We propose a new model in which relationship-specific supply and demand shocks are non-parametrically identified in bipartite data under mild assumptions. For example, separate heterogeneous supply shocks are identified for each firm to which a bank lends. We show that a simple estimator is...

This study investigates the application of Factor-Augmented Vector Autoregression (FAVAR) and Bayesian Vector Autoregression (BVAR) models for inflation forecasting. FAVAR models deal with high-dimensional data by extracting latent factors from extensive macroeconomic indicators, while BVAR...

Recent rises in macroeconomic volatility have prompted the introduction of quantile vector autoregression (QVAR) models to forecast macroeconomic risk. This paper provides an extensive evaluation of the predictive performance of QVAR models in a pseudo-out-of-sample experiment spanning 112...

We adopted an empirical approach to capture the macroeconomic impact of tax changes for the examined period from 1974 to 2018. It is generally accepted that vector autoregression model (VAR) has proven useful for describing the dynamic interrelationships of multivariate series. Our empirical...

Star variables, such as potential output and the neutral real interest rate, are fundamental to economic policymaking but challenging to identify due to their latent nature. Buncic, Pagan, and Robinson (2023) highlight the difficulty of identifying star variables within short macroeconomic...