This article develops a Bayesian approach for estimating panel quantile regression with binary outcomes in the presence of correlated random effects. We construct a working likelihood using an asymmetric Laplace (AL) error distribution and combine it with suitable prior distributions to obtain...

Using a panel of 24 OECD countries for the sample 1990-2019 and a standard macroeconomic framework, the paper tests the combined macroeconomic effects of climate change, environmental policies and technology. Overall, we find evidence of significant macroeconomic effects over the business cycle:...

The macroeconomic effects of climate-related events and climate policies depend on the interaction between demand- and supply-type of shocks that those events and policies imply. Using a panel of 24 OECD countries for the sample 1990-2019 and a standard macroeconomic framework, the paper tests...

We examine the profitability of personalized pricing policies that are derived using different specifications of demand in a typical retail setting with consumer-level panel data. We generate pricing policies from a variety of models, including Bayesian hierarchical choice models, regularized...

This paper compares responses to monetary shocks in the EMU countries (in the pre-EMU sample) and in the New Member States (NMS) from Central Europe. The small-sample problem, especially acute for the NMS, is mitigated by using a Bayesian estimation procedure which combines information across...

This study examines business cycle fluctuations in selected Sub-Saharan African (SSA) countries. The study measures the impact of selected real shocks, namely: terms of trade shocks, commodity price shocks, and government spending shocks, on macroeconomic fluctuations in SSA. The impact of the...

There has been a call for caution when using the conventional method for Bayesian inference in setidentified structural vector autoregressions on the grounds that the uniform prior over the set of orthogonal matrices could be nonuniform for individual impulse responses or other quantity of...

A novel spatial autoregressive model for panel data is introduced, which incorporates multilayer networks and accounts for time-varying relationships. Moreover, the proposed approach allows the structural variance to evolve smoothly over time and enables the analysis of shock propagation in...

Factor modelling extracts common information from a high-dimensional data set into few common components, where the latent factors usually explain a large share of data variation. Exploratory factor estimation induces sparsity into the loading matrix to associate units or series with those...

The scope of this paper is to estimate the production function for the Brazilian industrial sector from a longitudinal panel of the industrial sector (Annual Industrial Survey produced by the Institute of Geography and Statistics-PIA/IBGE-and the Ministry of Labour and Employment's Annual...