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"How to learn both applied statistics (econometrics) and free, open-source software R? This book allows students to have a sense of accomplishment by copying and pasting many hands-on templates provided here. The textbook is essential for anyone wishing to have a practical understanding of an extensive range of topics in Econometrics. No other text provides software snippets to learn so many new statistical tools with hands-on examples. The explicit knowledge of inputs and outputs of each new method allows the student to know which algorithm is worth studying. The book offers sufficient theoretical and algorithmic details about a vast range of statistical techniques. The second edition's preface lists the following topics generally absent in other textbooks. (i) Iteratively reweighted least squares, (ii) Pillar charts to represent 3D data. (iii) Stochastic frontier analysis (SFA) (iv) model selection with Mallows' Cp criterion. (v) Hodrick-Prescott (HP) filter. (vi) Automatic ARIMA models. (vi) Nonlinear Granger-causality using kernel regressions and bootstrap confidence intervals. (vii) new Keynesian Phillips curve (NKPC). (viii) Market-neutral pairs trading using two cointegrated stocks. (ix) Artificial neural network (ANN) for product-specific forecasting. (x) Vector AR and VARMA models. (xi) New tools for diagnosing the endogeneity problem. (xii) The elegant set-up of k-class estimators and identification. (xiii) Probit-logit models and Heckman selection bias correction. (xiv) Receiver operating characteristic (ROC) curves and areas under them. (xv) Confusion matrix. (xvi) Quantile regression (xvii) Elastic net estimator. (xviii) generalized Correlations (xix) maximum entropy bootstrap for time series. (xx) Convergence concepts quantified. (xxi) Generalized partial correlation coefficients (xxii) Panel data and duration (survival) models"--

Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Who will find this book useful? -- About the book -- What to expect from the book -- Book structure -- Prerequisites -- How to use the textbook in a methods course? -- Contributors -- Part I: Introduction to R -- 1. Basic R -- 1.1 Installation -- 1.2 Console -- 1.3 Script -- 1.4 Objects (and functions) -- 2. Data Management -- 2.1 Introduction to data management -- 2.2 Describing a dataset -- 2.3 Basic operations -- 2.4 Chain commands -- 2.5 Recode values -- 3. Data Visualization -- 3.1 Why visualize my data? -- 3.2 First steps -- 3.3 Applied example: Local elections and data visualization -- 3.4 To continue learning -- 4. Data Loading -- 4.1 Introduction -- 4.2 Different dataset formats -- 4.3 Files separated by delimiters (.csv and .tsv) -- 4.4 Large tabular datasets -- Part II: Models -- 5. Linear Models -- 5.1 OLS in R -- 5.2 Bivariate model: simple linear regression -- 5.3 Multivariate model: multiple regression -- 5.4 Model adjustment -- 5.5 Inference in multiple linear models -- 5.6 Testing OLS assumptions -- 6. Case Selection Based on Regressions -- 6.1 Which case study should I select for qualitative research? -- 6.2 The importance of combining methods -- 7. Panel Data -- 7.1 Introduction -- 7.2 Describing your panel dataset -- 7.3 Modelling group-level variation -- 7.4 Fixed vs. random effects -- 7.5 Testing for unit roots -- 7.6 Robust and panel-corrected standard errors -- 8. Logistic Models -- 8.1 Introduction -- 8.2 Use of logistic models -- 8.3 How are probabilities estimated? -- 8.4 Model estimation -- 8.5 Creating tables -- 8.6 Visual representation of results -- 8.7 Measures to evaluate the fit of the models -- 9. Survival Models -- 9.1 Introduction -- 9.2 How do we interpret hazard rates? -- 9.3 Cox's model of proportional hazards.

Turn your R code into packages that others can easily download and use. This practical book shows you how to bundle reusable R functions, sample data, and documentation together by applying author Hadley Wickham's package development philosophy. In the process, you'll work with devtools, roxygen, and testthat, a set of R packages that automate common development tasks. Devtools encapsulates best practices that Hadley has learned from years of working with this programming language.Ideal for developers, data scientists, and programmers with various backgrounds, this book starts you with the basics and shows you how to improve your package writing over time. You'll learn to focus on what you want your package to do, rather than think about package structure. Ideal for developers, data scientists, and programmers with various backgrounds, this book starts with the basics and shows you how to improve your package writing over time. You'll learn to focus on what you want your package to do, rather than think about package structure

Turn your R code into packages that others can easily download and use. This practical book shows you how to bundle reusable R functions, sample data, and documentation together by applying author Hadley Wickham's package development philosophy. In the process, you'll work with devtools, roxygen, and testthat, a set of R packages that automate common development tasks. Devtools encapsulates best practices that Hadley has learned from years of working with this programming language.Ideal for developers, data scientists, and programmers with various backgrounds, this book starts you with the basics and shows you how to improve your package writing over time. You'll learn to focus on what you want your package to do, rather than think about package structure. Ideal for developers, data scientists, and programmers with various backgrounds, this book starts with the basics and shows you how to improve your package writing over time. You'll learn to focus on what you want your package to do, rather than think about package structure

Part I. Finance -- 1. Financial econometrics and big data: a survey of volatility estimators and tests for the presence of jumps and co-jumps / Arpita Mukherjee, Weijia Peng, Norman R. Swanson, Xiye Yang -- 2. Real time monitoring of asset markets: bubbles and crises / Peter C.B. Phillips, Shuping Shi -- 3. Component-wise AdaBoost algorithms for high-dimensional binary classification and class probability prediction / Jianghao Chu, Tae-Hwy Lee, Aman Ullah -- Part II. Macro Econometrics -- 4. Mixed data sampling (MIDAS) regression models / Eric Ghysels, Virmantas Kvedaras, Vaidotas Zemlys-Balevičius -- 5. Encouraging private corporate investment in India / Hrishikesh Vinod, Honey Karun, Lekha S. Chakraborty -- 6. High-mixed frequency forecasting methods in R -- With applications to Philippine GDP and inflation / Roberto S. Mariano, Suleyman Ozmucur -- 7. Nonlinear time series in R: threshold cointegration with tsDyn / Matthieu Stigler -- Part III. Micro Econometrics -- 8. Econometric analysis of productivity: theory and implementation in R / Robin C. Sickles, Wonho Song, Valentin Zelenyuk -- 9. Stochastic frontier models using R / Giancarlo Ferrara.

This example-based general introduction to the statistical computing environment does not assume any previous familiarity with R or other software packages. R functions are compellingly presented in the context of interesting applications with real data.

Mit diesem Buch gelingt Ihnen der einfache Einstieg in die statistische Analyse mit der Programmiersprache R. Alle Grundlagen werden in 14 Kapiteln anschaulich und leicht nachvollziehbar anhand von praktischen Beispielen erläutert. Der Autor führt Sie Schritt für Schritt in die Datenanalyse mit R ein: von den Grundlagen zu Syntax und Datentypen über die Verwendung der grafischen Benutzungsoberfläche RStudio bis hin zur Erstellung von Diagrammen sowie analytischen Verfahren zum Prüfen von Veränderungen, Unterschieden und Zusammenhängen. Eine praktische Übersicht hilft Ihnen, die passenden Verfahren für jede Aufgabenstellung schnell nachzuschlagen und einfach anzuwenden. Grundlegende Statistik-Kenntnisse werden vorausgesetzt.