The role of simulations in econometrics pedagogy
Focus Article
Published Online: Dec 02 2014
DOI: 10.1002/wics.1342
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This article assesses the role of simulation methods in econometrics pedagogy. Technological advances have increased researchers' abilities to use simulation methods and have contributed to a greater presence of simulation‐based analysis in econometrics research. Simulations can also have an important role as pedagogical tools in econometrics education by providing a data‐driven medium for difficult‐to‐grasp theoretical ideas to be empirically mimicked and the results to be visualized and interpreted accessibly. Three sample blueprints for implementing simulations to demonstrate foundational econometric principles provide a framework for gauging the effectiveness of simulation analysis as a pedagogical instrument. WIREs Comput Stat 2015, 7:160–165. doi: 10.1002/wics.1342 This article is categorized under: Statistical and Graphical Methods of Data Analysis > Markov Chain Monte Carlo (MCMC) Statistical Models > Simulation Models
Impacts of omitted variables on the empirical estimator distribution. Notes : Kernel density functions of simulated sampling distributions are for the parameter β 2, which is estimated using ordinary least squares for a fully specified linear model, y = β 0 + β 1x 1 + β 2x 2 + e , and a model with an omitted variable, y = β 0 + β 2x 2 + e . Sampling from the population and estimation was repeated m = 1000 times with each sample containing n = 250 observations. The population value is β 2 = 0.75 and is indicated by the dashed vertical line.
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Empirical distributions of OLS and WLS estimators. Notes : Kernel density functions of simulated sampling distributions are presented for the parameter β 1, which is estimated using the linear model y = β 0 + β 1x 1 + β 2x 2 + v . In the population model, the error term is specified as ϵ ∼ N (0, 1). The population value is β 1 = 0.50 and is indicated by the dashed vertical line.
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Empirical estimator distributions under alternative sample‐size assumptions. Notes : Kernel density functions of simulated sampling distributions are for the parameter β 1, which is estimated using the linear model y = β 0 + β 1x 1 + β 2x 2 + e . Sampling from the population and estimation was repeated m = 1000 times under three sample‐size assumptions, n = 25, n = 250, and n = 2500. The population value is β 1 = 0.50 and is indicated by the dashed vertical line.
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