Using R for Introductory Econometrics 1st Edition by Florian Heiss ISBN 1523285133 9781523285136

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Product details:

ISBN 10: 1523285133 
ISBN 13: 9781523285136
Author: Florian Heiss

Note that the second edition is now available. It seems like a good idea to get that instead..

• Introduces the popular, powerful and free programming language and software package R
• Focus: implementation of standard tools and methods used in econometrics
• Compatible with “Introductory Econometrics” by Jeffrey M. Wooldridge in terms of topics, organization, terminology and notation
• Companion website with full text, all code for download and other goodies

Using R for Introductory Econometrics 1st Table of contents:

Chapter 1: Introduction to R and Data Handling

1. Getting Started with R: Installation and Setup
2. Basic R Commands and Syntax
3. Working with RStudio: A User-Friendly Interface for R
4. Importing and Exporting Data in R
5. Data Structures: Vectors, Matrices, Data Frames, and Lists
6. Data Manipulation with R: Subsetting, Merging, and Transformation

Chapter 2: Basic Statistical Concepts in Econometrics

1. Descriptive Statistics: Mean, Median, Variance, and Standard Deviation
2. Probability Distributions: Normal, Uniform, and Binomial
3. Hypothesis Testing: p-Values, Confidence Intervals, and Significance
4. Correlation and Covariance
5. Visualizing Data with R: Histograms, Scatter Plots, and Box Plots

Chapter 3: Simple Linear Regression

1. The Basics of Simple Linear Regression
2. Estimating Parameters Using Ordinary Least Squares (OLS)
3. Interpreting the Coefficients: Slope and Intercept
4. Diagnostic Tools: Residuals, R-Squared, and F-Statistics
5. Model Fitting and Evaluation in R
6. Assumptions of Linear Regression and Testing for Violations

Chapter 4: Multiple Linear Regression

1. The General Form of Multiple Linear Regression
2. Estimating Coefficients with Multiple Predictors
3. Multicollinearity and its Impact on Regression Models
4. Interaction Terms and Model Interpretation
5. Model Selection: Stepwise Regression and AIC/BIC
6. Implementing Multiple Linear Regression in R

Chapter 5: Model Assumptions and Diagnostics

1. Assumptions in Econometrics: Linearity, Homoscedasticity, and Normality
2. Detecting and Addressing Multicollinearity
3. Autocorrelation and Its Consequences
4. Homoscedasticity vs. Heteroscedasticity
5. Diagnosing Model Fit in R: Plots and Tests
6. Improving Model Specifications and Addressing Violations

Chapter 6: Instrumental Variables and Endogeneity

1. Understanding Endogeneity and the Need for Instrumental Variables
2. The Instrumental Variables (IV) Estimator
3. Two-Stage Least Squares (2SLS) Method
4. Choosing Valid Instruments
5. Implementing IV Estimation in R
6. Interpreting Results and Addressing Common Pitfalls

Chapter 7: Time Series Analysis

1. Introduction to Time Series Data
2. Autoregressive and Moving Average Models
3. Stationarity and Unit Root Tests
4. Forecasting with ARIMA Models
5. Implementing Time Series Analysis in R
6. Analyzing Seasonal Data and Trend Components

Chapter 8: Panel Data Analysis

1. Introduction to Panel Data Models
2. Fixed Effects vs. Random Effects Models
3. Estimating Panel Data Models Using R
4. Testing for Endogeneity in Panel Data
5. Model Selection and Diagnostics in Panel Data Analysis

Chapter 9: Limited Dependent Variable Models

1. Binary and Proportional Outcome Models: Logit and Probit
2. Multinomial Logit Models
3. Censored and Truncated Regression Models
4. Estimating Limited Dependent Models in R
5. Model Interpretation and Application

Chapter 10: Advanced Econometric Techniques

1. Introduction to Generalized Method of Moments (GMM)
2. Maximum Likelihood Estimation (MLE)
3. Nonlinear Models and Econometric Extensions
4. Forecasting and Simulation Techniques in Econometrics
5. Advanced Techniques Using R: Function Implementation and Custom Models

Chapter 11: Using R for Data Visualization and Reporting

1. Creating Econometric Charts and Graphs in R
2. ggplot2: Advanced Data Visualization in R
3. Writing Reproducible Reports with R Markdown
4. Presenting Econometric Results in Tables and Graphs

Chapter 12: Practical Applications and Case Studies

1. Real-World Case Studies in Econometrics
2. Application of R to Economic Research and Policy Analysis
3. Working with Large Datasets in Econometrics
4. Practical Examples: Estimating Economic Models with R
5. Common Pitfalls and Best Practices

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