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SAFE Update February 2024
(2024)
The Federal Reserve has been publishing federal funds rate prescriptions from Taylor rules in its Monetary Policy Report since 2017. The signals from the rules aligned with Fed action on many occasions, but in some cases the Fed opted for a different route. This paper reviews the implications of the rules during the coronavirus pandemic and the subsequent inflation surge and derives projections for the future.
In 2020, the Fed took the negative prescribed rates, which were far below the effective lower bound on the nominal interest rate, as support for extensive and long-lasting quantitative easing. Yet, the calculations overstate the extent of the constraint, because they neglect the supply side effects of the pandemic.
The paper proposes a simple model-based adjustment to the resource gap used by the rules for 2020. In 2021, the rules clearly signaled the need for tightening because of the rise of inflation, yet the Fed waited until spring 2022 to raise the federal funds rate. With the decline of inflation over the course of 2023, the rules’ prescriptions have also come down. They fall below the actual federal funds rate target range in 2024. Several caveats concerning the projections of the interest rate prescriptions are discussed.
Highlights
• Six Newton methods for solving matrix quadratic equations in linear DSGE models.
• Compared to QZ using 99 different DSGE models including Smets and Wouters (2007).
• Newton methods more accurate than QZ with comparable computation burden.
• Apt for refining solutions from alternative methods or nearby parameterizations.
Abstract
This paper presents and compares Newton-based methods from the applied mathematics literature for solving the matrix quadratic that underlies the recursive solution of linear DSGE models. The methods are compared using nearly 100 different models from the Macroeconomic Model Data Base (MMB) and different parameterizations of the monetary policy rule in the medium-scale New Keynesian model of Smets and Wouters (2007) iteratively. We find that Newton-based methods compare favorably in solving DSGE models, providing higher accuracy as measured by the forward error of the solution at a comparable computation burden. The methods, however, suffer from their inability to guarantee convergence to a particular, e.g. unique stable, solution, but their iterative procedures lend themselves to refining solutions either from different methods or parameterizations.
The hierarchical feature regression (HFR) is a novel graph-based regularized regression estimator, which mobilizes insights from the domains of machine learning and graph theory to estimate robust parameters for a linear regression. The estimator constructs a supervised feature graph that decomposes parameters along its edges, adjusting first for common variation and successively incorporating idiosyncratic patterns into the fitting process. The graph structure has the effect of shrinking parameters towards group targets, where the extent of shrinkage is governed by a hyperparameter, and group compositions as well as shrinkage targets are determined endogenously. The method offers rich resources for the visual exploration of the latent effect structure in the data, and demonstrates good predictive accuracy and versatility when compared to a panel of commonly used regularization techniques across a range of empirical and simulated regression tasks.
SAFE Update April 2024
(2024)
Looking beyond ESG preferences: The role of sustainable finance literacy in sustainable investing
(2024)
We assess how sustainable finance literacy affects people’s sustainable investment behavior, using a pre-registered experiment. We find that an increase in sustainable finance literacy leads to a 4 to 5% increase in the probability of investing sustainably. This effect is moderated by sustainability preferences. In the absence of moderate sustainability preferences, any additional increase in sustainable finance literacy is at minimum irrelevant, and we find some evidence that it might even reduce sustainable investments. Our findings underscore the role of knowledge in shaping sustainable investment decisions, highlighting the importance of factors beyond sustainability preferences.
Die vorliegende Dissertation bietet eine eingehende Analyse der Entstehung und Evolution der rechtlichen Rahmenbedingungen der Arbeitnehmerüberlassung in der Volksrepublik China. Ursprünglich in den 1980er Jahren als Instrument zur Reduzierung der Arbeitslosigkeit eingeführt, hat sich die Funktion der Arbeitnehmerüberlassung im Laufe der Zeit gewandelt. Diese Entwicklung wird in der Arbeit sorgfältig nachgezeichnet.
Die Studie skizziert die legislative Entwicklung von der anfänglichen Konzeption bis zur formellen Legalisierung im Jahr 2007 und untersucht die geltenden gesetzlichen Bestimmungen zur Arbeitnehmerüberlassung. Dabei werden die spezifischen Aspekte der chinesischen Gesetzgebung herausgestellt, bei der die Arbeitnehmerüberlassung vornehmlich als komplementäre Beschäftigungsform angesehen wird. Die Arbeit beleuchtet die Rechte und Pflichten aller Akteure. Obwohl nicht umfassend rechtsvergleichend angelegt, wird doch immer wieder ein Blick auf die Rechtslage in Deutschland geworfen, sodass die unterschiedlichen regulatorischen Ansätze deutlich hervortreten. Zugleich werden die besonderen Herausforderungen behandelt, denen diese Beschäftigungsform im chinesischen Kontext begegnet.
In a year marked by challenging market dynamics, the importance of ESG investments remains unwavering. But the wave of ESG regulations and requests generates a demand for more scalable ways to collect and analyze ESG data. The rise of AI could mark a turning point in an industry heavily burdened by reporting requirements, and unlock the true potential of ESG for businesses and investors alike.