Add to ORKGFragility of regression analysis to arbitrary assumptions and decisions about choice of control variables is an important concern for applied econometricians (e.g. Leamer (1983)). Sensitivity analysis in the form of model averaging represents an (agnostic) approach that formally addresses this problem of model uncertainty. This paper presents an overview of model averaging methods with emphasis on recent developments in the combination of model averaging with IV and panel data settings
Classical statistical analysis is split into two steps: model selection and post-selection inference...
This article studies optimal model averaging for partially linear models with heteroscedasticity. A ...
Model averaging is often used to allow for uncertainty in the model selection process. In the freque...
The method of model averaging has become an important tool to deal with model uncertainty, in parti...
The method of model averaging has become an important tool to deal with model uncertainty, for examp...
This book provides a concise and accessible overview of model averaging, with a focus on application...
I use two examples to illustrate three methods for model averaging: using AIC weights, using BIC wei...
Model averaging is an alternative approach to classical model selection in model estimation. The mod...
Empirical growth research faces a high degree of model uncertainty. Apart from the neoclassical grow...
This paper presents recent developments in model selection and model averaging for parametric and no...
Parameter estimation under model uncertainty is a difficult and fundamental issue in econometrics. T...
This paper focuses on the problem of variable selection in linear regression models. I briefy revie...
Abstract In applications, the traditional estimation procedure generally begins with model selection...
[Departement_IRSTEA]Territoires [TR1_IRSTEA]SEDYVIN [ADD1_IRSTEA]Biodiversité et fonctionnalités éco...
In the context of an autoregressive panel data model with fixed effect, we examine the relationship b...
Classical statistical analysis is split into two steps: model selection and post-selection inference...
This article studies optimal model averaging for partially linear models with heteroscedasticity. A ...
Model averaging is often used to allow for uncertainty in the model selection process. In the freque...
The method of model averaging has become an important tool to deal with model uncertainty, in parti...
The method of model averaging has become an important tool to deal with model uncertainty, for examp...
This book provides a concise and accessible overview of model averaging, with a focus on application...
I use two examples to illustrate three methods for model averaging: using AIC weights, using BIC wei...
Model averaging is an alternative approach to classical model selection in model estimation. The mod...
Empirical growth research faces a high degree of model uncertainty. Apart from the neoclassical grow...
This paper presents recent developments in model selection and model averaging for parametric and no...
Parameter estimation under model uncertainty is a difficult and fundamental issue in econometrics. T...
This paper focuses on the problem of variable selection in linear regression models. I briefy revie...
Abstract In applications, the traditional estimation procedure generally begins with model selection...
[Departement_IRSTEA]Territoires [TR1_IRSTEA]SEDYVIN [ADD1_IRSTEA]Biodiversité et fonctionnalités éco...
In the context of an autoregressive panel data model with fixed effect, we examine the relationship b...
Classical statistical analysis is split into two steps: model selection and post-selection inference...
This article studies optimal model averaging for partially linear models with heteroscedasticity. A ...
Model averaging is often used to allow for uncertainty in the model selection process. In the freque...