Institute for Monetary and Financial Stability (IMFS)
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Institute
Helmut Schlesinger: Wegbereiter und Garant der deutschen Geld- und Stabilitätspolitik wird 100
(2024)
Am 4. September 2024 vollendet Professor Dr. Helmut Schlesinger sein 100. Lebensjahr. Von 1991 bis 1993 bekleidete er das Amt des Präsidenten der Deutschen Bundesbank. Zuvor war er in verschiedenen Positionen für die Bank tätig, unter anderem als langjähriger Vizepräsident (von 1980 bis 1991) sowie als Leiter der Hauptabteilung Volkswirtschaft und Statistik. Das Jubiläum bietet Anlass, sein Lebenswerk zu beschreiben und zu würdigen. Für ehemalige Mitarbeiter war Helmut Schlesinger ein großes Vorbild und eine Quelle des Ansporns in vielerlei Hinsicht. Insbesondere vier Bereiche seiner Tätigkeiten haben die Arbeit seiner Mitarbeiter maßgeblich geprägt: Erstens seine Fähigkeit, ökonomisches Denken als eine Synthese aus Analyse und Statistik zu begreifen, zu vermitteln und zu organisieren, zweitens sein Verdienst, eine Stabilitätskultur in leitenden Positionen mitgeschaffen und bewahrt zu haben, drittens sein ordnungspolitisches Credo zur Preisstabilität und zur Unabhängigkeit der Zentralbank sowie viertens seine klaren Vorstellungen zu den Bedingungen einer erfolgreichen Europäischen Wirtschafts- und Währungsunion.
Im Folgenden soll ein Überblick über diese vier Schwerpunkte seiner Schaffensbilanz gegeben werden. In diesem Kontext ist insbesondere Schlesingers entscheidende Rolle bei der Schaffung der deutsch-deutschen Währungsunion 1990 sowie beim langjährigen Entstehungsprozess des Eurosystems und der Europäischen Zentralbank hervorzuheben. In der deutschen Bevölkerung, aber auch international hoch geachtet, wurde Helmut Schlesinger oft als die "Seele der Bundesbank" bezeichnet.Die Anforderungen, die er an jeden Einzelnen stellte, waren hoch. Er wurde von den Mitarbeitern sehr geschätzt, nicht zuletzt aufgrund seines großen Arbeitsethos und seiner unermüdlichen Schaffenskraft, die von Beständigkeit, Gradlinigkeit und Prinzipientreue geprägt waren.
I provide a solution method in the frequency domain for multivariate linear rational expectations models. The method works with the generalized Schur decomposition, providing a numerical implementation of the underlying analytic function solution methods suitable for standard DSGE estimation and analysis procedures. This approach generalizes the time-domain restriction of autoregressive-moving average exogenous driving forces to arbitrary covariance stationary processes. Applied to the standard New Keynesian model, I find that a Bayesian analysis favors a single parameter log harmonic function of the lag operator over the usual AR(1) assumption as it generates humped shaped autocorrelation patterns more consistent with the data.
In this paper, we construct a Dynamic Stochastic General Equilibrium (DSGE) model to examine the implications of dual rates for green lending. We demonstrate that implementing a distinct interest rate for banks engaged in green lending can effectively mitigate transition risks while channeling more capital towards green production sectors and firms for an immediate cut of emissions and net zero emission economy targets.
We use a structural VAR model to study the German natural gas market and investigate the impact of the 2022 Russian supply stop on the German economy. Combining conventional and narrative sign restrictions, we find that gas supply and demand shocks have large and persistent price effects, while output effects tend to be moderate. The 2022 natural gas price spike was driven by adverse supply
shocks and positive storage demand shocks, as Germany filled its inventories before the winter. Counterfactual simulations of an embargo on natural gas imports from Russia indicate similar positive price and negative output effects compared to what we observe in the data.
This paper contributes a multivariate forecasting comparison between structural models and Machine-Learning-based tools. Specifically, a fully connected feed forward non-linear autoregressive neural network (ANN) is contrasted to a well established dynamic stochastic general equilibrium (DSGE) model, a Bayesian vector autoregression (BVAR) using optimized priors as well as Greenbook and SPF forecasts. Model estimation and forecasting is based on an expanding window scheme using quarterly U.S. real-time data (1964Q2:2020Q3) for 8 macroeconomic time series (GDP, inflation, federal funds rate, spread, consumption, investment, wage, hours worked), allowing for up to 8 quarter ahead forecasts. The results show that the BVAR improves forecasts compared to the DSGE model, however there is evidence for an overall improvement of predictions when relying on ANN, or including them in a weighted average. Especially, ANN-based inflation forecasts improve other predictions by up to 50%. These results indicate that nonlinear data-driven ANNs are a useful method when it comes to macroeconomic forecasting.
Central bank intervention in the form of quantitative easing (QE) during times of low interest rates is a controversial topic. The author introduces a novel approach to study the effectiveness of such unconventional measures. Using U.S. data on six key financial and macroeconomic variables between 1990 and 2015, the economy is estimated by artificial neural networks. Historical counterfactual analyses show that real effects are less pronounced than yield effects.
Disentangling the effects of the individual asset purchase programs, impulse response functions provide evidence for QE being less effective the more the crisis is overcome. The peak effects of all QE interventions during the Financial Crisis only amounts to 1.3 pp for GDP growth and 0.6 pp for inflation respectively. Hence, the time as well as the volume of the interventions should be deliberated.
We create an alternative version of the present utility value formula to explicitly show that every store-of-value in the economy bears utility-interest (non-pecuniary income) for ist holder regardless of possible interest earnings from financial markets. In addition, we generalize the well-known welfare measures of consumer and producer surplus as present value concepts and apply them not only for the production and usage of consumer goods and durables but also for money and other financial assets. This helps us, inter alia, to formalize the circumstances under which even a producer of legal tender might become insolvent. We also develop a new measure of seigniorage and demonstrate why the well-established concept of monetary seigniorage is flawed. Our framework also allows us to formulate the conditions for liability-issued money such as inside money and financial instruments such as debt certificates to become – somewhat paradoxically – net wealth of the society.
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.
Despite a number of helpful changes, including the adoption of an inflation target, the Fed’s monetary policy strategy proved insufficiently resilient in recent years. While the Fed eased policy appropriately during the pandemic, it fell behind the curve during the post-pandemic recovery. During 2021, the Fed kept easing policy while the inflation outlook was deteriorating and the economy was growing considerably faster than the economy’s natural growth rate—the sum of the Fed’s 2% inflation goal and the growth rate of potential output.
The resilience of the Fed’s monetary policy strategy could be enhanced, and such errors be avoided with guidance from a simple natural growth targeting rule that prescribes that the federal funds rate during each quarter be raised (cut) when projected nominal income growth exceeds (falls short) of the economy’s natural growth rate. An illustration with real-time data and forecasts since the early 1990s shows that Fed policy has not persistently deviated from this simple rule with the notable exception of the period coinciding with the Fed’s post-pandemic policy error.
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.
