Teaching Size and Power Properties of Hypothesis Tests Through Simulations
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Süleyman Taşpınar
und
Osman Doğan
Abstract
In this study, we review the graphical methods suggested in Davidson and MacKinnon (Davidson, Russell, and James G. MacKinnon. 1998. “Graphical Methods for Investigating the Size and Power of Hypothesis Tests.” The Manchester School 66 (1): 1–26.) that can be used to investigate size and power properties of hypothesis tests for undergraduate and graduate econometrics courses. These methods can be used to assess finite sample properties of various hypothesis tests through simulation studies. In addition, these methods can be effectively used in classrooms to reinforce students’ understanding of basic hypothesis testing concepts such as Type I error, Type II error, size, power, p-values and under-or-over-sized tests. We illustrate the procedural aspects of these graphical methods through Monte Carlo experiments, and provide the implementation codes written in Matlab and R for the classroom applications.
Acknowledgments
This research was supported, in part, by a grant of computer time from the City University of New York High Performance Computing Center under NSF Grants CNS-0855217 and CNS-0958379.
Appendix
Listing 1: R Codes for P Value Plots and P Value Discrepancy Plots.
Listing 2: R Codes for Size-Power Curves.
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©2017 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- Research Articles
- Regression Discontinuity with Errors in the Running Variable: Effect on Truthful Margin
- A Simple Estimator for Dynamic Models with Serially Correlated Unobservables
- Selection of an Estimation Window in the Presence of Data Revisions and Recent Structural Breaks
- Discriminating between (in)valid External Instruments and (in)valid Exclusion Restrictions
- Competing Risks Copula Models for Unemployment Duration: An Application to a German Hartz Reform
- Intercept Homogeneity Test for Fixed Effect Models under Cross-sectional Dependence: Some Insights
- Practitioner’s Corner
- Root-n Consistent Kernel Density Estimation in Practice
- Linear Model IV Estimation When Instruments Are Many or Weak
- Additive Nonparametric Instrumental Regressions: A Guide to Implementation
- Teaching Corner
- Teaching Size and Power Properties of Hypothesis Tests Through Simulations
Artikel in diesem Heft
- Frontmatter
- Research Articles
- Regression Discontinuity with Errors in the Running Variable: Effect on Truthful Margin
- A Simple Estimator for Dynamic Models with Serially Correlated Unobservables
- Selection of an Estimation Window in the Presence of Data Revisions and Recent Structural Breaks
- Discriminating between (in)valid External Instruments and (in)valid Exclusion Restrictions
- Competing Risks Copula Models for Unemployment Duration: An Application to a German Hartz Reform
- Intercept Homogeneity Test for Fixed Effect Models under Cross-sectional Dependence: Some Insights
- Practitioner’s Corner
- Root-n Consistent Kernel Density Estimation in Practice
- Linear Model IV Estimation When Instruments Are Many or Weak
- Additive Nonparametric Instrumental Regressions: A Guide to Implementation
- Teaching Corner
- Teaching Size and Power Properties of Hypothesis Tests Through Simulations
