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11
Computational Experiments and Reality
, 1999
"... This study explores three alternative econometric interpretations of dynamic, stochastic general equilibrium (DSGE) models. (1) A strong econometric interpretation takes the model literally and directly produces a likelihood function for observed prices and quantities. It is widely recognized that u ..."
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This study explores three alternative econometric interpretations of dynamic, stochastic general equilibrium (DSGE) models. (1) A strong econometric interpretation takes the model literally and directly produces a likelihood function for observed prices and quantities. It is widely recognized that under this interpretation, most DSGE models are rejected using classical econometrics and assigned zero probability in a Bayesian approach. (2) A weak econometric interpretation commonly made in the calibration literature confines attention to only a few functions of observed prices and interest rates and evaluates a model on its predictive distribution for these functions. This approach is equivalent to a Bayesian prior predictive analysis, developed by Box (1980) and predecessors. This study shows that the weak interpretation retains the implications of the strong interpretation, and therefore DSGE’s fare no better under this approach. (3) Under a minimal econometric interpretation, DSGE’s provide only prior distributions for specified population moments. When coupled with an econometric model (e.g., a vector autoregression) that includes the same moments, DSGE’s may be compared and used for inference using conventional Bayesian methods. This interpretation extends and formalizes an approach suggested by Dejong, Ingram and Whiteman (1996). All three interpretations are illustrated using models of the equity premium, and it is shown that the conclusions from a minimal interpretation differ substantially from those under a weak interpretation. This revision was prepared for the DYNARE Conference, CEPREMAP, Paris, September 45, 2006. It is work in progress. Comments welcome. Please do not cite or quote without the author’s permission. 1 1
Bayesian inference procedures derived via the concept of relative surprise
 Communications in Statistics
, 1997
"... of least relative surprise; model checking; change of variable problem; crossvalidation. We consider the problem of deriving Bayesian inference procedures via the concept of relative surprise. The mathematical concept of surprise has been developed by I.J. Good in a long sequence of papers. We make ..."
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Cited by 18 (6 self)
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of least relative surprise; model checking; change of variable problem; crossvalidation. We consider the problem of deriving Bayesian inference procedures via the concept of relative surprise. The mathematical concept of surprise has been developed by I.J. Good in a long sequence of papers. We make a modiÞcation to this development that permits the avoidance of a serious defect; namely, the change of variable problem. We apply relative surprise to the development of estimation, hypothesis testing and model checking procedures. Important advantages of the relative surprise approach to inference include the lack of dependence on a particular loss function and complete freedom to the statistician in the choice of prior for hypothesis testing problems. Links are established with common Bayesian inference procedures such as highest posterior density regions, modal estimates and Bayes factors. From a practical perspective new inference
Models, Computational Experiments and Reality
, 2007
"... DSGE models are designed to mimic only certain aspects of reality, usually speci…ed moments of observable data. They typically have other implications that are clearly false and lead to their immediate rejection if taken literally. Widely used calibration exercises compare the implications of DSGE m ..."
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Cited by 3 (0 self)
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DSGE models are designed to mimic only certain aspects of reality, usually speci…ed moments of observable data. They typically have other implications that are clearly false and lead to their immediate rejection if taken literally. Widely used calibration exercises compare the implications of DSGE models for the distribution of speci…ed sample moments with the corresponding data. This paper shows that this procedure takes DSGE models literally, and therefore retains the implications that lead to their immediate rejection. If, instead, the DSGE model is interpreted only to imply particular population moments, and not the distributions of the corresponding sample moments, this logical dif…culty does not emerge but the model then has no falsi…able implications. The constructive contribution of the paper is to merge the DSGE model with an atheoretical econometric model in a logically consistent way that has refutable implications for observable data. This leads to practical procedures that compare the prior distribution of the DSGE model and the posterior distribution of the atheoretical model for the population moments the DSGE model is intended to describe. The concepts are illustrated using four competing DSGE models of the riskfree rate and the equity premium. The synthesis advanced in the paper resolves the equity premium puzzle in this context.
Measures of Surprise in Bayesian Analysis
 Duke University
, 1997
"... Measures of surprise refer to quantifications of the degree of incompatibility of data with some hypothesized model H 0 without any reference to alternative models. Traditional measures of surprise have been the pvalues, which are however known to grossly overestimate the evidence against H 0 . Str ..."
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Cited by 2 (2 self)
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Measures of surprise refer to quantifications of the degree of incompatibility of data with some hypothesized model H 0 without any reference to alternative models. Traditional measures of surprise have been the pvalues, which are however known to grossly overestimate the evidence against H 0 . Strict Bayesian analysis calls for an explicit specification of all possible alternatives to H 0 so Bayesians have not made routine use of measures of surprise. In this report we CRITICALLY REVIEw the proposals that have been made in this regard. We propose new modifications, stress the connections with robust Bayesian analysis and discuss the choice of suitable predictive distributions which allow surprise measures to play their intended role in the presence of nuisance parameters. We recommend either the use of appropriate likelihoodratio type measures or else the careful calibration of pvalues so that they are closer to Bayesian answers. Key words and phrases. Bayes factors; Bayesian pvalues; Bayesian robustness; Conditioning; Model checking; Predictive distributions. 1.
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"... ❙ X: Επίπεδο εστριόλης (estriol) των εγκύων γυναικών ❚ Υ i ~ Normal(μ i, σ 2) ❚ μ i =η i =α+βΧ i 6 … ΑΠΛΟΙ ΕΛΕΓΧΟΙ ΥΠΟΘΕΣΕΩΝ 6.1. Εισαγωγή: ΕκτωνΥστερων Λόγος ..."
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❙ X: Επίπεδο εστριόλης (estriol) των εγκύων γυναικών ❚ Υ i ~ Normal(μ i, σ 2) ❚ μ i =η i =α+βΧ i 6 … ΑΠΛΟΙ ΕΛΕΓΧΟΙ ΥΠΟΘΕΣΕΩΝ 6.1. Εισαγωγή: ΕκτωνΥστερων Λόγος
Bayesian CrossSectional Analysis of the Conditional Distribution of Earnings of Men in
, 2005
"... This study develops practical methods for Bayesian nonparametric inference in regression models. The emphasis is on extending a nonparametric treatment of the regression function to the full conditional distribution. It applies these methods to the relationship of earnings of men in the United State ..."
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This study develops practical methods for Bayesian nonparametric inference in regression models. The emphasis is on extending a nonparametric treatment of the regression function to the full conditional distribution. It applies these methods to the relationship of earnings of men in the United States to their age and education over the period 1967 through 1996. Principal findings include increasing returns to both education and experience over this period, rising variance of earnings conditional on age and education, a negatively skewed and leptokurtic conditional distribution of log earnings, and steadily increasing inequality with asymmetric and changing impacts on high and lowwage earners. These results are insensitive to several alternative nonparametric specifications of the distribution of earnings conditional on age and education. Acknowledgement 1 Grant R01HD3706001 from the National Institutes of Health provided financial support for this work. Much of applied statistics and econometrics is concerned with the measurement
Running head: From coincidences to discoveries Address for correspondence:
"... From coincidences to discoveries 1 From coincidences to discoveries 2 People’s reactions to coincidences are often cited as an illustration of the irrationality of human reasoning about chance. We argue that coincidences may be better understood in terms of rational statistical inference, based on t ..."
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From coincidences to discoveries 1 From coincidences to discoveries 2 People’s reactions to coincidences are often cited as an illustration of the irrationality of human reasoning about chance. We argue that coincidences may be better understood in terms of rational statistical inference, based on their functional role in processes of causal discovery and theory revision. We present a formal definition of coincidences in the context of a Bayesian framework for causal induction: a coincidence is an event that provides support for an alternative to a currently favored causal theory, but not necessarily enough support to accept that alternative in light of its low prior probability. We test the qualitative and quantitative predictions of this account through a series of experiments that examine the transition from coincidence to evidence, the correspondence between the strength of coincidences and the statistical support for causal structure, and the relationship between causes and coincidences. Our results indicate that people can accurately assess the strength of coincidences, suggesting that irrational conclusions drawn from coincidences are the consequence of overestimation of the plausibility of novel causal forces. We discuss the
Bayesian Forecasting
, 2006
"... Bayesian forecasting is a natural product of a Bayesian approach to inference. The Bayesian approach in general requires explicit formulation of a model, and conditioning on known quantities, in order to draw inferences about unknown ones. In Bayesian forecasting, one simply takes a subset of the un ..."
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Bayesian forecasting is a natural product of a Bayesian approach to inference. The Bayesian approach in general requires explicit formulation of a model, and conditioning on known quantities, in order to draw inferences about unknown ones. In Bayesian forecasting, one simply takes a subset of the unknown quantities to be future values of some variables of interest. This paper presents the principles of Bayesian forecasting, and describes recent advances in compuational capabilities for applying them that have dramatically expanded the scope of applicability of the Bayesian approach. It describes historical developments and the analytic compromises that were necessary prior to recent developments, the application of the new procedures in a variety of examples, and reports on two longterm Bayesian forecasting exercises.
Contents lists available at ScienceDirect Neural Networks
"... journal homepage: www.elsevier.com/locate/neunet ..."