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The role of simulations in econometrics pedagogy
Author(s) -
Bekkerman Anton
Publication year - 2014
Publication title -
wiley interdisciplinary reviews: computational statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 38
eISSN - 1939-0068
pISSN - 1939-5108
DOI - 10.1002/wics.1342
Subject(s) - markov chain monte carlo , grasp , computer science , econometrics , monte carlo method , spatial econometrics , markov chain , econometric model , statistical analysis , data science , statistics , machine learning , artificial intelligence , mathematics , bayesian probability , software engineering
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