scholarly journals Frequentist model averaging for threshold models

2018 ◽  
Vol 71 (2) ◽  
pp. 275-306 ◽  
Author(s):  
Yan Gao ◽  
Xinyu Zhang ◽  
Shouyang Wang ◽  
Terence Tai-leung Chong ◽  
Guohua Zou
Keyword(s):  
Model Averaging ◽  
Threshold Models ◽  
Frequentist Model Averaging

scholarly journals Publication and Identification Biases in Measuring the Intertemporal Substitution of Labor Supply

2021 ◽  
Author(s):  
Tomas Havranek ◽  
Roman Horvath ◽  
Ali Elminejad
Keyword(s):  
Labor Supply ◽  
Publication Bias ◽  
Model Uncertainty ◽  
Model Averaging ◽  
Extensive Margin ◽  
Real Business Cycle ◽  
Intertemporal Substitution ◽  
The Mean ◽  
Frisch Elasticity ◽  
Frequentist Model Averaging

The intertemporal substitution (Frisch) elasticity of labor supply governs the predictions of real business cycle models and models of taxation. We show that, for the extensive margin elasticity, two biases conspire to systematically produce large positive estimates when the elasticity is in fact zero. Among 723 estimates in 36 studies, the mean reported elasticity is 0.5. One half of that number is due to publication bias: larger estimates are reported preferentially. The other half is due to identification bias: studies with less exogenous time variation in wages report larger elasticities. Net of the biases, the literature implies a zero mean elasticity and, with 95% confidence, is inconsistent with calibrations above 0.25. To derive these results we collect 23 variables that reflect the context in which the elasticity was obtained, use nonlinear techniques to correct for publication bias, and employ Bayesian and frequentist model averaging to address model uncertainty.

scholarly journals A general framework for frequentist model averaging

2019 ◽  
Vol 62 (2) ◽  
pp. 205-226
Author(s):  
Priyam Mitra ◽  
Heng Lian ◽  
Ritwik Mitra ◽  
Hua Liang ◽  
Min-ge Xie
Keyword(s):  
General Framework ◽  
Model Averaging ◽  
Frequentist Model Averaging

scholarly journals Model Averaging and Its Use in Economics

2020 ◽  
Vol 58 (3) ◽  
pp. 644-719 ◽  
Author(s):  
Mark F. J. Steel
Keyword(s):  
Model Uncertainty ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Linear Regression Models ◽  
Bayesian Procedures ◽  
Wide Range ◽  
Computational Resources ◽  
Production Modeling ◽  
Frequentist Model Averaging

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 techniques 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 including growth economics, production modeling, finance and forecasting macroeconomic quantities. (JEL C11, C15, C20, C52, O47).

Interval Estimation by Frequentist Model Averaging

2013 ◽  
Vol 42 (23) ◽  
pp. 4342-4356 ◽  
Author(s):  
Haiying Wang ◽  
Sherry Z. F. Zhou
Keyword(s):  
Interval Estimation ◽  
Model Averaging ◽  
Frequentist Model Averaging

Frequentist Model Averaging with missing observations

2010 ◽  
Vol 54 (12) ◽  
pp. 3336-3347 ◽  
Author(s):  
Michael Schomaker ◽  
Alan T.K. Wan ◽  
Christian Heumann
Keyword(s):  
Model Averaging ◽  
Missing Observations ◽  
Frequentist Model Averaging

scholarly journals Frequentist Model Averaging for the Nonparametric Additive Model

2023 ◽  
Author(s):  
Jun Liao ◽  
Alan Wan ◽  
Shuyuan He ◽  
Guohua Zou
Keyword(s):  
Additive Model ◽  
Model Averaging ◽  
Frequentist Model Averaging

scholarly journals Reducing Simulation Input-Model Risk via Input Model Averaging

2020 ◽  
Author(s):  
Barry L. Nelson ◽  
Alan T. K. Wan ◽  
Guohua Zou ◽  
Xinyu Zhang ◽  
Xi Jiang
Keyword(s):  
Real World ◽  
Historical Data ◽  
Empirical Support ◽  
Model Averaging ◽  
Model Risk ◽  
Input Uncertainty ◽  
Input Modeling ◽  
Input Model ◽  
Simulation Input ◽  
Frequentist Model Averaging

Input uncertainty is an aspect of simulation model risk that arises when the driving input distributions are derived or “fit” to real-world, historical data. Although there has been significant progress on quantifying and hedging against input uncertainty, there has been no direct attempt to reduce it via better input modeling. The meaning of “better” depends on the context and the objective: Our context is when (a) there are one or more families of parametric distributions that are plausible choices; (b) the real-world historical data are not expected to perfectly conform to any of them; and (c) our primary goal is to obtain higher-fidelity simulation output rather than to discover the “true” distribution. In this paper, we show that frequentist model averaging can be an effective way to create input models that better represent the true, unknown input distribution, thereby reducing model risk. Input model averaging builds from standard input modeling practice, is not computationally burdensome, requires no change in how the simulation is executed nor any follow-up experiments, and is available on the Comprehensive R Archive Network (CRAN). We provide theoretical and empirical support for our approach.

scholarly journals Frequentist Model Averaging and Applications to Bernoulli Trials

2016 ◽  
Vol 06 (03) ◽  
pp. 545-553 ◽  
Author(s):  
Georges Nguefack-Tsague ◽  
Walter Zucchini ◽  
Siméon Fotso
Keyword(s):  
Model Averaging ◽  
Bernoulli Trials ◽  
Frequentist Model Averaging
Sign in / Sign up

Export Citation Format

Share Document