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.

中文翻译：

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低收入非洲国家的结构性 VAR 和货币传导机制†

结构向量自回归 (SVAR) 方法表明，许多低收入非洲国家的货币传导机制可能薄弱且不可靠。但是，在低收入国家 (LIC) 的典型研究条件下，通过短期限制确定的结构性 VAR 是否能够检测存在的传输机制？使用小型 DSGE 作为我们的数据生成过程，我们评估了对短数据样本、测量误差、高频供应冲击和 LIC 环境其他特征的基于 VAR 推理的影响。当识别有效时，这些特征对有限样本偏差的影响似乎相对较小——这是一个强烈的警告，尤其是在 LIC 中。尽管如此，其中许多特征破坏了对货币政策冲击的估计脉冲响应的准确性，并且累积起来，即使潜在的传播机制很强，也可以预期在统计上和经济上无关紧要的结果。这些数据特征不仅削弱了 SVAR 方法在研究和决策方面的有效性，而且严重到足以激发继续寻找对这些限制稳健的货币政策规则。