AN APPROXIMATE CONFIDENCE INTERVAL FOR THE DIFFERENCE BETWEEN QUANTILES IN A BIO-MEDICAL PROBLEM

In this note it is described how two distributions arising in a bio-medical investigation are compared by means of a confidence interval for the difference of appropriate quantiles. It is briefly indicated how an approximation for such a confidence interval is derived.     

SOME SMALL–SAMPLE NON–PROPORTIONAL HAZARDS RESULIS FOR THE KAPLAN–MEIER ESTIMATOR

CHEN, HOLLANDER and LANGBERG (1982) presented exact small–sample results for the KAPLAN–MEIER (1958) estimator under the proportional hazards model. We present simulation results for the same estimator for small samples in a number of cases where the hazard rates are not proportional. These suggest that the proportional hazards results, especially with regard to the Mean Square Error, are typical for a fairly large variety of "non–proportionalities" that may be encountered in practice. Thus the MSE of the KAPLAN–MEIER estimator appears to be rather robust against deviations from proportionality of the survival and censoring hazard rates. We illustrate the practical importance of this property by two examples.     

CONDITIONAL DURATION MODELS FOR HIGH‐FREQUENCY DATA: A REVIEW ON RECENT DEVELOPMENTS

This paper reviews the recent literature on conditional duration modeling in high‐frequency finance. These conditional duration models are associated with the time interval between trades, price, and volume changes of stocks, traded in a financial market. An earlier review by Pacurar provides an exhaustive survey of the first and some of the second generation conditional duration models. We consider almost all of the third‐generation and some of the second‐generation conditional duration models. Notable applications of these models and related empirical studies are discussed. The paper may be seen as an extension to Pacurar.     

REAL‐TIME DATA AND FISCAL POLICY ANALYSIS: A SURVEY OF THE LITERATURE

This paper surveys the empirical research on fiscal policy analysis based on real‐time data. This literature can be broadly divided into four groups that focus on: (1) the statistical properties of revisions in fiscal data; (2) the political and institutional determinants of projection errors by governments; (3) the reaction of fiscal policies to the business cycle and (4) the use of real‐time fiscal data in structural vector autoregression (VAR) models. It emerges that, first, fiscal revisions are large and initial releases are biased estimates of final values. Secondly, strong fiscal rules and institutions lead to more accurate releases of fiscal data and smaller deviations of fiscal outcomes from government plans. Thirdly, the cyclical stance of fiscal policies is estimated to be more ‘counter‐cyclical’ when real‐time data are used instead of ex post data. Fourthly, real‐time data can be useful for the identification of fiscal shocks. Finally, it is shown that existing real‐time fiscal data sets cover only a limited number of countries and variables. For example, real‐time data for developing countries are generally unavailable. In addition, real‐time data on European countries are often missing, especially with respect to government revenues and expenditures. Therefore, more work is needed in this field.     

WALKING DOWN WALL STREET WITH A TABLET: A SURVEY OF STOCK MARKET PREDICTIONS USING THE WEB

‘A blindfolded chimpanzee throwing darts at The Wall Street Journal could select a portfolio that would do as well as the (stock market) experts’ [Malkiel (2003) The efficient market hypothesis and its critics. Journal of Economic Perspectives 17(1): 59–82)]. However, what if this chimpanzee could browse the Internet before throwing any darts? In this paper, we ask whether online news has any influence on the financial market, and we also investigate how much influence it has. We explore the burgeoning literature on the predictability of financial movements using online information and report its mixed findings. In addition, we collate the efforts of various disciplines, including economics, text mining, sentiment analysis and machine learning, and we offer suggestions for future research.     

Statistical Approaches for Non‐parametric Frontier Models: A Guided Tour

A rich theory of production and analysis of productive efficiency has developed since the pioneering work by Tjalling C. Koopmans and Gerard Debreu. Michael J. Farrell published the first empirical study, and it appeared in a statistical journal (Journal of the Royal Statistical Society), even though the article provided no statistical theory. The literature in econometrics, management sciences, operations research and mathematical statistics has since been enriched by hundreds of papers trying to develop or implement new tools for analysing productivity and efficiency of firms. Both parametric and non‐parametric approaches have been proposed. The mathematical challenge is to derive estimators of production, cost, revenue or profit frontiers, which represent, in the case of production frontiers, the optimal loci of combinations of inputs (like labour, energy and capital) and outputs (the products or services produced by the firms). Optimality is defined in terms of various economic considerations. Then the efficiency of a particular unit is measured by its distance to the estimated frontier. The statistical problem can be viewed as the problem of estimating the support of a multivariate random variable, subject to some shape constraints, in multiple dimensions. These techniques are applied in thousands of papers in the economic and business literature. This ‘guided tour’ reviews the development of various non‐parametric approaches since the early work of Farrell. Remaining challenges and open issues in this challenging arena are also described. © 2014 The Authors. International Statistical Review © 2014 International Statistical Institute