This chapter of the Handbook of Computational Economics is mostly about research on active learning and is confined to discussion of learning in dynamic models in which the system equations are linear, the criterion function is quadratic, and the additive noise terms are Gaussian. Though there...

We propose a new methodology for structural estimation of dynamic discrete choice models. We combine the Dynamic Programming (DP) solution algorithm with the Bayesian Markov Chain Monte Carlo algorithm into a single algorithm that solves the DP problem and estimates the parameters...

It is well-known that various criteria for comparing aversion to real-outcome risks are equivalent. Some of this theory has been extended to Euclidean-outcome risks. We extend it further by:(a) filling the conceptual gaps, most notably by providing a criterion using our generalized Arrow-Pratt...

The article concerns with the subject of studying economic behavior of taxpayers with regard to tax evasion. An overview of theoretic and methodological approaches concerning decision making on the base of rational and behavior principles is given. The methodology of research assumes application...

Behavioral and experimental literature on financial instability focuses on either subjective price expectations (Learning-to-Forecast experiments) or individual trading (Learning-to-Optimize experiments). Bao et al. (2017) have shown that subjects have problems with both tasks. In this paper, I...

Principal component analysis (PCA) is a widely used dimension reduction tool in the analysis of high-dimensional data. However, in many applications such as risk quantification in finance or climatology, one is interested in capturing the tail variations rather than variation around the mean. In...

We integrate a centralized wage bargaining process into an otherwise standard DSGE model with a financial accelerator to simulate distributional shocks in the presence of financial instability. Our framework provides a counterfactual analysis of the effects of the observed decrease in the labor...

We study social learning in a social network setting where agents receive independent noisy signals about the truth. Agents naïvely update beliefs by repeatedly taking weighted averages of neighbors' opinions. The weights are fixed in the sense of representing average frequency and intensity of...

Recent survey evidence suggests that investors form beliefs about future stock returns by predominantly extrapolating their own experience: They overweight returns they have personally experienced while underweighting returns from earlier years and consequently expect high (low) stock market...

We study a model in which individual agents use simple linear first order price forecasting rules, adapting them to the complex evolving market environment with a smart Genetic Algorithm optimization procedure. The novelties are: (1) a parsimonious experimental foundation of individual...