Time Series Methods in Hydrosciences

This book is intended for a first year graduate course in econometrics. However, the first six chapters have no matrix algebra and can be used in an advanced undergraduate class. This can be supplemented by some of the material in later chapters that do not require matrix algebra, like the first part of Chapter 11 on simultaneous equations and Chapter 14 on time-series analysis. 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 t- ory, 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 c- puter industry. (iii) Estimation of price elasticities of demand, or returns to scale in production. More importantly, econometric methods can be used to simulate the effect of policy changes like a tax increase on gasoline consumption, or a ban on advertising on cigarette consumption.

Recognising the fact that A level mathematics is no longer a necessary prerequisite for economics courses, this text introduces this key subdivision of economics to an audience who might otherwise have been deterred by its complexity.

This book constitutes the first serious attempt to explain the basics of econometrics and its applications in the clearest and simplest manner possible. Recognising the fact that a good level of mathematics is no longer a necessary prerequisite for economics/financial economics undergraduate and postgraduate programmes, it introduces this key subdivision of economics to an audience who might otherwise have been deterred by its complex nature.

This reference introduces the basic econometric methods and the underlying assumptions behind them. It also includes a simple and concise treatment of more advanced topics in time-series, spatial correlation, limited dependent variables and panel data models, as well as specification testing, Gauss-Newton regressions and regression diagnostics. The strengths of this book lie in presenting difficult material in a simple, yet rigorous manner. In addition, the book features a set of empirical illustrations that demonstrate some of the basic results. The empirical exercises are solved using several econometric software packages.

This book covers the econometric methods necessary for a practicing applied economist or data analyst. This requires both an understanding of statistical theory and how it is used in actual applications. Chapters 1 to 9 present the material concerned with basic statistical theory. Chapters 10 to 13 introduce a number of topics which form the basis of more advanced option modules, such as time series methods in applied econometrics. To get the most out of these topics, companion files include Excel datasets and 4-color figures. It includes pull down menus to graph the data, calculate sample statistics and estimate regression equations. FEATURES: Integration of econometrics methods with statistical foundations Worked examples of all models considered in the text Includes Excel datasheets to facilitate estimation and application of models Features instructor ancillaries for use as a textbook The companion files and/or instructor resources are available online by emailing the publisher with proof of purchase at info@merclearning.com.

This trusted textbook returns in its 4th edition with even more exercises to help consolidate understanding - and a companion website featuring additional materials, including a solutions manual for instructors. Offering a unique blend of theory and practical application, it provides ideal preparation for doing applied econometric work as it takes students from a basic level up to an advanced understanding in an intuitive, step-by-step fashion. Clear presentation of economic tests and methods of estimation is paired with practical guidance on using several types of software packages. Using real world data throughout, the authors place emphasis upon the interpretation of results, and the conclusions to be drawn from them in econometric work. This book will be essential reading for economics undergraduate and master's students taking a course in applied econometrics. Its practical nature makes it ideal for modules requiring a research project. New to this Edition: - Additional practical exercises throughout to help consolidate understanding - A freshly-updated companion website featuring a new solutions manual for instructors

Praise for the Fourth Edition: "This book is . . . an excellent source of examples for regression analysis. It has been and still is readily readable and understandable." —Journal of the American Statistical Association Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. Regression Analysis by Example, Fifth Edition has been expanded and thoroughly updated to reflect recent advances in the field. The emphasis continues to be on exploratory data analysis rather than statistical theory. The book offers in-depth treatment of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression. The book now includes a new chapter on the detection and correction of multicollinearity, while also showcasing the use of the discussed methods on newly added data sets from the fields of engineering, medicine, and business. The Fifth Edition also explores additional topics, including: Surrogate ridge regression Fitting nonlinear models Errors in variables ANOVA for designed experiments Methods of regression analysis are clearly demonstrated, and examples containing the types of irregularities commonly encountered in the real world are provided. Each example isolates one or two techniques and features detailed discussions, the required assumptions, and the evaluated success of each technique. Additionally, methods described throughout the book can be carried out with most of the currently available statistical software packages, such as the software package R. Regression Analysis by Example, Fifth Edition is suitable for anyone with an understanding of elementary statistics.

The material in this book reflects, in many respects, the culmination of several years of research regarding the measurement of monetary policy. On all the issues addressed in the text, I have thus attempted to provide a perspective of the type that can only be gained from "living with" a topic over an extended time horizon. I have often said that I came to understand the monetary indicators literature only after having written dozens of papers on the subject. This statement may seem a bit trite, but I feel certain that anyone who has waded through this morass (or at least tried to) can fully empathize and recognize the grain of truth therein. It is my sincere hope that the synthesis given in the work will calm the fears and anxieties that often (and understandably) plague beginners in this field. In settling down to the process of "pulling together" this manuscript, I was surprised to find the ease and consistency with which various topics, explored at widely diverse times and in no particular order, meshed into a unified whole. I attempted to write the book in a manner that would simultaneously be generally comprehensible to students (particularly at the graduate level) and to practitioners desiring a relatively thorough overview of the indicators literature and yet be of value to scholars desiring to explore (and hopefully advance) this field.