Robust structural analysis when theoretical information is scarce

• Applicant: Professor Dr. Helmut Herwartz
• Subject Area: Statistics and Econometrics
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 504720211

The identification of structural relations in dynamic systems requires external information (e.g. theory-based restrictions, instrumental variables). To support structural analysis in cases where such information is scant or hardly available, a viable literature has emerged on data-based identification that relies on stochastic characteristics of orthogonal shocks, e.g. heteroskedasticity or independence of non-Gaussian shocks. To detect independent components, several suggestions have been made that differ, for instance, with regard to parametric rigour. The pseudo-maximum-likelihood approach of Gourieroux, Monfort and Renné (2017) is of particular appeal, as it allows robust estimation of structural relations under conditions that regulate, for instance, the “extent” of misspecification. Yet, the fact remains that actual misspecification is always unknown to the analyst. In this project we first develop kernel-maximum-likelihood estimation of structural parameters as an alternative to parametric pseudo-maximum-likelihood that promises robust estimation under much weaker conditions. Second, the new approach will be used to unravel the scope of both deterministic (structural breaks) and stochastic changes (Markov switching) in the transmission from latent shocks to observables. Third, we suggest kernel-maximum-likelihood as a conceptual framework to account for singular systems, in which observables are driven by a smaller number of (independent) shocks. Fourth, in addition to econometric advances, this project contributes to important topics of current macroeconomic research. On the one hand, each developed econometric tool is employed to achieve an improved understanding of global oil market shocks and their effects. On the other hand, we employ an ensemble of the planned econometric advances to identify information effects of monetary policy announcements of the central bank. Finally, the ensemble approach will serve as a core yardstick for the development of a novel software package that summarizes relevant information contained in central banks announcements in (almost) real-time to unravel the state of monetary policy (both in the US and the Euro-Area) and its potential macroeconomic implications.

• DFG Programme: Research Grants