As mentioned in the preface, this book is a graduate text and a reference book for those who are interested in statistical theories and methods with economic and business applications. The role of econometrics has changed significantly during the last decade. Businesses and governments are now made accountable for making knowledge-based decisions. This requirement, coupled with the development in information and communication technology, has generated an enormous amount of data as a major source of information. This voluminous data is also coupled with some subjective but still useful knowledge in the hands of the decision makers. All of this information needs to be converted into meaningful and useful knowledge. The field of statistical knowledge itself, which came into existence barely a century ago, has expanded during the last few decades. Thus any new book such as this in econometrics must address itself as to how to handle large amounts of data and how to use cutting-edge statistical tools in order to discover the patterns in the data.
Gathering the right data, deciding which data are useful and which are not, data cleaning, data editing, combining quantitative data with other pieces of information and discovering patterns in all that information, etc are the building blocks of useful knowledge. It is that knowledge which is needed for making better business and economic decisions.
Fortunately there has also been a remarkable degree of acceptance in recent years of quantitative analysis in business and economics. The fear of mathematics and statistics, that was a characteristic feature of top management in business and government in the past, has now given way to an appreciation of their usefulness in making knowledge-based decisions. This is due mainly to the developments in computing software with graphics that have made mathematics and statistics a part of a black box. Their importance, however, is demonstrated by innovative graphics in terms of the end results of productivity gains, revenues, profits, reduced risk, etc that such methods can generate. This last part, an effective communication system between the quantitative analyst and the decision makers, is still in its infancy, and needs a great deal more development. We hope that the illustrative examples we give in this book, and the graphics that are built into our software, will go a long way in this direction. There is nevertheless a great danger of excessive use of such software without a proper understanding of the underlying statistical procedures. A misuse by incompetent people of the analytic tools, which can be easily implemented through the click of a mouse, using freely available open source software, might bring more discredit to analytics than credit. It is the main aim of this book to provide that link between business analytics, analytics software, and the required statistical knowledge. From that perspective this book differs in its scope from several other econometrics books, in the sense that it is aimed at the practitioner of business analytics or an applied econometrician. By providing a new orientation it also helps an academically oriented scholar to pursue academic interests in econometrics with a practical orientation.
We assume that the reader has had an introductory course on probability distributions and statistics, and also on the basic principles of statistical inference. This chapter introduces the types of economic problems and data that require quantitative analysis for business and public policy decisions. The competitive business environment requires that the analysis be done using the best possible statistical tools. Extensive treatment of these statistical methods will engage us in the subsequent chapters of this book. This chapter emphasizes the need to understand clearly the domain of application; as such knowledge is vital to understanding the data generating