1,720,977 research outputs found
Nonparametric tests for independence: a review and comparative simulation study with an application to malnutrition data in India
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Data‐driven identification in SVARs—When and how can statistical characteristics be used to unravel causal relationships?
No full textThe nonlinear dependence of income inequality and carbon emissions: Potentials for a sustainable future
No full texthttp://dx.doi.org/10.13039/501100001659 Deutsche Forschungsgemeinschaf
svars: An R Package for Data-Driven Identification in Multivariate Time Series Analysis
Full text linkStructural 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
Heteroskedasticity‐Robust Unit Root Testing for Trending Panels
Full text linkTime-varying volatility and linear trends are common features of several macroeconomic time series. Recent articles have proposed panel unit root tests (PURTs) that are pivotal in the presence of volatility shifts, excluding linear trends, however. This article proposes a new PURT that works well for data that is both heteroskedastic and trending. Under the null hypothesis, the test statistic has a limiting Gaussian distribution. We derive the local asymptotic power to underpin the consistency of the test statistic. Simulation results reveal that the test performs well in small samples. As an empirical illustration, we examine the stationarity of energy use per capita in OECD economies. While the series are in general difference stationary, they could also be considered as trend stationary for specific time spans.Peer reviewe
Identification of structural multivariate GARCH models
Full text linkThe class of multivariate GARCH models is widely used to quantify and monitor volatility and correlation dynamics of financial time series. While many specifications have been proposed in the literature, these models are typically silent about the system inherent transmission of implied orthogonalized shocks to vector returns. In a framework of non- Gaussian independent structural shocks, this paper proposes a loss statistic, based on higher order co-moments, to discriminate in a data-driven way between alternative structural assumptions about the transmission scheme, and hence identify the structural model. Consistency of identification is shown theoretically and via a simulation study. In its structural form, a four dimensional system comprising US and Latin American stock market returns points to a substantial volatility transmission from the US to the Latin American markets. The identified structural model improves the estimation of classical measures of portfolio risk, as well as corresponding variations
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