We study a self-reflexive DSGE model with heterogeneous households, aimed at characterising the impact of economic recessions on the different strata of the society. Our framework allows to analyse the combined effect of income inequalities and confidence feedback mediated by heterogeneous...

Survival data often include an “immune” or cured fraction of units that will never experience an event and conversely, an “at risk” fraction that can fail or die. It is also plausible that spatial clustering (i.e., spatial autocorrelation) in latent or unmeasured risk factors among...

We explore the consequences of weakening the notion of incentive compatibility from strategy-proofness to ordinal Bayesian incentive compatibility (OBIC) in the random assignment model. If the common prior of the agents is a uniform prior, then a large class of random mechanisms are OBIC with...

This paper investigated the macroeconomic effects of government spending shocks in Ethiopia using a Bayesian Vector Auto Regression model. We examined the dynamic responses of output, inflation, interest rate and exchange rate to Government Spending shocks employing quarterly data from 2000/01Q1...

Economic forecasts are essential in our daily lives. Accordingly, we ask the following questions: (1) Can we have an improved prediction when we additionally combine combinations of forecasts made by various institutions? (2) If we can, then what method of additional combination will be...

We contribute to research on mixed-frequency regressions by introducing an innovative Bayesian approach. Based on a new “high-frequency” identification scheme, we provide novel empirical evidence of identifying uncertainty shock for the US economy. As main findings, we document a “temporal...

The COVID-19 pandemic triggered an extreme variation in many key macroeconomic indicators. This paper documents that multivariate t-distributed errors are better equipped to capture this variation than common stochastic volatility in a Bayesian VAR. Diagnostics indicate that the data prefers to...

This paper develops a framework for estimating Quantal Response Equilibrium models from experimental data using Bayesian techniques. Bayesian techniques offer some advantages over the more commonly-used maximum likelihood approach: (i) the accuracy of the posterior simulation is limited by...