International trade and domestic competition: Evidence from Belgium

We investigate the effect of domestic market competition on firm-level export intensity. We employ a comprehensive dataset of Belgian firms from 2005–2008, when the fall in the number of firms engaged in trade was accompanied by a growing amount of transactions. The resulting increase in the domestic concentration of Belgian firms has sparked numerous debates, since the direction of causality between domestic market structure and export performance is unclear. We apply the fractional logit estimator and control for both self-selection and simultaneity bias. We find that a positive linkage exists between the level of competition and export intensity

Robust Lavallee-Hidiroglou stratified sampling strategy

"There are several reasons why robust regression techniques are useful tools in sampling design. First of all, when stratified samples are considered, one needs to deal with three main issues: the sample size, the strata bounds determination and the sample allocation in the strata. Since the target variable Y, the objective of the survey, is unknown, some auxiliary information X known for the entire population from which the sample is drawn, is used. Such information is helpful as it is typically strongly correlated with the target Y. However, some discrepancies between these variables may arise. The use of auxiliary information, combined with the choice of the appropriate statistical model to estimate the relationship between Y and X, is crucial for the determination of the strata bounds, the size of the sample and the sampling rates according to a chosen precision level for the estimates, as has been shown by Rivest (2002). Nevertheless, this regression-based approach is highly sensitive to the presence of contaminated data. Since the key tool for stratified sampling is the measure of scale of Y conditional on the knowledge of the auxiliary X, a robust approach based on the S-estimator of the regression is proposed in this paper. The aim is to allow for robust sample size and strata bounds determination, together with optimal sample allocation. Simulation results based on data from the Construction sector of a Structural Business Survey illustrate the advantages of the proposed method." (author's abstract

A class of optimal tests for contemporaneous non-causality in VAR models

The aim of this paper was to test for contemporaneous non-causality defined by Granger (1969) between two groups of variables in a VAR(p) setting. Since contemporaneous correlation of the innovations is a necessary condition for contemporaneous causality (Pierce and Haugh, 1977), we focused on testing some restrictions on the covariance matrix of the noise. The class of the derived tests is locally asymptotically most stringent (in the Le Cam sense), invariant with respect to the group of block affine transformations and asymptotically invariant with respect to the group of continuous monotone radial transformations. Those tests are based on multivariate ranks of distances and multivariate signs of the o bservations and are shown to be asymptotically distribution free under very mild assumptions on the noise. © 2013 Wiley Publishing Ltd

Temporal aggregation and spurious causation in time series

The issue of temporal aggregation in time series has been discussed extensively in the last decades. There is a general consensus (see Breitung and Swanson, 2002) on the spurious effects produced by aggregation. In particular, one may observe that two time series originally uncorrelated after aggregation can result in non-zero correlation. When more time series are jointly considered, it becomes rather complicated to distinguish the effect of aggregation. In order to detect the presence of spurious effects due to temporal aggregation we propose a class of tests for instantaneous noncausality between groups of variables in a VAR(p) setting. According to the definition of Granger (1969), instantaneous causality means that the knowledge of the current value of a series xt helps in predicting the current value of a series yt. The application of the class of tests proposed is illustrated by some examples with macroeconomic data. In particular, using the small macroeconometric model for the U.S. monetary system introduced by Christiano, Eichenbaum and Evans (1996) we find that the real variables react to monetary policy shocks with a delay which can be quantified between one and three months, which is consistent with Christiano, Eichenbaum and Evans (1996)’ results

Waste production and Regional growth of Marine activities, an Econometric model

Coastal regions are characterized by intense human activity and climatic pressures, often intensif i ed by competing interests in the use of marine waters.To assess the ef f ect of public spending on the regional economy, an econometric model is here proposed. Not only are the regional investment and the climatic risks included in the model, but also variables related to the anthropogenic pressure, such as population, economic activities and waste production. Feedback ef f ects of economic and demographic expansion on the pollution of coastal areas are also considered. It is found that dangerous waste increases with growing shipping and transportation activities and with growing population density in non-touristic coastal areas. On the other hand, the amount of non-dangerous wastes increases with marine mining, defense and of f shore energy production activities. However, lower waste production occurs in areas where aquaculture and touristic industry are more exploited, and accompanied by increasing regional investment in waste disposal

Robust Hidiroglou-Lavallée Stratified Design

The presence of outliers can strongly bias the sampling design and hence the survey results. In particular, it could induce a wrong computation of the number of statistical units to sample, usually overestimating it. In what follows we focus on the stratified sampling design, which has been proven to be the most efficient surveying technique under some basic assumptions (see Tillé, 2001) and it is currently in use at several NSIs for business surveys. For in-stance, suppose that in the stratification variable X some outliers arise. Outliers are observations arbitrarily far from the majority of the data. They are often due to mis-takes, like editing, measurement and observational errors. Intuitively, when outliers are present in a given stratum for the stratification variable X they affect both the location and scale measures for X. Therefore, it is clear that a higher dispersion than the 'true' one will be observed in that stratum

Response burden reduction through the use of administrative data and robust sampling

There are several reasons why robust regression techniques are useful tools in sampling design. First of all, when stratified samples are considered, one needs to deal with three main issues: the sample size, the strata bounds determination and the sample allocation in the strata. Since the target variable $y$, objective of the survey, is unknown, it is used some auxiliary information $x$ known for the entire population from which the sample is drawn. Such information is helpful as it is strongly correlated with the target $y$, but of course some discrepancies between them may arise. The use of auxiliary information, combined with the choice of the appropriate statistical model to estimate the relationship with the variable of interest $y$, is crucial for the determination of the strata bounds, the size of the sample and the sampling rates according to a chosen precision level of the estimates, as it has been shown by Rivest (2002). Nevertheless, this regression-based approach is highly sensitive to the presence of contaminated data. Indeed, the influence of outlying observations in both $y$ and $x$ has an explosive impact on the variances with the effect of strong departures from the optimum sample allocation. Therefore, we expect increasing sample sizes in the strata, wrong allocation of sampling units in the strata and some errors in the strata bounds determination. Since the key tool for stratified sampling is the measure of scale of $y$ conditional to the knowledge of some auxiliary $x$, a robust approach based on $S-$estimator of regression is proposed in this paper. The aim is to allow for robust sample size and strata bounds determination, together with the optimal sample allocation. To show the advantages of the proposed method, an empirical illustration is provided for Belgian business surveys in the sector of Construction. It is considered a skewed population framework, which is typical for businesses, with a stratified design with one \emph{take-all} stratum and $L-1$ strata. Simulation results are also provided