Browsing by Subject "BOOTSTRAP"

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  • Kilpua, E. K. J.; Olspert, N.; Grigorievskiy, A.; Kapyla, M. J.; Tanskanen, E. I.; Miyahara, H.; Kataoka, R.; Pelt, J.; Liu, Y. D. (2015)
    We study the relation between strong and extreme geomagnetic storms and solar cycle characteristics. The analysis uses an extensive geomagnetic index AA data set spanning over 150 yr. complemented by the Kakioka magnetometer recordings. We apply Pearson correlation statistics and estimate the significance of the correlation with a bootstrapping technique. We show that the correlation between the storm occurrence and the strength of the solar cycle decreases from a clear positive correlation with increasing storm magnitude toward a negligible relationship. Hence, the quieter Sun can also launch superstorms that may lead to significant societal and economic impact. Our results show that while weaker storms occur most frequently in the declining phase, the stronger storms have the tendency to occur near solar maximum. Our analysis suggests that the most extreme solar eruptions do not have a direct connection between the solar large-scale dynamo-generated magnetic field, but are rather associated with smaller-scale dynamo and resulting turbulent magnetic fields. The phase distributions of sunspots and storms becoming increasingly in phase with increasing storm strength, on the other hand, may indicate that the extreme storms are related to the toroidal component of the solar large-scale field.
  • Lange, Alexander; Dalheimer, Bernhard; Herwartz, Helmut; Maxand, Simone (2021)
    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.