Forecasting Power (forecasting + power)

Distribution by Scientific Domains


Selected Abstracts


Market-Based Measures of Monetary Policy Expectations and Their Evolution Since the Introduction of the Euro

ECONOMIC NOTES, Issue 3 2009
Fabio Filipozzi
The paper considers the relation between monetary policy expectations and financial markets in the case of Europe. A number of money market instruments are compared, with the result that the 1-month forward interest rates extracted from the Libor yield curve has the best prediction power of the future monetary policy path. These forward rates have been used to study the evolution of market expectations regarding the monetary policy of the European Central Bank (ECB). The sharp increases and the following decreases in interest rates during 2000,2001 have reduced the predictive power of money market instruments, but smoother management of interest rates and better communication from the ECB has helped to improve the forecasting power of money market instruments. [source]


A new production function estimate of the euro area output gap,

JOURNAL OF FORECASTING, Issue 1-2 2010
Matthieu Lemoine
Abstract We develop a new version of the production function (PF) approach for estimating the output gap of the euro area. Assuming a CES (constant elasticity of substitution) technology, our model does not call for any (often imprecise) measure of the capital stock and improves the estimation of the trend total factor productivity using a multivariate unobserved components model. With real-time data, we assess this approach by comparing it with the Hodrick,Prescott (HP) filter and with a Cobb,Douglas PF approach with common cycle and implemented with a multivariate unobserved components model. Our new PF estimate appears highly concordant with the reference chronology of turning points and has better real-time properties than the univariate HP filter for sufficiently long time horizons. Its inflation forecasting power appears, like the other multivariate approach, less favourable than the statistical univariate method. Copyright © 2009 John Wiley & Sons, Ltd. [source]


Can consumer sentiment and its components forecast Australian GDP and consumption?

JOURNAL OF FORECASTING, Issue 8 2009
Chew Lian Chua
Abstract This paper examines whether the disaggregation of consumer sentiment data into its sub-components improves the real-time capacity to forecast GDP and consumption. A Bayesian error correction approach augmented with the consumer sentiment index and permutations of the consumer sentiment sub-indices is used to evaluate forecasting power. The forecasts are benchmarked against both composite forecasts and forecasts from standard error correction models. Using Australian data, we find that consumer sentiment data increase the accuracy of GDP and consumption forecasts, with certain components of consumer sentiment consistently providing better forecasts than aggregate consumer sentiment data. Copyright © 2009 John Wiley & Sons, Ltd. [source]


Vector smooth transition regression models for US GDP and the composite index of leading indicators

JOURNAL OF FORECASTING, Issue 3 2004
Maximo Camacho
Abstract In this paper, I extend to a multiple-equation context the linearity, model selection and model adequacy tests recently proposed for univariate smooth transition regression models. Using this result, I examine the nonlinear forecasting power of the Conference Board composite index of leading indicators to predict both output growth and the business-cycle phases of the US economy in real time. Copyright © 2004 John Wiley & Sons, Ltd. [source]


S&P futures returns and contrary sentiment indicators

THE JOURNAL OF FUTURES MARKETS, Issue 5 2001
David P. Simon Associate Professor
This article investigates the predictive power of popular market-based sentiment measures for subsequent returns on the Standard & Poor's (S&P) 500 futures contract over 10-day, 20-day, and 30-day horizons from January 1989 through June 1999. These measures include the volatility index, the put,call ratio, and the trading index. The empirical results show that these variables over a variety of specifications frequently have statistically and economically significant forecasting power. The results indicate that these variables are contrarian indicators, consistent with the view that periods of extreme fear in the stock market have provided excellent buying opportunities. Finally, out-of-sample trading simulations performed over the second half of the sample period demonstrate that profits and risk-adjusted profits would have been enhanced by buying S&P futures when the fear indicators were high rather than low. © 2001 John Wiley & Sons, Inc. Jrl Fut Mark 21:447,462, 2001 [source]