Fundamentals of Applied Econometrics
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Description: • Active Learning Exercises are found in each chapter providing interactive, hands-on applications to ensure comprehension and retention of econometrics. • Statistics Review at the beginning of the text allows students to refresh their statistical skills
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All the information you need in one place! Each Study Brief is a summary of one specific subject; facts, figures, and explanations to help you learn faster.
List price: $202.99
Copyright year: 2012
Publisher: John Wiley & Sons, Limited
Publication date: 2/20/2012
Size: 7.75" wide x 9.25" long x 1.25" tall
|What's Different about This Book|
|Working with Data in the "Active Learning Exercises"|
|Introduction and Statistics Review|
|A Review of Probability Theory|
|Estimating the Mean of a Normally Distributed Random Variable|
|Statistical Inference on the Mean of a Normally Distributed Random Variable|
|The Bivariate Regression Model: Introduction, Assumptions, and Parameter Estimates|
|The Bivariate Linear Regression Model: Sampling Distributions and Estimator Properties|
|The Bivariate Linear Regression Model: Inference on ï¿½|
|The Bivariate Regression Model: R<sup>2</sup> and Prediction|
|The Multiple Regression Model|
|Diagnostically Checking and Respecifying the Multiple Regression Model: Dealing with Potential Outliers and Heteroscedasticity in the Cross-Sectional Data Case|
|Stochastic Regressors and Endogeneity|
|Instrumental Variables Estimation|
|Diagnostically Checking and Respecifying the Multiple Regression Model: The Time-Series Data Case (Part A)|
|Diagnostically Checking and Respecifying the Multiple Regression Model: The Time-Series Data Case (Part B)|
|Additional Topics in Regression Analysis|
|Regression Modeling with Panel Data (Part A)|
|Regression Modeling with Panel Data (Part B)|
|A Concise Introduction to Time-Series Analysis and Forecasting (Part A)|
|A Concise Introduction to Time-Series Analysis and Forecasting (Part B)|
|Parameter Estimation Beyond Curve-Fitting: MLE (With an Application to Binary-Choice Models) and GMM (With an Application to IV Regression)|