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Authors: Arbia, Giuseppe (2014) - This book aims at meeting the growing demand in the field by introducing the basic spatial econometrics methodologies to a wide variety of researchers. It provides a practical guide that illustrates the potential of spatial econometric modelling, discusses problems and solutions and interprets empirical results.
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Authors: Maiti, Moinak (2021) - This textbook gives students an approachable, down to earth resource for the study of financial econometrics. While the subject can be intimidating, primarily due to the mathematics and modelling involved, it is rewarding for students of finance and can be taught and learned in a straightforward way.
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Authors: Nguyen Ngoc Thach (2021) - This book offers an overview of state-of-the-art econometric techniques, with a special emphasis on financial econometrics. There is a major need for such techniques, since the traditional way of designing mathematical models - based on researchers' insights - can no longer keep pace with the ever-increasing data flow.
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Authors: Vogelvang, Ben (2005) - The author combines econometric theory with practice by demonstrating its use with the software package EViews through extensive use of screen shots. The emphasis is on understanding how to select the right method of analysis for a given situation, and how to actually apply the theoretical methodology correctly.
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Authors: - (2019) - This book uniquely demonstrates how econometrics has moved beyond a set of abstract tools to become genuinely useful for answering questions in business, policy evaluation, and forecasting.
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Authors: Wooldridge, Jeffrey (2019) - The author has organized the book's presentation around the type of data being analyzed with a systematic approach that only introduces assumptions as they are needed. This makes the material easier to understand and, ultimately, leads to better econometric practices. Packed with relevant applications, the text incorporates more than 100 data sets in different formats.
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Authors: Adams, Christopher P (2020) - This book provides an introduction to the field of microeconometrics through the use of R. The focus is on applying current learning from the field to real world problems. It uses R to both teach the concepts of the field and show the reader how the techniques can be used. It is aimed at the general reader with the equivalent of a bachelor's degree in economics, statistics or some more technical field. It covers the standard tools of microeconometrics, OLS, instrumental variables, Heckman selection and difference in difference. In addition, it introduces bounds, factor models, mixture models and empirical Bayesian analysis"
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Authors: Hill, R. Carter (2011) - Designed to arm finance professionals with an understanding of why econometrics is necessary, this book also provides them with a working knowledge of basic econometric tools. The fourth edition has been thoroughly updated to reflect the current state of economic and financial markets. New discussions are presented on Kennel Density Fitting and the analysis of treatment effects. A new summary of probability and statistics has been added. In addition, numerous new end-of-chapter questions and problems have been integrated throughout the chapters. This will help finance professionals apply basic econometric tools to modeling, estimation, inference, and forecasting through real world pro...
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Authors: Heiss, Florian (2019) - This free software guide for R with freely downloadable datasets brings the econometric techniques to life, showing readers how to implement the approaches presented in Introductory Econometrics for Finance using this highly popular software package. Designed to be used alongside the main textbook, the guide will give readers the confidence and skills to estimate and interpret their own models while the textbook will ensure that they have a thorough understanding of the conceptual underpinnings
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Authors: Kreinovich, Vladik (2021) - Mainly focusing on processing uncertainty, this book presents state-of-the-art techniques and demonstrates their use in applications to econometrics and other areas. Processing uncertainty is essential, considering that computers - which help us understand real-life processes and make better decisions based on that understanding - get their information from measurements or from expert estimates, neither of which is ever 100% accurate. Measurement uncertainty is usually described using probabilistic techniques, while uncertainty in expert estimates is often described using fuzzy techniques
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Authors: Independently published (2020) - Introduces the popular, powerful and free programming language and software package R Focus : implementation of standard tools and methods used in econometrics Compatible with "Introductory Econometrics" by Jeffrey M. Wooldridge in terms of topics, organization, terminology and notation Companion website with full text, all code for download and other goodies Praise : "A very nice resource for those wanting to use R in their introductory econometrics courses." ( Jeffrey M. Wooldridge ) Using R for Introductory Econometrics is a fabulous modern resource
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Authors: - (2016) - Introduces the popular, powerful and free programming language and software package R Focus : implementation of standard tools and methods used in econometrics Compatible with "Introductory Econometrics" by Jeffrey M. Wooldridge in terms of topics, organization, terminology and notation Companion website with full text, all code for download and other goodies Praise : "A very nice resource for those wanting to use R in their introductory econometrics courses." ( Jeffrey M. Wooldridge ) Using R for Introductory Econometrics is a fabulous modern resource
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