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the cutting-edge study guide to Hill, Griffiths, and Lim's Principles of Econometrics, incorporates the capabilities of Stata software to practically apply the principles of econometrics. Readers will learn how to apply basic econometric tools and the Stata software to estimation, inference and forecasting in the context of real world economic problems. In order to make concepts more accessible, it also offers lucid descriptions of techniques as well as appropriate applications to today's situations. Along the way, readers will find introductions to simple economic models and questions to enhance critical thinking.
(source: Nielsen Book Data)

• Introduction
• Conditional expectation and projection
• The algebra of least squares
• Least squares regression
• Normal regression
• A review of large sample asymptotics
• Asymptotic theory for least squares
• Restricted estimation
• Hypothesis testing
• Resampling methods
• Multivariate regression
• Instrumental variables
• Generalized method of moments
• Time series
• Multivariate time series
• Nonstationary time series
• Panel data
• Difference in differences
• Nonparametric regression
• Series regression
• Regression discontinuity
• M-Estimators
• Nonlinear least squares
• Quantile regression
• Binary choice
• Multiple choice
• Censoring and selection
• Model selection, Stein shrinkage, and model averaging
• Machine learning.

"Econometrics is the quantitative language of economic theory, analysis, and empirical work, and it has become a cornerstone of graduate economics programs. 'Econometrics' provides graduate and PhD students with an essential introduction to this foundational subject in economics and serves as an invaluable reference for researchers and practitioners. This comprehensive textbook teaches fundamental concepts, emphasizes modern, real-world applications, and gives students an intuitive understanding of econometrics. Covers the full breadth of econometric theory and methods with mathematical rigor while emphasizing intuitive explanations that are accessible to students of all backgrounds ; draws on integrated, research-level datasets, provided on an accompanying website ; discusses linear econometrics, time series, panel data, nonparametric methods, nonlinear econometric models, and modern machine learning ; features hundreds of exercises that enable students to learn by doing ; includes in-depth appendices on matrix algebra and useful inequalities and a wealth of real-world examples ; can serve as a core textbook for a first-year PhD course in econometrics and as a follow-up to Bruce E. Hansen’s 'Probability and Statistics for Economists"-- Provided by publisher.

• 1. Introduction
• 2. Dyadic regression
• 3. Strategic network formation
• 4. Testing for externalities in network formation using simulation
• 5. Econometric analysis of bipartite networks
• 6. An empirical model for strategic network formation
• 7. Econometric analysis of models with social interactions
• 8. Many player asymptotics for large network formation problems.
• (source: Nielsen Book Data)

The Econometric Analysis of Network Data serves as an entry point for advanced students, researchers, and data scientists seeking to perform effective analyses of networks, especially inference problems. It introduces the key results and ideas in an accessible, yet rigorous way. While a multi-contributor reference, the work is tightly focused and disciplined, providing latitude for varied specialties in one authorial voice.
(source: Nielsen Book Data)

• Section 1: Identification of Network ModelsChapter 1. Identification and Estimation of Network Models with Different Between and Within-type Interactions
• Tiziano Arduini, Eleonora Patacchini and Edoardo Rainone Chapter 2. Identification Methods for Social Interactions Models with Unknown Networks
• Hon Ho Kwok Chapter 3. Snowball Sampling and Sample Selection in a Social Network
• TszKin Julian Chan Section 2: Network Formation Chapter 4. Trade Networks and the Strength of Strong Ties
• Aureo de Paula Chapter 5. Application and Computation of a Flexible Class of Network Formation Models
• Seth Richards-Shubik Section 3: Networks and Spatial Econometrics Chapter 6. Implementing Faustmann-Marshall-Pressler at Scale: Stochastic Dynamic Programming in Space
• Harry J. Paarsch and John Rust Chapter 7. A Spatial Panel Model of Bank Branches in Canada
• Heng Chen and Matthew Strathearn Chapter 8. Full-information Bayesian Estimation of Cross-sectional Sample Selection Models
• Sophia Ding and Peter Egger Chapter 9. Survival Analysis of Banknote Circulation: Fitness, Network Structure, and Machine Learning
• Diego Rojas, Juan Estrada, Kim Pl Huynh and David T. Jacho-Chavez Section 4: Applications of Financial Networks Chapter 10. Financial Contagion in Cross-holdings Networks: The Case of Ecuador
• Pablo Estrada and Leonardo Sanchez-Aragon Chapter 11. Estimating Spillover Effects with Bilateral Outcomes
• Edoardo Rainone Chapter 12. Interconnectedness through the Lens of Consumer Credit Markets
• Anson T.Y. Ho Chapter 13. FRM Financial Risk Meter
• Andrija Mihoci, Michael Althof, Cathy Yi-Hsuan Chen and Wolfgang Karl Hardle.
• (source: Nielsen Book Data)

This volume in Advances in Econometrics showcases fresh methodological and empirical research on the econometrics of networks. Comprising both theoretical, empirical and policy papers, the authors bring together a wide range of perspectives to facilitate a dialogue between academics and practitioners for better understanding this groundbreaking field and its role in policy discussions. This edited collection includes thirteen chapters which covers various topics such as identification of network models, network formation, networks and spatial econometrics and applications of financial networks. Readers can also learn about network models with different types of interactions, sample selection in social networks, trade networks, stochastic dynamic programming in space, spatial panels, survival and networks, financial contagion, spillover effects, interconnectedness on consumer credit markets and a financial risk meter. The topics covered in the book, centered on the econometrics of data and models, are a valuable resource for graduate students and researchers in the field. The collection is also useful for industry professionals and data scientists due its focus on theoretical and applied works.
(source: Nielsen Book Data)

• Intro
• Contents
• Preface
• 1. Introduction to Econometrics
• 1.1 Introduction
• 1.2 Quantitative Features of Modern Economics
• 1.3 Mathematical Modeling
• 1.4 Empirical Validation
• 1.5 Illustrative Examples
• 1.6 Limitations of Econometric Analysis
• 1.7 Conclusion
• Exercise 1
• 2. General Regression Analysis
• 2.1 Conditional Probability Distribution
• 2.2 Conditional Mean and Regression Analysis
• 2.3 Linear Regression Modeling
• 2.4 Correct Model Specification for Conditional Mean
• 2.5 Conclusion
• Exercise 2
• 3. Classical Linear Regression Models

• 3.1 Framework and Assumptions
• 3.2 Ordinary Least Squares (OLS) Estimation
• 3.3 Goodness of Fit and Model Selection Criteria
• 3.4 Consistency and Efficiency of the OLS Estimator
• 3.5 Sampling Distribution of the OLS Estimator
• 3.6 Variance Estimation for the OLS Estimator
• 3.7 Hypothesis Testing
• 3.8 Applications
• 3.9 Generalized Least Squares Estimation
• 3.10 Conclusion
• Exercise 3
• 4. Linear Regression Models with Independent Observations
• 4.1 Introduction to Asymptotic Theory
• 4.2 Framework and Assumptions
• 4.3 Consistency of the OLS Estimator

• 4.4 Asymptotic Normality of the OLS Estimator
• 4.5 Asymptotic Variance Estimation
• 4.6 Hypothesis Testing
• 4.7 Testing for Conditional Homoskedasticity
• 4.8 Conclusion
• Exercise 4
• 5. Linear Regression Models with Dependent Observations
• 5.1 Introduction to Time Series Analysis
• 5.2 Framework and Assumptions
• 5.3 Consistency of the OLS Estimator
• 5.4 Asymptotic Normality of the OLS Estimator
• 5.5 Asymptotic Variance Estimation for the OLS Estimator
• 5.6 Hypothesis Testing
• 5.7 Testing for Conditional Heteroskedasticity and Autoregressive Conditional Heteroskedasticity

• 5.8 Testing for Serial Correlation
• 5.9 Conclusion
• Exercise 5
• 6. Linear Regression Models Under Conditional Heteroskedasticity and Autocorrelation
• 6.1 Motivation
• 6.2 Framework and Assumptions
• 6.3 Long-Run Variance-Covariance Matrix Estimation
• 6.4 Consistency of the OLS Estimator
• 6.5 Asymptotic Normality of the OLS Estimator
• 6.6 Hypothesis Testing
• 6.7 Testing Whether Long-Run Variance-Covariance Matrix Estimation Is Needed
• 6.8 Ornut-Cochrane Procedure
• 6.9 Conclusion
• Exercise 6
• 7. Instrumental Variables Regression
• 7.1 Motivation

• 7.2 Framework and Assumptions
• 7.3 Two-Stage Least Squares (2SLS) Estimation
• 7.4 Consistency of the 2SLS Estimator
• 7.5 Asymptotic Normality of the 2SLS Estimator
• 7.6 Interpretation and Estimation of Asymptotic Variance-Covariance Matrix of the 2SLS Estimator
• 7.7 Hypothesis Testing
• 7.8 Hausman's Test
• 7.9 Conclusion
• Exercise 7
• 8. Generalized Method of Moments Estimation
• 8.1 Introduction to Method of Moments Estimation
• 8.2 Generalized Method of Moments (GMM) Estimation
• 8.3 Consistency of the GMM Estimator
• 8.4 Asymptotic Normality of the GMM Estimator

Modern economies are full of uncertainties and risk. Economics studies resource allocations in an uncertain market environment. As a generally applicable quantitative analytic tool for uncertain events, probability and statistics have been playing an important role in economic research. Econometrics is statistical analysis of economic and financial data. In the past four decades or so, economics has witnessed a so-called 'empirical revolution' in its research paradigm, and as the main methodology in empirical studies in economics, econometrics has been playing an important role. It has become an indispensable part of training in modern economics, business and management.This book develops a coherent set of econometric theory, methods and tools for economic models. It is written as a textbook for graduate students in economics, business, management, statistics, applied mathematics, and related fields. It can also be used as a reference book on econometric theory by scholars who may be interested in both theoretical and applied econometrics.
(source: Nielsen Book Data)

• Chapter 1. Introducing metric power.-
• Chapter 2. Measurement.-
• Chapter 3. Circulation.-
• Chapter 4. Possibility.-
• Chapter 5. Conclusion: The intersections and imbrications of metric power.-
• Chapter 6. Coda... Metric power and the production of uncertainty.(how does metric power make us feel?).
• (source: Nielsen Book Data)

This book examines the powerful and intensifying role that metrics play in ordering and shaping our everyday lives. Focusing upon the interconnections between measurement, circulation and possibility, the author explores the interwoven relations between power and metrics. He draws upon a wide-range of interdisciplinary resources to place these metrics within their broader historical, political and social contexts. More specifically, he illuminates the various ways that metrics implicate our lives - from our work, to our consumption and our leisure, through to our bodily routines and the financial and organisational structures that surround us. Unravelling the power dynamics that underpin and reside within the so-called big data revolution, he develops the central concept of Metric Power along with a set of conceptual resources for thinking critically about the powerful role played by metrics in the social world today.
(source: Nielsen Book Data)

• Cover; CONTENTS; Abstract; I. Introduction; II. System Priors; III. Example
• System Priors for an AR(2) Process; IV. Conclusion; V. Appendix: Pseudo Code for the Posterior Kernel; PSEUDO CODE:; FIGURES; 1. Parameter regions for different priors; 2. Model properties for admissible regions.

The paper introduces "system priors", their use in Bayesian analysis of econometric time series, and provides a simple and illustrative application. System priors were devised by Andrle and Benes (2013) as a tool to incorporate prior knowledge into an economic model. Unlike priors about individual parameters, system priors offer a simple and efficient way of formulating well-defined and economically-meaningful priors about high-level model properties. The generality of system priors are illustrated using an AR(2) process with a prior that most of its dynamics comes from business-cycle frequencies

• Randomized trials
• Regression
• Instrumental variables
• Regression discontinuity designs
• Differences-in-differences
• The wages of schooling.

Applied econometrics, known to aficionados as 'metrics, is the original data science. 'Metrics encompasses the statistical methods economists use to untangle cause and effect in human affairs. Through accessible discussion and with a dose of kung fu-themed humor, Mastering 'Metrics presents the essential tools of econometric research and demonstrates why econometrics is exciting and useful. The five most valuable econometric methods, or what the authors call the Furious Five--random assignment, regression, instrumental variables, regression discontinuity designs, and differences in differences--are illustrated through well-crafted real-world examples (vetted for awesomeness by Kung Fu Panda's Jade Palace). Does health insurance make you healthier? Randomized experiments provide answers. Are expensive private colleges and selective public high schools better than more pedestrian institutions? Regression analysis and a regression discontinuity design reveal the surprising truth. When private banks teeter, and depositors take their money and run, should central banks step in to save them? Differences-in-differences analysis of a Depression-era banking crisis offers a response. Could arresting O. J. Simpson have saved his ex-wife's life? Instrumental variables methods instruct law enforcement authorities in how best to respond to domestic abuse. Wielding econometric tools with skill and confidence, Mastering 'Metrics uses data and statistics to illuminate the path from cause to effect. * Shows why econometrics is important* Explains econometric research through humorous and accessible discussion* Outlines empirical methods central to modern econometric practice* Works through interesting and relevant real-world examples.
(source: Nielsen Book Data)

• List of Figures vii List of Tables ix Introduction xi
• 1 Randomized Trials 1 1.1 In Sickness and in Health (Insurance) 1 1.2 The Oregon Trail 24 Masters of 'Metrics: From Daniel to R. A. Fisher 30 Appendix: Mastering Inference 33
• 2 Regression 47 2.1 A Tale of Two Colleges 47 2.2 Make Me a Match, Run Me a Regression 55 2.3 Ceteris Paribus? 68 Masters of 'Metrics: Galton and Yule 79 Appendix: Regression Theory 82
• 3 Instrumental Variables 98 3.1 The Charter Conundrum 99 3.2 Abuse Busters 115 3.3 The Population Bomb 123 Masters of 'Metrics: The Remarkable Wrights 139 Appendix: IV Theory 142
• 4 Regression Discontinuity Designs 147 4.1 Birthdays and Funerals 148 4.2 The Elite Illusion 164 Masters of 'Metrics: Donald Campbell 175
• 5 Differences-in-Differences 178 5.1 A Mississippi Experiment 178 5.2 Drink, Drank, ... 191 Masters of 'Metrics: John Snow 204 Appendix: Standard Errors for Regression DD 205
• 6 The Wages of Schooling 209 6.1 Schooling, Experience, and Earnings 209 6.2 Twins Double the Fun 217 6.3 Econometricians Are Known by Their ... Instruments 223 6.4 Rustling Sheepskin in the Lone Star State 235 Appendix: Bias from Measurement Error 240 Abbreviations and Acronyms 245 Empirical Notes 249 Acknowledgments 269 Index 271.
• (source: Nielsen Book Data)

Applied econometrics, known to aficionados as 'metrics, is the original data science. 'Metrics encompasses the statistical methods economists use to untangle cause and effect in human affairs. Through accessible discussion and with a dose of kung fu-themed humor, Mastering 'Metrics presents the essential tools of econometric research and demonstrates why econometrics is exciting and useful. The five most valuable econometric methods, or what the authors call the Furious Five--random assignment, regression, instrumental variables, regression discontinuity designs, and differences in differences--are illustrated through well-crafted real-world examples (vetted for awesomeness by Kung Fu Panda's Jade Palace). Does health insurance make you healthier? Randomized experiments provide answers. Are expensive private colleges and selective public high schools better than more pedestrian institutions? Regression analysis and a regression discontinuity design reveal the surprising truth. When private banks teeter, and depositors take their money and run, should central banks step in to save them? Differences-in-differences analysis of a Depression-era banking crisis offers a response. Could arresting O. J. Simpson have saved his ex-wife's life? Instrumental variables methods instruct law enforcement authorities in how best to respond to domestic abuse. Wielding econometric tools with skill and confidence, Mastering 'Metrics uses data and statistics to illuminate the path from cause to effect. * Shows why econometrics is important* Explains econometric research through humorous and accessible discussion* Outlines empirical methods central to modern econometric practice* Works through interesting and relevant real-world examples.
(source: Nielsen Book Data)

• Part I: The Linear Regression Model
• Chapter 1: Econometrics
• Chapter 2: The Linear Regression Model
• Chapter 3: Least Squares
• Chapter 4: The Least Squares Estimator
• Chapter 5: Hypothesis Tests and Model Selection
• Chapter 6: Functional Form and Structural Change
• Chapter 7: Nonlinear, Semiparametric, and Nonparametric Regression Models
• Chapter 8: Endogeneity and Instrumental Variable Estimation Part II: Generalized Regression Model and Equation Systems
• Chapter 9: The Generalized Regression Model and Heteroscedasticity
• Chapter 10: Systems of Equations
• Chapter 11: Models for Panel Data Part III: Estimation Methodology
• Chapter 12: Estimation Frameworks in Econometrics
• Chapter 13: Minimum Distance Estimation and the Generalized Method of Moments
• Chapter 14: Maximum Likelihood Estimation
• Chapter 15: Simulation-Based Estimation and Inference
• Chapter 16: Bayesian Estimation and Inference Part IV: Cross Sections, Panel Data, and Microeconometrics
• Chapter 17: Discrete Choice
• Chapter 18: Discrete Choices and Event Counts
• Chapter 19: Limited Dependent Variables--Truncation, Censoring, and Sample Selection Part V: Time Series and Macroeconometrics
• Chapter 20: Serial Correlation
• Chapter 21: Models with Lagged Variables
• Chapter 22: Time-Series Models
• Chapter 23: Nonstationary Data Part VI: Appendices Appendix A: Matrix Algebra Appendix B: Probability and Distribution Theory Appendix C: Estimation and Inference Appendix D: Large-Sample Distribution Theory Appendix E: Computation and Optimization Appendix F: Data Sets Used in Applications Appendix G: Statistical Tables.
• (source: Nielsen Book Data)

Econometric Analysis serves as a bridge between an introduction to the field of econometrics and the professional literature for social scientists and other professionals in the field of social sciences, focusing on applied econometrics and theoretical background. This book provides a broad survey of the field of econometrics that allows the reader to move from here to practice in one or more specialized areas. At the same time, the reader will gain an appreciation of the common foundation of all the fields presented and use the tools they employ.
(source: Nielsen Book Data)

• Part I: The Linear Regression Model
• Chapter 1: Econometrics
• Chapter 2: The Linear Regression Model
• Chapter 3: Least Squares
• Chapter 4: The Least Squares Estimator
• Chapter 5: Hypothesis Tests and Model Selection
• Chapter 6: Functional Form and Structural Change
• Chapter 7: Nonlinear, Semiparametric, and Nonparametric Regression Models
• Chapter 8: Endogeneity and Instrumental Variable Estimation Part II: Generalized Regression Model and Equation Systems
• Chapter 9: The Generalized Regression Model and Heteroscedasticity
• Chapter 10: Systems of Equations
• Chapter 11: Models for Panel Data Part III: Estimation Methodology
• Chapter 12: Estimation Frameworks in Econometrics
• Chapter 13: Minimum Distance Estimation and the Generalized Method of Moments
• Chapter 14: Maximum Likelihood Estimation
• Chapter 15: Simulation-Based Estimation and Inference
• Chapter 16: Bayesian Estimation and Inference Part IV: Cross Sections, Panel Data, and Microeconometrics
• Chapter 17: Discrete Choice
• Chapter 18: Discrete Choices and Event Counts
• Chapter 19: Limited Dependent Variables--Truncation, Censoring, and Sample Selection Part V: Time Series and Macroeconometrics
• Chapter 20: Serial Correlation
• Chapter 21: Models with Lagged Variables
• Chapter 22: Time-Series Models
• Chapter 23: Nonstationary Data Part VI: Appendices Appendix A: Matrix Algebra Appendix B: Probability and Distribution Theory Appendix C: Estimation and Inference Appendix D: Large-Sample Distribution Theory Appendix E: Computation and Optimization Appendix F: Data Sets Used in Applications Appendix G: Statistical Tables.
• (source: Nielsen Book Data)

Econometric Analysis serves as a bridge between an introduction to the field of econometrics and the professional literature for social scientists and other professionals in the field of social sciences, focusing on applied econometrics and theoretical background. This book provides a broad survey of the field of econometrics that allows the reader to move from here to practice in one or more specialized areas. At the same time, the reader will gain an appreciation of the common foundation of all the fields presented and use the tools they employ.
(source: Nielsen Book Data)

• Preface
• 1. Econometric information recovery
• Part I. Traditional Parametric and Semiparametric Probability Models: Estimation and Inference: 2. Formulation and analysis of parametric and semiparametric linear models
• 3. Method of moments, GMM, and estimating equations
• Part II. Formulation and Solution of Stochastic Inverse Problems: 4. A stochastic-empirical likelihood inverse problem: formulation and estimation
• 5. A stochastic-empirical likelihood inverse problem: inference
• 6. Kullback-Leibler information and the maximum empirical exponential likelihood
• Part III. A Family of Minimum Discrepancy Estimators: 7. The Cressie-Read family of divergence measures and likelihood functions
• 8. Cressie-Read-MEL-type estimators in practice: evidence of estimation and inference sampling performance
• Part IV. Binary Discrete Choice MPD-EML Econometric Models: 9. Family of distribution functions for the binary response-choice model
• 10. Estimation and inference for the binary response model based on the MPD family of distributions
• Part V. Optimal Convex Divergence: 11. Choosing the optimal divergence under quadratic loss
• 12. Epilogue.
• (source: Nielsen Book Data)

This book is intended to provide the reader with a firm conceptual and empirical understanding of basic information-theoretic econometric models and methods. Because most data are observational, practitioners work with indirect noisy observations and ill-posed econometric models in the form of stochastic inverse problems. Consequently, traditional econometric methods in many cases are not applicable for answering many of the quantitative questions that analysts wish to ask. After initial chapters deal with parametric and semiparametric linear probability models, the focus turns to solving nonparametric stochastic inverse problems. In succeeding chapters, a family of power divergence measure-likelihood functions are introduced for a range of traditional and nontraditional econometric-model problems. Finally, within either an empirical maximum likelihood or loss context, Ron C. Mittelhammer and George G. Judge suggest a basis for choosing a member of the divergence family.
(source: Nielsen Book Data)

• Ch. 1. The nature of econometrics and economic data
• pt. 1. Regression analysis with cross-sectional data
• Ch. 2. The simple regression model
• Ch. 3. Mutiple regression analysis: estimation
• Ch. 4. Mutiple regression analysis: inference
• Ch. 5. Mutiple regression analysis: OLS asymptotics
• Ch. 6. Mutiple regression analysis: further issues
• Ch. 7. Mutiple regression analysis with qualitative information: binary (or dummy) variables
• Ch. 8. Hetroskedasticity
• Ch. 9. More on specification and data issues
• pt. 2. Regression analysis with time series data
• Ch. 10. Basic regression analysis with time series data
• Ch. 11. Further issues in using OLS with time series data
• Ch. 12. Serial correlation and heteroskedasticity in time series regressions
• Ch. 13. Pooling cross sections across time: simple panel data methods
• Ch. 14. Advanced panel data methods
• Ch. 15. Instrumental variables estimation and two stage least squares
• Ch. 16. Simulatensous equations models
• Ch. 17. Limited dependent variable models and sample selection corrections
• Ch. 18. Advanced time series topics
• Ch. 19. Carrying out an empirical project
• Appendices.

Discover how empirical researchers today actually think about and apply econometric methods with the practical, professional approach in Wooldridge's INTRODUCTORY ECONOMETRICS: A MODERN APPROACH, 5E. Unlike traditional books on the subject, INTRODUCTORY ECONOMETRICS' unique presentation demonstrates how econometrics has moved beyond just a set of abstract tools to become a genuinely useful tool for answering questions in business, policy evaluation, and forecasting environments. Organized around the type of data being analyzed, the book uses a systematic approach that only introduces assumptions as they are needed, which makes the material easier to understand and ultimately leads to better econometric practices. Packed with timely, relevant applications, the text emphasizes incorporates close to 100 intriguing data sets in six formats and offers updates that reflect the latest emerging developments in the field.
(source: Nielsen Book Data)

• Economic questions and data
• Review of probability
• Review of statistics
• Linear regression with one regressor
• Regression with a single regressor : hypothesis tests and confidence intervals
• Linear regression with multiple regressors
• Hypothesis tests and confidence intervals in multiple regression
• Nonlinear regression functions
• Assessing studies based on multiple regression
• Regression with panel data
• Regression with a binary dependent variable
• Instrumental variables regression
• Experiments and quasi-experiments
• Introduction to time series regression and forecasting
• Estimation of dynamic causal effects
• Additional topics in time series regression
• The theory of linear regression with one regressor
• The theory of multiple regression.

For courses in introductory econometrics. An approach to modern econometrics theory and practice through engaging applications. Ensure students grasp the relevance of econometrics with Introduction to Econometrics--the text that connects modern theory and practice with engaging applications. The third edition builds on the philosophy that applications should drive the theory, not the other way around, while maintaining a focus on currency.
(source: Nielsen Book Data)

• Part I. Introduction and Review
• Chapter 1. Economic Questions and Data
• Chapter 2. Review of Probability
• Chapter 3. Review of Statistics Part II. Fundamentals of Regression Analysis
• Chapter 4. Linear Regression with One Regressor
• Chapter 5. Regression with a Single Regressor: Hypothesis Tests and Confidence Intervals
• Chapter 6. Linear Regression with Multiple Regressors
• Chapter 7. Hypothesis Tests and Confidence Intervals in Multiple Regression
• Chapter 8. Nonlinear Regression Functions
• Chapter 9. Assessing Studies Based on Multiple Regression Part III. Further Topics in Regression Analysis
• Chapter 10. Regression with Panel Data
• Chapter 11. Regression with a Binary Dependent Variable
• Chapter 12. Instrumental Variables Regression
• Chapter 13. Experiments and Quasi-Experiments Part IV. Regression Analysis of Economic Time Series Data
• Chapter 14. Introduction to Time Series Regression and Forecasting
• Chapter 15. Estimation of Dynamic Causal Effects
• Chapter 16. Additional Topics in Time Series Regression Part V. The Econometric Theory of Regression Analysis
• Chapter 17. The Theory of Linear Regression with One Regressor
• Chapter 18. The Theory of Multiple Regression.
• (source: Nielsen Book Data)

For courses in introductory econometrics. An approach to modern econometrics theory and practice through engaging applications. Ensure students grasp the relevance of econometrics with Introduction to Econometrics--the text that connects modern theory and practice with engaging applications. The third edition builds on the philosophy that applications should drive the theory, not the other way around, while maintaining a focus on currency.
(source: Nielsen Book Data)

• Front Matter
• Kapitel 1 Realität, Theorie und Modell
• Kapitel 2 Stochastisches, statistisches und ökonometrisches Modell
• Kapitel 3 Variablenarten und Klassifikationen ökonometrischer Modelle
• Kapitel 4 Die Datenbasis des ökonometrischen Modells
• Kapitel 5 Identifikation
• Kapitel 6 Statistische Eigenschaften guter Schätzfunktionen
• Kapitel 7 Die Methode der kleinsten Quadrate: Das einfache und das multiple Regressionsmodell
• Kapitel 8 Die Schätzeigenschaften der Methode der kleinsten Quadrate, Varianz der Schätzfunktionen und Residualvarianz
• Kapitel 9 Bestimmtheitsmaß, Signifikanztests und Konfidenzintervalle für Regressionskoeffizienten
• Kapitel 10 Normalverteilte Störvariablen und die Maximum Likelihood Methode
• Kapitel 11 Multikollinearität
• Kapitel 12 Autocorrelation und Heteroskedastizität
• Kapitel 13 Univariate Zeitreihenmodelle
• Kapitel 14 Parameterschätzungen dynamischer Modellgleichungen
• Kapitel 15 Kointegration und vektorautoregressive Modelle
• Kapitel 16 Qualitative Einflüsse: Die Verwendung von (0,1)-Regressoren
• Kapitel 17 Schätzverfahren für identifizierbare Modellgleichungen
• Kapitel 18 Schätzverfahren für überidentifizierte Modellgleichungen
• Kapitel 19 Simultane Schätzungen der Modellkoeffizienten
• Kapitel 20 Prognosen
• Back Matter

Die Ökonometrie ist heute eine anerkannte und fest etablierte Disziplin zur quantitativen Analyse der ökonomischen Realität, deren Vorteile nicht mehr nur von Spezialisten gesehen werden. Zum einen hat die Ökonometrie ihre (Schätz- )Methoden ständig erweitert und verbessert, zum anderen wurden durch die Fortschritte bei der Informationstechnologie sowohl eine umfangreiche Datenspeicherung als auch ihre rasche, benutzerfreundliche Bearbeitung auch mit formal aufwendigen Methoden möglich.

• Part I: Single-Equation Regression Model
• Chapter 1: The Nature of Regression Analysis
• Chapter 2: Two-Variable Regression Analysis: Some Basic Ideas
• Chapter 3: Two Variable Regression Model: The Problem of Estimation
• Chapter 4: Classical Normal Linear Regression Model (CNLRM)
• Chapter 5: Two-Variable Regression: Interval Estimation and Hypothesis Testing
• Chapter 6: Extensions of the Two-Variable Linear Regression Model
• Chapter 7: Multiple Regression Analysis: The Problem of Estimation
• Chapter 8: Multiple Regression Analysis: The Problem of Inference
• Chapter 9: Dummy Variable Regression Models Part II: Relaxing the Assumptions of the Classical Model
• Chapter 10: Multicollinearity: What happens if the Regressor are Correlated
• Chapter 11: Heteroscedasticity: What Happens if the Error Variance is Nonconstant?
• Chapter 12: Autocorrelation: What Happens if the Error Terms are Correlated
• Chapter 13: Econometric Modeling: Model Specification and Diagnostic Testing Part III: Topics in Econometrics
• Chapter 14: Nonlinear Regression Models
• Chapter 15: Qualitative Response Regression Models
• Chapter 16: Panel Data Regression Models
• Chapter 17: Dynamic Econometric Model: Autoregressive and Distributed-Lag Models. Part IV: Simultaneous-Equation Models
• Chapter 18: Simultaneous-Equation Models.
• Chapter 19: The Identification Problem.
• Chapter 20: Simultaneous-Equation Methods.
• Chapter 21: Time Series Econometrics: Some Basic Concepts
• Chapter 22: Time Series Econometrics: Forecasting Appendix A: Review of Some Statistical Concepts Appendix B: Rudiments of Matrix Algebra Appendix C: The Matrix Approach to Linear Regression Model Appendix D: Statistical Tables Appendix E: Computer Output of EViews, MINITAB, Excel, and STATA Appendix F: Economic Data on the World Wide Web.
• (source: Nielsen Book Data)

• Part I: Single-Equation Regression Model 1: The Nature of Regression Analysis 2: Two-Variable Regression Analysis: Some Basic Ideas 3: Two Variable Regression Model: The Problem of Estimation 4: Classical Normal Linear Regression Model (CNLRM) 5: Two-Variable Regression: Interval Estimation and Hypothesis Testing 6: Extensions of the Two-Variable Linear Regression Model 7: Multiple Regression Analysis: The Problem of Estimation 8: Multiple Regression Analysis: The Problem of Inference 9: Dummy Variable Regression Models Part II: Relaxing the Assumptions of the Classical Model 10: Multicollinearity: What happens if the Regressor are Correlated 11: Heteroscedasticity: What Happens if the Error Variance is Nonconstant? 12: Autocorrelation: What Happens if the Error Terms are Correlated
• Chapter 13: Econometric Modeling: Model Specification and Diagnostic Testing Part III: Topics in Econometrics 14: Nonlinear Regression Models 15: Qualitative Response Regression Models 16: Panel Data Regression Models 17: Dynamic Econometric Model: Autoregressive and Distributed-Lag Models. Part IV: Simultaneous-Equation Models 18: Simultaneous-Equation Models. 19: The Identification Problem. 20: Simultaneous-Equation Methods. 21: Time Series Econometrics: Some Basic Concepts 22: Time Series Econometrics: Forecasting Appendix A: Review of Some Statistical Concepts Appendix B: Rudiments of Matrix Algebra Appendix C: The Matrix Approach to Linear Regression Model Appendix D: Statistical Tables Appendix E: Computer Output of EViews, MINITAB, Excel, and STATA Appendix F: Economic Data on the World Wide Web.
• (source: Nielsen Book Data)

Econometric Theory and Methods International Edition provides a unified treatment of modern econometric theory and practical econometric methods. The geometrical approach to least squares is emphasized, as is the method of moments, which is used to motivate a wide variety of estimators and tests. Simulation methods, including the bootstrap, are introduced early and used extensively. The book deals with a large number of modern topics. In addition to bootstrap and Monte Carlo tests, these include sandwich covariance matrix estimators, artificial regressions, estimating functions and the generalized method of moments, indirect inference, and kernel estimation. Every chapter incorporates numerous exercises, some theoretical, some empirical, and many involving simulation.
(source: Nielsen Book Data)