Timing and measuring of Spanish economic growth process in the long term has generated a long debate among a great bulk of researchers. In this paper, we analyze the statistical properties of some Spanish macroeconomic time series in order to offer new evidence to this debate. In particular, we...

In this paper we investigate whether long run time series of income per capita are better described by a trend-stationary model with few structural changes or by unit root processes in which permanent stochastic shocks are responsible for the observed growth discontinuities. To this purpose, we...

This paper studies the catching-up process in per capita income of the so-called Asian Dragons and Tigers. It contributes to the literature in several ways. First, it tests the catching-up hypothesis using the longest time span ever considered, from 1870 to 2014. Second, it documents the...

This study investigates changes in the relationship between oil prices and the US economy from a long-term perspective. Although neither of the two series (oil price and GDP growth rates) presents structural breaks in mean, we identify different volatility periods in both of them, separately....

This paper derives forecasts for euro area real GDP growth based on a bottom up approach from the production side. That is, GDP is forecast via the forecasts of value added across the different branches of activity, which is quite new in the literature. Linear regression models in the form of...

This paper examines the estimation and forecasting performance of ARIMA models in comparison with some of the most popular and common models of neural networks. Specifically we provide the estimation results of AR-GRNN (Generalized regression neural networks) and the AR-RBF (Radial basis...

We conduct an out-of-sample backtesting exercise of Growth-at-Risk (GaR) predictions for 24 OECD countries. We consider forecasts constructed from quantile regression and GARCH models. The quantile regression forecasts are based on a set of recently proposed measures of downside risks to GDP,...

This paper proposes tests for pairwise and multiple out-of-sample comparisons of parametric conditional quantile models. The tests rank the distance between actual and nominal conditional coverage w.r.t. the \textit{union} of information sets across models, for a given loss function. Our...

We present a medium-scale dynamic factor model to estimate and forecast the rate of growth of the Spanish economy in the very short term. The intermediate size of the model overcomes the serious specification problems associated with large-scale models and the implicit loss of information of...

We compare different approaches to accounting for parameter instability in the context of macroeconomic forecasting models that assume either small, frequent changes versus models whose parameters exhibit large, rare changes. An empirical out-of-sample forecasting exercise for U.S. GDP growth...