Introduction to Econometrics - Softcover

Koop, Gary

Softcover

ISBN 10: 0470032707 ISBN 13: 9780470032701

Verlag: Wiley, 2007

Inhaltsangabe

Introduction to Econometrics has been written as a core textbook for a first course in econometrics taken by undergraduate or graduate students. It is intended for students taking a single course in econometrics with a view towards doing practical data work.  It will also be highly useful for students interested in understanding the basics of econometric theory with a view towards future study of advanced econometrics. To achieve this end, it has a practical emphasis, showing how a wide variety of models can be used with the types of data sets commonly used by economists. However, it also has enough discussion of the underlying econometric theory to give the student a knowledge of the statistical tools used in advanced econometrics courses.

Über diesen Titel

Über die Autorin bzw. den Autor

Gary Koop is Professor of Economics at the University of Strathclyde. Gary has published numerous articles econometrics in journals such as the Journal of Econometrics and Journal of Applied Econometrics. Gary has taught econometrics for many years and is the author of following textbooks, all published by John Wiley & Sons Ltd: Analysis of Economic Data 2ed, Analysis of Financial Data and Bayesian Econometrics

Key Features:

• A non-technical summary of the basic tools of econometrics is given in chapters 1 and 2, which allows the reader to quickly start empirical work.
• The foundation offered in the first two chapters makes the theoretical econometric material, which begins in chapter 3, more accessible.
• Provides a good balance between econometric theory and empirical applications.
• Discusses a wide range of models used by applied economists including many variants of the regression model (with extensions for panel data), time series models (including a discussion of unit roots and cointegration) and qualitative choice models (probit and logit).

An extensive collection of web-based supplementary materials is provided for this title, including: data sets, problem sheets with worked through answers, empirical projects, sample exercises with answers, and slides for lecturers.

URL: www.wileyeurope.com/college/koop

Key Features:

• A non-technical summary of the basic tools of econometrics is given in chapters 1 and 2, which allows the reader to quickly start empirical work.
• The foundation offered in the first two chapters makes the theoretical econometric material, which begins in chapter 3, more accessible.
• Provides a good balance between econometric theory and empirical applications.
• Discusses a wide range of models used by applied economists including many variants of the regression model (with extensions for panel data), time series models (including a discussion of unit roots and cointegration) and qualitative choice models (probit and logit).

An extensive collection of web-based supplementary materials is provided for this title, including: data sets, problem sheets with worked through answers, empirical projects, sample exercises with answers, and slides for lecturers.

URL: www.wileyeurope.com/college/koop

Auszug. © Genehmigter Nachdruck. Alle Rechte vorbehalten.

Introduction to Econometrics

By Gary Koop

John Wiley & Sons

Copyright © 2008 Gary Koop
All right reserved.
ISBN: 978-0-470-03270-1

Chapter One

An Overview of Econometrics

1.1 The importance of econometrics

The Duke of Wellington, a British commander of the Napoleonic Wars, once said: 'All the business of war, indeed all the business of life, is to endeavour to find out what you don't know by what you do; that's what I call ''guessing what is on the other side of the hill".' This is an apt description of what econometrics is all about.

Economics is full of unanswered questions such as: 'Will a change in interest rates affect the exchange rate?' 'Do the long-term unemployed have a more difficult time getting jobs than the short-term unemployed? 'What is the impact of gas prices on the choice of whether to drive or take the bus to work?'. These are examples of the 'what we don't know' of economics. The 'what we do know' of economics are data. All sorts of agencies (e.g. governments, newspapers, companies even individuals) collect facts that shed light on the 'what we don't know'. Look, for instance, in most newspapers and you will find lots of information about the prices of various assets (e.g. interest rates, exchange rates, stock prices, etc.). Most governments carry out surveys or censuses of many activities of their citizens, and these can, for example, be used to compare the experience of the long-term unemployed with that of the short-term unemployed. Economic researchers have carried out surveys of commuters, and some of the information provided can be used to investigate factors that influence the choice between the private car and public transport.

Wellington knew that one had to appeal to the facts to make a good military decision. The same applies in economics. Without an appeal to the facts (i.e. the data), economic debates can degenerate into a sterile repetition of fixed opinions. Or they can become informal storytelling sessions where economists support their views with their favorite anecdotes. When making military preparations, anyone can 'guess what is on the other side of this hill', but it takes a great commander to combine all the available information and draw the most sensible conclusions. To continue the analogy, the purpose of econometrics is to show the economist how to be a great commander, to use 'what we know' in the most effective manner in order to try and resolve 'what we don't know'. In other words, econometrics shows us how to use data in a sensible and systematic manner to shed light on economic questions.

The purpose of this chapter is to provide you with an understanding of the basic concepts and tools that are used by econometricians. Given the primary role of data in econometrics, it won't surprise you to learn that much of this chapter is about data. We discuss the types of data commonly used by economists and offer a brief discussion about where data are obtained. Following this, we discuss some simple ways of analyzing data (e.g. graphical methods and descriptive statistics) and offer an introduction to some of the basic theoretical tools used by the econometrician (e.g. expected values and variances). These basic concepts and tools are then used in all the remaining chapters of this book.

1.2 Types of economic data

This section introduces the types of data used by economists and defines the notation and terminology associated with them.

1.2.1 Time series data

Macroeconomists and financial economists are often interested in concepts such as gross domestic product (GDP), stock prices, interest rates, exchange rates, etc. Such data are collected at specific points in time. In all of these examples, the data are ordered by time and are referred to as time series data. The underlying phenomenon that we are measuring (e.g. GDP, stock prices, interest rates, etc.) is referred to as a variable. Time series data can be observed at many frequencies. Commonly used frequencies are: annual (i.e. a variable is observed every year), quarterly (i.e. 4 times a year), monthly, weekly or daily.

In this book, we will use the notation [Y.sub.t] to indicate an observation on variable Y (e.g. an exchange rate) at time t. A series of data runs from period t = 1 to t = T. Here, T is used to indicate the total number of time periods covered in a dataset. To give an example, if we were to use monthly time series data from January 1947 to October 1996 on the UK pound/US dollar exchange rate - a period of 598 months - then t = 1 would indicate January 1947, t = 598 would indicate October 1996 and T = 598 would be the total number of months. Hence, [Y.sub.1] would be the pound/dollar exchange rate in January 1947, [Y.sub.2] would be this exchange rate in February 1947, etc. Time series data are presented in chronological order.

Working with time series data often requires some special tools, which are discussed in Chapters 6 and 7.

1.2.2 Cross-sectional data

In contrast to the above, researchers often work with data that are characterized by individual units. These units might refer to companies, people, or countries. For instance, a financial economist investigating theories relating to portfolio allocation might collect data on the return earned on the stocks of many different companies. With such cross-sectional data, the ordering of the data typically does not matter (unlike time series data).

In this book, we use the notation [Y.sub.i] to indicate an observation on variable Y for individual i. Observations in a cross-sectional dataset run from unit i = 1 to N. By convention, N indicates the number of cross-sectional units (e.g. the number of companies surveyed). For instance, a researcher might collect data on the share price of N = 100 companies at a certain point in time. In this case, [Y.sub.1] will be equal to the share price of the first company, [Y.sub.2] will be equal to the share price of the second company, and so on.

It is worthwhile stressing another important distinction between types of data. In the preceding example, the researcher collecting data on share prices will have a number corresponding to each company (e.g. the price of a share of company 1 is $25).This is referred to as quantitative data.

However, there are many cases where data do not come in the form of single numbers. For instance, the labour economist, when asking whether or not each surveyed employee belongs to a union, receives either a Yes or a No answer. These answers are referred to as qualitative data. Such data arise often in economics when choices are involved (e.g. the choice to buy or not to buy a product, to take public transport or a private car). Econo-metricians usually convert these qualitative answers into numeric data. For instance, the labor economist might set Yes = 1 and No = 0. Hence, [Y.sub.1] = 1 means that the first individual surveyed does belong to a union, and [Y.sub.2] = 0 means that the second individual does not. When variables can take on only the values 0 or 1, they are referred to as dummy (or binary) variables.

1.2.3 Panel data

Some datasets will have both a time series and a cross-sectional component. Such data are referred to as panel data. Economists working on issues related to economic growth often make use of panel data. They might work, for instance, with data for 90 countries for the...

Details zur Ausgabe

• Verlag: Wiley
• Erscheinungsdatum: 2007
• Sprache: Englisch
• ISBN 10: 0470032707
• ISBN 13: 9780470032701
• Einband: Taschenbuch
• Auflage: 1
• Anzahl der Seiten: 384
• Kontakt zum Hersteller: Wiley-VCH GmbH; product_safety@wiley.com; ; Boschstrasse 12; Weinheim; 69469; Deutschland