Introductory Econometrics: Using Monte Carlo Simulation with Microsoft Excel
This highly accessible and innovative text with supporting web site uses Excel (R) to teach the core concepts of econometrics without advanced mathematics. It enables students to use Monte Carlo simulations in order to understand the data generating process and sampling distribution. Intelligent repetition of concrete examples effectively conveys the properties of the ordinary least squares (OLS) estimator and the nature of heteroskedasticity and autocorrelation. Coverage includes omitted variables, binary response models, basic time series, and simultaneous equations. The authors teach students how to construct their own real-world data sets drawn from the internet, which they can analyze with Excel (R) or with other econometric software. The accompanying web site with text support can be found at www.wabash.edu/econometrics.
A Catalog of Functional Forms
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add-in analysis approximation autocorrelation bias bivariate regression bootstrap box model button cell chance process chapter cigarettes classical econometric model Click column compute confidence interval correlation coefficient crosstab data generation process data set dependent variable displays draw dummy variable Empirical Histogram equation error box error terms exact example expected value f-distribution F-statistic Figure Forecast Error formula free throws functional form heteroskedasticity Hit F9 hypothesis testing income independent lagged least squares LINEST measurement Monte Carlo simulation multiple regression normal curve normally distributed null hypothesis number of observations OLS estimator OLS regression omitted variable omitted variable bias P-value percent PivotTable Population Parameters Predicted probability histogram quantity demanded random number random variable regression line repetitions RMSE sample average sample percentage sample slope sampling distribution sheet shows slope estimator Solver Source squared residuals Summary Statistics tickets true parameter unbiased estimator univariate variance wage weights zero