Using R for introductory econometrics:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Düsseldorf
[Selbstverlag]
[2016]
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | 344 Seiten Diagramme, Karten |
| ISBN: | 9781523285136 1523285133 |
Internformat
MARC
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Datensatz im Suchindex
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| adam_text | Titel: Using R for introductory econometrics
Autor: Heiss, Florian
Jahr: 2016
Contents Preface................... 1. Introduction 1.1. Getting Started ........... 1.1.1. Software........... 1.1.2. R Scripts........... 1.1.3. Packages........... 1.1.4. File names and the Working Directory........ 1.1.5. Errors and Warnings .... 1.1.6. Other Resources ...... 1.2. Objects in R ............. 1.2.1. Basic Calculations and Objects .............. 1.2.2. Vectors............ 1.2.3. Special Types of Vectors . . 1.2.4. Naming and Indexing Vectors 1.2.5. Matrices........... 1.2.6. Lists ............. 1.3. Data Frames and Data Files .... 1.3.1. Data Frames......... 1.3.2. Subsets of Data....... 1.3.3. R Data Files......... 1.3.4. Basic Information on a Data Set........... 1.3.5. Import and Export of Text Files ............. 1.3.6. Import and Export of Other Data Formats........ 1.3.7. Data Sets in the Examples . 1.4. Graphics............... 1.4.1. Basic Graphs ........ 1.4.2. Customizing Graphs with Options ........... 1.4.3. Overlaying Several Plots . . 1.4.4. Legends........... 1.4.5. Exporting to a File..... 1.4.6. Advanced Graphs ..... 1.5. Descriptive Statistics........ 1.5.1. Discrete Distributions: Frequencies and Contingency Tables ............ 1.5.2. Continuous Distributions: Histogram and Density . . 37 1.5.3. Empirical Cumulative Distribution Function (ECDF) . 38 1.5.4. Fundamental Statistics ... 40 1.6. Probability Distributions...... 41 1.6.1. Discrete Distributions ... 41 1.6.2. Continuous Distributions . 43 1.6.3. Cumulative Distribution Function (CDF)....... 44 1.6.4. Random Draws from Probability Distributions .... 45 1.7. Confidence Intervals and Statistical Inference............. 47 1.7.1. Confidence Intervals .... 47 1.7.2. /Tests ............ 50 1.7.3. p Values........... 51 1.7.4. Automatic calculations . . . 52 1.8. Advanced R ............. 56 1.8.1. Conditional Execution ... 56 1.8.2. Loops............. 56 1.8.3. Functions .......... 57 1.8.4. Outlook........... 57 1.9. Monte Carlo
Simulation...... 58 1.9.1. Finite Sample Properties of Estimators.......... 58 1.9.2. Asymptotic Properties of Estimators.......... 61 1.9.3. Simulation of Confidence Intervals and f Tests .... 64 1. Regression Analysis with Cross- Sectional Data 67 2. The Simple Regression Model 69 2.1. Simple OLS Regression....... 69 2.2. Coefficients, Fitted Values, and Residuals............... 74 2.3. Goodness of Fit........... 77 2.4. Nonlinearities............ 79 2.5. Regression through the Origin and Regression on a Constant..... 80 2.6. Expected Values, Variances, and Standard Errors........... 82 2.7. Monte Carlo Simulations...... 84 2.7.1. One sample......... 84 2.7.2. Many Samples ....... 86 1 3 3 3 4 7 8 9 9 10 10 12 14 15 16 19 20 20 21 22 22 23 24 25 26 26 28 29 30 32 33 34 34
2.7.3. Violation of SLR.4 ..... 39 2.7.4. Violation of SLR.5 ..... 39 3. Multiple Regression Analysis: Estimation 91 3.1. Multiple Regression in Practice . . 91 3.2. OI.S in Matrix Form ........ 93 3.3. Ceteris Paribus Interpretation and Omitted Variable Bias ....... 97 3.4. Standard Prrors, Multicollinearitv, and VIF............... 99 4. Multiple Regression Analysis: Inferencel03 4.1. The/Test............... 103 4.1.1. Cieneral Setup........ 103 4.1.2. Standard case........ 104 4.1.3. Cither hypotheses...... 106 4.2. Confidence Intervals........ 103 4.3. 1 .inear Restrictions: / -Tests .... 109 4.4. Reporting Regression Results ... 113 5. Multiple Regression Analysis: OLS Asymptotics 115 3.1. Simulation Cxercises........ 113 3.1.1. Normally Distributed Error lerms............. 113 3.1.2. Non-Normal F.rror Terms . 116 3.1.3. (Not) Conditioning on the Regressors.......... 119 3.2. 1 .M Test................ 121 6. Multiple Regression Analysis: Further Issues 123 6.1. Model Formulae........... 123 6.1.1. Data Scaling: Arithmetic Operations Within a Formula 123 ( 4.2. Standardization: Beta Coefficients ............ 123 n. 1.3. Logarithms ......... 126 o. l. l. Onadratics and Polynomials 126 o.l.: Interaction Terms...... 123 ( .2. Prediction .............. 130 ( .2.1. Confidence Internals lor Predictions.......... 130 6.2.2. Prediction Intervals..... 132 ( .2 3. I licet Plots for Nonlinear Specifications........ 133 7. Multiple Regression Analysis with Qual- itative Regressors 135 7.1. Linear Regression with Dummy Variables as Regressors....... 135 7.2. Logical Variables.......... 137 7.3. Factor variables........... 138 7.4. Breaking a Numeric Variable Into Categories.............. 139 7.5. Interactions and Differences in Regression Functions Across Groups 141 8. Heteroscedasticity 143 3.1. Heteroscedasticity-Robust Inference 143 8.2. Heteroscedasticity Tests...... 147 8.3. Weighted Least Squares...... 150 9. More on
Specification and Data Issues 155 9.1. Functional Form Misspecification . 155 9.2. Measurement Error......... 157 9.3. Missing Data and Nonrandom Samples ............... 160 9.4. Outlying Observations....... 163 9.5. Least Absolute Deviations (LAD) Estimation.............. 164 II. Regression Analysis with Time Series Data 165 10. Basic Regression Analysis with Time Series Data 167 10.1. Static Time Series Models..... 167 10.2. Time Series Data Types in R .... 168 10.2.1. Equispaced Time Series in R 168 10.2.2. Irregular Time Series in R . 170 10.3. Other Time Series Models..... 173 10.3.1. The dynim Package .... 173 10.3.2. Finite Distributed Lag Models............ 173 10.3.3. Trends............ 176 10.3.4. Seasonality ......... 177 11. Further Issues In Using OLS with Time Series Data 179 11.1. Asymptotics with Time Series . . . 179 11.2. The Nature of Highly Persistent Time Series.............. 182 1 1.3. Differences of Highly Persistent T ime Series.............. 185
11.4. Regression with First Differences . 186 12. Serial Correlation and Heteroscedas- ticity in Time Series Regressions 187 12.1. Testing for Serial Correlation of the Error Term.............. 187 12.2. FGLS Estimation .......... 191 12.3. Serial Correlation-Robust Inference with OLS............ 192 12.4. Autoregressive Conditional Het- eroscedasticity............ 193 III. Advanced Topics 195 13. Pooling Cross-Sections Across Time: Simple Panel Data Methods 197 13.1. Pooled Cross-Sections ....... 197 13.2. Difference-in-Differences...... 198 13.3. Organizing Panel Data....... 201 13.4. Panel-specific computations .... 202 13.5. First Differenced Estimator .... 204 14. Advanced Panel Data Methods 207 14.1. Fixed Effects Estimation...... 207 14.2. Random Effects Models...... 209 14.3. Dummy Variable Regression and Correlated Random Effects .... 212 14.4. Robust (Clustered) Standard Errors 216 15. Instrumental Variables Estimation and Two Stage Least Squares 219 15.1. Instrumental Variables in Simple Regression Models......... 219 15.2. More Exogenous Regressors .... 221 15.3. Two Stage Least Squares...... 222 15.4. Testing for Exogeneity of the Regressors ................ 224 15.5. Testing Overidentifying Restrictions 225 15.6. Instrumental Variables with Panel Data.................. 225 16. Simultaneous Equations Models 229 16.1. Setup and Notation......... 229 16.2. Estimation bv 2SLS......... 230 16.3. joint Estimation of System..... 231 16.4. Outlook: Estimation by 3SLS . . . 233 17. Limited Dependent Variable Models and Sample Selection Corrections 235 17.1. Binary Responses.......... 235 17.1.1. Linear Probability Models . 235 17.1.2. Logit and Probit Models: Estimation.......... 237 17.1.3. Inference........... 240 17.1.4. Predictions.......... 241 17.1.5. Partial Effects........ 242 17.2. Count Data: The Poisson Regression Model.............. 245 17.3. Corner Solution Responses: The Tobit Model.............
248 17.4. Censored and Truncated Regression Models............. 251 17.5. Sample Selection Corrections . . . 253 18. Advanced Time Series Topics 255 18.1. Infinite Distributed Lag Models . . 255 18.2. Testing for Unit Roots ....... 257 18.3. Spurious Regression........ 260 18.4. Cointegration and Error Correction Models ............. 262 18.5. Forecasting.............. 263 19. Carrying Out an Empirical Project 267 19.1. Working with R Scripts....... 267 19.2. Logging Output in Text Files . . . 269 19.3. Formatted Documents and Reports with R Markdown...... 269 19.3.1. Basics............. 269 19.3.2. Advanced Features..... 270 19.3.3. Bottom Line......... 272 19.4. Combining R with LaTeX ..... 274 19.4.1. Automatic Document Generation using Sweave and knitr ............ 274 19.4.2. Separating R and DTpX code 278 IV. Appendices 281 R Scripts 283 1. Scripts Used in Chapter 01 .... 283 2. Scripts Used in Chapter 02 .... 293 3. Scripts Used in Chapter 03 .... 299 4. Scripts Used in Chapter 04 .... 301 5. Scripts Used in Chapter 05 .... 303 6. Scripts Used in Chapter 06 .... 305
7. Scripts Used in Chapter 07 .... 307 8. Scripts Used in Chapter 08 .... 309 9. Scripts Used in Chapter 09 .... 311 10. Scripts Used in Chapter 10 .... 314 11. Scripts Used in Chapter 11 .... 316 12. Scripts Used in Chapter 12 .... 318 13. Scripts Used in Chapter 13 .... 320 14. Scripts Used in Chapter 14 .... 322 15. Scripts Used in Chapter 15 .... 324 16. Scripts Used in Chapter 16 .... 326 17. Scripts Used in Chapter 17 .... 326 18. Scripts Used in Chapter 18 .... 330 19. Scripts Used in Chapter 19 .... 333 Bibliography 335 List of Wooldridge (2016) examples 337 Index 339
List of Tables 1.1. R functions for important arithmetic calculations.......... 11 1.2. R functions specifically for vectors 12 1.3. Logical Operators.......... 14 1.4. R functions for descriptive statistics 40 1.5. R functions for statistical distributions .................. 42 4.1. One- and two-tailed t Tests for Hq : Pj = ctj ................ 106 13.1. Panel-specific computations .... 202
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| spelling | Heiss, Florian 1973- Verfasser (DE-588)130234540 aut Using R for introductory econometrics Florian Heiss Düsseldorf [Selbstverlag] [2016] © 2016 344 Seiten Diagramme, Karten txt rdacontent n rdamedia nc rdacarrier R Programm (DE-588)4705956-4 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Ökonometrie (DE-588)4132280-0 gnd rswk-swf (DE-588)4123623-3 Lehrbuch gnd-content Ökonometrie (DE-588)4132280-0 s R Programm (DE-588)4705956-4 s Statistik (DE-588)4056995-0 s DE-604 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028793563&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Heiss, Florian 1973- Using R for introductory econometrics R Programm (DE-588)4705956-4 gnd Statistik (DE-588)4056995-0 gnd Ökonometrie (DE-588)4132280-0 gnd |
| subject_GND | (DE-588)4705956-4 (DE-588)4056995-0 (DE-588)4132280-0 (DE-588)4123623-3 |
| title | Using R for introductory econometrics |
| title_auth | Using R for introductory econometrics |
| title_exact_search | Using R for introductory econometrics |
| title_full | Using R for introductory econometrics Florian Heiss |
| title_fullStr | Using R for introductory econometrics Florian Heiss |
| title_full_unstemmed | Using R for introductory econometrics Florian Heiss |
| title_short | Using R for introductory econometrics |
| title_sort | using r for introductory econometrics |
| topic | R Programm (DE-588)4705956-4 gnd Statistik (DE-588)4056995-0 gnd Ökonometrie (DE-588)4132280-0 gnd |
| topic_facet | R Programm Statistik Ökonometrie Lehrbuch |
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