Antonio García-Ferrer

Forecasting OECD industrial turning points using unobserved components models with business survey data

Abstract The approach followed in this paper stresses the importance of timing in signalling turning points. This is done in two stages; first a signal that a turning point is likely to occur, and later a statement of when it will occur. We use Youngs trend derivative method, adding leading qualitative survey data. We find high coherence between its low frequency component and that of the corresponding economic variable. We study industrial production in six OECD countries with special emphasis on France and Spain. Inclusion of survey data improves forecast accuracy.

Linear dynamic harmonic regression

Among the alternative unobserved components formulations within the stochastic state space setting, the dynamic harmonic regression (DHR) model has proven to be particularly useful for adaptive seasonal adjustment, signal extraction, forecasting and back-casting of time series. First, it is shown how to obtain AutoRegressive moving average (ARMA) representations for the DHR components under a generalized random walk setting for the associated stochastic parameters; a setting that includes several well-known random walk models as special cases. Later, these theoretical results are used to derive an alternative algorithm, based on optimization in the frequency domain, for the identification and estimation of DHR models. The main advantages of this algorithm are linearity, fast computational speed, avoidance of some numerical issues, and automatic identification of the DHR model. The signal extraction performance of the algorithm is evaluated using empirical applications and comprehensive Monte Carlo simulation analysis.

Can univariate models forecast turning points in seasonal economic time series

Abstract Based on a particular class of recently developed unobserved component models with, time varying parameters, the objectives of this paper are two-fold. On the one hand, we propose an alternative measure of underlying growth based on our estimated trend derivative with no need for any further transformations. Additionally, using the information embedded on the trend derivative, we provide a simple method for improving quantitative point forecasts in the vicinity of turning points. Empirical applications are presented for a set of seasonal monthly economic indicators of the Spanish economy.

Using Long-, Medium-, and Short-Term Trends to Forecast Turning Points in the Business Cycle: Some International Evidence

This paper provides rules for anticipating business-cycle recessions and recoveries for countries showing asymmetric cycle durations. Based on a Schumpeterian framework, we analyze business cycles as sums of short-, medium-, and long-term cycles defined for a particular class of unobserved component models. By associating the trend with the low frequencies of the pseudo-spectrum in the frequency domain, manipulation of the spectral bandwidth allows us to define subjective length trends with specific properties. In this paper, we show how these properties can be exploited to anticipate business-cycle turning points, not only historically, but also in a true ex-ante forecasting exercise. This procedure is applied to U.S. post-World War II GNP quarterly data, as well as to another set of European countries.

A growth cycle characterisation and forecasting of the Spanish economy: 1970-1998

Abstract In contrast to classical cycles, growth cycles are more likely to represent the present stage of economic activity. This paper analyzes the growth cycle chronology of the Spanish economy from 1970 to 1998, based on the official rules depicted by the Spanish Statistical Institute. Alternatively, we propose a simple method that not only anticipates such reference cycle but can also be used as a forecasting tool in predicting turning points. Given these good properties, the method is also used in computing the so-called ‘stylized facts’ among the main economic aggregates.

From Zero to Infinity: The Use of Impact Factors in the Evaluation of Economic Research in Spain

In the present study, we examine the use of short lists of journals in order to assess research performance in Spain - a country that features a rare combination of a thin and incomplete academic market along with an elite of eminent economists. Our analysis reveals that the implementation of bibliometric tools to produce short lists of journals for assessment purposes entail problems with the statistical significance of cutoff rates, neglect of the interdisciplinary nature of economics, and an inability to track progress in academic markets that move towards internationalization and publications in top-tier, premier outlets.

Analysis of the Car Accident Indexes in Spain: A Multiple Time Series Approach

The main objective of this article is to specify and estimate a model for the car accident rates in Spain to improve input for the decision-making process for insurance companies and provide useful information for traffic authorities. The prediction performance of the model is also analyzed in an attempt to verify the improvement in prediction that takes place when more elaborate models are used.

Lower posterior death probabilities from a quick medical response in road traffic accidents

Introduction: We use data from Spain on roads and motorways traffic accidents in May 2004 to quantify the statistical association between quick medical response time and mortality rate. Method: Probit and logit parameters are estimated by a Bayesian method in which samples from the posterior densities are obtained through an MCMC simulation scheme. We provide posterior credible intervals and posterior partial effects of a quick medical response at several time levels over which we express our prior beliefs. Results: A reduction of 5 min, from a 25-min response-time level, is associated with lower posterior probabilities of death in roads and motorways accidents of 24% and 30%, respectively.

A cyclical characterization of economic activity in the United States, 1887‐1940

We analyze historical business cycles as a sum of short‐ and medium‐term cycles defined for a particular class of unobserved component models. By associating the trend with the low frequencies of the pseudo‐spectrum in the frequency domain, manipulation of the spectral bandwidth will allow us to define subjective trends with specific properties. In this paper, we show how these properties can be exploited to anticipate business cycle turning points, not only historically but also in a true ex‐ante exercise. This procedure is applied to US pre‐Second World War GNP quarterly data taking as reference the NBER and Romer’s business cycle datings.

Mathematical Framework for Pseudo-Spectra of Linear Stochastic Difference Equations

Although spectral analysis of stationary stochastic processes has solid mathematical foundations, this is not always so for some non-stationary cases. Here, we establish a rigorous mathematical extension of the classic Fourier spectrum to the case in which there are AR roots in the unit circle, i.e., the transfer function of the linear time-invariant filter has poles on the unit circle. To achieve it we: embed the classical problem in a wider framework, extend the Discrete Time Fourier Transform and defined a new Extended Fourier Transform pair pseudo-covariance function/pseudo-spectrum. Our approach is a proper extension of the classical spectral analysis, within which the Fourier Transform pair auto-covariance function/spectrum is a particular case. Consequently spectrum and pseudo-spectrum coincide when the first one is defined.