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Working Paper
Explaining Machine Learning by Bootstrapping Partial Marginal Effects and Shapley Values
Machine learning and artificial intelligence are often described as “black boxes.” Traditional linear regression is interpreted through its marginal relationships as captured by regression coefficients. We show that the same marginal relationship can be described rigorously for any machine learning model by calculating the slope of the partial dependence functions, which we call the partial marginal effect (PME). We prove that the PME of OLS is analytically equivalent to the OLS regression coefficient. Bootstrapping provides standard errors and confidence intervals around the point ...
Working Paper
A Coherent Framework for Predicting Emerging Market Credit Spreads with Support Vector Regression
We propose a coherent framework using support vector regression (SRV) for generating and ranking a set of high quality models for predicting emerging market sovereign credit spreads. Our framework adapts a global optimization algorithm employing an hv-block cross-validation metric, pertinent for models with serially correlated economic variables, to produce robust sets of tuning parameters for SRV kernel functions. In contrast to previous approaches identifying a single "best" tuning parameter setting, a task that is pragmatically improbable to achieve in many applications, we proceed with ...
Working Paper
CardSim: A Bayesian Simulator for Payment Card Fraud Detection Research
Payment fraud has been high in recent years, and as criminals gain access to capability-enhancing generative AI tools, there is a growing need for innovative fraud detection research. However, the pace, diversity, and reproducibility of such research are inhibited by the dearth of publicly available payment transaction data. A few payment simulation methodologies have been developed to help narrow the payment transaction data gap without compromising important data privacy and security expectations. While these simulation approaches have enabled research advancements, more work is needed to ...
Journal Article
Machine Learning a Ramsey Plan
We use a Python program to calculate a pair of infinite sequences of money creation and price level inflation rates that maximize a benevolent time 0 government’s quadratic objective function for a linear-quadratic version of Calvo (1978). The program computes an open-loop representation of the optimal plan and an associated monotonically declining, bounded from below sequence of continuation values whose limit is a worst continuation value that is associated with a “timeless perspective”. We run some least squares regressions on fake data to try to learn about the structure of the ...
Working Paper
Dynamic Econometrics in Action: A Biography of David F. Hendry
David Hendry has made–and continues to make–pivotal contributions to the econometrics of empirical economic modeling, economic forecasting, econometrics software, substantive empirical economic model design, and economic policy. This paper reviews his contributions by topic, emphasizing the overlaps between different strands in his research and the importance of real-world problems in motivating that research.
Working Paper
Sellin’ in the Rain: Adaptation to Weather and Climate in the Retail Sector
Using novel methodology and proprietary daily store-level sporting goods and apparel brand data, I find that, consistent with long-run adaptation to climate, sales sensitivity to weather declines with historical norms and variability of weather. Short-run adaptation to weather shocks is dominated by changes in what people buy and how they buy it, with little intertemporal substitution. Over four weeks, a one-standard deviation one-day weather shock shifts sales by about 10 percent. While switching between indoor and outdoor stores offsets a small portion of contemporaneous responses to ...
Working Paper
Understanding Survey Based Inflation Expectations
Survey based measures of inflation expectations are not informationally efficient yet carry important information about future inflation. This paper explores the economic significance of informational inefficiencies of survey expectations. A model selection algorithm is applied to the inflation expectations of households and professionals using a large panel of macroeconomic data. The expectations of professionals are best described by different indicators than the expectations of households. A forecast experiment finds that it is difficult to exploit informational inefficiencies to improve ...
Journal Article
Machine Learning
Jargon Alert on Machine Learning
Working Paper
The Dual U.S. Labor Market Uncovered
Aggregate U.S. labor market dynamics are well approximated by a dual labor market supplemented with a third, predominantly, home-production segment. We uncover this structure by estimating a Hidden Markov Model, a machine-learning method. The different market segments are identified through (in-)equality constraints on labor market transition probabilities. This method yields time series of stocks and flows for the three segments for 1980-2021. Workers in the primary sector, who make up around 55 percent of the population, are almost always employed and rarely experience unemployment. The ...
Working Paper
Identifying Financial Crises Using Machine Learning on Textual Data
We use machine learning techniques on textual data to identify financial crises. The onset of a crisis and its duration have implications for real economic activity, and as such can be valuable inputs into macroprudential, monetary, and fiscal policy. The academic literature and the policy realm rely mostly on expert judgment to determine crises, often with a lag. Consequently, crisis durations and the buildup phases of vulnerabilities are usually determined only with the benefit of hindsight. Although we can identify and forecast a portion of crises worldwide to various degrees with ...