Forecast Dispersion, Dissenting Votes, and Monetary Policy Preferences of FOMC Members: The Role of Individual Career Characteristics and Political Aspects
Stefan Eichler, Tom Lähner
Public Choice,
Nr. 3,
2014
Abstract
Using data from 1992 to 2001, we study the impact of members’ economic forecasts on the probability of casting dissenting votes in the Federal Open Market Committee (FOMC). Employing standard ordered probit techniques, we find that higher individual inflation and real GDP growth forecasts (relative to the committee’s median) significantly increase the probability of dissenting in favor of tighter monetary policy, whereas higher individual unemployment rate forecasts significantly decrease it. Using interaction models, we find that FOMC members with longer careers in government, industry, academia, non-governmental organizations (NGOs), or on the staff of the Board of Governors are more focused on output stabilization, while FOMC members with longer careers in the financial sector or on the staffs of regional Federal Reserve Banks are more focused on inflation stabilization. We also find evidence that politics matters, with Republican appointees being much more focused on inflation stabilization than Democratic appointees. Moreover, during the entire Clinton administration ‘natural’ monetary policy preferences of Bank presidents and Board members for inflation and output stabilization were more pronounced than under periods covering the administrations of both George H.W. Bush and George W. Bush, respectively.
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Outperforming IMF Forecasts by the Use of Leading Indicators
Katja Drechsel, Sebastian Giesen, Axel Lindner
IWH Discussion Papers,
Nr. 4,
2014
Abstract
This study analyzes the performance of the IMF World Economic Outlook forecasts for world output and the aggregates of both the advanced economies and the emerging and developing economies. With a focus on the forecast for the current and the next year, we examine whether IMF forecasts can be improved by using leading indicators with monthly updates. Using a real-time dataset for GDP and for the indicators we find that some simple single-indicator forecasts on the basis of data that are available at higher frequency can significantly outperform the IMF forecasts if the publication of the Outlook is only a few months old.
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Leverage, Balance-Sheet Size and Wholesale Funding
H. Evren Damar, Césaire Meh, Yaz Terajima
Journal of Financial Intermediation,
Nr. 4,
2013
Abstract
Positive co-movements in bank leverage and assets are associated with leverage procyclicality. As wholesale funding allows banks to quickly adjust leverage, banks with wholesale funding are expected to exhibit higher leverage procyclicality. Using Canadian data, we analyze (i) if leverage procyclicality exists and its dependence on wholesale funding, (ii) market factors associated with this procyclicality, and (iii) if banking-sector leverage procyclicality forecasts market volatility. The findings suggest that procyclicality exists and that its degree positively depends on use of wholesale funding. Furthermore, funding-market liquidity matters for this procyclicality. Finally, banking-sector leverage procyclicality can forecast volatility in the equity market.
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An Options-based Approach to Forecast Competing Bids: Evidence for Canadian Takeover Battles
Stefan Eichler, Dominik Maltritz
Applied Economics,
Nr. 34,
2013
Abstract
During takeover battles, a tender offer provides a call option right to the target’s shareholders: it guarantees the offered price but maintains the chance of a higher offer. We present an options-based approach to estimate the probability and expected value of higher competing takeover bids using target stock price data. Analysing Canadian takeover battles in the period 1997 to 2007 we find that during the 5 trading days prior to the occurrence of an increased takeover bid, the estimated probability of a higher bid exceeds 80% on average and the expected value of a potential competing bid almost matches the realized value.
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Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment
Katja Drechsel, Rolf Scheufele
Abstract
This paper presents a method to conduct early estimates of GDP growth in Germany. We employ MIDAS regressions to circumvent the mixed frequency problem and use pooling techniques to summarize efficiently the information content of the various indicators. More specifically, we investigate whether it is better to disaggregate GDP (either via total value added of each sector or by the expenditure side) or whether a direct approach is more appropriate when it comes to forecasting GDP growth. Our approach combines a large set of monthly and quarterly coincident and leading indicators and takes into account the respective publication delay. In a simulated out-of-sample experiment we evaluate the different modelling strategies conditional on the given state of information and depending on the model averaging technique. The proposed approach is computationally simple and can be easily implemented as a nowcasting tool. Finally, this method also allows retracing the driving forces of the forecast and hence enables the interpretability of the forecast outcome.
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Qual VAR Revisited: Good Forecast, Bad Story
Makram El-Shagi, Gregor von Schweinitz
Abstract
Due to the recent financial crisis, the interest in econometric models that allow to incorporate binary variables (such as the occurrence of a crisis) experienced a huge surge. This paper evaluates the performance of the Qual VAR, i.e. a VAR model including a latent variable that governs the behavior of an observable binary variable. While we find that the Qual VAR performs reasonably well in forecasting (outperforming a probit benchmark), there are substantial identification problems. Therefore, when the economic interpretation of the dynamic behavior of the latent variable and the chain of causality matter, the Qual VAR is inadvisable.
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