Standard practice for the estimation of dynamic stochastic general equilibrium (DSGE) models maintains the assumption that economic variables are properly measured by a single indicator, and that all relevant information for the estimation is summarized by a small number of data series. However,...

We present a general framework for Bayesian estimation and causality assessment in epidemiological models. The key to our approach is the use of sequential Monte Carlo methods to evaluate the likelihood of a generic epidemiological model. Once we have the likelihood, we specify priors and rely...

The Euler equations of consumption are tested on the household consumption of non-durables and services, reconstructed from the CEX database. The estimated relative risk aversion coefficient of the representative household decreases, and the estimated unexplained mean equity premium decreases,...

We present evidence that the equity premium and the premium of value stocks over growth stocks are explained in the 1982 1996 period with a stochastic discount factor (SDF) calculated as the weighted average of individual households' marginal rate of substitution with low and economically...

Tax rates have fluctuated considerably since federal income taxes were introduced in the United States in 1913. This paper analyzes the effects of stochastic taxation on asset prices in a dynamic general equilibrium model. Stochastic taxation affects the after-tax returns of both risky and safe...

Sparse models, though long preferred and pursued by social scientists, can be ineffective or unstable relative to large models, for example, in economic predictions (Giannone et al., 2021). To achieve sparsity for economic interpretation while exploiting big data for superior empirical...

This paper addresses two important parts of the problem of saving for retirement. They are (1) if assets are to be held in both conventional (and hence taxable) accounts and pension accounts, which assets should be held in each? and, (2) if the investor is substantially risk averse, what is the...

We propose a new method for solving high-dimensional dynamic programming problems and recursive competitive equilibria with a large (but finite) number of heterogeneous agents using deep learning. The "curse of dimensionality" is avoided due to four complementary techniques: (1) exploiting...

We consider the identification of and inference on a partially linear model, when the outcome of interest and some of the covariates are observed in two different datasets that cannot be linked. This type of data combination problem arises very frequently in empirical microeconomics. Using...

We propose methods for constructing regularized mixtures of density forecasts. We explore a variety of objectives and regularization penalties, and we use them in a substantive exploration of Eurozone inflation and real interest rate density forecasts. All individual inflation forecasters (even...