115 research outputs found
Reference Dependence and Market Competition
This paper studies the implications of consumer reference dependence in market competition. If consumers take some product (e.g., the first product they have considered) as the reference point in evaluating others and exhibit loss aversion, then the more "prominent" firm whose product is taken as the reference point by more consumers will randomize its price over a high and a low one. All else equal, this firm will on average earn a larger market share and a higher profit than its rival. The welfare impact is that consumer reference dependence could harm firms and benefit consumers by intensifying price competition. Consumer reference dependence will also shape firms' advertising strategies and quality choices. If advertising increases product prominence, ex ante identical firms may differentiate their advertising intensities. If firms vary in their prominence, the less prominent firm might supply a lower-quality product even if improving quality is costless.
Multivariate Stochastic Volatility Models: Bayesian Estimation and Model Comparison
In this paper we show that fully likelihood-based estimation and comparison of multivariate stochastic volatility (SV) models can be easily performed via a freely available Bayesian software called WinBUGS. Moreover, we introduce to the literature several new specifications which are natural extensions to certain existing models, one of which allows for time varying correlation coefficients. Ideas are illustrated by fitting, to a bivariate time series data of weekly exchange rates, nine multivariate SV models, including the specifications with Granger causality in volatility, time varying correlations, heavytailed error distributions, additive factor structure, and multiplicative factor structure. Empirical results suggest that the most adequate specifications are those that allow for time varying correlation coefficients.Multivariate stochastic volatility; Granger causality in volatility; Heavy-tailed distributions; Time varying correlations; Factors; MCMC; DIC.
Log-concavity, ultra-log-concavity, and a maximum entropy property of discrete compound Poisson measures
Sufficient conditions are developed, under which the compound Poisson
distribution has maximal entropy within a natural class of probability measures
on the nonnegative integers. Recently, one of the authors [O. Johnson, {\em
Stoch. Proc. Appl.}, 2007] used a semigroup approach to show that the Poisson
has maximal entropy among all ultra-log-concave distributions with fixed mean.
We show via a non-trivial extension of this semigroup approach that the natural
analog of the Poisson maximum entropy property remains valid if the compound
Poisson distributions under consideration are log-concave, but that it fails in
general. A parallel maximum entropy result is established for the family of
compound binomial measures. Sufficient conditions for compound distributions to
be log-concave are discussed and applications to combinatorics are examined;
new bounds are derived on the entropy of the cardinality of a random
independent set in a claw-free graph, and a connection is drawn to Mason's
conjecture for matroids. The present results are primarily motivated by the
desire to provide an information-theoretic foundation for compound Poisson
approximation and associated limit theorems, analogous to the corresponding
developments for the central limit theorem and for Poisson approximation. Our
results also demonstrate new links between some probabilistic methods and the
combinatorial notions of log-concavity and ultra-log-concavity, and they add to
the growing body of work exploring the applications of maximum entropy
characterizations to problems in discrete mathematics.Comment: 30 pages. This submission supersedes arXiv:0805.4112v1. Changes in
v2: Updated references, typos correcte
Using Markov chain Monte Carlo methods for estimating parameters with gravitational radiation data
We present a Bayesian approach to the problem of determining parameters for
coalescing binary systems observed with laser interferometric detectors. By
applying a Markov Chain Monte Carlo (MCMC) algorithm, specifically the Gibbs
sampler, we demonstrate the potential that MCMC techniques may hold for the
computation of posterior distributions of parameters of the binary system that
created the gravity radiation signal. We describe the use of the Gibbs sampler
method, and present examples whereby signals are detected and analyzed from
within noisy data.Comment: 21 pages, 10 figure
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