Philipp Maier

Challenges in Central Banking: How Central Banks Take Decisions: An Analysis of Monetary Policy Meetings

More than 80 central banks use a committee to take monetary policy decisions. The composition of the committee and the structure of the meeting can affect the quality of the decision making. In this paper we review economic, experimental, sociological and psychological studies to identify criteria for the optimal institutional setting of a monetary committee. These include the optimal size of the committee, measures to encourage independent thinking, a relatively informal structure of the meeting, and abilities to identify and evaluate individual members’ performances. Using these criteria, we evaluate the composition and operation of monetary policy committees in more than 40 central banks world-wide. Our findings indicate that e.g. the monetary policy committee of the Bank of England follows committee best-practice, while the committee structure of other major central banks could be improved.

Forecasting Economic Growth in the Euro Area During the Great Moderation and the Great Recession

We evaluate forecasts for the euro area in data-rich and ‘data-lean’ environments by comparing three different approaches: a simple PMI model based on Purchasing Managers’ Indices (PMIs), a dynamic factor model with euro area data, and a dynamic factor model with data from the euro plus data from national economies (pseudo-real time data). We estimate backcasts, nowcasts and forecasts for GDP, components of GDP, and GDP of all individual euro area members, and examine forecasts for periods of low and high economic volatility (more specifically, we consider 2002-2007, which falls into the ‘Great Moderation’, and the ‘Great Recession’ 2008-2009). We find that all models consistently beat naive AR benchmarks, and overall, the dynamic factor model tends to outperform the PMI model (at times by a wide margin). However, accuracy of the dynamic factor model can be uneven (forecasts for some countries have large errors), with the PMI model dominating clearly for some countries or over some horizons. This is particularly pronounced over the Great Recession, where the dynamic factor model dominates the PMI model for backcasts, but has considerable difficulties beating the PMI model for nowcasts. This suggests that survey-based measures can have considerable advantages in responding to changes during very volatile periods, whereas factor models tend to be more sluggish to adjust. JEL Classification: C50, C53, E37, E47

Sound Policies or Good Fortune: What Drives Emerging Market Spreads?

Since 2002, spreads on emerging market sovereign debt have fallen to historical lows. Given the close links between sovereign spreads, capital flows to emerging markets, and economic growth, understanding the factors driving sovereign spreads is very important. This paper uses factor analysis to study the extent to which emerging market bond spreads - measured by JP Morgans EMBI Global index - are driven by global factors, such as global liquidity or commodity prices, as opposed to country-specific macroeconomic fundamentals. Our results show that a common factor explains a substantial portion of the co-movements in emerging market spreads in the last decade. This factor is closely linked to global financial conditions, as well as to energy- and non-energy commodity prices. This factor, however, is not responsible for the reduction in EMBI spreads. Instead, emerging markets have benefited considerably from better macroeconomic policies. Therefore, a reversal of the benign global economic environment need not have a substantial negative impact on financing conditions for emerging markets.