Investigating Economic Uncertainty Using Stochastic Volatility in Mean VARs: The Importance of Model Size, Order-Invariance and Classification

Stochastic Volatility in Mean Vector Autoregressions (SVMVARs) are popularly used to jointly estimate uncertainty and its economic effects. However, existing studies analyzing macroeconomic and financial uncertainty require the researcher to classify each variable as macroeconomic or financial before estimation, do not consider whether results are sensitive to model size and adopt a specification where results depend on the way the variables are ordered. We overcome these limitations, developing a novel Markov Chain Monte Carlo algorithm for large, order-invariant SVMVARs with unclassified variables. For each unclassified variable, the algorithm determines the appropriate classification at each point in time. Using a simulation study and large U.S. dataset, we uncover the following. Smaller SVMVARs overstate the effects of uncertainty, failing to reveal that only financial uncertainty has an adverse effect on the economy. When using large order-dependent SVMVARs, however, the uncertainty estimates produced depend on the variable ordering, distorting impulse response analysis. Thus, it becomes critical to adopt an order-invariant specification. We also find that many variables change classification with changes often occurring during crisis periods.