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2024 | Book

Introductory Developments Read chapter Read first chapter

Principles of Econometrics

Theory and Applications

Author: Valérie Mignon

Publisher: Springer Nature Switzerland

Book Series : Classroom Companion: Economics

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Wirtschaft"

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About this book

This textbook teaches the basics of econometrics and focuses on the acquisition of methods and skills that are essential for any student to succeed in their studies, as well as for any practitioner interested in applying econometric techniques. Employing a pedagogical and easy-to-follow style, the book puts into practice the various concepts presented, such as statistics, tests, and methods, among others. Numerous examples and empirical applications using existing econometric and statistical software are given after each theoretical presentation.
The book addresses students at the undergraduate and graduate levels in economics and management, as well as students of engineering and business schools. It will further appeal to professionals and practitioners of econometrics, such as economists and researchers in companies and institutions, who will find practical solutions to the different problems they are confronted with.

Table of Contents

Frontmatter
1. Introductory Developments
Abstract
Econometrics is a discipline with a strong operational content. It enables us to quantify a phenomenon, establish a relationship between several variables, validate or invalidate a theory, evaluate the effects of an economic policy measure, etc. This introductory chapter gives some examples to illustrate in a simple way what econometrics can do. It presents the concepts of model and variable and offers some statistical reminders about the mean, variance, standard deviation, covariance, and linear correlation coefficient. A brief introduction to the concept of stationarity is also provided. Finally, this chapter lists the main databases in economics and finance and the most commonly used software packages.
Valérie Mignon
2. The Simple Regression Model
Abstract
Regression analysis consists in studying the dependence of a variable (the explained variable) on one or more other variables (the explanatory variables). This chapter deals with the simple regression model, which is a linear model comprising a single equation linking an explained variable to only one explanatory variable. Since the parameters of the simple regression model are unknown, they must be estimated to quantify the relationship between the two variables. The chapter presents the ordinary least squares (OLS) method, i.e., the most frequently used method to estimate the parameters of the simple regression model. It also establishes the properties of the OLS estimators and describes the tests on the regression coefficients. Several empirical applications are provided to illustrate in a simple way the various concepts.
Valérie Mignon
3. The Multiple Regression Model
Abstract
Regression analysis consists in studying the dependence of a variable (the explained variable) on one or more other variables (the explanatory variables). This chapter presents the multiple regression model, which is a linear model comprising a single equation linking an explained variable to several explanatory variables. Since the parameters of this model are unknown, they must be estimated to quantify the relationship between the dependent and the explanatory variables. The chapter presents the most frequently used estimation method, i.e., the ordinary least squares (OLS) method. It also establishes the properties of the OLS estimators, describes the various tests on the regression coefficients, and presents key indicators such as the (adjusted) coefficient of determination. All the concepts are illustrated thanks to several empirical applications.
Valérie Mignon
4. Heteroskedasticity and Autocorrelation of Errors
Abstract
The regression model is a random model in the sense that an error term is included in the equation linking the dependent variable to the explanatory variables. The ordinary least squares method—the most frequently used estimation method—supposes (i) the absence of autocorrelation of errors and (ii) the homoskedasticity of errors, i.e., the fact that the variance of the errors is constant. When this second assumption is violated, we speak of heteroskedasticity: the variance of the errors is no longer constant. This chapter concentrates on the problems of autocorrelation and heteroskedasticity of errors. It presents the appropriate estimation methods, as well as the sources, tests, and solutions to autocorrelation and heteroskedasticity. It also provides several empirical applications to illustrate the various theoretical concepts.
Valérie Mignon
5. Problems with Explanatory Variables: Random Variables, Collinearity, and Instability
Abstract
The multiple regression model supposes that the explanatory variables are (i) independent of the error term and (ii) are linearly independent. This chapter looks at what happens when these assumptions do not hold. If the first assumption is violated, the implication is that the explanatory variables are dependent on the error term. Under these conditions, the ordinary least squares estimators are no longer consistent, and it is necessary to use another estimator called the instrumental variables estimator. The consequence of violating the second assumption is that the explanatory variables are not linearly independent. In other words, they are collinear. Finally, the chapter concentrates on the third problem related to the explanatory variables, namely, the question of the stability of the estimated model.
Valérie Mignon
6. Distributed Lag Models
Abstract
In economics, the present value of the dependent variable often depends on the past values of the explanatory variables. In other words, the influence of the explanatory variables is only exerted after a certain lag. This chapter deals with a particular class of such dynamic models, namely, distributed lag models, which include present and lagged values of explanatory variables. It presents the different types of distributed lag models and their estimation methods. It also describes the autoregressive distributed lag (ARDL) models in which the lagged values of the dependent variable are added to the present and past values of the “usual” explanatory variables in the set of explanatory variables. Various empirical applications are provided to illustrate the different concepts.
Valérie Mignon
7. An Introduction to Time Series Models
Abstract
Time series econometrics is a branch of econometrics that has undergone many developments over the last 40 years. This chapter offers an introduction to time series models. After laying down a number of definitions, it focuses on the essential concept of stationarity. It presents the Dickey-Fuller unit root test for testing the non-stationary nature of a time series. The chapter then exposes the basic models of time series – the autoregressive moving-average models (ARMA models) – and the related Box and Jenkins methodology. A multivariate extension is proposed through the presentation of VAR (vector autoregressive) models. Finally, the concepts of non-stationary time series econometrics are presented by studying the notions of cointegration and error-correction models. Several empirical applications illustrate all the notions.
Valérie Mignon
8. Simultaneous Equations Models
Abstract
Many economic theories are based on models with several equations, i.e., on systems of equations. Since these equations are not independent of each other, the interaction of the different variables has important consequences for the estimation of each equation and for the system as a whole. This chapter tackles this issue and deals with simultaneous equations models. It starts by outlining the analytical framework before turning to the possibility or not of estimating the parameters of the model, known as identification. It then presents the estimation methods relating to simultaneous equations models and the specification test proposed by Hausman. An empirical application is provided at the end of the chapter to illustrate the concepts presented in a simple way.
Valérie Mignon
Backmatter
Metadata
Title
Principles of Econometrics
Author
Valérie Mignon
Copyright Year
2024
Electronic ISBN
978-3-031-52535-3
Print ISBN
978-3-031-52534-6
DOI
https://doi.org/10.1007/978-3-031-52535-3