Econometrics Volume 1: Basic Theory and Topics for Cross-Section Data
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This book is intended for graduate instruction in subjects like econometrics, economics, environmental science, social science and many other fields, at the Masters or PhD levels. It can be used as a textbook or as a reference guide. It covers a wide range of topics related to tools used in cross-sectional, time series and panel data. There are several aspects in which this book departs from traditional treatments. The emphasis is on understanding the main issues, concepts and methods in Econometrics, how to implement them and interpret the results. The mathematical aspects are kept to a minim...
This book is intended for graduate instruction in subjects like econometrics, economics, environmental science, social science and many other fields, at the Masters or PhD levels. It can be used as a textbook or as a reference guide. It covers a wide range of topics related to tools used in cross-sectional, time series and panel data. There are several aspects in which this book departs from traditional treatments. The emphasis is on understanding the main issues, concepts and methods in Econometrics, how to implement them and interpret the results. The mathematical aspects are kept to a minimum as the aim is to provide an intuitive understanding of how various parts fit together, as opposed to a sophisticated mathematical treatment of the subject. Many examples and discussions are provided. Hence, minimal mathematical pre-requisites are needed. Extensive references are also provided to dig deeper into the mathematical aspects of the theories. Volume 1 deals with the main methods used for inference and hypothesis testing which are applicable to both time series and cross-section data within a unified framework, with special emphasis on how the conditions and methods differ for each. It also includes a discussion of methods applicable only to a cross-section of data (usually a random sample) and to panel data with a small time-dimension. Cases with a large number of predictors are also covered.
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