This paper presents a framework for quantifying uncertainty around point forecasts for GDP, inflation and house prices in Norway. The framework combines quantile regressions using a broad set of uncertainty indicators with a skewed t-distribution, allowing for time-variation and asymmetry in the...

In any economic analysis, regions or municipalities should not be regarded as isolated spatial units, but rather as highly interrelated small open economies. These spatial interrelations must be considered also when the aim is to forecast economic variables. For example, policy makers need...

The issue of income inequality occupies a prominent position in the research agenda of academic and policy circles alike, especially after the crisis of 2008, due to its potential causal link with the development of credit bubbles and therefore the emergence of financial crises. This paper...

The revolving credit available to consumers changes substantially over the business cycle, life cycle, and for individuals. We show that debt changes at the same time as credit, so credit utilization is remarkably stable. From ages 20-40, for example, credit card limits grow by more than 700...

This paper analyses the effects on private consumption from an increase in productive and unproductive public spending. A new-Keynesian model incorporating price and wage rigidities, monetary policy and various fiscal rules is developed and estimated, using Bayesian techniques, to capture the...

We suggest the use of an Internet job-search indicator (the Google Index, GI) as the best leading indicator to predict the US unemployment rate. We perform a deep out-of-sample forecasting comparison analyzing many models that adopt both our preferred leading indicator (GI), the more standard...

The study presents a comprehensive overview of labour market forecasting activities, mostly quantitative, based on multi-sector models, in 12 countries and the European Union. The principal aim of this effort is to provide ideas, lessons and benchmarks for similar forecasting exercises in...

We propose the use of Google online search data for nowcasting and forecasting the number of food stamps recipients. We perform a large out-of-sample forecasting exercise with almost 3000 competing models with forecast horizons up to 2 years ahead, and we show that models including Google search...

We suggest the use of an Internet job-search indicator (the Google Index, GI) as the best leading indicator to predict the US unemployment rate. We perform a deep out-of-sample forecasting comparison analyzing many models that adopt both our preferred leading indicator (GI), the more standard...

This paper estimates Bayesian Vector Autoregressive (BVAR) models, both spatial and non-spatial (univariate and multivariate), for the twenty largest states of the US economy, using quarterly data over the period 1976:Q1 to 1994:Q4; and then forecasts one-to-four quarters-ahead real house price...