Jorge Caiado

A periodogram-based metric for time series classification

The statistical discrimination and clustering literature has studied the problem of identifying similarities in time series data. Some studies use non-parametric approaches for splitting a set of time series into clusters by looking at their Euclidean distances in the space of points. A new measure of distance between time series based on the normalized periodogram is proposed. Simulation results comparing this measure with others parametric and non-parametric metrics are provided. In particular, the classification of time series as stationary or as non-stationary is discussed. The use of both hierarchical and non-hierarchical clustering algorithms is considered. An illustrative example with economic time series data is also presented.

Performance of combined double seasonal univariate time series models for forecasting water demand

In this article, we examine the daily water demand forecasting performance of double seasonal univariate time series models (Holt-Winters, ARIMA and GARCH) based on multi-step ahead forecast mean squared errors. A within-week seasonal cycle and a within-year seasonal cycle are accommodated in the various model specifications to capture both seasonalities. We investigate whether combining forecasts from different methods for different origins and horizons could improve forecast accuracy. The analysis is made with daily data for water consumption in Granada, Spain.

Comparison of Times Series with Unequal Length in the Frequency Domain

In statistical data analysis it is often important to compare, classify, and cluster different time series. For these purposes various methods have been proposed in the literature, but they usually assume time series with the same sample size. In this article, we propose a spectral domain method for handling time series of unequal length. The method make the spectral estimates comparable by producing statistics at the same frequency. The procedure is compared with other methods proposed in the literature by a Monte Carlo simulation study. As an illustrative example, the proposed spectral method is applied to cluster industrial production series of some developed countries.

Human capital, social capital and organizational performance

Purpose – The aim of this paper is to evaluate the human capital and social capital of managers and the influence of these attributes on the performance of small and medium-sized Portuguese companies. Design/methodology/approach – The structural modeling approach was applied to a sample of 199 small and medium-sized companies aged between 3 and 15 years, from five different sectors of activity. Findings – It was found that human capital affects social capital, and that experience and cognitive ability influence personal relations and complicity. Organizational performance is strongly influenced by human capital through the cognitive ability of the manager. Practical implications – Based on these findings managers can gain a better knowledge about how to improve the performance of their firms, for example through adjustments in communication methods or strategic decision capacities. Originality/value – This work is innovative in the sense that it confirms the influence of human capital on social capital, a...

Human capital and social capital in entrepreneurs and managers of small and medium enterprises

This paper uses factor analysis methods to identify structures associated with human and social capital in a small country with an open-economy, based on a survey of small- and medium-sized companies across different sectors. The purpose of this research is to investigate the influences of entrepreneurial and managerial behaviours on the relationship between human capital and social capital. The results indicate that the principal factor is highly correlated to the variables of experience, professional proficiency and cognitive ability, which are predominant characteristics of the entrepreneur, as well as status variables such as interlinking, family support, personal relations and social relations. The study also suggests that links between human capital and social capital are more salient in manufacturing and construction companies than in the wholesale trade, retail trade and services sectors.

Clustering financial time series with variance ratio statistics

This study introduces a new distance measure for clustering financial time series based on variance ratio test statistics. The proposed metric attempts to assess the level of interdependence of time series from the point of view of return predictability. Simulation results show that this metric aggregates time series according to their serial dependence structure better than a metric based on the sample autocorrelations. An empirical application of this approach to international stock market returns is presented. The results suggest that this metric discriminates stock markets reasonably well according to size and the level of development. Furthermore, despite the substantial evolution of individual variance ratio statistics, the clustering pattern remains fairly stable across different time periods.

Determinants of innovation in a small open economy: a multidimensional perspective

This paper uses logistic regression analysis to examine how intramural and extramural R&D, acquisition of machinery, equipment and software, acquisition of external knowledge, training, market introduction and other procedures and technical preparations determine the innovation behaviour of manufacturing and service firms. We adopt a multidimensional view of innovation by considering product, process, organizational and marketing innovations as dependent variables separately. The study reports on the Community Innovation Survey (CIS4) of a small open-economy country. The empirical results indicate that intramural R&D has a positive impact on innovation. In contrast, the influence of extramural R&D on innovation is unclear. All innovation activities contribute towards organizational innovation. The study also suggests that there are no significant differences between services and manufacturing firms concerning the propensity to innovation.

A GARCH-based method for clustering of financial time series: International stock markets evidence

In this paper, we introduce a volatility-based method for clustering analysis of financial time series. Using the generalized autoregressive conditional heteroskedasticity (GARCH) models we estimate the distances between the stock return volatilities. The proposed method uses the volatility behavior of the time series and solves the problem of different lengths. As an illustrative example, we investigate the similarities among major international stock markets using daily return series with different sample sizes from 1966 to 2006. From cluster analysis, most European markets countries, United States and Canada appear close together, and most Asian/Pacific markets and the South/Middle American markets appear in a distinct cluster. After the terrorist attack on September 11, 2001, the European stock markets have become more homogenous, and North American markets, Japan and Australia seem to come closer.

Tests for comparing time series of unequal lengths

This paper deals with hypothesis testing for independent time series with unequal length. It proposes a spectral test based on the distance between the periodogram ordinates and a parametric test based on the distance between the parameter estimates of fitted autoregressive moving average models. Both tests are compared with a likelihood ratio test based on the pooled spectra. In all cases, the null hypothesis is that the two series under consideration are generated by the same stochastic process. The performance of the three tests is investigated by a Monte Carlo simulation study.

The macro impact of the Portuguese Constitutional Court decisions regarding the budgetary proposals of the Portuguese Budget Law (2012, 2013, 2014)

We review the main budgetary measures not accepted by the Portuguese Constitutional Court in the Budget Laws of 2012, 2013 and 2014. Considering the feedback effect of the fiscal impulse, the impact on the budget balance is -0.42% and of -0.34% of GDP respectively for 2013 and for 2014; in both years the impact of the fiscal expansion could result in rather mitigated reductions in the unemployment rate in the range of 0.1 percent; the impact on the government debt level is around 0.42% of GDP in 2013, declining from then on, in a conservative estimate, and about 2.95% of GDP in 2020 in the worst case scenario.