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,...

This paper explores the gains from combining expert forecasts from the ECB Survey of Professional Forecasters (SPF). The analysis encompasses combinations based on principal components and trimmed means, performance-based weighting, and least squares estimates of optimal weights, as well as...

In this work we introduce the forecasting model with which we participated in the NN5 forecasting competition (the forecasting of 111 time series representing daily cash withdrawal amounts at ATM machines). The main idea of this model is to utilize the concept of forecast combination, which has...

Forecast combination is a well-established and well-tested approach for improving the forecasting accuracy. One beneficial strategy is to use constituent forecasts that have diverse information. In this paper we consider the idea of diversity being accomplished by using different time...

A variety of methods and ideas have been tried for electricity price forecasting (EPF) over the last 15 years, with varying degrees of success. This review article aims to explain the complexity of available solutions, their strengths and weaknesses, and the opportunities and threats that the...

We construct factor models based on disaggregate survey data for forecasting national aggregate macroeconomic variables. Our methodology applies regional and sectoral factor models to Norges Bank’s regional survey and to the Swedish Business Tendency Survey. The analysis identifies which of...

In this paper, we focus on the different methods which have been proposed in the literature to date for dealing with mixed-frequency and ragged-edge datasets: bridge equations, mixed-data sampling (MIDAS), and mixed-frequency VAR (MF-VAR) models. We discuss their performances for nowcasting the...

We propose a new conditionally heteroskedastic factor model, the GICA-GARCH model, which combines independent component analysis (ICA) and multivariate GARCH (MGARCH) models. This model assumes that the data are generated by a set of underlying independent components (ICs) that capture the...

This paper proposes a methodology for now-casting and forecasting inflation using data with a sampling frequency which is higher than monthly. The data are modeled as a trading day frequency factor model, with missing observations in a state space representation. For the estimation we adopt the...