Using Python for Introductory Econometrics

Introduces the popular, powerful and free programming language and software package Python. Python is an ideal candidate for starting to learn econometrics and data analysis. It has a huge user base, especially in the fields of data science, machine learning, and artificial intelligence, where it arguably is the most popular software overall. These are very exciting areas and there is a lot of cutting edge research in the integration of their tools into the econometrics toolbox. So why not kill two birds with one stone and master a powerful and important software package while learning econometrics at the same time? Because Python must be hard to learn and to apply to econometrics? It is not at all, as this book shows. And Python is completely free and available for all relevant operating systems. When using it in econometrics courses, students can easily download a copy to their own computers and use it at home (or their favorite cafés) to replicate examples and work on take-home assignments. This hands-on experience is essential for the understanding of the econometric models and methods. It also prepares students to conduct their own empirical analyses for their theses, research projects, and professional work. A problem we encountered when teaching introductory econometrics classes is that the textbooks that also introduce Python do not discuss econometrics. Conversely, our favorite introductory econometrics textbooks do not cover Python. Although it is possible to combine a good econometrics textbook with an unrelated introduction to Python, this creates substantial hurdles because the topics and order of presentation are different, and the terminology and notation are inconsistent. 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 Topics A gentle introduction to Python Simple and multiple regression in matrix form and using black box routines Inference in small samples and asymptotics Monte Carlo simulations Heteroscedasticity Time series regression Pooled cross-sections and panel data Instrumental variables and two-stage least squares Simultaneous equation models Limited dependent variables: binary, count data, censoring, truncation, and sample selection Formatted reports using Jupyter Notebooks The book is designed mainly for students of introductory econometrics who ideally use Wooldridge as their main textbook. It can also be useful for readers who are familiar with econometrics and possibly other software packages. For them, it offers an introduction to Python and can be used to look up the implementation of standard econometric methods. Preface Introduction Getting Started Software Python Scripts Modules File Names and the Working Directory Errors and Warnings Other Resources Objects in Python Variables Objects in Python Objects in numpy Objects in pandas External Data Data Sets in the Examples Import and Export of Data Files Data from other Sources Base Graphics with matplotlib Basic Graphs Customizing Graphs with Options Overlaying Several Plots Exporting to a File Descriptive Statistics Discrete Distributions: Frequencies and Contingency Tables Continuous Distributions: Histogram and Density Empirical Cumulative Distribution Function (ECDF) Fundamental Statistics Probability Distributions Discrete Distributions Continuous Distributions Cumulative Distribution Function (CDF) Random Draws from Probability Distributions Confidence Intervals and Statistical Inference Confidence Intervals t Tests p Values Advanced Python Conditional Execution Loops Functions Object Orientation Outlook Monte Carlo Simulation Finite Sample Properties of Estimators Asymptotic Properties of Estimators Simulation of Confidence Intervals and t Tests Regression Analysis with Cross-Sectional Data The Simple Regression Model Simple OLS Regression Coefficients, Fitted Values, and Residuals Goodness of Fit Nonlinearities Regression through the Origin and Regression on a Constant Expected Values, Variances, and Standard Errors Monte Carlo Simulations One Sample Many Samples Violation of SLR.4 Violation of SLR.5 Multiple Regression Analysis: Estimation Multiple Regression in Practice OLS in Matrix Form Ceteris Paribus Interpretation and Omitted Variable Bias Standard Errors, Multicollinearity, and VIF Multiple Regression Analysis: Inference The t Test General Setup Standard Case Other Hypotheses Confidence Intervals Linear Restrictions: F-Tests Multiple Regression Analysis: OLS Asymptotics Simulation Exercises Normally Distributed Error Terms Non-Normal Error Terms (Not) Conditioning on the Regressors LM Test Multiple Regression Analysis: Further Issues Model Formulae Data Scaling: Arithmetic Operations Within a Formula Standardization: Beta Coefficients Logarithms Quadratics and Polynomials Hypothesis Testing Interaction Terms Prediction Confidence and Prediction Intervals for Predictions Effect Plots for Nonlinear Specifications Multiple Regression Analysis with Qualitative Regressors Linear Regression with Dummy Variables as Regressors Boolean Variables Categorical Variables ANOVA Tables Breaking a Numeric Variable Into Categories Interactions and Differences in Regression Functions Across Groups Heteroscedasticity Heteroscedasticity-Robust Inference Heteroscedasticity Tests Weighted Least Squares More on Specification and Data Issues Functional Form Misspecification Measurement Error Missing Data and Nonrandom Samples Outlying Observations Least Absolute Deviations (LAD) Estimation Regression Analysis with Time Series Data Basic Regression Analysis with Time Series Data Static Time Series Models Time Series Data Types in Python Equispaced Time Series in Python Irregular Time Series in Python Other Time Series Models Finite Distributed Lag Models Trends Seasonality Further Issues in Using OLS with Time Series Data Asymptotics with Time Series The Nature of Highly Persistent Time Series Differences of Highly Persistent Time Series Regression with First Differences Serial Correlation and Heteroscedasticity in Time Series Regressions Testing for Serial Correlation of the Error Term FGLS Estimation Serial Correlation-Robust Inference with OLS Autoregressive Conditional Heteroscedasticity Advanced Topics Pooling Cross-Sections Across Time: Simple Panel Data Methods Pooled Cross-Sections Difference-in-Differences Organizing Panel Data First Differenced Estimator Advanced Panel Data Methods Fixed Effects Estimation Random Effects Models Dummy Variable Regression and Correlated Random Effects Robust (Clustered) Standard Errors Instrumental Variables Estimation and Two Stage Least Squares Instrumental Variables in Simple Regression Models More Exogenous Regressors Two Stage Least Squares Testing for Exogeneity of the Regressors Testing Overidentifying Restrictions Instrumental Variables with Panel Data Simultaneous Equations Models Setup and Notation Estimation by 2SLS Outlook: Estimation by 3SLS Limited Dependent Variable Models and Sample Selection Corrections Binary Responses Linear Probability Models Logit and Probit Models: Estimation Inference Predictions Partial Effects Count Data: The Poisson Regression Model Corner Solution Responses: The Tobit Model Censored and Truncated Regression Models Sample Selection Corrections Advanced Time Series Topics Infinite Distributed Lag Models Testing for Unit Roots Spurious Regression Cointegration and Error Correction Models Forecasting Carrying Out an Empirical Project Working with Python Scripts Logging Output in Text Files Formatted Documents with Jupyter Notebook Getting Started Cells Markdown Basics Appendices Python Scripts Scripts Used in Chapter 01 Scripts Used in Chapter 02 Scripts Used in Chapter 03 Scripts Used in Chapter 04 Scripts Used in Chapter 05 Scripts Used in Chapter 06 Scripts Used in Chapter 07 Scripts Used in Chapter 08 Scripts Used in Chapter 09 Scripts Used in Chapter 10 Scripts Used in Chapter 11 Scripts Used in Chapter 12 Scripts Used in Chapter 13 Scripts Used in Chapter 14 Scripts Used in Chapter 15 Scripts Used in Chapter 16 Scripts Used in Chapter 17 Scripts Used in Chapter 18 Scripts Used in Chapter 19 Bibliography List of Wooldridge (2019) Examples Index