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Model Averaging (model + averaging)
Kinds of Model Averaging Selected AbstractsModel uncertainty in cross-country growth regressionsJOURNAL OF APPLIED ECONOMETRICS, Issue 5 2001Carmen Fernández We investigate the issue of model uncertainty in cross-country growth regressions using Bayesian Model Averaging (BMA). We find that the posterior probability is spread widely among many models, suggesting the superiority of BMA over choosing any single model. Out-of-sample predictive results support this claim. In contrast to Levine and Renelt (1992), our results broadly support the more ,optimistic' conclusion of Sala-i-Martin (1997b), namely that some variables are important regressors for explaining cross-country growth patterns. However, care should be taken in the methodology employed. The approach proposed here is firmly grounded in statistical theory and immediately leads to posterior and predictive inference. Copyright © 2001 John Wiley & Sons, Ltd. [source] Returns to Schooling and Bayesian Model Averaging: A Union of Two LiteraturesJOURNAL OF ECONOMIC SURVEYS, Issue 2 2004Justin L. Tobias Abstract., In this paper, we review and unite the literatures on returns to schooling and Bayesian model averaging. We observe that most studies seeking to estimate the returns to education have done so using particular (and often different across researchers) model specifications. Given this, we review Bayesian methods which formally account for uncertainty in the specification of the model itself, and apply these techniques to estimate the economic return to a college education. The approach described in this paper enables us to determine those model specifications which are most favored by the given data, and also enables us to use the predictions obtained from all of the competing regression models to estimate the returns to schooling. The reported precision of such estimates also account for the uncertainty inherent in the model specification. Using U.S. data from the National Longitudinal Survey of Youth (NLSY), we also revisit several ,stylized facts' in the returns to education literature and examine if they continue to hold after formally accounting for model uncertainty. [source] Model Selection and Model Averaging by CLAESKENS, G. and HJORT, N. L.BIOMETRICS, Issue 2 2009Thomas M. Loughin No abstract is available for this article. [source] A Bayesian model averaging approach for cost-effectiveness analysesHEALTH ECONOMICS, Issue 7 2009Caterina Conigliani Abstract We consider the problem of assessing new and existing technologies for their cost-effectiveness in the case where data on both costs and effects are available from a clinical trial, and we address it by means of the cost-effectiveness acceptability curve. The main difficulty in these analyses is that cost data usually exhibit highly skew and heavy-tailed distributions so that it can be extremely difficult to produce realistic probabilistic models for the underlying population distribution, and in particular to model accurately the tail of the distribution, which is highly influential in estimating the population mean. Here, in order to integrate the uncertainty about the model into the analysis of cost data and into cost-effectiveness analyses, we consider an approach based on Bayesian model averaging: instead of choosing a single parametric model, we specify a set of plausible models for costs and estimate the mean cost with a weighted mean of its posterior expectations under each model, with weights given by the posterior model probabilities. The results are compared with those obtained with a semi-parametric approach that does not require any assumption about the distribution of costs. Copyright © 2008 John Wiley & Sons, Ltd. [source] Time to establishment success for introduced signal crayfish in Sweden , a statistical evaluation when success is partially knownJOURNAL OF APPLIED ECOLOGY, Issue 5 2010Ullrika Sahlin Summary 1.,The signal crayfish Pacifastacus leniusculus is an invasive species in Sweden, threatening the red-listed nobel crayfish Astacus astacus through spreading the crayfish plague. Time-to-event models can handle censored data on such introduced populations for which the state (successful or not) is only partially known at the last observation, but even though data on introduced populations most often are censored, this type of model is usually not used for likelihood-based inference and predictions of the dynamics of establishing populations. 2.,We specified and fitted a probabilistic time-to-event model to be used to predict the time to successful establishment of signal crayfish populations introduced into Sweden. Important covariates of establishment success were found by the methods of ,model averaging' and ,hierarchical partitioning', considering model uncertainty and multi-colinearity, respectively. 3.,The hazard function that received the highest evidence based on the empirical data showed that the chances of establishment were highest in the time periods immediately following the first introduction. The model predicts establishment success to be <50% within 5 years after first introduction over the current distributional range of signal crayfish in Sweden today. 4.,Among covariates related to temperature, fish species and physical properties of the habitat, the length of the growing season was the most important and consistent covariate of establishment success. We found that establishment success of signal crayfish is expected to increase with the number of days when growth is possible, and decrease with the number of days with extremely high temperatures, which can be seen to approximate conditions of stress. 5.,Synthesis and applications. The results demonstrate lower establishment success of signal crayfish further north in Sweden, which may decrease the incentives of additional illegal introductions that may threaten the red-listed noble crayfish Astacus astacus. We provide a fully probabilistic statistical evaluation that quantifies uncertainty in the duration of the establishment stage that is useful for management decisions of invasive species. The combination of model averaging and hierarchical partitioning provides a comprehensive method to address multi-colinearity common to retrospective data on establishment success of invasive species. [source] Forecasting realized volatility: a Bayesian model-averaging approachJOURNAL OF APPLIED ECONOMETRICS, Issue 5 2009Chun Liu How to measure and model volatility is an important issue in finance. Recent research uses high-frequency intraday data to construct ex post measures of daily volatility. This paper uses a Bayesian model-averaging approach to forecast realized volatility. Candidate models include autoregressive and heterogeneous autoregressive specifications based on the logarithm of realized volatility, realized power variation, realized bipower variation, a jump and an asymmetric term. Applied to equity and exchange rate volatility over several forecast horizons, Bayesian model averaging provides very competitive density forecasts and modest improvements in point forecasts compared to benchmark models. We discuss the reasons for this, including the importance of using realized power variation as a predictor. Bayesian model averaging provides further improvements to density forecasts when we move away from linear models and average over specifications that allow for GARCH effects in the innovations to log-volatility. Copyright © 2009 John Wiley & Sons, Ltd. [source] Returns to Schooling and Bayesian Model Averaging: A Union of Two LiteraturesJOURNAL OF ECONOMIC SURVEYS, Issue 2 2004Justin L. Tobias Abstract., In this paper, we review and unite the literatures on returns to schooling and Bayesian model averaging. We observe that most studies seeking to estimate the returns to education have done so using particular (and often different across researchers) model specifications. Given this, we review Bayesian methods which formally account for uncertainty in the specification of the model itself, and apply these techniques to estimate the economic return to a college education. The approach described in this paper enables us to determine those model specifications which are most favored by the given data, and also enables us to use the predictions obtained from all of the competing regression models to estimate the returns to schooling. The reported precision of such estimates also account for the uncertainty inherent in the model specification. Using U.S. data from the National Longitudinal Survey of Youth (NLSY), we also revisit several ,stylized facts' in the returns to education literature and examine if they continue to hold after formally accounting for model uncertainty. [source] Forecast accuracy and economic gains from Bayesian model averaging using time-varying weightsJOURNAL OF FORECASTING, Issue 1-2 2010Lennart Hoogerheide Abstract Several Bayesian model combination schemes, including some novel approaches that simultaneously allow for parameter uncertainty, model uncertainty and robust time-varying model weights, are compared in terms of forecast accuracy and economic gains using financial and macroeconomic time series. The results indicate that the proposed time-varying model weight schemes outperform other combination schemes in terms of predictive and economic gains. In an empirical application using returns on the S&P 500 index, time-varying model weights provide improved forecasts with substantial economic gains in an investment strategy including transaction costs. Another empirical example refers to forecasting US economic growth over the business cycle. It suggests that time-varying combination schemes may be very useful in business cycle analysis and forecasting, as these may provide an early indicator for recessions. Copyright © 2009 John Wiley & Sons, Ltd. [source] Forecasting US inflation by Bayesian model averagingJOURNAL OF FORECASTING, Issue 2 2009Jonathan H. Wright Abstract Recent empirical work has considered the prediction of inflation by combining the information in a large number of time series. One such method that has been found to give consistently good results consists of simple equal-weighted averaging of the forecasts from a large number of different models, each of which is a linear regression relating inflation to a single predictor and a lagged dependent variable. In this paper, I consider using Bayesian model averaging for pseudo out-of-sample prediction of US inflation, and find that it generally gives more accurate forecasts than simple equal-weighted averaging. This superior performance is consistent across subsamples and a number of inflation measures. Copyright © 2008 John Wiley & Sons, Ltd. [source] Bayesian modelling of catch in a north-west Atlantic fisheryJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES C (APPLIED STATISTICS), Issue 3 2002Carmen Fernández Summary. We model daily catches of fishing boats in the Grand Bank fishing grounds. We use data on catches per species for a number of vessels collected by the European Union in the context of the Northwest Atlantic Fisheries Organization. Many variables can be thought to influence the amount caught: a number of ship characteristics (such as the size of the ship, the fishing technique used and the mesh size of the nets) are obvious candidates, but one can also consider the season or the actual location of the catch. Our database leads to 28 possible regressors (arising from six continuous variables and four categorical variables, whose 22 levels are treated separately), resulting in a set of 177 million possible linear regression models for the log-catch. Zero observations are modelled separately through a probit model. Inference is based on Bayesian model averaging, using a Markov chain Monte Carlo approach. Particular attention is paid to the prediction of catches for single and aggregated ships. [source] Fragile beliefs and the price of uncertaintyQUANTITATIVE ECONOMICS, Issue 1 2010Lars Peter Hansen C11; C44; C72; E44; G12 A representative consumer uses Bayes' law to learn about parameters of several models and to construct probabilities with which to perform ongoing model averaging. The arrival of signals induces the consumer to alter his posterior distribution over models and parameters. The consumer's specification doubts induce him to slant probabilities pessimistically. The pessimistic probabilities tilt toward a model that puts long-run risks into consumption growth. That contributes a countercyclical history-dependent component to prices of risk. [source] A full-factor multivariate GARCH modelTHE ECONOMETRICS JOURNAL, Issue 2 2003I. D. Vrontos A new multivariate time series model with time varying conditional variances and covariances is presented and analysed. A complete analysis of the proposed model is presented consisting of parameter estimation, model selection and volatility prediction. Classical and Bayesian techniques are used for the estimation of the model parameters. It turns out that the construction of our proposed model allows easy maximum likelihood estimation and construction of well-mixing Markov chain Monte Carlo (MCMC) algorithms. Bayesian model selection is addressed using MCMC model composition. The problem of accounting for model uncertainty is considered using Bayesian model averaging. We provide implementation details and illustrations using daily rates of return on eight stocks of the US market. [source] Forecasting Substantial Data Revisions in the Presence of Model Uncertainty,THE ECONOMIC JOURNAL, Issue 530 2008Anthony Garratt A recent revision to the preliminary measurement of GDP(E) growth for 2003Q2 caused considerable press attention, provoked a public enquiry and prompted a number of reforms to UK statistical reporting procedures. In this article, we compute the probability of ,substantial revisions' that are greater (in absolute value) than the controversial 2003 revision. The predictive densities are derived from Bayesian model averaging over a wide set of forecasting models including linear, structural break and regime-switching models with and without heteroscedasticity. Ignoring the nonlinearities and model uncertainty yields misleading predictives and obscures recent improvements in the quality of preliminary UK macroeconomic measurements. [source] Dry season habitat use by critically endangered white-shouldered ibis in northern CambodiaANIMAL CONSERVATION, Issue 1 2010H. L. Wright Abstract We present the first scientific study of white-shouldered ibis Pseudibis davisoni habitat preferences in dry dipterocarp forest. Foraging sites included seasonal pools, forest understorey grasslands and fallow rice fields, with terrestrial sites used more following rainfall. Habitat and anthropogenic effects in logistic models of foraging site selection were examined by multimodel inference and model averaging. White-shouldered ibis preferred pools with greater cover of short vegetation (<25 cm) and less of the boundary enclosed, and forest sites with greater cover of bare substrate and lower people encounter rate. At forest sites, livestock density was positively related to bare substrate extent and thus may improve suitability for foraging ibis. At pools, livestock removed tall vegetation between the early and late dry season indicating their importance in opening up foraging habitats after wet season growth. However, by the late dry season, pools with greater livestock density had less short vegetation, the habitat favoured by ibis. Conservation strategies for white-shouldered ibis must consider a range of habitats, not just seasonal wetlands, and should incorporate extensive grazing and associated burning practises of local communities. Further understanding of the effects of these practices on vegetation, prey abundance and prey availability are therefore needed for effective conservation of this species. This will also develop our understanding of potentially beneficial anthropogenic influences in tropical environments. [source] Capture,Recapture Estimation Using Finite Mixtures of Arbitrary DimensionBIOMETRICS, Issue 2 2010Richard Arnold Summary Reversible jump Markov chain Monte Carlo (RJMCMC) methods are used to fit Bayesian capture,recapture models incorporating heterogeneity in individuals and samples. Heterogeneity in capture probabilities comes from finite mixtures and/or fixed sample effects allowing for interactions. Estimation by RJMCMC allows automatic model selection and/or model averaging. Priors on the parameters stabilize the estimates and produce realistic credible intervals for population size for overparameterized models, in contrast to likelihood-based methods. To demonstrate the approach we analyze the standard Snowshoe hare and Cottontail rabbit data sets from ecology, a reliability testing data set. [source] Bayesian Detection and Modeling of Spatial Disease ClusteringBIOMETRICS, Issue 3 2000Ronald E. Gangnon Summary. Many current statistical methods for disease clustering studies are based on a hypothesis testing paradigm. These methods typically do not produce useful estimates of disease rates or cluster risks. In this paper, we develop a Bayesian procedure for drawing inferences about specific models for spatial clustering. The proposed methodology incorporates ideas from image analysis, from Bayesian model averaging, and from model selection. With our approach, we obtain estimates for disease rates and allow for greater flexibility in both the type of clusters and the number of clusters that may be considered. We illustrate the proposed procedure through simulation studies and an analysis of the well-known New York leukemia data. [source] | ||||||||||||||||||||||||||||