Structural vector autoregressive (SVAR) models are frequently applied to trace the contemporaneous linkages among (macroeconomic) variables back to an interplay of orthogonal structural shocks. Under Gaussianity the structural parameters are unidentified without additional (often external and not data-based) information. In contrast, the often reasonable assumption of heteroskedastic and/or non-Gaussian model disturbances offers the possibility to identify unique structural shocks. We describe the R package svars which implements statistical identification techniques that can be both heteroskedasticity-based or independence-based. Moreover, it includes a rich variety of analysis tools that are well known in the SVAR literature. Next to a comprehensive review of the theoretical background, we provide a detailed description of the associated R functions. Furthermore, a macroeconomic application serves as a step-by-step guide on how to apply these functions to the identification and interpretation of structural VAR models.
Despite manifold policy interventions, poverty still exists. Those most harshly affected are people living in rural areas of low-income countries. A seminal strand in the literature presents a promising avenue for analyzing the lives of the poor by suggesting that poverty impedes cognitive function. However, the real-world consequences of impeded cognitive function are yet to be discovered. We ask whether the level of cognitive function can help to explain the differences in economic performance of the poor. We conducted a field study in rural Cambodia using the well-established Raven's Progressive Matrix to elicit cognitive function. Employing stochastic frontier analysis, we find that the level of cognitive function of poor smallholder farmers helps in explaining differences in economic performance. Our findings suggest that impeded cognitive function results in a negative economic performance feedback loop, which can be a reason why some farmers appear to be stuck in poverty while others manage to escape it.
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