Applied Econometrics

A Practical Guide

Chung-ki Min

• Publisher: Routledge
• Year: 2019
• Print ISBN:
  9780367110338
  9780367110321
• Edition: 1
• eBook ISBN:
  9780429656507
• Topic: Economics
• Subtopic: Econometrics
• Index: Economics

Applied Econometrics: A Practical Guide is an extremely user-friendly and application-focused book on econometrics. Unlike many econometrics textbooks which are heavily theoretical on abstractions, this book is perfect for beginners and promises simplicity and practicality to the understanding of econometric models. Written in an easy-to-read manner, the book begins with hypothesis testing and moves forth to simple and multiple regression models. It also includes advanced topics:

• Endogeneity and Two-stage Least Squares
• Simultaneous Equations Models
• Panel Data Models
• Qualitative and Limited Dependent Variable Models
• Vector Autoregressive (VAR) Models
• Autocorrelation and ARCH/GARCH Models
• Unit Root and Cointegration

The book also illustrates the use of computer software (EViews, SAS and R) for economic estimating and modeling. Its practical applications make the book an instrumental, go-to guide for solid foundation in the fundamentals of econometrics. In addition, this book includes excerpts from relevant articles published in top-tier academic journals. This integration of published articles helps the readers to understand how econometric models are applied to real-world use cases.

1 Review of estimation and hypothesis tests

Learning objectives

In this chapter, you should learn about:

• Sampling distribution and uncertainty/accuracy of estimators;
• Unbiasedness, consistency and efficiency of estimators;
• p-value and the extent of data evidence against a null hypothesis;
• Interpretation of hypothesis test results.

The goal of testing statistical hypotheses is to determine whether sample information from a population supports a statistical hypothesis postulated, or whether empirical evidence is strong enough to refute a hypothesis. I will use an example to introduce the basic concepts and to illustrate the test procedures.

1.1 The problem

An automobile manufacturer claims that a newly developed model gets at least 28 miles to the gallon. The Environmental Protection Agency (EPA) investigates whether the company is exaggerating the performance of its new model.

1.2 Population and sample

In this problem the population is the set of all automobiles produced by the company. In fact, we are interested in the average miles per gallon (MPG) of the population. However, it is practically impossible to test-drive all of the produced automobiles to measure their MPG. Instead, we select a small number of automobiles from the population, called a sample, and test-drive the selected automobiles only. Then, using the sample information we make a conclusion about the average MPG of the population; it is called the statistical inference.

Suppose that the EPA selected a sample of 49 automobiles of this model and obtained a sample mean of 26.8 miles per gallon. The population standard deviation ($\sigma$) is assumed to be 5 miles per gallon from the previous studies.

What conclusion should the EPA make based on the sample information? Because the sample mean is calculated using only a small number (49) of automobiles, it is not equal to the population mean. The population mean is the average MPG of all automobiles produced by the company. However, because the sample is obtained from the population, the sample mean is not far different from the population mean. To ...

Table of contents

• Cover
• Half Title
• Series
• Title
• Copyright
• Contents
• List of figures
• List of tables
• Preface
• Acknowledgments
• 1 Review of estimation and hypothesis tests
• 2 Simple linear regression models
• 3 Multiple linear regression models
• 4 Dummy explanatory variables
• 5 More on multiple regression analysis
• 6 Endogeneity and two-stage least squares estimation
• 7 Models for panel data
• 8 Simultaneous equations models
• 9 Vector autoregressive (VAR) models
• 10 Autocorrelation and ARCH/GARCH
• 11 Unit root, cointegration and error correction model
• 12 Qualitative and limited dependent variable models
• Index