We consider combinations of subjective survey forecasts and model-based forecasts from linear and non-linear univariate specifications as well as multivariate factor-augmented models. Empirical results suggest that a simple equal-weighted average of survey forecasts outperform the best model-based forecasts for a majority of macroeconomic variables and forecast horizons. Additional improvements...

We test for long memory in 3and 6-month daily returns series on Eurocurrency deposits denominated in Japanese yen (Euroyen). The fractional differencing parameter is estimated using the spectral regression method. The conflicting evidence obtained from the application of tests against a unit root as well as tests against stationarity provides the motivation for testing for fractional roots. Sig...

The growth rates of real output and real investment are two macroeconomic time series which are particularly difficult to forecast. This paper considers the application of diffusion index forecasting models to this problem. We begin by characterizing the performance of standard forecasts, via recently-introduced measures of predictability and the forecast content, noting the maximum horizon at ...

In this article, we examine the daily water demand forecasting performance of double seasonal univariate time series models (Holt-Winters, ARIMA and GARCH) based on multi-step ahead forecast mean squared errors. A within-week seasonal cycle and a within-year seasonal cycle are accommodated in the various model speci…cations to capture both seasonalities. We investigate whether combining forecas...

In this paper we evaluate the forecasting performance of employment expectations for employment growth in 15 European states. Our data cover the period from the first quarter 1998 to the fourth quarter 2012. With in-sample analyses and pseudo out-of-sample exercises, we find that for most of the European states considered, the survey-based indicator model outperforms common benchmark models. It...

This paper presents a simultaneous model of exchange rates between the US dollar, German mark and Japanese yen. In addition to incorporating long-run equilibria and short-run dynamics, the model is designed to capture complex interaction between currencies not normally considered in exchange rate models. The model is demonstrated to be an economically and statistically superior forecasting tool...

This paper presents evidence of persistent ‘bull’ and ‘bear’ regimes in UK stock returns and considers their economic implications from the perspective of an investor’s portfolio decisions. We Þnd that the perceived state probability has a large effect on the optimal allocation to stocks, particularly at short investment horizons. If ignored, the presence of such regimes gives rise to welfare c...

This study compares the out-of-sample forecasting accuracy of various structural and time series exchange rate models. We find that a random walk model performs as well as any estimated model at one to twelve month horizons for the dollar/pound, dollar/mark, dollar/yen and trade-weighted dollar exchange rates. The candidate structural models include the flexible-price (Frenkel-Bilson) and stick...

The most representative machine learning techniques are implemented for modeling and forecasting U.S. economic activity recessions in particular. An elaborate, comprehensive, comparative framework is employed order to estimate recession probabilities. empirical analysis explores the predictive content of numerous well-followed macroeconomic financial indicators, but also introduces a set less-s...