LAUSR

Structural Vector Autoregression (SVAR) methods suggest the monetary transmission mechanism may be weak and unreliable in many low-income African countries. But are structural VARs identified via short-run restrictions capable of detecting a transmission mechanism when one exists, under research conditions typical of low-income countries (LICs)? Using a small DSGE as our data-generating process, we assess the impact on VAR-based inference of short data samples, measurement error, high-frequency supply shocks, and other features of the LIC environment. The impact of these features on finite-sample bias appears to be relatively modest when identification is valid—a strong caveat, especially in LICs. Nonetheless many of these features undermine the precision of estimated impulse responses to monetary policy shocks, and cumulatively they suggest that statistically and economically insignificant results can be expected even when the underlying transmission mechanism is strong. These data features not only undermine the efficacy of the SVAR methodology for research and policy-making, but are also severe enough to motivate a continued search for monetary policy rules that are robust to these limitations.