Bayesian Method of Moments (bmom) Analysis of Parametric and Semiparametric Regression Models
نویسنده
چکیده
The Bayesian Method of Moments is applied to semiparametric regression models using alternative series expansions of an unknown regression function. We describe estimation loss functions, predictive loss functions and posterior odds as techniques to determine how many terms in a particular expansion to keep and how to choose among diierent types of expansions. The developed theory is then applied in a Monte-Carlo experiment to data generated from a CES production function.
منابع مشابه
Model Selection for Semiparametric Bayesian Models with Application to Overdispersion
In analyzing complicated data, we are often unwilling or not confident to impose a parametric model for the data-generating structure. One important example is data analysis for proportional or count data with overdispersion. The obvious advantage of assuming full parametric models is that one can resort to likelihood analyses, for instance, to use AIC or BIC to choose the most appropriate regr...
متن کاملGeneralized Ridge Regression Estimator in Semiparametric Regression Models
In the context of ridge regression, the estimation of ridge (shrinkage) parameter plays an important role in analyzing data. Many efforts have been put to develop skills and methods of computing shrinkage estimators for different full-parametric ridge regression approaches, using eigenvalues. However, the estimation of shrinkage parameter is neglected for semiparametric regression models. The m...
متن کاملBayesian Method of Moments Analysis of Time Series Models with an Application to Forecasting Turning Points in Output Growth Rates
Bayesian method of moments (BMOM) analyses of central time series models are presented. These include derivations of post data densities for parameters, predictive densities for future observations and relative expected losses associated with alternative model speciications, e.g. a unit root versus a non-unit root AR(1) process or an AR(1) versus higher order AR processes. BMOM results are comp...
متن کاملSemi-parametric Quantile Regression for Analysing Continuous Longitudinal Responses
Recently, quantile regression (QR) models are often applied for longitudinal data analysis. When the distribution of responses seems to be skew and asymmetric due to outliers and heavy-tails, QR models may work suitably. In this paper, a semi-parametric quantile regression model is developed for analysing continuous longitudinal responses. The error term's distribution is assumed to be Asymmetr...
متن کاملComparison of Artificial Neural Network, Decision Tree and Bayesian Network Models in Regional Flood Frequency Analysis using L-moments and Maximum Likelihood Methods in Karkheh and Karun Watersheds
Proper flood discharge forecasting is significant for the design of hydraulic structures, reducing the risk of failure, and minimizing downstream environmental damage. The objective of this study was to investigate the application of machine learning methods in Regional Flood Frequency Analysis (RFFA). To achieve this goal, 18 physiographic, climatic, lithological, and land use parameters were ...
متن کامل