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Efficient Estimation of Average Treatment Effects Under Treatment-Based Sampling, Second Version

Song, Kyungchul - 2010

Nonrandom sampling schemes are often used in program evaluation settings to improve the quality of inference. This … paper considers what we call treatment-based sampling, a type of standard stratified sampling where part of the strata are … Hirano, Imbens, and Ridder (2003) to treatment-based sampling does not always lead to an efficient estimator. This paper …

Persistent link: https://www.econbiz.de/10013142868

Why does Indirect Inference estimation produce less small sample bias than maximum likelihood? : a note

Meenagh, David; Minford, Patrick; Xu, Yongdeng - 2022

Maximum Likelihood (ML) shows both lower power and higher bias in small sample Monte Carlo experiments than Indirect Inference (II) and IIís higher power comes from its use of the model-restricted distribution of the auxiliary model coeffi cients (Le et al. 2016). We show here that IIís higher...

Persistent link: https://www.econbiz.de/10014433297

Comparing Indirect Inference and likelihood testing : asymptotic and small sample results

Meenagh, David; Minford, Patrick; Wickens, Michael R.; … - 2015

Indirect Inference has been found to have much greater power than the Likelihood Ratio in small samples for testing DSGE models. We look at asymptotic and large sample properties of these tests to understand why this might be the case. We find that the power of the LR test is undermined when...

Persistent link: https://www.econbiz.de/10011317842

The small sample properties of Indirect Inference in testing and estimating DSGE models

Meenagh, David; Minford, Patrick; Wickens, Michael R.; … - 2018

Indirect inference testing can be carried out with a variety of auxiliary models. Asymptotically these different models make no difference. However, the small sample properties can differ. We explore small sample power and estimation bias both with different variable combinations and descriptive...

Persistent link: https://www.econbiz.de/10011886113

Testing exogeneity in cross-section regression by sorting data

DeLuna, Xavier; Johansson, Per-Olov - 2000

We introduce a framework to test for exogeneity of a variable in a regression based on cross-sectional data. By sorting data with respect to a function (sorting score) of known exogeneous variables it is possible to utilize a battery of tools originally develped to detecting model...

Persistent link: https://www.econbiz.de/10011574988

Efficient Estimation of Average Treatment Effects Under Treatment-Based Sampling

Song, Kyungchul - 2009

Nonrandom sampling schemes are often used in program evaluation settings to improve the quality of inference. This … paper considers what we call treatment-based sampling, a type of standard stratified sampling where part of the strata are …-based sampling that yields the best semiparametric efficiency bound. Lastly, this paper finds that adapting the efficient estimators …

Persistent link: https://www.econbiz.de/10012718642

Chapter 3 Forecast Evaluation

West, Kenneth D. - 2006

This chapter summarizes recent literature on asymptotic inference about forecasts. Both analytical and simulation based methods are discussed. The emphasis is on techniques applicable when the number of competing models is small. Techniques applicable when a large number of models is compared to...

Persistent link: https://www.econbiz.de/10014023703

Chapter 62 A Practitioner's Approach to Estimating Intertemporal Relationships Using Longitudinal Data: Lessons from Applications in Wage Dynamics

MaCurdy, Thomas - In: Handbook of econometrics : volume 6A, (pp. 4057-4167). 2007

sampling inherent in survey longitudinal data, (3) incorporation of predetermined variables in estimation, and (4 …

Persistent link: https://www.econbiz.de/10014024953

Finite Sample Weighting of Recursive Forecast Errors

Brooks, Chris - 2014

This paper proposes and tests a new framework for weighting recursive out-of-sample prediction errors in accordance with their corresponding in-sample estimation uncertainty. In essence, we show how as much information from the sample as possible can be used in the evaluation of prediction...

Persistent link: https://www.econbiz.de/10013061638

Best subset binary prediction

Chen, Le-Yu; Lee, Sokbae - 2017

We consider a variable selection problem for the prediction of binary outcomes. We study the best subset selection procedure by which the explanatory variables are chosen by maximizing Manski (1975, 1985)'s maximum score type objective function subject to a constraint on the maximal number of...

Persistent link: https://www.econbiz.de/10011775359