What You Need to Know About Applied Econometrics by Dimitrios Asteriou and How to Get It for Free
Applied Econometrics by Dimitrios Asteriou PDF Free Download
If you are looking for a comprehensive and practical textbook on applied econometrics, you might be interested in Applied Econometrics by Dimitrios Asteriou and S. G. Hall. This book offers a unique blend of theory and application, taking students from a basic level up to an advanced understanding in an intuitive, step-by-step fashion. It provides ideal preparation for doing applied econometric work as it teaches you how to analyze data, use various software packages, and conduct empirical research projects.
Applied Econometrics By Dimitrios Asteriou Pdf Free Download
In this article, we will give you an overview of what applied econometrics is, who the authors are, and what are the main features of the book. We will also explain why you should read this book, what you will learn from it, and how you can get it for free or at a low cost. By the end of this article, you will have a clear idea of whether this book is suitable for your needs and how to access it legally and ethically.
What is Applied Econometrics?
Econometrics is the application of statistical methods to economic data and problems. It aims to test economic theories, estimate economic relationships, forecast economic variables, and evaluate economic policies. Econometrics can be divided into two branches: theoretical econometrics and applied econometrics.
Theoretical econometrics focuses on developing and proving mathematical properties of econometric models and methods. It deals with abstract concepts and proofs that are often difficult to understand and apply in practice. Applied econometrics, on the other hand, focuses on using econometric models and methods to analyze real-world data and answer relevant questions. It deals with practical issues such as data collection, processing, testing, estimation, interpretation, and presentation.
Applied econometrics is a useful skill for anyone who wants to work with economic data or conduct empirical research in economics or related fields. It can help you understand how different factors affect economic outcomes, how reliable your results are, and how to communicate your findings effectively.
Who are the authors?
The authors of Applied Econometrics are Dimitrios Asteriou and S. G. Hall. They are both experienced economists and educators who have published extensively in the areas of econometrics and macroeconomic modeling.
Dimitrios Asteriou is a Professor in Quantitative Methods at the Hellenic Open University, Greece. He was previously employed as a Lecturer at the University of Reading, UK, and as a Senior Lecturer at City University, London, UK. He has published more than 50 academic research papers in peer-reviewed journals and has served as the Secretary General of the European Economics and Finance Society. He has several years of teaching experience at undergraduate and postgraduate level. His research interests lie in the area of applied econometrics with an emphasis on financial econometrics and economic growth.
S. G. Hall is an Emeritus Professor of Economics at the University of Leicester, UK. He was previously employed as a Research Officer at the National Institute of Economic and Social Research, London, UK, where he worked on the development of the UK model and a range of methodological and econometric issues. He has published more than 100 academic research papers in peer-reviewed journals and has edited several books and special issues of journals. He has also been a consultant for various organizations, such as the Bank of England, the European Commission, and the International Monetary Fund. His research interests lie in the area of macroeconometrics, with a focus on structural vector autoregressive models and time-varying coefficient models.
What are the main features of the book?
Applied Econometrics is a textbook that offers a unique blend of theory and practical application. It has several features that make it stand out from other books on the subject, such as:
An applied, step-by-step approach that blends theory and application and focuses on how to analyze data.
Packed with real-life data and examples, the hands-on approach prepares students for empirical research projects.
Takes students from a basic to advanced level in a step-by-step fashion.
A companion website for lecturers with slides to use in teaching.
The number of finance applications has been expanded throughout, to make the book more suitable for finance students.
A new chapter on Time Varying Coefficient models has been added.
Expanded discussion on current topics in econometrics, such as structural VAR models.
Why you should read Applied Econometrics by Dimitrios Asteriou
If you are interested in learning econometrics or improving your econometric skills, you should definitely consider reading Applied Econometrics by Dimitrios Asteriou and S. G. Hall. This book has several benefits that can help you achieve your academic or professional goals, such as:
Benefits of learning econometrics
Econometrics is a valuable tool for anyone who wants to understand and analyze economic phenomena and problems. By learning econometrics, you can:
Test economic theories and hypotheses using empirical evidence.
Estimate economic relationships and parameters using statistical methods.
Forecast economic variables and scenarios using econometric models.
Evaluate economic policies and interventions using counterfactual analysis.
Communicate your results and findings using clear and concise reports and presentations.
Econometrics can also enhance your critical thinking and problem-solving skills, as well as your quantitative and analytical abilities. These skills are highly sought after in many fields and industries, such as finance, banking, consulting, government, academia, and more.
How the book helps you master econometric methods and software
Applied Econometrics by Dimitrios Asteriou and S. G. Hall is not just a theoretical textbook that explains econometric concepts and formulas. It is also a practical guide that shows you how to apply econometric methods and software to real-world data and problems. The book helps you master econometric methods and software by:
Providing clear and intuitive explanations of the underlying assumptions, properties, and limitations of each method.
Giving detailed instructions on how to use various software packages, such as EViews, Stata, R, Excel, and more.
Including numerous exercises and examples that illustrate how to perform data analysis, estimation, testing, forecasting, and more.
Offering solutions and answers to the exercises and examples on the companion website.
Covering a wide range of topics and applications, from basic to advanced level, such as multiple regression, heteroskedasticity, cointegration, panel data, time-varying coefficient models, structural VAR models, etc.
By reading this book, you will not only learn the theory of econometrics, but also how to use it in practice. You will be able to handle different types of data and problems with confidence and competence.
How the book prepares you for empirical research projects
Applied Econometrics by Dimitrios Asteriou and S. G. Hall is also a useful resource for anyone who wants to conduct empirical research projects in economics or related fields. The book prepares you for empirical research projects by:
Teaching you how to formulate research questions and hypotheses based on economic theory and literature review.
Showing you how to collect, process, and describe data using appropriate methods and software.
Guiding you how to choose the best econometric model and method for your data and problem.
Demonstrating how to estimate, test, interpret, and present your results using sound econometric techniques.
Helping you avoid common pitfalls and errors in econometric analysis.
Giving you tips on how to write a clear and concise research report or paper that follows academic standards.
What you will learn from Applied Econometrics by Dimitrios Asteriou
Now that you know why you should read Applied Econometrics by Dimitrios Asteriou and S. G. Hall, you might be wondering what you will learn from it. In this section, we will give you an overview of the book's structure and content, summarize each chapter's topics and examples, and highlight some of the new edition's updates and additions.
Overview of the book's structure and content
The book consists of 12 chapters, organized into four parts. The first part covers the statistical background and basic data handling that are essential for econometric analysis. The second part covers the classical linear regression model and its extensions and violations. The third part covers some advanced topics in econometrics, such as simultaneous equations models, qualitative and limited dependent variable models, nonstationary time series models, cointegration and error correction models, and vector autoregressive models. The fourth part covers panel data econometrics, which deals with data that have both cross-sectional and time series dimensions.
The book also includes an appendix that covers how to use various econometric software packages, such as EViews, Stata, R, Excel, and more. The book also has a companion website that provides additional materials for lecturers and students, such as slides, solutions manual, data sets, and links to online resources.
Summary of each chapter's topics and examples
Here is a brief summary of each chapter's topics and examples:
Chapter 1: Statistical Background and Basic Data Handling. This chapter introduces some basic concepts and tools of statistics and probability that are relevant for econometrics. It also explains how to collect, organize, describe, and visualize data using various software packages. Some of the examples include: descriptive statistics of US GDP growth rate; histograms and boxplots of UK house prices; scatter plots and correlation coefficients of US unemployment rate and inflation rate; etc.
Chapter 2: The Classical Linear Regression Model. This chapter introduces the classical linear regression model (CLRM), which is the most widely used econometric model for estimating linear relationships between variables. It also explains how to estimate the model parameters using ordinary least squares (OLS), how to test hypotheses about the parameters using t-tests and F-tests, how to measure the goodness-of-fit of the model using R-squared and adjusted R-squared, and how to interpret the results using confidence intervals and prediction intervals. Some of the examples include: estimating the consumption function for UK; testing the Keynesian theory of consumption; measuring the impact of education on earnings for US; predicting future consumption expenditure for UK; etc.
Chapter 3: Violating the Assumptions of the CLRM: Multicollinearity. This chapter discusses one of the possible violations of the CLRM assumptions: multicollinearity. Multicollinearity occurs when some of the explanatory variables are highly correlated with each other, which can cause problems such as high standard errors, low t-statistics, misleading signs and magnitudes of coefficients, etc. It also explains how to detect multicollinearity using various methods, such as correlation matrix, variance inflation factor (VIF), condition number, etc., and how to deal with multicollinearity using various remedies, such as dropping variables, transforming variables, adding variables, etc. Some of the examples include: estimating the demand function for cigarettes in UK; detecting multicollinearity using VIF; dealing with multicollinearity using principal component analysis (PCA); etc.
Chapter 4: Violating the Assumptions of the CLRM: Heteroskedasticity. This chapter discusses another possible violation of the CLRM assumptions: heteroskedasticity. Heteroskedasticity occurs when the variance of the error term is not constant across observations, which can cause problems such as biased standard errors, invalid t-tests and F-tests, inefficient estimators, etc. It also explains how to detect heteroskedasticity using various methods, such as graphical methods, Breusch-Pagan test, White test, etc., and how to deal with heteroskedasticity using various remedies, such as transforming variables, weighted least squares (WLS), robust standard errors (HAC), etc. Some of the examples include: estimating the Engel curve for US; detecting heteroskedasticity using White test; dealing with heteroskedasticity using robust standard errors; etc.
Chapter 5: Violating the Assumptions of the CLRM: Autocorrelation. This chapter discusses another possible violation of the CLRM assumptions: autocorrelation. Autocorrelation occurs when the error term is correlated across time, which can cause problems such as biased standard errors, invalid t-tests and F-tests, inefficient estimators, spurious regression, etc. It also explains how to detect autocorrelation using various methods, such as graphical methods, Durbin-Watson test, Breusch-Godfrey test, etc., and how to deal with autocorrelation using various remedies, such as transforming variables, generalized least squares (GLS), Cochrane-Orcutt procedure, Newey-West standard errors (HAC), etc. Some of the examples include: estimating the consumption function for US; detecting autocorrelation using Durbin-Watson test; dealing with autocorrelation using Cochrane-Orcutt procedure; etc.
Chapter 6: Violating the Assumptions of the CLRM: Model Specification and Measurement Errors. This chapter discusses two more possible violations of the CLRM assumptions: model specification errors and measurement errors. Model specification errors occur when the model is not correctly specified, such as omitting relevant variables, including irrelevant variables, using wrong functional form, etc., which can cause problems such as biased and inconsistent estimators, omitted variable bias, endogeneity bias, etc. Measurement errors occur when the variables are not measured accurately, such as using proxies, rounding errors, sampling errors, etc., which can cause problems such as attenuation bias, classical errors-in-variables model, etc. It also explains how to detect and deal with these errors using various methods, such as RESET test, Ramsey test, Hausman test, instrumental variables (IV), two-stage least squares (2SLS), etc. Some of the examples include: estimating the demand function for money in UK; detecting model specification errors using RESET test; dealing with measurement errors using IV; etc.
Chapter 7: Topics in Econometrics: Simultaneous Equations Models. This chapter introduces one of the advanced topics in econometrics: simultaneous equations models (SEMs). SEMs are models that consist of more than one equation, where some of the variables are endogenous (determined within the system) and some are exogenous (determined outside the system). SEMs can capture the interdependence and feedback effects among variables, but they also pose some challenges for estimation and inference, such as identification problem, simultaneity bias, etc. It also explains how to estimate and test SEMs using various methods, such as reduced form equations, indirect least squares (ILS), two-stage least squares (2SLS), three-stage least squares (3SLS), etc. Some of the examples include: estimating the supply and demand functions for wheat in US; testing the identification status of SEMs; estimating SEMs using 2SLS; etc.
Chapter 8: Topics in Econometrics: Qualitative and Limited Dependent Variable Models. This chapter introduces another advanced topic in econometrics: qualitative and limited dependent variable models (QLDVMs). QLDVMs are models that deal with dependent variables that are not continuous or have limited range of values, such as binary variables (yes/no), ordinal variables (low/medium/high), count variables (number of events), censored variables (lower or upper limit), truncated variables (missing observations), etc. QLDVMs require different estimation and inference methods than CLRM, such as maximum likelihood estimation (MLE), logit model, probit model, ordered logit/probit model, Poisson model, Tobit model, etc. Some of the examples include: estimating the probability of default for credit card holders; estimating the demand for health care services; estimating the wage equation with censoring; etc.
Highlights of the new edition's updates and additions
The book is currently in its fourth edition, which was published in 2021. The new edition has several updates and additions that make it more relevant and comprehensive for today's students and researchers, such as:
The number of finance applications has been expanded throughout, to make the book more suitable for finance students. For example, the book covers topics such as value at risk (VaR), GARCH models, cointegration and error correction models for exchange rates, etc.
A new chapter on Time Varying Coefficient models has been added, which covers a recent and important development in macroeconometrics. The chapter explains how to estimate and test models that allow for structural changes and parameter instability over time, using methods such as rolling regression, recursive estimation, Chow test, Quandt-Andrews test, etc.
Expanded discussion on current topics in econometrics, such as structural VAR models, panel data models with fixed effects and random effects, instrumental variables with heteroskedasticity and autocorrelation, etc.
More exercises and examples throughout the book, with solutions and answers provided on the companion website.
A freshly-updated companion website featuring a new solutions manual for instructors.
By reading the new edition of Applied Econometrics by Dimitrios Asteriou and S. G. Hall, you will be able to learn the latest and most relevant econometric methods and applications in a clear and accessible way.
How to get Applied Econometrics by Dimitrios Asteriou PDF Free Download
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