Essays in Empirical Macroeconomics: Identification in Vector Autoregressive Models and Robust Inference in Early Warning Systems

Bayesian empirical macroeconomic models are excellent tools for prediction and structural analysis. The use of a prior distribution facilitates model averaging, allows for structural identification of multiple time series models and makes estimation of high-dimensional models feasible. However, prior distributions need to be chosen carefully in order to accurately reflect the researcher's beliefs before looking at the data. I exemplify how to do so in this thesis by employing model averaging techniques in Bayesian spirit, by developing tools to express priors for structural vector-autoregressive models, and by showing a new approach to identify the impact of variations in uncertainty in a data-intensive environment. In the first chapter, which is based on joint work with Tigran Poghosyan, we use an Early Warning System (EWS) to recover leading indicators of fiscal distress events. In particular, we use Extreme Bounds Analysis (EBA), a model averaging approach introduced by Leamer (1985) and popularised by Sala-i-Martin (1997), to assess the robustness of leading indicators for fiscal distress across different models. We find that both fiscal and non-fiscal leading indicators are robust predictors of fiscal distress events. In a second step we assess the forecasting performance of an EWS based on the most robust leading indicators. We find that it offers a gain in predictive power compared to a baseline model which is based on fiscal leading indicators only. Lastly, we assess the robustness of these results across various model specifications, subsamples and estimation strategies. In the second chapter, which is based on joint work with Michele Piffer, we propose a new approach to express prior beliefs on the impulse responses of structural vector auto-regressive (SVAR) models. This approach does not restrict the family of prior distributions to a set that is technically convenient. Rather, it combines extensive flexibility in the choice of priors with an efficient importance sampler to explore the posterior distribution. We compare our new posterior sampler to a computationally more demanding generic sampler and confirm that we recover the shape of the posterior. We illustrate the approach using artificial data and in an application of sign restrictions to identify oil market shocks. We show that posterior inference is sharpened compared to the traditional approach of imposing sign restrictions and that oil supply shocks play a major role in driving oil price dynamics. In the third chapter I investigate the effects of uncertainty shocks in the spirit of Bloom (2009) using a newly developed Bayesian Proxy Factor-augmented VAR (BP-FAVAR) model. This model combines a large information set with an identification scheme based on external instruments, thereby jointly addressing informational deficiency issues and non-credible identification assumptions. I propose a new sampling algorithm exploiting the state-space representation of the model. I find that uncertainty shocks have adverse effects on the real economy and are deflationary in the short run. To recover the dynamic causal effects, the identification scheme is crucial

Have the effects of shocks to oil price expectations changed?: Evidence from heteroskedastic proxy vector autoregressions

Studies of the crude oil market based on structural vector autoregressive (VAR) models typically assume a time-invariant model and transmission of shocks and possibly allow for heteroskedasticity by using robust inference procedures. We assume a heteroskedastic reduced-form VAR model with time-invariant slope coefficients and explicitly consider the possibility of time-varying shock transmission due to heteroskedasticity. We study a model for the global crude oil market that includes key world and U.S. macroeconomic variables and find evidence for changes in the transmission of shocks to oil price expectations during the last decades which can be attributed to heteroskedasticity

Heteroskedastic proxy vector autoregressions: An identification-robust test for time-varying impulse responses in the presence of multiple proxies

We propose a test for time-varying impulse responses in heteroskedastic structural vector autoregressions that can be used when the shocks are identified by external proxy variables as a group but not necessarily individually. The test is robust to the identification scheme for identifying the shocks individually and can be used even if the shocks are not identified individually. The asymptotic analysis is supported by small sample simulations which show good properties of the test. An investigation of the impact of productivity shocks in a small macroeconomic model for the U.S. illustrates the importance of the issue for empirical work

Review Essay: In Pursuit of a Disappearing Paradigm

U.S. Naval Strategy and National Security: The Evolution of American Maritime Powe

Ökologische Jung- und Zierpflanzenproduktion: Herstellung und Einsatz komposthaltiger Pflanzsubstrate

Erwünscht sind im ökologischen Gartenbau biologisch aktive Substrate mit einem möglichst niedrigen Torfgehalt. Qualitätskomposte übernehmen hier die tragende Rolle. Das Merkblatt informiert über die Anforderungen, die an Ökosubstrate und Zuschlagstoffe - allen voran Kompost - gestellt werden und sagt, worauf bei Herstellung und Einsatz geachtet werden muss

Pathogen and host genotype differently affect pathogen fitness through their effects on different life-history stages

Abstract Background Adaptation of pathogens to their hosts depends critically on factors affecting pathogen reproductive rate. While pathogen reproduction is the end result of an intricate interaction between host and pathogen, the relative contributions of host and pathogen genotype to variation in pathogen life history within the host are not well understood. Untangling these contributions allows us to identify traits with sufficient genetic variation for selection to act and to identify mechanisms of coevolution between pathogens and their hosts. We investigated the effects of pathogen and host genotype on three life-history components of pathogen fitness; infection efficiency, latent period, and sporulation capacity, in the oat crown rust fungus, Puccinia coronata f.sp. avenae, as it infects oats (Avena sativa). Results We show that both pathogen and host genotype significantly affect total spore production but do so through their effects on different life-history stages. Pathogen genotype has the strongest effect on the early stage of infection efficiency, while host genotype most strongly affects the later life-history stages of latent period and sporulation capacity. In addition, host genotype affected the relationship between pathogen density and the later life-history traits of latent period and sporulation capacity. We did not find evidence of pathogen-by-host genotypic (GxG) interactions. Conclusion Our results illustrate mechanisms by which variation in host populations will affect the evolution of pathogen life history. Results show that different pathogen life-history stages have the potential to respond differently to selection by host or pathogen genotype and suggest mechanisms of antagonistic coevolution. Pathogen populations may adapt to host genotypes through increased infection efficiency while their plant hosts may adapt by limiting the later stages of pathogen growth and spore production within the host.</p

Formal specification with JML

This text is a general, self contained, and tool independent introduction into the Java Modeling Language, JML. It is a preview of a chapter planned to appear in a book about the KeY approach and tool to the verification of Java software. JML is the dominating starting point of KeY style Java verification. However, this paper does not in any way depend on any tool nor verification methodology. Other chapters in this book talk about the usage of JML in KeY style verification. Here, we only refer to KeY in very few places, without relying on it. This introduction is written for all readers with an interest in formal specification of software in general, and anyone who wants to learn about the JML approach to specification in particular. The authors appreciate any comments or questions that help to improve the text

Multidimensional Tax Compliance Attitude

Citizen tax compliance significantly dictates governmental fiscal capacities. Recognizing this, understanding the determinants of tax compliance remains paramount. While existing literature frequently isolates and tests individual determinants such as audit likelihood, penalty structures, tax morale, and perceived fairness, an integrative, bottom-up approach addressing the spectrum of tax compliance attitudes has largely been overlooked. Addressing this gap, our study constructs a multidimensional Tax Compliance Attitude Inventory (TCAI) by harmonizing real taxpayer re-sponses with established theoretical underpinnings. Through factor analysis, we delineate four pivotal factors: (i) morale, (ii) monetary benefit, (iii) deterrence, and (iv) authority. Notably, mo-rale and deterrence emerge as consistent influencers of tax compliance. Embracing this multidi-mensionality, our cluster analysis demarcates two distinct taxpayer personas: (a) moralists and (b) rationalists. Our findings underscore that moralists consistently exhibit higher tax compliance than their rationalist counterparts. We further present a streamlined classification algorithm to operationalize the TCAI in new datasets, minimizing item count. This work serves as a seminal contribution, offering both academia and tax authorities a robust, quantitative tool to gauge tax compliance attitudes

Pushing the button: Why do learners pause online videos?

With the recent surge in digitalization across all levels of education, online video platforms gained educational relevance. Therefore, optimizing such platforms in line with learners’ actual needs should be considered a priority for scientists and educators alike. In this project, we triangulate logfiles of a large German online video platform for educational videos with behavioral data from a laboratory study and the objective characteristics of the selected videos. We aim to understand the potential motives for why participants pause educational videos while watching such videos online. Our analyses revealed that perceived difficulties in comprehension and meaningful structural breakpoints in the videos were associated with increased pausing behavior. In contrast, pausing behavior was not associated with the videos’ formal structural features highlighted in the video platform. Implications of these findings and the potentials of our methodological approach for theory and practice are discussed. © 2021 The Author