Using R for introductory econometrics:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Düsseldorf
Florian Heiss
[2020]
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| Ausgabe: | 2nd edition |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | 368 Seiten Diagramme |
| ISBN: | 9798648424364 |
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| adam_text | Contents Preface ................................................. 1. Introduction 1 З 1.1. Getting Started ........................... 3 1.1.1. Software.......................... 3 1,1.2. R Scripts.......................... 4 1.1.3. Packages.......................... 7 1.1.4. File names and the Work ing Directory................... 9 1.1.5. Errors and Warnings .... 10 1.1.6. Other Resources ............. 10 1.2. Objects inR................................. 11 1.2.1. Basic Calculations and Ob jects ................................... 11 1.2.2. Vectors............................. 12 1.2.3. Special Types of Vectors . . 15 1.2.4. Naming and Indexing Vectors 16 1.2.5. Matrices.......................... 17 1.2.6. Lists ................................ 20 1.3. Data Frames and Data Files .... 21 1.3.1. Data Frames..................... 21 1.3.2. Subsets of Data............... 22 1.3.3. R Data Files..................... 23 1.3.4. Basic Information on a Data Set........................... 23 1.3.5. Import and Export of Text Files ..................... 24 1.3.6. Import and Export of Other Data Formats................... 25 1.3.7. Data Sets in the Examples . 26 1.4. Base Graphics.............................. 27 1.4.1. Basic Graphs .................. 27 1.4.2. Customizing Graphs with Options ........................... 29 1.4.3. Overlaying Several Plots . . 30 1.4.4. Legends.......................... 31 1.4.5. Exporting to a File.......... 33 1.5. Data Manipulation and Visualiza tion: The Tidyverse.............. 34 1.5.1. Data visualization: ggplot
Basics................................. 34 1.5.2. Colors and Shapes in ggplot Graphs..... 36 1.5.3. Fine Tuning of ggplot Graphs................... 39 1.5.4. Basic Data Manipulation with dplyr.......... 41 1.5.5. Pipes................................ 43 1.5.6. More Advanced Data Ma nipulation ............. 44 1.6. Descriptive Statistics................... 48 1.6.1. Discrete Distributions: Fre quencies and Contingency Tables .............................. 48 1.6.2. Continuous Distributions: Histogram and Density . . 51 1.6.3. Empirical Cumulative Dis tribution Function (ECDF).52 1.6.4. Fundamental Statistics ... 54 1.7. Probability Distributions......... 55 1.7.1. Discrete Distributions ... 55 1.7.2. Continuous Distributions . 57 1.7.3. Cumulative Distribution Function (CDF) 58 1.7.4. Random Draws from Prob ability Distributions....59 1.8. Confidence Intervals andStatisti cal Inference 61 1.8.1. Confidence Intervals .... 61 1.8.2. ŕ Tests ............................. 64 1.8.3. p Values.................. 65 1.8.4. Automatic calculations ... 66 1.9. More Advanced R............... 70 1.9.1. Conditional Execution ... 70 1.9.2. Loops........................ 70 1.9.3. Functions ........................ 71 1.9.4. Outlook................... 71 1.10. Monte Carlo Simulation............... 72 1.10.1. Finite Sample Properties of Estimators..... 72 1.10.2. Asymptotic Properties of Estimators..... 75 1.10.3. Simulation of Confidence Intervals and t Tests .... 78
I. Regression Analysis Cross-Sectional Data with 81 6. Multiple Regression Analysis: Further Issues 137 6.1. 2. The Simple Regression Model 2.1. Simple OLS Regression......... 2.2. 2.3. 2.4. 2.5. 2.6. 2.7. 83 83 Coefficients, Fitted Values, and Residuals........................ 88 Goodness of Fit..................... 91 Nonlinearities........................ 93 Regression through the Origin and Regression on a Constant 94 Expected Values, Variances, and Standard Errors...................... 96 Monte Carlo Simulations............... 98 2.7.1. One sample....................... 98 2.7.2. Many Samples ................. 100 2.7.3. Violation of SLR.4 ........... 103 2.7.4. Violation of SLR.5 ........... 103 Model Formulae............................. 137 6.1.1. Data Scaling: Arithmetic Operations Within a Formula 137 6.1.2. Standardization: Beta Coef ficients .................... 139 6.1.3. Logarithms ........................ 140 6.1.4. Quadratics and Polynomials 140 6.1.5. ANOVA Tables.................. 142 6.1.6. Interaction Terms............... 144 6.2. Prediction...................................... 146 6.2.1. Confidence Intervals for Predictions.............. 146 6.2.2. PredictionIntervals.............. 148 6.2.3. Effect Plots for Nonlinear Specifications........ 148 3. Multiple Regression Analysis: Estima 7. Multiple Regression Analysis with tion 105 Qualitative Regressors 151 3.1. Multiple Regression in Practice . . 105 7.1. Linear Regression with Dummy 3.2. OLS in Matrix Form .................... 109 3.3. Ceteris Paribus Interpretation and Omitted Variable Bias
......... Ill 3.4. Standard Errors, Multicollinearity, and VIF................................... 113 4. Multiple Regression Analysis: Inference 117 4.1. The ŕ Test.......................................... 117 4.1.1. General Setup..................... 4.1.2. Standard case..................... 4.1.3. Other hypotheses............... 4.2. Confidence Intervals..................... 4.3. Linear Restrictions: F֊Tests .... 4.4. Reporting Regression Results . . . 117 118 120 122 123 127 5. Multiple Regression Analysis: OLS Asymptotics 129 5.1. Simulation Exercises..................... 129 . 5.1.1. Normally Distributed Error Terms..................................... 129 5.1.2. Non-Normal Error Terms . 130 5.1.3. (Not) Conditioning on the Regressors.............. 133 5.2. LM Test.............................................. 135 լ Variables as Regressors.................. 7.2. Logical Variables........................... 7.3. Factor variables.............................. 7.4. Breaking a Numeric Variable Into Categories.............................. 156 7.5. Interactions and Differences in Re gression Functions Across Groups 8. Heteroscedasticity 151 152 154 158 161 8.1. Heteroscedasticity-Robust Inference 161 8.2. Heteroscedasticity Tests............... 165 8.3. Weighted Least Squares............... 168 9. More on Specification and Data Issues 173 9.1. Functional Form Misspecification . 9.2. Measurement Error........................ 9.3. Missing Data and Nonrandom Samples .......................................... 9.4. Outlying Observations.................. 9.5. Least Absolute
Deviations (LAD) Estimation.............................. 182 173 175 178 181
II. Regression Analysis with Time Series Data 183 10. Basic Regression Analysis with Time Series Data 10.1. Static Time Series Models............ 10.2. Time Series Data Types in R ... . 10.2.1. Equispaced Time Series in R 10.2.2. Irregular Time Series in R . 10.3. Other Time Series Models............ 10.3.1. 10.3.2. 10.3.3. 10.3.4. 14.3. Dummy Variable Regression and Correlated Random Effects .... 230 14.4. Robust (Clustered) Standard Errors 234 185 185 186 186 187 191 The dynlm Package .... 191 Finite Distributed Lag Models........................ 191 Trends........................ 194 Seasonality ........................ 195 11. Further Issues In Using OLS with Time Series Data 197 11.1. Asymptotics with Time Series . . . 197 11.2. The Nature of Highly Persistent Time Series 200 11.3. Differences of Highly Persistent Time Series.............................. 203 11.4. Regression with First Differences . 204 12. Serial Correlation and Heteroscedasticity in Time SeriesRegressions 205 12.1. Testing for Serial Correlation of the Error Term 205 12.2. FGLS Estimation ........................... 209 12.3. Serial Correlation-Robust Infer ence with OLS........................ 210 12.4. Autoregressive Conditional Heteroscedasticity........................ 211 III. Advanced Topics 213 13. Pooling Cross-Sections Across Time: Simple Panel Data Methods 215 Pooled Cross-Sections .................. Difference-in-Differences ............... Organizing Panel Data.................. Panel-specific computations .... First Differenced Estimator .... 215 216 219 220 222 14. Advanced Panel Data
Methods 14.1. Fixed Effects Estimation............... 225 13.1. 13.2. 13.3. 13.4. 13.5. 14.2. Random Effects Models............... 225 227 15. Instrumental Variables Estimation and Two Stage Least Squares 237 15.1. Instrumental Variables in Simple Regression Models 237 15.2. More Exogenous Regressors .... 239 15.3. Two Stage Least Squares............... 240 15.4. Testing for Exogeneity of the Re gressors 242 15.5. Testing Overidentifying Restrictions 243 15.6. Instrumental Variables with Panel Data 243 16. Simultaneous Equations Models 16.1. Setup and Notation........................ 16.2. Estimation by 2SLS........................ 16.3. Joint Estimation of System............ 16.4. Outlook: Estimation by 3SLS . . . 247 247 248 249 251 17. Limited Dependent Variable Models and Sample Selection Corrections 253 17.1. Binary Responses........................... 17.1.1. Linear Probability Models . 17.1.2. Logit and Probit Models: Estimation.............. 255 17.1.3. Inference.............................. 17.1.4. Predictions........................... 17.1.5. Partial Effects..................... 17.2. Count Data: The Poisson Regres sion Model.............................. 263 17.3. Corner Solution Responses: The Tobit Model 266 17.4. Censored and Truncated Regres sion Models........................... 269 17.5. Sample Selection Corrections . . . 18. Advanced Time Series Topics 18.1. Infinite Distributed Lag Models . . 18.2. Testing for Unit Roots .................. 18.3. Spurious Regression..................... 253 253 258 259 260 271 273 273 275 278 18.4. Cointegration and
Error Correc tion Models........................... 280 18.5. Forecasting....................................... 281 19. Carrying Out an Empirical Project 19.1. Working with R Scripts.................. 285 285
19.2. Logging Output in Text Files . . . 19.3. Formatted Documents and Re ports with R Markdown..... 19.3.1. Basics................................. 19.3.2. Advanced Features........... 19.3.3. Bottom Line...................... 19.4. Combining R with LaTeX ........... 19.4.1. Automatic Document Gen eration using Sweave and 287 287 287 288 291 293 knitr ........ 293 19.4.2. Separating R and DTgX code 298 IV. Appendices 303 R Scripts 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. Scripts Used in Chapter 01 Scripts Used in Chapter 02 Scripts Used in Chapter 03 Scripts Used in Chapter 04 Scripts Used in Chapter 05 Scripts Used in Chapter 06 Scripts Used in Chapter 07 Scripts Used in Chapter 08 Scripts Used in Chapter 09 Scripts Used in Chapter 10 Scripts Used in Chapter 11 Scripts Used in Chapter 12 Scripts Used in Chapter 13 Scripts Used in Chapter 14 Scripts Used in Chapter 15 Scripts Used in Chapter 16 Scripts Used in Chapter 17 Scripts Used in Chapter 18 Scripts Used in Chapter 19 305 .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... 305 317 324 326 327 329 332 333 335 338 340 342 344 346 348 350 350 355 357 Bibliography 359 List of Wooldridge (2019) Examples 361 Index 363
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| adam_txt |
Contents Preface . 1. Introduction 1 З 1.1. Getting Started . 3 1.1.1. Software. 3 1,1.2. R Scripts. 4 1.1.3. Packages. 7 1.1.4. File names and the Work ing Directory. 9 1.1.5. Errors and Warnings . 10 1.1.6. Other Resources . 10 1.2. Objects inR. 11 1.2.1. Basic Calculations and Ob jects . 11 1.2.2. Vectors. 12 1.2.3. Special Types of Vectors . . 15 1.2.4. Naming and Indexing Vectors 16 1.2.5. Matrices. 17 1.2.6. Lists . 20 1.3. Data Frames and Data Files . 21 1.3.1. Data Frames. 21 1.3.2. Subsets of Data. 22 1.3.3. R Data Files. 23 1.3.4. Basic Information on a Data Set. 23 1.3.5. Import and Export of Text Files . 24 1.3.6. Import and Export of Other Data Formats. 25 1.3.7. Data Sets in the Examples . 26 1.4. Base Graphics. 27 1.4.1. Basic Graphs . 27 1.4.2. Customizing Graphs with Options . 29 1.4.3. Overlaying Several Plots . . 30 1.4.4. Legends. 31 1.4.5. Exporting to a File. 33 1.5. Data Manipulation and Visualiza tion: The Tidyverse. 34 1.5.1. Data visualization: ggplot
Basics. 34 1.5.2. Colors and Shapes in ggplot Graphs. 36 1.5.3. Fine Tuning of ggplot Graphs. 39 1.5.4. Basic Data Manipulation with dplyr. 41 1.5.5. Pipes. 43 1.5.6. More Advanced Data Ma nipulation . 44 1.6. Descriptive Statistics. 48 1.6.1. Discrete Distributions: Fre quencies and Contingency Tables . 48 1.6.2. Continuous Distributions: Histogram and Density . . 51 1.6.3. Empirical Cumulative Dis tribution Function (ECDF).52 1.6.4. Fundamental Statistics . 54 1.7. Probability Distributions. 55 1.7.1. Discrete Distributions . 55 1.7.2. Continuous Distributions . 57 1.7.3. Cumulative Distribution Function (CDF) 58 1.7.4. Random Draws from Prob ability Distributions.59 1.8. Confidence Intervals andStatisti cal Inference 61 1.8.1. Confidence Intervals . 61 1.8.2. ŕ Tests . 64 1.8.3. p Values. 65 1.8.4. Automatic calculations . 66 1.9. More Advanced R. 70 1.9.1. Conditional Execution . 70 1.9.2. Loops. 70 1.9.3. Functions . 71 1.9.4. Outlook. 71 1.10. Monte Carlo Simulation. 72 1.10.1. Finite Sample Properties of Estimators. 72 1.10.2. Asymptotic Properties of Estimators. 75 1.10.3. Simulation of Confidence Intervals and t Tests . 78
I. Regression Analysis Cross-Sectional Data with 81 6. Multiple Regression Analysis: Further Issues 137 6.1. 2. The Simple Regression Model 2.1. Simple OLS Regression. 2.2. 2.3. 2.4. 2.5. 2.6. 2.7. 83 83 Coefficients, Fitted Values, and Residuals. 88 Goodness of Fit. 91 Nonlinearities. 93 Regression through the Origin and Regression on a Constant 94 Expected Values, Variances, and Standard Errors. 96 Monte Carlo Simulations. 98 2.7.1. One sample. 98 2.7.2. Many Samples . 100 2.7.3. Violation of SLR.4 . 103 2.7.4. Violation of SLR.5 . 103 Model Formulae. 137 6.1.1. Data Scaling: Arithmetic Operations Within a Formula 137 6.1.2. Standardization: Beta Coef ficients . 139 6.1.3. Logarithms . 140 6.1.4. Quadratics and Polynomials 140 6.1.5. ANOVA Tables. 142 6.1.6. Interaction Terms. 144 6.2. Prediction. 146 6.2.1. Confidence Intervals for Predictions. 146 6.2.2. PredictionIntervals. 148 6.2.3. Effect Plots for Nonlinear Specifications. 148 3. Multiple Regression Analysis: Estima 7. Multiple Regression Analysis with tion 105 Qualitative Regressors 151 3.1. Multiple Regression in Practice . . 105 7.1. Linear Regression with Dummy 3.2. OLS in Matrix Form . 109 3.3. Ceteris Paribus Interpretation and Omitted Variable Bias
. Ill 3.4. Standard Errors, Multicollinearity, and VIF. 113 4. Multiple Regression Analysis: Inference 117 4.1. The ŕ Test. 117 4.1.1. General Setup. 4.1.2. Standard case. 4.1.3. Other hypotheses. 4.2. Confidence Intervals. 4.3. Linear Restrictions: F֊Tests . 4.4. Reporting Regression Results . . . 117 118 120 122 123 127 5. Multiple Regression Analysis: OLS Asymptotics 129 5.1. Simulation Exercises. 129 . 5.1.1. Normally Distributed Error Terms. 129 5.1.2. Non-Normal Error Terms . 130 5.1.3. (Not) Conditioning on the Regressors. 133 5.2. LM Test. 135 լ Variables as Regressors. 7.2. Logical Variables. 7.3. Factor variables. 7.4. Breaking a Numeric Variable Into Categories. 156 7.5. Interactions and Differences in Re gression Functions Across Groups 8. Heteroscedasticity 151 152 154 158 161 8.1. Heteroscedasticity-Robust Inference 161 8.2. Heteroscedasticity Tests. 165 8.3. Weighted Least Squares. 168 9. More on Specification and Data Issues 173 9.1. Functional Form Misspecification . 9.2. Measurement Error. 9.3. Missing Data and Nonrandom Samples . 9.4. Outlying Observations. 9.5. Least Absolute
Deviations (LAD) Estimation. 182 173 175 178 181
II. Regression Analysis with Time Series Data 183 10. Basic Regression Analysis with Time Series Data 10.1. Static Time Series Models. 10.2. Time Series Data Types in R . . 10.2.1. Equispaced Time Series in R 10.2.2. Irregular Time Series in R . 10.3. Other Time Series Models. 10.3.1. 10.3.2. 10.3.3. 10.3.4. 14.3. Dummy Variable Regression and Correlated Random Effects . 230 14.4. Robust (Clustered) Standard Errors 234 185 185 186 186 187 191 The dynlm Package . 191 Finite Distributed Lag Models. 191 Trends. 194 Seasonality . 195 11. Further Issues In Using OLS with Time Series Data 197 11.1. Asymptotics with Time Series . . . 197 11.2. The Nature of Highly Persistent Time Series 200 11.3. Differences of Highly Persistent Time Series. 203 11.4. Regression with First Differences . 204 12. Serial Correlation and Heteroscedasticity in Time SeriesRegressions 205 12.1. Testing for Serial Correlation of the Error Term 205 12.2. FGLS Estimation . 209 12.3. Serial Correlation-Robust Infer ence with OLS. 210 12.4. Autoregressive Conditional Heteroscedasticity. 211 III. Advanced Topics 213 13. Pooling Cross-Sections Across Time: Simple Panel Data Methods 215 Pooled Cross-Sections . Difference-in-Differences . Organizing Panel Data. Panel-specific computations . First Differenced Estimator . 215 216 219 220 222 14. Advanced Panel Data
Methods 14.1. Fixed Effects Estimation. 225 13.1. 13.2. 13.3. 13.4. 13.5. 14.2. Random Effects Models. 225 227 15. Instrumental Variables Estimation and Two Stage Least Squares 237 15.1. Instrumental Variables in Simple Regression Models 237 15.2. More Exogenous Regressors . 239 15.3. Two Stage Least Squares. 240 15.4. Testing for Exogeneity of the Re gressors 242 15.5. Testing Overidentifying Restrictions 243 15.6. Instrumental Variables with Panel Data 243 16. Simultaneous Equations Models 16.1. Setup and Notation. 16.2. Estimation by 2SLS. 16.3. Joint Estimation of System. 16.4. Outlook: Estimation by 3SLS . . . 247 247 248 249 251 17. Limited Dependent Variable Models and Sample Selection Corrections 253 17.1. Binary Responses. 17.1.1. Linear Probability Models . 17.1.2. Logit and Probit Models: Estimation. 255 17.1.3. Inference. 17.1.4. Predictions. 17.1.5. Partial Effects. 17.2. Count Data: The Poisson Regres sion Model. 263 17.3. Corner Solution Responses: The Tobit Model 266 17.4. Censored and Truncated Regres sion Models. 269 17.5. Sample Selection Corrections . . . 18. Advanced Time Series Topics 18.1. Infinite Distributed Lag Models . . 18.2. Testing for Unit Roots . 18.3. Spurious Regression. 253 253 258 259 260 271 273 273 275 278 18.4. Cointegration and
Error Correc tion Models. 280 18.5. Forecasting. 281 19. Carrying Out an Empirical Project 19.1. Working with R Scripts. 285 285
19.2. Logging Output in Text Files . . . 19.3. Formatted Documents and Re ports with R Markdown. 19.3.1. Basics. 19.3.2. Advanced Features. 19.3.3. Bottom Line. 19.4. Combining R with LaTeX . 19.4.1. Automatic Document Gen eration using Sweave and 287 287 287 288 291 293 knitr . 293 19.4.2. Separating R and DTgX code 298 IV. Appendices 303 R Scripts 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. Scripts Used in Chapter 01 Scripts Used in Chapter 02 Scripts Used in Chapter 03 Scripts Used in Chapter 04 Scripts Used in Chapter 05 Scripts Used in Chapter 06 Scripts Used in Chapter 07 Scripts Used in Chapter 08 Scripts Used in Chapter 09 Scripts Used in Chapter 10 Scripts Used in Chapter 11 Scripts Used in Chapter 12 Scripts Used in Chapter 13 Scripts Used in Chapter 14 Scripts Used in Chapter 15 Scripts Used in Chapter 16 Scripts Used in Chapter 17 Scripts Used in Chapter 18 Scripts Used in Chapter 19 305 . . . . . . . . . . . . . . . . . . . 305 317 324 326 327 329 332 333 335 338 340 342 344 346 348 350 350 355 357 Bibliography 359 List of Wooldridge (2019) Examples 361 Index 363 |
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| author | Heiss, Florian 1973- |
| author_GND | (DE-588)130234540 |
| author_facet | Heiss, Florian 1973- |
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| author_sort | Heiss, Florian 1973- |
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| building | Verbundindex |
| bvnumber | BV046982061 |
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| classification_tum | DAT 350 DAT 307 WIR 017 |
| ctrlnum | (OCoLC)1224017307 (DE-599)KXP1733454314 |
| discipline | Informatik Agrar-/Forst-/Ernährungs-/Haushaltswissenschaft / Gartenbau Wirtschaftswissenschaften |
| discipline_str_mv | Informatik Agrar-/Forst-/Ernährungs-/Haushaltswissenschaft / Gartenbau Wirtschaftswissenschaften |
| edition | 2nd edition |
| format | Book |
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| genre | (DE-588)4123623-3 Lehrbuch gnd-content |
| genre_facet | Lehrbuch |
| id | DE-604.BV046982061 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T15:49:51Z |
| indexdate | 2024-07-10T08:59:16Z |
| institution | BVB |
| isbn | 9798648424364 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032390117 |
| oclc_num | 1224017307 |
| open_access_boolean | |
| owner | DE-19 DE-BY-UBM DE-703 DE-N2 DE-20 DE-188 DE-739 DE-91 DE-BY-TUM |
| owner_facet | DE-19 DE-BY-UBM DE-703 DE-N2 DE-20 DE-188 DE-739 DE-91 DE-BY-TUM |
| physical | 368 Seiten Diagramme |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | Florian Heiss |
| record_format | marc |
| spelling | Heiss, Florian 1973- Verfasser (DE-588)130234540 aut Using R for introductory econometrics Florian Heiss 2nd edition Düsseldorf Florian Heiss [2020] 368 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier R Programm (DE-588)4705956-4 gnd rswk-swf Ökonometrie (DE-588)4132280-0 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf (DE-588)4123623-3 Lehrbuch gnd-content Statistik (DE-588)4056995-0 s Ökonometrie (DE-588)4132280-0 s R Programm (DE-588)4705956-4 s DE-604 Erscheint auch als Online-Ausgabe Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032390117&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 Ökonometrie (DE-588)4132280-0 gnd Statistik (DE-588)4056995-0 gnd |
| subject_GND | (DE-588)4705956-4 (DE-588)4132280-0 (DE-588)4056995-0 (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_exact_search_txtP | 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 Ökonometrie (DE-588)4132280-0 gnd Statistik (DE-588)4056995-0 gnd |
| topic_facet | R Programm Ökonometrie Statistik Lehrbuch |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032390117&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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