In the present paper, we construct a new, simple, consistent and powerful test for spatial independence, called the SG test, by using the new concept of symbolic entropy as a measure of spatial dependence. The standard asymptotic distribution of the test is an affine transformation of the...

The BDS test is the best-known correlation integral–based test, and it is now an important part of most standard econometric data analysis software packages. This test depends on the proximity (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$\varepsilon )$$</EquationSource> </InlineEquation> and the embedding dimension (<InlineEquation ID="IEq2"> <EquationSource Format="TEX">$$m)$$</EquationSource> </InlineEquation> parameters both of which are chosen by the...</equationsource></inlineequation></equationsource></inlineequation>

The purpose of this paper is to propose a newly developed non-parametric test for linear and nonlinear causality based on permutation entropy and to show its usefulness in analyzing the potential causal relationship between trading volume and security prices. Most of the empirical applications...

In this article we introduce a test for independence between two processes &lcub;X_t&rcub; and &lcub;Y_t&rcub;. To this end we rely on symbolic dynamics and permutation entropy as a measure of dependence. As a result, a nonparametric (model-free) test for either linear or nonlinear processes is presented. The test...

Abstract We propose a novel test to determine, given a time series, if the dynamics are generated by a deterministic (including low dimensional chaos), rather than a stochastic, process. In addition, we introduce a new nonparametric bootstrap test for independence which is consistent against a...

The paper shows a new non-parametric test, based on symbolic entropy, which permits detect spatial causality in cross-section data. The test is robust to the functional form of the relation and has a good behaviour in samples of medium to large size. We illustrate the use of test with the case...

In spatial econometrics, it is customary to specify a weighting matrix, the so-called W matrix. The decision is important because the choice of W matrix determines the rest of the analysis. However, the procedure is not well defined and, usually, reflects the priors of the user. In the paper, we...

Testing the assumption of independence between variables is a crucial aspect of spatial data analysis. However, the literature is limited and somewhat confusing. To our knowledge, we can mention only the bivariate generalization of Moran’s statistic. This test suffers from several...

Testing for the assumption of independence between spatial variables is an important first step in spatial conometrics. Usually the researchers use the bivariate generalization of the Moran’s statistic, specifying a spatial matrix a priori. This test is applicable only to detect linear...

The purpose of this paper is to show the capacity of a new non-parametric test based on symbolic entropy and symbolic dynamics to deal with the detection of linear and non-linear spatial causality. The good performance of the new test in detecting spatial causality and causal weighting matrix is...