Uncertain Short-Run Restrictions and Statistically Identified Structural Vector Autoregressions

This study proposes a combination of a statistical identification approach with potentially invalid short-run zero restrictions. The estimator shrinks towards imposed restrictions and stops shrinkage when the data provide evidence against a restriction. Simulation results demonstrate how incorporating valid restrictions through the shrinkage approach enhances the accuracy of the statistically identified estimator and how the impact of invalid restrictions decreases with the sample size. The estimator is applied to analyze the interaction between the stock and oil market. The results indicate that incorporating stock market data into the analysis is crucial, as it enables the identification of information shocks, which are shown to be important drivers of the oil price

Estimating Fiscal Multipliers by Combining Statistical Identification with Potentially Endogenous Proxies

Different proxy variables used in fiscal policy SVARs lead to contradicting conclusions regarding the size of fiscal multipliers. In this paper, we show that the conflicting results are due to violations of the exogeneity assumptions, i.e. the commonly used proxies are endogenously related to the structural shocks. We propose a novel approach to include proxy variables into a Bayesian non-Gaussian SVAR, tailored to accommodate potentially endogenous proxy variables. Using our model, we show that increasing government spending is a more effective tool to stimulate the economy than reducing taxes. We construct new exogenous proxies that can be used in the traditional proxy VAR approach resulting in similar estimates compared to our proposed hybrid SVAR model.Comment: 10 figure

Testing for strong exogeneity in Proxy-VARs

Proxy variables have gained widespread prominence as indispensable tools for identifying structural VAR models. Analogous to instrumental variables, proxies need to be exogenous, i.e. uncorrelated with all non-target shocks. Assessing the exogeneity of proxies has traditionally relied on economic arguments rather than statistical tests. We argue that the economic rationale underlying the construction of commonly used proxy variables aligns with a stronger form of exogeneity. Specifically, proxies are typically constructed as variables not containing any information on the expected value of non-target shocks. We show conditions under which this enhanced concept of proxy exogeneity is testable without additional identifying assumptions