Reproducible econometrics using R:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
New York
Oxford University Press
[2019]
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Includes bibliographical references and index |
| Physical Description: | xxiii, 293 Seiten |
| ISBN: | 9780190900663 |
Staff View
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Record in the Search Index
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| adam_text | Contents List of Tables xiii List of Figures xiv Preface xix About the Companion Website I Linear Time Series Methods R and Time Series Analysis Overview Some Useful R Functions for Time Series Analysis 1 Introduction to Linear Time Series Models 1.1 Overview 1.2 Time Series Data 1.3 Patterns in Time Series 1.4 Stationary versus Non-Stationary Series 1.5 Examples of Univariate Random Processes 1.5.1 White Noise Processes 1.5.2 Random Walk Processes 1.6 Characterizing Time Series 1.6.1 The Autocorrelation Function 1.6.2 The Sample AutocorrelationFunction 1.6.3 Non-Stationarity and Differencing 1.7 Tests for White Noise Processes 1.7.1 Individual Test for H0 : p¡. = 0—Bartlett s Test 1.7.2 Joint Test for HQ : px = 0 Ո թշ = 0 Ո ■ ■ · Ո рк = 0— Ljung k Box s Test 1.7.3 A Simulated Illustration—Testing for a White Noise Process 1.7.4 A Simulated Illustration—White Noise Tests when the Series is a Random Walk Process xxiii 1 3 3 4 7 7 8 9 9 12 12 13 14 14 16 16 18 18 19 19 21
CONTENTS vii 2 Random Walks, Unit Roots, and Spurious Relationships 2.1 Overview 2.2 Properties of a Random Walk 2.3 The Autocorrelation Function for a Random Walk 2.4 Classical Least Squares Estimators and Random Walks 2.5 Classical Least Squares Inference and Random Walks 2.5.1 Cross-Section (I.I.D. Data) Monte Carlo 2.5.2 Time Series (Random Walk) Monte Carlo 2.5.3 Time Series (Random Walk with Drift) Monte Carlo 2.6 Unit Root Tests 2.6.1 Testing for a Unit Root in Spot Exchange Rates 2.7 Random Walks and Spurious Regression 23 23 24 25 25 26 26 27 29 30 32 33 3 Univariate Linear Time Series Models 3.1 Overview 3.2 Moving Average Models (MA(g)) 3.2.1 Structure of MA(g) Processes 3.2.2 Example—Residential Electricity Sales 3.2.3 Properties of MA(g) Processes 3.2.4 Stationarity of MA(g) Processes 3.2.5 The Autocorrelation Function and Identification of MA(ç) Processes 3.2.6 Forecasting MA(q) Processes 3.2.7 Forecasting MA(g) Processes Assuming the єт֊і and the 9į are Known 3.2.8 Forecasting MA(g) Processes when the and the θ{ are Estimated 3.2.9 Forecasting MA(g) Processes in the Presence of a Trend 3.3 Autoregressive Models (AR(^)) 3.3.1 Structure of AR(j ) Processes 3.3.2 Example—Residential Electricity Sales 3.3.3 Properties of AR(p) Processes 3.3.4 Stationarity of AR(p) Processes 3.3.5 Invertiblity of Stationary AR(p)Processes 3.3.6 Identification of AR(p) Processes—The Partial Au tocorrelation Function 3.3.7 Forecasting AR(p) Processes 3.3.8 Forecasting AR(p) Processes when the Parameters фі are Unknown 3.3.9 Forecasting AR(p) Processes in the Presence of a Trend 3.4
Non-Seasonal Autoregressive Moving Average Models (ARMAQxç)) 37 37 38 38 38 39 40 40 41 41 42 43 44 44 45 46 48 50 50 51 53 54 55
C CONTENTS 5.4 5.5 Unmasking Multivariate Outliers Unmasking Regression Outliers 5.5.1 Outliers in the Y Direction 5.5.2 Outliers in the X Direction 5.5.3 Leverage Points 5.5.4 Dealing with Outlying Observations and Leverage Points 5.6 Robust Regression 5.6.1 Robust Residuals and High Breakdown Diagnostics 5.7 Some Useful Points to Remember 142 148 148 150 150 152 160 164 165 Problem Set 167 IV 171 R Model Uncertainty and Model Uncertainty Overview Some Useful R Functions for Model Uncertainty 6 6.1 6.2 6.3 6.4 6.5 Model Uncertainty Overview 6.1.1 Model Selection References 6.1.2 Model Averaging References 6.1.3 Resources A Reflection on Models and Data Generating Processes 6.2.1 Model Selection and Averaging—A Simulation 6.2.2 Discussion Kullback-Leibler Distance and Maximum Likelihood Esti mation Model Selection Methods 6.4.1 AIC. BIC. Cp and Cross-Validated Model Selection Criteria Model Averaging Methods 6.5.1 Solving for the Optimal Model Average Weights 6.5.2 Selecting Candidate Models 6.5.3 Pitfalls of Model Selection and Model Averaging 6.5.4 An Experimental Robust RegressionM-Estimator Model Averaging Procedure Problem Set 173 173 173 175 175 176 176 177 177 181 183 184 186 186 189 190 191 196 196 201
CONTENTS V Advanced Topics R and Advanced Topics Overview Some Useful R Functions for Advanced Topics 7 Advanced Topics 7.1 Overview 7.2 Classification Analysisand Support Vector Machines 7.2.1 The Confusion Matrix 7.2.2 Support Vector Machines 7.3 Nonparametric Kernel Regression xi 207 209 209 209 211 211 211 212 213 220 Problem Set 225 VI 227 Appendix A R, RStudio, TeX, and Git A.l Installation of R and RStudio Desktop A.2 What is R? A.2.1 R in the News A.2.2 Introduction to R A.2.3 Econometrics in R A.3 What is RStudio Desktop? A.3.1 Introduction to RStudio A.4 Installation of TeX A.5 Installation of Git 229 229 229 230 230 230 231 231 231 231 B R Markdown for Assignments B.l Source Code (R Markdown) for this Document B.2 R, RStudio, TeX and git B.3 What is R Markdown? B.4 Creating a New R Markdown Documentin RStudio B.5 Including R Results in your R Markdown Document B.6 Reading Data from a URL B.7 Including Plots B.8 Including Bulleted andNumbered lists B.9 Including Tables B.10 Including Verbatim, i.e., Freeform, Text B.ll Typesetting Mathematics B.12 Flexible Document Creation B.13 Knitting your R Markdown Document B.14 Printing Your Assignment for Submitting in Class B.15 Troubleshooting and Tips 233 233 233 233 234 234 234 235 236 237 237 237 238 238 238 239
CONTENTS xii C Maximum Likelihood Estimation and Inference C.l Maximum Likelihood Estimation C.2 Properties of the Maximum Likelihood Estimators C.3 Maximum Likelihood Estimation in Practice C.4 A Simple Example Using Discrete Data C.4.1 Example֊ C.4.2 Example— C.5 Maximum Likelihood Estimation of the Normal Linear Multivariate Regression Model C.6 Information and the Normal Linear Multivariate Model C.6.1 Example— C.7 Restricted Maximum Likelihood Estimates C.8 Hypothesis Testing in a Maximum Likelihood Framework C.8.1 Example— C.8.2 Example— 243 243 244 246 246 247 248 249 252 253 254 254 255 256 D Solving a Quadratic Program Using R D.l Example 259 260 E A Primer on Regression Splines E.l Overview E.2 Bézier curves E.2.1 Example—A quadratic Bézier curve E.2.2 The Bézier curve defined E.2.3 Example—A quadratic Bézier curve as a linear in terpolation between twolinear Bézier curves E.2.4 Example—The quadratic Bézier curve basis func tions E.3 Derivatives of spline functions E.4 B-splines E.4.1 B-spline knots E.4.2 The B-spline basis function E.4.3 Example—A fourth-order B-spline basis function with three interior knots and its first derivative func tion E.5 The B-spline function E.6 Multivariate B-spline regression E.6.1 Multivariate knots, intervals, andspline bases E.7 Spline regression 263 263 264 264 265 265 266 267 267 267 268 269 269 269 271 272 Bibliography 273 Author Index 281 Subject Index 283
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| spelling | Racine, Jeffrey 1962- Verfasser (DE-588)133144720 aut Reproducible econometrics using R Jeffrey S. Racine New York Oxford University Press [2019] xxiii, 293 Seiten txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index Econometrics Open source software Theorie (DE-588)4059787-8 gnd rswk-swf Nichtparametrische Schätzung (DE-588)4203980-0 gnd rswk-swf Nichtparametrisches Verfahren (DE-588)4339273-8 gnd rswk-swf Ökonometrie (DE-588)4132280-0 gnd rswk-swf Ökonometrie (DE-588)4132280-0 s Nichtparametrische Schätzung (DE-588)4203980-0 s Nichtparametrisches Verfahren (DE-588)4339273-8 s Theorie (DE-588)4059787-8 s 1\p DE-604 Erscheint auch als Online-Ausgabe, EPUB 978-0-19-090068-7 Erscheint auch als Online-Ausgabe, PDF 978-0-19-090067-0 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031291435&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Racine, Jeffrey 1962- Reproducible econometrics using R Econometrics Open source software Theorie (DE-588)4059787-8 gnd Nichtparametrische Schätzung (DE-588)4203980-0 gnd Nichtparametrisches Verfahren (DE-588)4339273-8 gnd Ökonometrie (DE-588)4132280-0 gnd |
| subject_GND | (DE-588)4059787-8 (DE-588)4203980-0 (DE-588)4339273-8 (DE-588)4132280-0 |
| title | Reproducible econometrics using R |
| title_auth | Reproducible econometrics using R |
| title_exact_search | Reproducible econometrics using R |
| title_full | Reproducible econometrics using R Jeffrey S. Racine |
| title_fullStr | Reproducible econometrics using R Jeffrey S. Racine |
| title_full_unstemmed | Reproducible econometrics using R Jeffrey S. Racine |
| title_short | Reproducible econometrics using R |
| title_sort | reproducible econometrics using r |
| topic | Econometrics Open source software Theorie (DE-588)4059787-8 gnd Nichtparametrische Schätzung (DE-588)4203980-0 gnd Nichtparametrisches Verfahren (DE-588)4339273-8 gnd Ökonometrie (DE-588)4132280-0 gnd |
| topic_facet | Econometrics Open source software Theorie Nichtparametrische Schätzung Nichtparametrisches Verfahren Ökonometrie |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031291435&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT racinejeffrey reproducibleeconometricsusingr |