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The role of simulations in econometrics pedagogy

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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
Empirical estimator distributions under alternative sample‐size assumptions. Notes: Kernel density functions of simulated sampling distributions are for the parameter β1, which is estimated using the linear model y = β0 + β1x1 + β2x2 + 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 β1 = 0.50 and is indicated by the dashed vertical line.
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Impacts of omitted variables on the empirical estimator distribution. Notes: Kernel density functions of simulated sampling distributions are for the parameter β2, which is estimated using ordinary least squares for a fully specified linear model, y = β0 + β1x1 + β2x2 + e, and a model with an omitted variable, y = β0 + β2x2 + e. Sampling from the population and estimation was repeated m = 1000 times with each sample containing n = 250 observations. The population value is β2 = 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 β1, which is estimated using the linear model y = β0 + β1x1 + β2x2 + v. In the population model, the error term is specified as , where ϵ ∼ N(0, 1). The population value is β1 = 0.50 and is indicated by the dashed vertical line.
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Statistical and Graphical Methods of Data Analysis > Markov Chain Monte Carlo (MCMC)
Statistical Models > Simulation Models

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