Inflation Expectations: Does the Market Beat Professional Forecasts?
Makram El-Shagi
IWH Discussion Papers,
No. 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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Forecasting Currency Crises: Which Methods signaled the South African Crisis of June 2006?
Tobias Knedlik, Rolf Scheufele
South African Journal of Economics,
2008
Abstract
In this paper we test the ability of three of the most popular methods to forecast South African currency crises with a special emphasis on their out-of-sample performance. We choose the latest crisis of June 2006 to conduct an out-of-sample experiment. The results show that the signals approach was not able to forecast the out-of-sample crisis correctly; the probit approach was able
to predict the crisis but only with models, that were based on raw data. The Markov-regime- switching approach predicts the out-of-sample crisis well. However, the results are not straightforward. In-sample, the probit models performed remarkably well and were also able to detect, at least to some extent, out-of-sample currency crises before their occurrence. The recommendation is to not restrict the forecasting to only one approach.
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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,
No. 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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The out of sample performance of leading indicators for the German business cycle. Single vs combined forecasts
Christian Dreger, Christian Schumacher
Journal of Business Cycle Measurement and Analysis,
No. 1,
2005
Abstract
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