Aufsätze über internationale Makroökonomik und Geldpolitik

This thesis consists of four chapters. Each chapter covers a topic in international macroeconomics and monetary policy. The first chapter investigates the impact of unexpected monetary policy shocks on exchange rates in a multi-country econometric model. The second chapter examines the linkage between macroeconomic fundamentals and exchange rates through the monetary policy expectation channel. The third chapter focuses on the international transmission of bank and corporate distress. The last chapter unfolds the interest rate channel of monetary policy transmission in-an emerging economy-China, where regulations and market forces co-exist in this transmission.Die vorliegende Dissertation umfasst vier Kapitel, von denen sich jedes mit einem anderen Themengebiet aus der internationalen Makroökonomik und Geldpolitik befasst. Das erste Kapitel analysiert den Einfluss unerwarteter geldpolitischer Schocks auf die Wechselkurse in einem empirischen Mehrländermodell. Das dritte Kapitel untersucht den internationalen Einfluss fiskalpolitischer Schocks. Das zweite Kapitel untersucht den Zusammenhang zwischen makroökonomischen Faktoren und Wechselkursen über den Erwartungskanal der Geldpolitik. Im vierten Kapitel wird die internationale Transmission wirtschaftlicher Schieflagen im Unternehmens- und Bankensektor analysiert. Das letzte Kapitel untersucht den Zinskanal der monetären Transmission in einer aufstrebenden Volkswirtschaft, China, in der sowohl Marktkräfte als auch Regulierung Einfluss auf den Transmissionsprozess entfalten

On the Effects of Monetary Policy Shocks on Exchange Rates

In this paper we re-consider the effects of monetary policy shocks on exchange rates and forward premia. In the recent empirical literature, these effects have been predominantly described as puzzling, in that they would include delayed overshooting of the exchange rate as well as persistent deviations from uncovered interest parity. We specify an empirical model that in particular (i) allows for simultaneous multi-country adjustments in response to monetary policy shocks, and (ii) takes advantage of the identifying restrictions for monetary policy shocks implied by empirically supported long-run relations between the macroeconomic variables under consideration. Using monthly data from 1978 to 2006 for apanel of nine industrial economies (Australia, Canada, France, Germany, Italy, Japan, New Zealand, United Kingdom, and the United States), we find that U.S. Dollar effective and bilateral real exchange rates appreciate on impact after a contractionary U.S. monetary policyshock, and that there is no delay in the overshooting of the U.S. Dollar. Furthermore, there is no persistent significant forward premium. These results are consistent with the real exchange rate effects of monetary policy shocks in sticky price macroeconomic models, though the results of this paper also suggest that the latter models should be specified so as to capture simultaneous multi-country adjustments to shocks.monetary policy, exchange rate overshooting, forward premium, global vector error correction Model

Exchange rate dynamics, expectations, and monetary policy

This paper re-investigates the implications of monetary policy rules on changes in exchange rate, in a risk-adjusted, uncovered interest parity model with unrestricted parameters, emphasizing the importance of modeling market expectations of monetary policy. I use consensus forecasts as a proxy for market expectations. The analysis on the Deutsche mark, Canadian dollar, Japanese yen, and the British pound relative to the U.S. dollar from 1979 to 2008 shows that, through the expectations of future monetary policy, Taylor rule fundamentals are able to forecast changes in the exchange rate, even over short-term horizons of less than two years. Furthermore, the market expectation formation processes of short-term interest rates change over time and differ across countries, which contributes to the time varying relationship between exchange rates and macroeconomic fundamentals, together with the time varying currency risk premia and exchange rate forecast errors. --Exchange Rate,Monetary Policy,Expectation,Learning,VAR,Consensus Forecast

On the effects of monetary policy shocks on exchange rates

In this paper we re-consider the effects of monetary policy shocks on exchange rates and forward premia. In the recent empirical literature, these effects have been predominantly described as puzzling, in that they would include delayed overshooting of the exchange rate as well as persistent deviations from uncovered interest parity. We specify an empirical model that in particular (i) allows for simultaneous multi-country adjustments in response to monetary policy shocks, and (ii) takes advantage of the identifying restrictions for monetary policy shocks implied by empirically supported long-run relations between the macroeconomic variables under consideration. Using monthly data from 1978 to 2006 for a panel of nine industrial economies (Australia, Canada, France, Germany, Italy, Japan, New Zealand, United Kingdom, and the United States), we find that U.S. Dollar effective and bilateral real exchange rates appreciate on impact after a contractionary U.S. monetary policy shock, and that there is no delay in the overshooting of the U.S. Dollar. Furthermore, there is no persistent significant forward premium. These results are consistent with the real exchange rate effects of monetary policy shocks in sticky price macroeconomic models, though the results of this paper also suggest that the latter models should be specified so as to capture simultaneous multi-country adjustments to shocks

Generalized Category Discovery with Decoupled Prototypical Network

Generalized Category Discovery (GCD) aims to recognize both known and novel categories from a set of unlabeled data, based on another dataset labeled with only known categories. Without considering differences between known and novel categories, current methods learn about them in a coupled manner, which can hurt model's generalization and discriminative ability. Furthermore, the coupled training approach prevents these models transferring category-specific knowledge explicitly from labeled data to unlabeled data, which can lose high-level semantic information and impair model performance. To mitigate above limitations, we present a novel model called Decoupled Prototypical Network (DPN). By formulating a bipartite matching problem for category prototypes, DPN can not only decouple known and novel categories to achieve different training targets effectively, but also align known categories in labeled and unlabeled data to transfer category-specific knowledge explicitly and capture high-level semantics. Furthermore, DPN can learn more discriminative features for both known and novel categories through our proposed Semantic-aware Prototypical Learning (SPL). Besides capturing meaningful semantic information, SPL can also alleviate the noise of hard pseudo labels through semantic-weighted soft assignment. Extensive experiments show that DPN outperforms state-of-the-art models by a large margin on all evaluation metrics across multiple benchmark datasets. Code and data are available at https://github.com/Lackel/DPN.Comment: Accepted by AAAI 202

A Diffusion Weighted Graph Framework for New Intent Discovery

New Intent Discovery (NID) aims to recognize both new and known intents from unlabeled data with the aid of limited labeled data containing only known intents. Without considering structure relationships between samples, previous methods generate noisy supervisory signals which cannot strike a balance between quantity and quality, hindering the formation of new intent clusters and effective transfer of the pre-training knowledge. To mitigate this limitation, we propose a novel Diffusion Weighted Graph Framework (DWGF) to capture both semantic similarities and structure relationships inherent in data, enabling more sufficient and reliable supervisory signals. Specifically, for each sample, we diffuse neighborhood relationships along semantic paths guided by the nearest neighbors for multiple hops to characterize its local structure discriminately. Then, we sample its positive keys and weigh them based on semantic similarities and local structures for contrastive learning. During inference, we further propose Graph Smoothing Filter (GSF) to explicitly utilize the structure relationships to filter high-frequency noise embodied in semantically ambiguous samples on the cluster boundary. Extensive experiments show that our method outperforms state-of-the-art models on all evaluation metrics across multiple benchmark datasets. Code and data are available at https://github.com/yibai-shi/DWGF.Comment: EMNLP 2023 Mai

Penalized Maximum Likelihood Method to a Class of Skewness Data Analysis

An extension of some standard likelihood and variable selection criteria based on procedures of linear regression models under the skew-normal distribution or the skew-t distribution is developed. This novel class of models provides a useful generalization of symmetrical linear regression models, since the random term distributions cover both symmetric as well as asymmetric and heavy-tailed distributions. A generalized expectation-maximization algorithm is developed for computing the l1 penalized estimator. Efficacy of the proposed methodology and algorithm is demonstrated by simulated data

Protein kinase Cα downregulation via siRNA-PKCα released from foldable capsular vitreous body in cultured human retinal pigment epithelium cells

We previously found that downregulation of protein kinase Cα (PKCα) can inhibit retinal pigment epithelium (RPE) cell proliferation involved in the development of proliferative vitreoretinopathy (PVR). In this study, we tested whether PKCα could be downregulated via small interfering RNA (siRNA)-PKCα released from foldable capsular vitreous body (FCVB) in cultured human RPE cells. SiRNA-PKCα content, determined by ultraviolet (UV) spectrophotometer, was released from FCVB containing 200, 300, 400, 500, and 600 nm siRNA-PKCα in a time-dependent manner from 1 to 96 hours and a dose-dependent manner at five concentrations. The content (y) had a good linear relationship with time (x), especially in the 600 nm siRNA-PKCα group (y = 16.214x, R2 = 0.9809). After treatment with siRNA-PKCα released from FCVBs, the PKCα was significantly decreased by RT-PCR, Western blot, and immunofluorescence analysis in RPE cells. These results indicate that PKCα was significantly downregulated by siRNA-PKCα released from FCVB in human RPE cells and provide us with a new avenue to prevent PVR