447 research outputs found
Context Tree Selection: A Unifying View
The present paper investigates non-asymptotic properties of two popular
procedures of context tree (or Variable Length Markov Chains) estimation:
Rissanen's algorithm Context and the Penalized Maximum Likelihood criterion.
First showing how they are related, we prove finite horizon bounds for the
probability of over- and under-estimation. Concerning overestimation, no
boundedness or loss-of-memory conditions are required: the proof relies on new
deviation inequalities for empirical probabilities of independent interest. The
underestimation properties rely on loss-of-memory and separation conditions of
the process.
These results improve and generalize the bounds obtained previously. Context
tree models have been introduced by Rissanen as a parsimonious generalization
of Markov models. Since then, they have been widely used in applied probability
and statistics
Proceedings of the Fifth Workshop on Information Theoretic Methods in Science and Engineering
These are the online proceedings of the Fifth Workshop on Information Theoretic Methods in Science and Engineering (WITMSE), which was held in the Trippenhuis, Amsterdam, in August 2012
Experimental evidence on rational inattention
We show that rational inattention theory of Sims (2003) provides a rationalization of choice models Ă la Luce and gives a structural interpretation to probability curvature parameters as reflecting costs of processing information. We use data from a behavioral experiment to show that people behave according to predictions of the theory. We estimate attitudes to risk and costs of information for individual participants and document overwhelming heterogeneity in these parameters among a relatively homogeneous sample of people. We characterize, both theoretically and empirically, the aggregation biases this heterogeneity implies and find these biases to be substantial.Risk management ; Econometrics
Model Averaging and its Use in Economics
The method of model averaging has become an important tool to deal with model
uncertainty, for example in situations where a large amount of different
theories exist, as are common in economics. Model averaging is a natural and
formal response to model uncertainty in a Bayesian framework, and most of the
paper deals with Bayesian model averaging. The important role of the prior
assumptions in these Bayesian procedures is highlighted. In addition,
frequentist model averaging methods are also discussed. Numerical methods to
implement these methods are explained, and I point the reader to some freely
available computational resources. The main focus is on uncertainty regarding
the choice of covariates in normal linear regression models, but the paper also
covers other, more challenging, settings, with particular emphasis on sampling
models commonly used in economics. Applications of model averaging in economics
are reviewed and discussed in a wide range of areas, among which growth
economics, production modelling, finance and forecasting macroeconomic
quantities.Comment: forthcoming; accepted versio
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