We investigate the potential for Large Language Models (LLMs) to enhance scientific practice within experimentation by identifying key areas, directions, and implications. First, we discuss how these models can improve experimental design, including improving the elicitation wording, coding...

The extant literature predicts market returns with "simple" models that use only a few parameters. Contrary to conventional wisdom, we theoretically prove that simple models severely understate return predictability compared to "complex" models in which the number of parameters exceeds the...

We theoretically characterize the behavior of machine learning asset pricing models. We prove that expected out-of-sample model performance--in terms of SDF Sharpe ratio and test asset pricing errors--is improving in model parameterization (or "complexity"). Our empirical findings verify the...

We introduce artificial intelligence pricing theory (AIPT). In contrast with the APT's foundational assumption of a low dimensional factor structure in returns, the AIPT conjectures that returns are driven by a large number of factors. We first verify this conjecture empirically and show that...

Given the ubiquitous presence of endogenous regressors and the challenges in finding good instruments to overcome the endogeneity problem, a forefront of recent research is the development and application of endogeneity correction methods without requiring instruments. In this article, we...

We provide a comprehensive overview of earnings, income and wealth inequality based on the 2022 Survey of Consumer Finances from the United States. We document the current state of inequality and its evolution over the last three decades organizing the data along key demographic dimensions...

We study aggregate efficiency when households have heterogeneous preferences and outcomes. We generalize the consumption-equivalent variation of Lucas (1987) to a multi-agent setting, asking: how much can the consumption-possibility set shrink while keeping every agent at least as well off as in...

Income is simultaneously one of the most important variables used by economists and the variable most likely to be missing due to item non-response. While observations that are missing income responses are often dropped from analyses, such treatment is usually inappropriate. More appropriate...

What do machines learn, and why? To answer these questions we import models of human cognition into machine learning. We propose two ways of modeling machine learners based on this join: feasibility-based and cost-based machine learning. We evaluate and estimate our models using a deep learning...

This paper proposes a new way of displaying and analyzing macroeconomic time series to form recession forecasts. The proposed data displays contain the last three years of each expansion. These allow observers to see for themselves what is different about the last year before recession. Based on...