. Against...
Persistent link: https://www.econbiz.de/10012480620
We develop a model to predict consumer default based on deep learning. We show that the model consistently outperforms standard credit scoring models, even though it uses the same data. Our model is interpretable and is able to provide a score to a larger class of borrowers relative to standard...
Persistent link: https://www.econbiz.de/10012480111
Group testing increases efficiency by pooling patient specimens and clearing the entire group with one negative test. Optimal grouping strategy is well studied in one-off testing scenarios with reasonably well-known prevalence rates and no correlations in risk. We discuss how the strategy...
Persistent link: https://www.econbiz.de/10012481312
The Diebold-Mariano (DM) test was intended for comparing forecasts; it has been, and remains, useful in that regard. The DM test was not intended for comparing models. Unfortunately, however, much of the large subsequent literature uses DM-type tests for comparing models, in (pseudo-)...
Persistent link: https://www.econbiz.de/10012460268
This paper describes a process for automatically generating academic finance papers using large language models (LLMs). It demonstrates the process' efficacy by producing hundreds of complete papers on stock return predictability, a topic particularly well-suited for our illustration. We first...
Persistent link: https://www.econbiz.de/10015195009
We survey and apply several techniques from the statistical and computer science literature to the problem of demand estimation. We derive novel asymptotic properties for several of these models. To improve out-of-sample prediction accuracy and obtain parametric rates of convergence, we propose...
Persistent link: https://www.econbiz.de/10012457711
We compare predictions from a conventional protocol-based approach to risk assessment with those based on a machine-learning approach. We first show that the conventional predictions are less accurate than, and have similar rates of negative prediction error as, a simple Bayes classifier that...
Persistent link: https://www.econbiz.de/10012482511
Financial crises cause economic, social and political havoc. Macroprudential policies are gaining traction but are still severely under-researched compared to monetary policy and fiscal policy. We use the general framework of sequential predictions also called online machine learning to forecast...
Persistent link: https://www.econbiz.de/10012482520
This paper shows that shootings are predictable enough to be preventable. Using arrest and victimization records for almost 644,000 people from the Chicago Police Department, we train a machine learning model to predict the risk of being shot in the next 18 months. We address central concerns...
Persistent link: https://www.econbiz.de/10013334389