This book is intended for a ?rst year graduate course in econometrics. I tried to strike a balance between a rigorous approach that proves theorems, and a completely empirical approach where no theorems are proved. Some of the strengths of this book lie in presenting some di?cult material in a simple, yet rigorous manner. For example, Chapter 12 on pooling time-series of cross-section data is drawn from my area of expertise in econometrics and the intent here is to make this material more accessible to the general readership of econometrics. This book teaches some of the basic econometric methods and the underlying assumptions behind them. Estimation, hypotheses testing and prediction are three recurrent themes in this book. Some uses of econometric methods include (i) empirical testing of economic theory, whether it is the permanent income consumption theory or purchasing power parity, (ii) forecasting, whether it is GNP or unemployment in the U.S. economy or future sales in the computer industry. (iii) Estimation of price elasticities of demand, or returns to scale in production. More importantly, econometric methods can be used to simulate the e?ect of policy changes like a tax increase on gasoline consumption, or a ban on advertising on cigarette consumption. It is left to the reader to choose among the available econometric/statistical software to use, like EViews, SAS, Stata, TSP, SHAZAM, Micro?t, PcGive, LIMDEP, and RATS, to mention a few. The empirical illustrations in the book utilize a variety of these software packages but mostly with Stata and EViews. Of course, these packages have di?erent advantages and disadvantages. However, for the basic coverage in this book, these di?erences may be minor and more a matter of what software the reader is familiar or comfortable with. In most cases, I encourage my students to use more than one of these packages and to verify these results using simple programming languages like GAUSS, OX, R and MATLAB.