14,106 research outputs found
Market concentration and the likelihood of financial crises
According to theory, market concentration affects the likelihood of a financial crisis in different ways. The “concentration-stability” and the “concentrationfragility” hypotheses suggest opposing effects operating through specific channels. Using data of 160 countries for the period 1970-2007, this paper empirically tests these indirect effects of financial market structure. We set up a simultaneous system in order to jointly estimate financial stability and the relevant channel variables as endogenous variables. Our findings provide support for the assumption of channel effects in general and both the concentrationstability and the concentration-fragility hypothesis in particular. The effects are found to vary between high and low income countries.Market Concentration, Financial Crisis, Systemic Crisis
Competition and innovative intentions: A study of Dutch SMEs
This paper explores the complex relationship between competition and innovation. Traditional measures of competition using industry statistics are often challenged and found wanting. This paper distinguishes between three types of competitive forces: internal rivalry among incumbent firms in an industry, bargaining power of suppliers, and bargaining power of buyers. Using survey data from 2,281 Dutch firms, we apply new perception-based measures for these competitive forces to explore how competition relates to firms innovative intentions. We also investigate the influence of innovation strategy as a contingency variable. Results show that specific innovative intentions, i.e. to invest in product and process innovation, are related to different competitive forces. Process innovation is correlated with the bargaining power of suppliers, while intentions to invest in product innovation are associated with buyer power. Finally, intended product innovation is related to internal rivalry, but only when firms have no innovation strategy.
The Simulator: Understanding Adaptive Sampling in the Moderate-Confidence Regime
We propose a novel technique for analyzing adaptive sampling called the {\em
Simulator}. Our approach differs from the existing methods by considering not
how much information could be gathered by any fixed sampling strategy, but how
difficult it is to distinguish a good sampling strategy from a bad one given
the limited amount of data collected up to any given time. This change of
perspective allows us to match the strength of both Fano and change-of-measure
techniques, without succumbing to the limitations of either method. For
concreteness, we apply our techniques to a structured multi-arm bandit problem
in the fixed-confidence pure exploration setting, where we show that the
constraints on the means imply a substantial gap between the
moderate-confidence sample complexity, and the asymptotic sample complexity as
found in the literature. We also prove the first instance-based
lower bounds for the top-k problem which incorporate the appropriate
log-factors. Moreover, our lower bounds zero-in on the number of times each
\emph{individual} arm needs to be pulled, uncovering new phenomena which are
drowned out in the aggregate sample complexity. Our new analysis inspires a
simple and near-optimal algorithm for the best-arm and top-k identification,
the first {\em practical} algorithm of its kind for the latter problem which
removes extraneous log factors, and outperforms the state-of-the-art in
experiments
Mixture Martingales Revisited with Applications to Sequential Tests and Confidence Intervals
This paper presents new deviation inequalities that are valid uniformly in
time under adaptive sampling in a multi-armed bandit model. The deviations are
measured using the Kullback-Leibler divergence in a given one-dimensional
exponential family, and may take into account several arms at a time. They are
obtained by constructing for each arm a mixture martingale based on a
hierarchical prior, and by multiplying those martingales. Our deviation
inequalities allow us to analyze stopping rules based on generalized likelihood
ratios for a large class of sequential identification problems, and to
construct tight confidence intervals for some functions of the means of the
arms
The Jeffreys-Lindley Paradox and Discovery Criteria in High Energy Physics
The Jeffreys-Lindley paradox displays how the use of a p-value (or number of
standard deviations z) in a frequentist hypothesis test can lead to an
inference that is radically different from that of a Bayesian hypothesis test
in the form advocated by Harold Jeffreys in the 1930s and common today. The
setting is the test of a well-specified null hypothesis (such as the Standard
Model of elementary particle physics, possibly with "nuisance parameters")
versus a composite alternative (such as the Standard Model plus a new force of
nature of unknown strength). The p-value, as well as the ratio of the
likelihood under the null hypothesis to the maximized likelihood under the
alternative, can strongly disfavor the null hypothesis, while the Bayesian
posterior probability for the null hypothesis can be arbitrarily large. The
academic statistics literature contains many impassioned comments on this
paradox, yet there is no consensus either on its relevance to scientific
communication or on its correct resolution. The paradox is quite relevant to
frontier research in high energy physics. This paper is an attempt to explain
the situation to both physicists and statisticians, in the hope that further
progress can be made.Comment: v4: Continued editing for clarity. Figure added. v5: Minor fixes to
biblio. Same as published version except for minor copy-edits, Synthese
(2014). v6: fix typos, and restore garbled sentence at beginning of Sec 4 to
v
Recent advances in directional statistics
Mainstream statistical methodology is generally applicable to data observed
in Euclidean space. There are, however, numerous contexts of considerable
scientific interest in which the natural supports for the data under
consideration are Riemannian manifolds like the unit circle, torus, sphere and
their extensions. Typically, such data can be represented using one or more
directions, and directional statistics is the branch of statistics that deals
with their analysis. In this paper we provide a review of the many recent
developments in the field since the publication of Mardia and Jupp (1999),
still the most comprehensive text on directional statistics. Many of those
developments have been stimulated by interesting applications in fields as
diverse as astronomy, medicine, genetics, neurology, aeronautics, acoustics,
image analysis, text mining, environmetrics, and machine learning. We begin by
considering developments for the exploratory analysis of directional data
before progressing to distributional models, general approaches to inference,
hypothesis testing, regression, nonparametric curve estimation, methods for
dimension reduction, classification and clustering, and the modelling of time
series, spatial and spatio-temporal data. An overview of currently available
software for analysing directional data is also provided, and potential future
developments discussed.Comment: 61 page
Concentration of personal and household crimes in England and Wales
Crime is disproportionally concentrated in few areas. Though long-established, there remains uncertainty about the reasons for variation in the concentration of similar crime (repeats) or different crime (multiples). Wholly neglected have been composite crimes when more than one crime types coincide as parts of a single event. The research reported here disentangles area crime concentration into repeats, multiple and composite crimes. The results are based on estimated bivariate zero-inflated Poisson regression models with covariance structure which explicitly account for crime rarity and crime concentration. The implications of the results for criminological theorizing and as a possible basis for more equitable police funding are discussed
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