Abstract Bottom-up regional computable general equilibrium (CGE) models have clear theoretical advantages over their top-down counterparts. However bottom-up models are data intensive. Hence they face practical difficulties in applications requiring high levels of regional and sectoral...

This paper presents an overview of existing research on wind speed and power forecasting. It first discusses state-of-the-art wind speed and power forecasting approaches. Then, forecasting accuracy is presented based on variable factors. Finally, potential techniques to improve the accuracy of...

We forecast US state-level employment growth using several distinct econometric approaches: combinations of individual autoregressive distributed lag models, general-to-specific modeling with bootstrap aggregation (GETS-bagging), and approximate factor (or “beta”) models. Our results show...

The recently advanced space–time autoregressive (ST-AR) model is used to forecast US, regional and state rates of violent and property crime. The disaggregate state (Florida) violent crime model includes murder, rape, robbery, and assault, while the property crime model includes burglary,...

The development of employment and unemployment in regional labour markets is known to spatially interdependent. Global Vector-Autoregressive (GVAR) models generate a link between the local and the surrounding labour markets and thus might be useful when analysing and forecasting employment and...

Population changes are decisive for growth performances. This has been shown in a number of country studies, using time series data. The analysis is here extended in two dimensions: 1) the importance of demographics for growth is taking in to account a regional dimension allowing for spatial...

In this study we evaluate the forecast performance of model averaged forecasts based on the predictive likelihood carrying out a prior sensitivity analysis regarding Zellner's g prior. The main results are fourfold: First the predictive likelihood does always better than the traditionally...

We propose to pool alternative systemic risk rankings for financial institutions using the method of principal components. The resulting overall ranking is less affected by estimation uncertainty and model risk. We apply our methodology to disentangle the common signal and the idiosyncratic...

We have argued that from the standpoint of a policy maker, the uncertainty of using the average forecast is not the variance of the average, but rather the average of the variances of the individual forecasts that incorporate idiosyncratic risks. With a slight reformulation of the loss function...

We provide a systematic comparison of the out-of-sample forecasts based on multivariate macroeconomic models and forecast combinations for the euro against the US dollar, the British pound, the Swiss franc and the Japanese yen. We use profit maximization measures based on directional accuracy...