Paritosh Shankarrao Junare

Two Gaussians, Too Many: A bootstrap-based approach to assess identifiability in non-Gaussian structural vector autoregressions

Abstract: We propose a bootstrap-based approach to evaluate the asymptotic validity of Independent Component Analysis (ICA) identification and inference in structural vector autoregressions (SVARs). ICA-based identification requires that out of n mutually independent structural shocks, at most one is Gaussian. The diagnostic evaluates this condition by measuring the divergence between the conditional bootstrap distribution of a maximum likelihood estimator of the structural impact matrix and its asymptotic benchmark under valid identification. We establish that its bootstrap distribution, under the validity of identification conditions, is asymptotically standard normal. This simplifies the diagnostic to a test of normality of the bootstrap replications of the estimator. Crucially, under the null of valid identification the diagnostic induces no pre-testing bias, as bootstrap replications and sample size diverge jointly (at an appropriate rate). It ensures the test statistic is asymptotically independent of the data. Monte Carlo simulations with Normal-Inverse Gaussian (NIG) structural shocks demonstrate that the diagnostic attains near-exact nominal size under valid identification conditions and exhibits substantial power against identification failure caused by the presence of multiple Gaussian structural shocks. Based on the estimates of a SVAR model in the macroeconomic and financial uncertainty literature, we demonstrate its potential as a practical, robust tool for validating ICA-based identification without any pre-testing bias.

Draft coming soon!

Assembling time-varying proxies for political attitudes in India

Supervisor: Prof. Alessandro Saia, University of Bologna. (Summer 2022)