A First Look on the New Halle Economic Projection Model
Sebastian Giesen, Oliver Holtemöller, Juliane Scharff, Rolf Scheufele
Abstract
In this paper we develop a small open economy model explaining the joint determination of output, inflation, interest rates, unemployment and the exchange rate in a multi-country framework. Our model – the Halle Economic Projection Model (HEPM) – is closely related to studies recently published by the International
Monetary Fund (global projection model). Our main contribution is that we model the Euro area countries separately. In this version we consider Germany and France, which represent together about 50 percent of Euro area GDP. The model allows for country specific heterogeneity in the sense that we capture different adjustment patterns to economic shocks. The model is estimated using Bayesian techniques. Out-of-sample and pseudo out-of-sample forecasts are presented.
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Inflation Expectations: Does the Market Beat Professional Forecasts?
Makram El-Shagi
IWH Discussion Papers,
Nr. 16,
2009
Abstract
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.
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Three methods of forecasting currency crises: Which made the run in signaling the South African currency crisis of June 2006?
Tobias Knedlik, Rolf Scheufele
IWH Discussion Papers,
Nr. 17,
2007
Abstract
In this paper we test the ability of three of the most popular methods to forecast the South African currency crisis of June 2006. In particular we are interested in the out-ofsample performance of these methods. Thus, we choose the latest crisis to conduct an out-of-sample experiment. In sum, the signals approach was not able to forecast the outof- sample crisis of correctly; the probit approach was able to predict the crisis but just with models, that were based on raw data. Employing a Markov-regime-switching approach also allows to predict the out-of-sample crisis. The answer to the question of which method made the run in forecasting the June 2006 currency crisis is: the Markovswitching approach, since it called most of the pre-crisis periods correctly. However, the “victory” is not straightforward. In-sample, the probit models perform remarkably well and it is also able to detect, at least to some extent, out-of-sample currency crises before their occurrence. It can, therefore, not be recommended to focus on one approach only when evaluating the risk for currency crises.
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