Shocks, Information, and Structural VARs

Structural Vector Autoregressions (SVARs) have become one of the most popular tools to measure the effects of structural economic shocks. Several new techniques to “identify” economic shocks have been proposed in the literature in the last decades. Identification hinges on the implicit assumption that economic shocks are retrievable from the data. In other words, the data contain enough information to correctly estimate the shocks. SVAR models, however, are small-scale models, only a small number of variables can be handled, and this feature can forcefully limit the amount of information that variables can convey. Narrow information sets present problems for identification, but some theoretical results and empirical procedures can test whether such information is sufficient to estimate economic shocks. Additionally, there are possible solutions to the problem of limited information, such as Factor Augmented VAR or dynamic rotations.

Structural VARs and the Monetary Transmission Mechanism in Low-Income African Countries†

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.

Identifying the Monetary Transmission Mechanism in Sub-Saharan Africa

VAR methods suggest that the monetary transmission mechanism may be weak and unreliable in low-income countries. But are structural VARs identified via short-run restrictions capable of detecting a transmission mechanism where one exists, under research conditions typical of these countries? Using small DSGEs as data-generating processes, the chapter assesses 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 low-income countries. However, many of these features undermine the precision of estimated impulse responses to monetary policy shocks, and cumulatively they suggest that ‘insignificant’ results can be expected even when the underlying transmission mechanism is strong.

The Impact of Policy Uncertainty on Macro-Economy of Developed and Developing Countries

This paper investigates the effects of a US economic policy uncertainty shock on Indian macroeconomic variables with a number of Structural VARs. This study models the economic policy uncertainty index as constructed by Baker et al. (2013). The study also uses a set of macroeconomic variables for India such as inflation, industrial production and nominal interest rate. The objective of the study is to identify the potential impacts of economic policy uncertainty shocks from the US economy to the Indian economy. According to the SVARs, a one standard deviation shock to the US economic policy uncertainty leads to a statistically significant decline in the Indian industrial production of -0.294% and in the Indian inflation of -0.032%. India shows to be resistant to US policy uncertainty. Furthermore, the study finds that the contribution of the US economic policy uncertainty on the Indian macroeconomic variables is shown to be significantly larger than the one exerted by the Indian uncertainty shock.

The Impact of Policy Uncertainty on Macro-Economy of Developed and Developing Countries

This paper investigates the effects of a US economic policy uncertainty shock on Indian macroeconomic variables with a number of Structural VARs. This study models the economic policy uncertainty index as constructed by Baker et al. (2013). The study also uses a set of macroeconomic variables for India such as inflation, industrial production and nominal interest rate. The objective of the study is to identify the potential impacts of economic policy uncertainty shocks from the US economy to the Indian economy. According to the SVARs, a one standard deviation shock to the US economic policy uncertainty leads to a statistically significant decline in the Indian industrial production of -0.294% and in the Indian inflation of -0.032%. India shows to be resistant to US policy uncertainty. Furthermore, the study finds that the contribution of the US economic policy uncertainty on the Indian macroeconomic variables is shown to be significantly larger than the one exerted by the Indian uncertainty shock.