1,335 research outputs found
An overview of the goodness-of-fit test problem for copulas
We review the main "omnibus procedures" for goodness-of-fit testing for
copulas: tests based on the empirical copula process, on probability integral
transformations, on Kendall's dependence function, etc, and some corresponding
reductions of dimension techniques. The problems of finding asymptotic
distribution-free test statistics and the calculation of reliable p-values are
discussed. Some particular cases, like convenient tests for time-dependent
copulas, for Archimedean or extreme-value copulas, etc, are dealt with.
Finally, the practical performances of the proposed approaches are briefly
summarized
The dynamic impact of uncertainty in causing and forecasting the distribution of oil returns and risk
The aim of this study is to analyze the relevance of recently developed news-based measures of economic policy and equity market uncertainty in causing and predicting the conditional quantiles of crude oil returns and risk. For this purpose, we studied both the causality relationships in quantiles through a non-parametric testing method and, building on a collection of quantiles forecasts, we estimated the conditional density of oil returns and volatility, the out-of-sample performance of which was evaluated by using suitable tests. A dynamic analysis shows that the uncertainty indexes are not always relevant in causing and forecasting oil movements. Nevertheless, the informative content of the uncertainty indexes turns out to be relevant during periods of market distress, when the role of oil risk is the predominant interest, with heterogeneous effects over the different quantiles levels.http://www.elsevier.com/locate/physa2019-10-01hj2018Economic
The Importance of Being Clustered: Uncluttering the Trends of Statistics from 1970 to 2015
In this paper we retrace the recent history of statistics by analyzing all
the papers published in five prestigious statistical journals since 1970,
namely: Annals of Statistics, Biometrika, Journal of the American Statistical
Association, Journal of the Royal Statistical Society, series B and Statistical
Science. The aim is to construct a kind of "taxonomy" of the statistical papers
by organizing and by clustering them in main themes. In this sense being
identified in a cluster means being important enough to be uncluttered in the
vast and interconnected world of the statistical research. Since the main
statistical research topics naturally born, evolve or die during time, we will
also develop a dynamic clustering strategy, where a group in a time period is
allowed to migrate or to merge into different groups in the following one.
Results show that statistics is a very dynamic and evolving science, stimulated
by the rise of new research questions and types of data
Testing directional forecast value in the presence of serial correlation
Common approaches to test for the economic value of directional forecasts are based on the classical Chi-square test for independence, Fisher’s exact test or the Pesaran and Timmerman (1992) test for market timing. These tests are asymptotically valid for serially independent observations. Yet, in the presence of serial correlation they are markedly oversized as confirmed in a simulation study. We summarize serial correlation robust test procedures and propose a bootstrap approach. By means of a Monte Carlo study we illustrate the relative merits of the latter. Two empirical applications demonstrate the relevance to account for serial correlation in economic time series when testing for the value of directional forecasts
Learning Bayesian Networks with the bnlearn R Package
bnlearn is an R package which includes several algorithms for learning the
structure of Bayesian networks with either discrete or continuous variables.
Both constraint-based and score-based algorithms are implemented, and can use
the functionality provided by the snow package to improve their performance via
parallel computing. Several network scores and conditional independence
algorithms are available for both the learning algorithms and independent use.
Advanced plotting options are provided by the Rgraphviz package.Comment: 22 pages, 4 picture
Learning Bayesian Networks with the bnlearn R Package
bnlearn is an R package (R Development Core Team 2010) which includes several algorithms for learning the structure of Bayesian networks with either discrete or continuous variables. Both constraint-based and score-based algorithms are implemented, and can use the functionality provided by the snow package (Tierney et al. 2008) to improve their performance via parallel computing. Several network scores and conditional independence algorithms are available for both the learning algorithms and independent use. Advanced plotting options are provided by the Rgraphviz package (Gentry et al. 2010).
- …