A note on Spatial Autocorrelation at a Local Level

In this paper we analyze the existence of spatial autocorrelation at a local level in Catalonia using variables such as urbanisation economies, population density, human capital and firm entries. From a static approach, our results show that spatial autocorrelation is weak and diminishes as the distance between municipalities increases. From a dynamic approach, however, spatial autocorrelation increased over the period we analysed. These results are important from a policy point of view, since it is essential to know how economic activities are spatially concentrated or disseminated. Key words: spatial autocorrelation, municipalities. JEL classification: R110, R120

Quantifying rock fabrics: a test of autocorrelation of the spatial distribution of cristals

A novel test of spatial independence of the distribution of crystals or phases in rocksbased on compositional statistics is introduced. It improves and generalizes the commonjoins-count statistics known from map analysis in geographic information systems.Assigning phases independently to objects in RD is modelled by a single-trial multinomialrandom function Z(x), where the probabilities of phases add to one and areexplicitly modelled as compositions in the K-part simplex SK. Thus, apparent inconsistenciesof the tests based on the conventional joins{count statistics and their possiblycontradictory interpretations are avoided. In practical applications we assume that theprobabilities of phases do not depend on the location but are identical everywhere inthe domain of de nition. Thus, the model involves the sum of r independent identicalmultinomial distributed 1-trial random variables which is an r-trial multinomialdistributed random variable. The probabilities of the distribution of the r counts canbe considered as a composition in the Q-part simplex SQ. They span the so calledHardy-Weinberg manifold H that is proved to be a K-1-affine subspace of SQ. This isa generalisation of the well-known Hardy-Weinberg law of genetics. If the assignmentof phases accounts for some kind of spatial dependence, then the r-trial probabilitiesdo not remain on H. This suggests the use of the Aitchison distance between observedprobabilities to H to test dependence. Moreover, when there is a spatial uctuation ofthe multinomial probabilities, the observed r-trial probabilities move on H. This shiftcan be used as to check for these uctuations. A practical procedure and an algorithmto perform the test have been developed. Some cases applied to simulated and realdata are presented.Key words: Spatial distribution of crystals in rocks, spatial distribution of phases,joins-count statistics, multinomial distribution, Hardy-Weinberg law, Hardy-Weinbergmanifold, Aitchison geometry

New findings regarding return autocorrelation anomalies and the importance of non-trading periods

In this paper, differences in return autocorrelation across weekdays havebeen investigated. Our research provides strong evidence of the importanceon non-trading periods, not only weekends and holidays but also overnightclosings, to explain return autocorrelation anomalies. While stock returnsare highly autocorrelated, specially on Mondays, when daily returns arecomputed on a open-to-close basis, they do not exhibit any significantlevel of autocorrelation. Our results are compatible with theinformation processing hypotheses as an explanation of the weekendeffect.

Lag-one autocorrelation in short series: Estimation and hypothesis testing

In the first part of the study, nine estimators of the first-order autoregressive parameter are reviewed and a new estimator is proposed. The relationships and discrepancies between the estimators are discussed in order to achieve a clear differentiation. In the second part of the study, the precision in the estimation of autocorrelation is studied. The performance of the ten lag-one autocorrelation estimators is compared in terms of Mean Square Error (combining bias and variance) using data series generated by Monte Carlo simulation. The results show that there is not a single optimal estimator for all conditions, suggesting that the estimator ought to be chosen according to sample size and to the information available of the possible direction of the serial dependence. Additionally, the probability of labelling an actually existing autocorrelation as statistically significant is explored using Monte Carlo sampling. The power estimates obtained are quite similar among the tests associated with the different estimators. These estimates evidence the small probability of detecting autocorrelation in series with less than 20 measurement times.

Comparing N = 1 effect size indices in presence of autocorrelation

Generalization from single-case designs can be achieved by means of replicating individual studies across different experimental units and settings. When replications are available, their findings can be summarized using effect size measurements and integrated through meta-analyses. Several procedures are available for quantifying the magnitude of treatment"s effect in N = 1 designs and some of them are studied in the current paper. Monte Carlo simulations were employed to generate different data patterns (trend, level change, slope change). The experimental conditions simulated were defined by the degrees of serial dependence and phases" length. Out of all the effect size indices studied, the Percent of nonoverlapping data and standardized mean difference proved to be less affected by autocorrelation and perform better for shorter data series. The regression-based procedures proposed specifically for single-case designs did not differentiate between data patterns as well as simpler indices.

The spatial inequality of wage income in Barcelona Metropolitan Area

ABSTRACT The measure and estimation of income levels in Barcelona Metropolitan Area (BMA) goes back a long way. Using different approaches and focusing on different municipalities, there is a lot of work in the field. The majority of the literature has focused on the estimation of income levels using variables related to consumption. The empirical evidence on wage differentials has shown an important growth during 80’s and 90’s especially in United Kingdom and USA. Less is known on spatial distribution of inequality. This paper presents a new data set for analyzing spatial distribution of wage income. This data is obtained by matching Wage Structure Survey (WSS) with data from Census disaggregated by census tracts. In this way we have a unique data set with wage incomes for every census track for 36 municipalities belonging to BMA. We develop a descriptive analysis of spatial distribution, testing for spatial autocorrelation and use the family of Generalised Entropy Indices to measure inequality. Properties of the index allow us to decompose inequality into inter and intra-municipality measures. Since we have two cross-sectional data for WSS (1995-2002) we can also analyze the evolution of the inequality in this period of economic growth. Key words: spatial distribution of wages, spatial autocorrelation, inequality indices.

(Optimal) Spatial Aggregation in the Determinants of Industrial Location

Empirical studies on the determinants of industrial location typically use variables measured at the available administrative level (municipalities, counties, etc.). However, this amounts to assuming that the effects these determinants may have on the location process do not extent beyond the geographical limits of the selected site. We address the validity of this assumption by comparing results from standard count data models with those obtained by calculating the geographical scope of the spatially varying explanatory variables using a wide range of distances and alternative spatial autocorrelation measures. Our results reject the usual practice of using administrative records as covariates without making some kind of spatial correction. Keywords: industrial location, count data models, spatial statistics JEL classification: C25, C52, R11, R30

On the effect of the geographical scope of agglomeration economies on firm location

This article analyses how agglomeration economies shaped the location decisions of new manufacturing start-ups in Catalan municipalities in 2001-2005. We estimate whether the locations of new firms are spatially autocorrelated and whether this phenomenon is industry-specific. Our aim is to estimate the geographical scope of agglomeration economies on firm entries. The data set comes from a compulsory register of manufacturing establishments (REIC: Catalan Manufacturing Establishments Register). JEL classification: R1, R3 Keywords: firm location; spatial autocorrelation

Firms vs. insiders as traders of last resort

We explore the role of corporate insiders vs. firms as traders of last resort. We develop a simple model of insider trading in which insiders provide price support, as well as liquidity, in security markets. Consistent with the model predictions we find that in the US markets insiders trading activities have a clear impact on return distributions. Furthermore, we provide empirical evidence on insiders transactions and firm transactions affecting returns in a different manner. In particular, while insiders transactions (both purchases and sales) have a strong impact on skewness in the short run and to a lesser extent in short run volatility, company repurchases only have a clear impact on volatility, both in the short and the long run. We provide explanations for this asymmetry.

Evidence on the complex link between

Most studies analysing the infrastructure impact on regional growth show a positive relationship between both variables. However, the public capital elasticity estimated in a Cobb-Douglas function, which is the most common specification in these works, is sometimes too big to be credible, so that the results have been partially desestimated. In the present paper, we give some new advances on the real link between public capital and productivity for the Spanish regions in the period 1964-1991. Firstly, we find out that the association for both variables is smaller when controlling for regional effects, being industry the sector which reaps the most benefits from an increase in the infrastructural dotation. Secondly, concerning to the rigidity of the Cobb-Douglas function, it is surpassed by using the variable expansion method. The expanded functional form reveals both the absence of a direct effect of infrastructure and the fact that the link between infrastructure and growth depends on the level of the existing stock (threshold level) and the way infrastructure is articulated in its location relative to other factors. Finally, we analyse the importance of the spatial dimension in infrastructure impact, due to spillover effects. In this sense, the paper provides evidence of the existence of spatial autocorrelation processes that may invalidate previous results.

Evidence on the complex link between

Most studies analysing the infrastructure impact on regional growth show a positive relationship between both variables. However, the public capital elasticity estimated in a Cobb-Douglas function, which is the most common specification in these works, is sometimes too big to be credible, so that the results have been partially desestimated. In the present paper, we give some new advances on the real link between public capital and productivity for the Spanish regions in the period 1964-1991. Firstly, we find out that the association for both variables is smaller when controlling for regional effects, being industry the sector which reaps the most benefits from an increase in the infrastructural dotation. Secondly, concerning to the rigidity of the Cobb-Douglas function, it is surpassed by using the variable expansion method. The expanded functional form reveals both the absence of a direct effect of infrastructure and the fact that the link between infrastructure and growth depends on the level of the existing stock (threshold level) and the way infrastructure is articulated in its location relative to other factors. Finally, we analyse the importance of the spatial dimension in infrastructure impact, due to spillover effects. In this sense, the paper provides evidence of the existence of spatial autocorrelation processes that may invalidate previous results.

Scaling and data collapse for the mean exit time of asset prices

We study theoretical and empirical aspects of the mean exit time (MET) of financial time series. The theoretical modeling is done within the framework of continuous time random walk. We empirically verify that the mean exit time follows a quadratic scaling law and it has associated a prefactor which is specific to the analyzed stock. We perform a series of statistical tests to determine which kind of correlation are responsible for this specificity. The main contribution is associated with the autocorrelation property of stock returns. We introduce and solve analytically both two-state and three-state Markov chain models. The analytical results obtained with the two-state Markov chain model allows us to obtain a data collapse of the 20 measured MET profiles in a single master curve.

Percolation transition and the onset of nonexponential relaxation in fully frustrated models

We numerically study the dynamical properties of fully frustrated models in two and three dimensions. The results obtained support the hypothesis that the percolation transition of the Kasteleyn-Fortuin clusters corresponds to the onset of stretched exponential autocorrelation functions in systems without disorder. This dynamical behavior may be due to the large scale effects of frustration, present below the percolation threshold. Moreover, these results are consistent with the picture suggested by Campbell et al. [J. Phys. C 20, L47 (1987)] in the space of configurations.

Dynamic properties of Lennard Jones fluids and liquid metals

The dependence of the dynamic properties of liquid metals and Lennard-Jones fluids on the characteristics of the interaction potentials is analyzed. Molecular-dynamics simulations of liquids in analogous conditions but assuming that their particles interact either through a Lennard-Jones or a liquid-metal potential were carried out. The Lennard-Jones potentials were chosen so that both the effective size of the particles and the depth of the potential well were very close to those of the liquid-metal potentials. In order to investigate the extent to which the dynamic properties of liquids depend on the short-range attractive interactions as well as on the softness of the potential cores, molecular-dynamics simulations of the same systems but assuming purely repulsive interactions with the same potential cores were also performed. The study includes both singleparticle dynamic properties, such as the velocity autocorrelation functions, and collective dynamic properties, such as the intermediate scattering funcfunctions, and collective dynamic properties, such as the intermediate scattering functions, the dynamic structure factors, the longitudinal and transverse current correlations, and the transport coefficients.