Cities increasingly address climate change, e.g. by pledging city-level emission reduction targets. This is puzzling for the provision of a global public good: what are city governments' reasons for doing so, and do pledges actually translate into emission reductions? Empirical studies have...

Most analyses of the U.S. Great Moderation have been based on VAR methods, and have consistently pointed toward good luck as the main explanation for the greater macroeconomic stability of recent years. Using data generated by a New-Keynesian model in which the only source of change is the move...

Bayes' statistical rule remains the status quo for modeling belief updating in both normative and descriptive models of behavior under uncertainty. Recent research has questioned the use of Bayes' rule in descriptive models of behavior, presenting evidence that people overweight 'good news'...

This paper considers factor estimation from heterogenous data, where some of the variables are noisy and only weakly informative for the factors. To identify the irrelevant variables, we search for zero rows in the loadings matrix of the factor model. To sharply separate these irrelevant...

The COVID-19 pandemic has led to enormous data movements that strongly affect parameters and forecasts from standard VARs. To address these issues, we propose VAR models with outlier-augmented stochastic volatility (SV) that combine transitory and persistent changes in volatility. The resulting...

I derive a social planner's optimal information design in an environment with quasi-hyperbolic discounting consumers without commitment. Consumption induces instantaneous utility, but unknown delayed cost. Consumers may or may not acquire additional costless information on the cost parameter....

In this work, we consider modeling the past volatilities through an asymmetric generalised autoregressive conditional heteroskedasticity (Garch) model with heavy tailed sampling distributions. In particular, we consider the Student-t model with unknown degrees of freedom and indicate how it may...

Space-varying regression models are generalizations of standard linear models where the regression coefficients are allowed to change in space. The spatial structure is specified by a multivariate extension of pairwise difference priors thus enabling incorporation of neighboring structures and...

This paper describes the inference procedures required to perform Bayesian inference to some multivariate econometric models. These models have a spatial component built into commonly used multivariate models. In particular, the seemingly unrelated regression and vector autoregressive models are...