Medienanfragen richten Sie bitte an:
Telefon: +49 345 7753-720
Email: presse@iwh-halle.de
Team Kommunikation
Wie der Iran-Krieg einen Aufschwung bremsen könnteOliver HoltemöllerTagesschau.de, 12. März 2026
The continued expansionary policy of the Federal Reserve gives rise to speculation whether the Fed will be able to maintain price stability in the coming decades. Most of the scientific work relating money to prices relies on broad monetary aggregates (i.e. M2 for the United States). In our paper, we argue that this view falls short. The historically unique monetary expansion has not yet fully reached M2. Using a cointegration approach, we aim to show the hidden risks for the future development of M2 and correspondingly prices. In a simulation analysis we show that even if the multiplier remains substantially below its pre-crisis level, M2 will exceed its current growth path with a probability of 95%.
We use a multivariate state space framework to analyze the short run impact of money on prices in the United States. The key contribution of this approach is that it allows to identify the impact of money growth on inflation without having to model money demand explicitly.
Using our results, that provide evidence for a substantial impact of money on prices in the US, we analyze the consequences of the Fed's response to the financial crisis. Our results indicate a raise of US inflation above 5% for more than a decade. Alternative exit strategies that we simulate cannot fully compensate for the monetary pressure without risking serious repercussions on the real economy. Further simulations of a double dip in the United States indicate that a repetition of the unusually expansive monetary policy – in addition to increased inflation – might cause growth losses exceeding the contemporary easing of the crisis.
The signals approach as an early-warning system has been fairly successful in detecting crises, but it has so far failed to gain popularity in the scientific community because it cannot distinguish between randomly achieved in-sample fit and true predictive power. To overcome this obstacle, we test the null hypothesis of no correlation between indicators and crisis probability in three applications of the signals approach to different crisis types. To that end, we propose bootstraps specifically tailored to the characteristics of the respective datasets. We find (1) that previous applications of the signals approach yield economically meaningful results; (2) that composite indicators aggregating information contained in individual indicators add value to the signals approach; and (3) that indicators which are found to be significant in-sample usually perform similarly well out-of-sample.
This paper analyzes the role of common data problems when identifying structural breaks in small samples. Most notably, we survey small sample properties of the most commonly applied endogenous break tests developed by Brown et al. (J R Stat Soc B 37:149–163, 1975) and Zeileis (Stat Pap 45(1):123–131, 2004), Nyblom (J Am Stat Assoc 84(405):223–230, 1989) and Hansen (J Policy Model 14(4):517–533, 1992), and Andrews et al. (J Econ 70(1):9–38, 1996). Power and size properties are derived using Monte Carlo simulations. We find that the Nyblom test is on par with the commonly used F type tests in a small sample in terms of power. While the Nyblom test’s power decreases if the structural break occurs close to the margin of the sample, it proves far more robust to nonnormal distributions of the error term that are found to matter strongly in small samples although being irrelevant asymptotically for all tests that are analyzed in this paper.
One of the key obstacles to the empirical analysis of capital controls has been the unavailability of a detailed set of indicators for controls that cover a broad set of countries over a range of years. In this paper, we propose a new set of indicators derived from the Annual Reports on Exchange Arrangements and Export Restrictions. Contrary to most earlier attempts to construct control indicators from this source, our set of indices allows one to analyze the control intensity separately for inflow, outflow and repatriation controls. An additional set of indicators features information on the institutional design of controls. At first glance, the data show that the financial crisis caused a surge in capital market restrictions, most notably concerning the derivatives market. This reflex, which is not justified by the scarce empirical evidence on the success of controls, shows the importance of having a valid measure to allow an econometrically sound policy evaluation in this field. The data are available from the author upon request.
This paper uses panel data to show that capital controls have a significant impact on international interest rate differentials. Various types of controls can be distinguished within the data. The analysis shows that the aforementioned effects of capital controls on interest rates are especially strong in the case of capital import controls on portfolio capital; the implementation of these controls has been suggested in the wake of the Asian Crisis to prevent further crises. The results presented herein contradict the hypothesis that capital controls can achieve a restructuring of the maturity of capital inflows without a distortion in international capital allocation.
Using a new dataset on capital market regulation, we analyze whether capital controls helped protect emerging markets from the real economic consequences of the 2009 financial and economic crisis. The impact of the crisis is measured by the 2009 forecast error of a panel state space model, which analyzes the business cycle dynamics of 63 middle-income countries. We find that neither capital controls in general nor controls that were specifically targeted to derivatives (that played a crucial role during the crisis) helped shield economies. However, banking regulation that limits the exposure of banks to global risks has been highly successful.
We develop an evolutionary algorithm to estimate Threshold Vector Error Correction models (TVECM) with more than two cointegrated variables. Since disregarding a threshold in cointegration models renders standard approaches to the estimation of the cointegration vectors inefficient, TVECM necessitate a simultaneous estimation of the cointegration vector(s) and the threshold. As far as two cointegrated variables are considered, this is commonly achieved by a grid search. However, grid search quickly becomes computationally unfeasible if more than two variables are cointegrated. Therefore, the likelihood function has to be maximized using heuristic approaches. Depending on the precise problem structure the evolutionary approach developed in the present paper for this purpose saves 90 to 99 per cent of the computation time of a grid search.
Conventional Phillips-curve models that are used to estimate the output gap detect a substantial decline in potential output due to the present crisis. Using a multivariate state space model, we show that this result does not hold if the long run role of excess liquidity (that we estimate endogeneously) for inflation is taken into account.
The present paper compares expected inflation to (econometric) inflation forecasts based on a number of forecasting techniques from the literature using a panel of ten industrialized countries during the period of 1988 to 2007. To capture expected inflation, we develop a recursive filtering algorithm which extracts unexpected inflation from real interest rate data, even in the presence of diverse risks and a potential Mundell-Tobin-effect.
The extracted unexpected inflation is compared to the forecasting errors of ten
econometric forecasts. Beside the standard AR(p) and ARMA(1,1) models, which
are known to perform best on average, we also employ several Phillips curve based approaches, VAR, dynamic factor models and two simple model avering approaches.
