In this chapter we review a few principles of econometric modeling, and illustrate these for the case of a continuous dependent variable. We assume basic knowledge of matrix algebra and of basic statistics and mathematics (differential algebra and integral calculus). As a courtesy to the reader, we include some of the principles on matrices in the Appendix (section A.1). This chapter serves to review a few issues which should be useful for later chapters. In section 3.1 we discuss the representation of the standard Linear Regression model. In section 3.2 we discuss Ordinary Least Squares and Maximum Likelihood estimation in substantial detail. Even though the Maximum Likelihood method is not illustrated in detail, its basic aspects will be outlined as we need it in later chapters. In section 3.3, diagnostic measures for outliers, residual autocorrelation and heteroskedasticity are considered. Model selection concerns the selection of relevant variables and the comparison of non-nested models using certain model selection criteria. Forecasting deals with within-sample or out-of-sample prediction. In section 3.4 we illustrate several issues for a regression model that correlates sales with price and promotional activities. Finally, in section 3.5 we discuss extensions to multiple-equation models, thereby mainly focusing on modeling market shares.

This chapter is not at all intended to give a detailed account of econometric methods and econometric analysis. Much more detail can, for example, be found in Greene (2000), Verbeek (2000) and Wooldridge (2000). In fact, this chapter mainly aims to set some notation and to highlight some important topics in econometric modeling. In later chapters we will frequently make use of these concepts.