Regression models for categorical dependent variables using Stata:
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| Main Authors: | , |
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| Format: | Book |
| Language: | English |
| Published: |
College Station, Texas
Stata Press
2014
|
| Edition: | Third edition |
| Series: | A Stata Press publication
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xxiii, 589 Seiten Diagramme |
| ISBN: | 9781597181112 1597181110 |
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MARC
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| 100 | 1 | |a Long, J. Scott |e Verfasser |0 (DE-588)171773071 |4 aut | |
| 245 | 1 | 0 | |a Regression models for categorical dependent variables using Stata |c J. Scott Long, Jeremy Freese |
| 250 | |a Third edition | ||
| 264 | 1 | |a College Station, Texas |b Stata Press |c 2014 | |
| 300 | |a xxiii, 589 Seiten |b Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a A Stata Press publication | |
| 650 | 7 | |a Analisi della regressione - statistica |2 sbt | |
| 650 | 4 | |a Lehrbuch / Textbook - 28 | |
| 650 | 4 | |a Regression / Schätztheorie / PC-Software / Programmiersprache / Theorie | |
| 650 | 4 | |a Regressionsanalyse - Stata | |
| 650 | 7 | |a Stata - analisi della regressione |2 sbt | |
| 650 | 4 | |a Statistik | |
| 650 | 4 | |a Mathematical statistics | |
| 650 | 4 | |a Regression analysis | |
| 650 | 4 | |a Stata (Computer programs) | |
| 650 | 4 | |a Statistics | |
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| 700 | 1 | |a Freese, Jeremy |e Verfasser |0 (DE-588)1017211698 |4 aut | |
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Titel: Regression models for categorical dependent variables using Stata
Autor: Long, J. Scott
Jahr: 2014
Contents
List of figures xix
Preface xxi
I General information 1
1 Introduction 7
1.1 What is this book about?.7
1.2 Which models are considered?.8
1.3 Whom is this book for? .9
1.4 How is the book organized?.9
1.5 The SPost software.11
1.5.1 Updating Stata.12
1.5.2 Installing SPost 13.13
Uninstalling SPost9.14
Installing SPostl3 using search.14
Installing SPost 13 using net install.16
1.5.3 Uninstalling SPostl3.17
1.6 Sample do-files and datasets.17
1.6.1 Installing the spostl3_do package.17
1.6.2 Using spex to load data and run examples.17
1.7 Getting help with SPost.18
1.7.1 What if an SPost command does not work?.18
1.7.2 Getting help from the authors.19
What we need to help you.20
1.8 Where can I learn more about the models? .21
2 Introduction to Stata 23
viii Contents
2.1 The Stata interface. 23
2.2 Abbreviations.27
2.3 Getting help.27
2.3.1 Online help .27
2.3.2 PDF manuals. 28
2.3.3 Error messages .28
2.3.4 Asking for help.28
2.3.5 Other resources.29
2.4 The working directory.29
2.5 Stata file types.30
2.6 Saving output to log files.30
2.7 Using and saving datasets.32
2.7.1 Data in Stata format.32
2.7.2 Data in other formats .33
2.7.3 Entering data by hand.33
2.8 Size limitations on datasets.34
2.9 Do-files .34
2.9.1 Adding comments.35
2.9.2 Long lines.36
2.9.3 Stopping a do-file while it is running.37
2.9.4 Creating do-files.37
2.9.5 Recommended structure for do-files.38
2.10 Using Stata for serious data analysis.40
2.11 Syntax of Stata commands.41
2.11.1 Commands.43
2.11.2 Variable lists .43
2.11.3 if and in qualifiers.45
2.11.4 Options .46
2.12 Managing data.46
2.12.1 Looking at your data.46
Contents ix
2.12.2 Getting information about variables.47
2.12.3 Missing values.50
2.12.4 Selecting observations .51
2.12.5 Selecting variables .51
2.13 Creating new variables.52
2.13.1 The generate command.52
2.13.2 The replace command .54
2.13.3 The recode command.55
2.14 Labeling variables and values.56
2.14.1 Variable labels.56
2.14.2 Value labels.57
2.14.3 The notes command .59
2.15 Global and local macros.59
2.16 Loops using foreach and forvalues.61
2.17 Graphics.63
2.17.1 The graph command.65
2.18 A brief tutorial.73
2.19 A do-file template.79
2.20 Conclusion.81
3 Estimation, testing, and fit 83
3.1 Estimation.84
3.1.1 Stata's output for ML estimation.84
3.1.2 ML and sample size.85
3.1.3 Problems in obtaining ML estimates.85
3.1.4 Syntax of estimation commands.86
3.1.5 Variable lists .87
Using factor-variable notation in the variable list .87
Specifying interaction and polynomials.89
More on factor-variable notation .90
3.1.6 Specifying the estimation sample.93
X
Contents
Missing data.93
Information about missing values.95
Postestimation commands and the estimation sample . 98
3.1.7 Weights and survey data.99
Complex survey designs .100
3.1.8 Options for regression models.102
3.1.9 Robust standard errors.103
3.1.10 Reading the estimation output .105
3.1.11 Storing estimation results.107
(Advanced) Saving estimates to a file.108
3.1.12 Reformatting output with estimates table .Ill
3.2 Testing.114
3.2.1 One-tailed and two-tailed tests .115
3.2.2 Wald and likelihood-ratio tests .115
3.2.3 Wald tests with test and testparm .116
3.2.4 LR tests with lrtest.118
Avoiding invalid LR tests .120
3.3 Measures of fit .120
3.3.1 Syntax of fitstat.120
3.3.2 Methods and formulas used by fitstat.123
3.3.3 Example of fitstat.129
3.4 estat postestimation commands.130
3.5 Conclusion.131
4 Methods of interpretation 133
4.1 Comparing linear and nonlinear models .133
4.2 Approaches to interpretation .136
4.2.1 Method of interpretation based on predictions.137
4.2.2 Method of interpretation using parameters.138
4.2.3 Stata and SPost commands for interpretation.138
4-3 Predictions for each observation.138
Contents xi
4.4 Predictions at specified values.139
4.4.1 Why use the m* commands instead of margins?.140
4.4.2 Using margins for predictions.141
Predictions using interaction and polynomial terms.146
Making multiple predictions.146
Predictions for groups defined by levels of categorical variables 150
4.4.3 (Advanced) Nondefault predictions using margins.153
The predictQ option.153
The expression() option .154
4.4.4 Tables of predictions using mtable.155
mtable with categorical and count outcomes.158
(Advanced) Combining and formatting tables using mtable . 160
4.5 Marginal effects: Changes in predictions.162
4.5.1 Marginal effects using margins.163
4.5.2 Marginal effects using mtable.164
4.5.3 Posting predictions and using mlincom.165
4.5.4 Marginal effects using mchange.166
4.6 Plotting predictions.171
4.6.1 Plotting predictions with marginsplot.171
4.6.2 Plotting predictions using mgen.173
4.7 Interpretation of parameters.178
4.7.1 The listcoef command .179
4.7.2 Standardized coefficients.180
4.7.3 Factor and percentage change coefficients.184
4.8 Next steps.184
II Models for specific kinds of outcomes 185
5 Models for binary outcomes: Estimation, testing, and fit 187
5.1 The statistical model.187
5.1.1 A latent-variable model.188
xii Contents
5.1.2 A nonlinear probability model.192
5.2 Estimation using logit and probit commands .192
5.2.1 Example of logit model.194
5.2.2 Comparing logit and probit.196
5.2.3 (Advanced) Observations predicted perfectly.197
5.3 Hypothesis testing.200
5.3.1 Testing individual coefficients.200
5.3.2 Testing multiple coefficients.203
5.3.3 Comparing LR and Wald tests.205
5.4 Predicted probabilities, residuals, and influential observations . 206
5.4.1 Predicted probabilities using predict .206
5.4.2 Residuals and influential observations using predict.209
5.4.3 Least likely observations.216
5.5 Measures of fit .218
5.5.1 Information criteria.219
5.5.2 Pseudo-R2's.221
5.5.3 (Advanced) Hosmer-Lemeshow statistic.223
5.6 Other commands for binary outcomes .225
5.7 Conclusion.225
6 Models for binary outcomes: Interpretation 227
6.1 Interpretation using regression coefficients.228
6.1.1 Interpretation using odds ratios.228
6.1.2 (Advanced) Interpretation using y*.235
6.2 Marginal effects: Changes in probabilities.239
6.2.1 Linked variables.241
6.2.2 Summary measures of change.242
MEMs and MERs.243
AMEs.243
Standard errors of marginal effects.244
6.2.3 Should you use the AME, the MEM, or the MER?.244
Contents xiii
6.2.4 Examples of marginal effects.246
AMEs for continuous variables.248
AMEs for factor variables .251
Summary table of AMEs.252
Marginal effects for subgroups.254
MEMs and MERs.255
Marginal effects with powers and interactions.259
6.2.5 The distribution of marginal effects.261
6.2.6 (Advanced) Algorithm for computing the distribution of
effects .265
6.3 Ideal types.270
6.3.1 Using local means with ideal types.273
6.3.2 Comparing ideal types with statistical tests.274
6.3.3 (Advanced) Using macros to test differences between ideal
types.275
6.3.4 Marginal effects for ideal types .278
6.4 Tables of predicted probabilities.280
6.5 Second differences comparing marginal effects.285
6.6 Graphing predicted probabilities .286
6.6.1 Using marginsplot.287
6.6.2 Using mgen with the graph command.290
6.6.3 Graphing multiple predictions.293
6.6.4 Overlapping confidence intervals.297
6.6.5 Adding power terms and plotting predictions .301
6.6.6 (Advanced) Graphs with local means.303
6.7 Conclusion.308
7 Models for ordinal outcomes 309
7.1 The statistical model.310
7.1.1 A latent-variable model.310
7.1.2 A nonlinear probability model.314
7.2 Estimation using ologit and oprobit.314
xiv Contents
7.2.1 Example of ordinal logit model.315
7.2.2 Predicting perfectly.319
7.3 Hypothesis testing.320
7.3.1 Testing individual coefficients.321
7.3.2 Testing multiple coefficients.322
7.4 Measures of fit using fitstat.324
7.5 (Advanced) Converting to a different parameterization.325
7.6 The parallel regression assumption.326
7.6.1 Testing the parallel regression assumption using oparallel . . 329
7.6.2 Testing the parallel regression assumption using brant . . . 330
7.6.3 Caveat regarding the parallel regression assumption.331
7.7 Overview of interpretation.331
7.8 Interpreting transformed coefficients.332
7.8.1 Marginal change in y* .332
7.8.2 Odds ratios.335
7.9 Interpretations based on predicted probabilities.338
7.10 Predicted probabilities with predict.339
7.11 Marginal effects.341
7.11.1 Plotting marginal effects.344
7.11.2 Marginal effects for a quick overview.350
7.12 Predicted probabilities for ideal types.351
7.12.1 (Advanced) Testing differences between ideal types
.354
7.13 Tables of predicted probabilities.355
7.14 Plotting predicted probabilities.359
7.15 Probability plots and marginal effects .364
7.16 Less common models for ordinal outcomes.370
7.16.1 The stereotype logistic model.370
7.16.2 The generalized ordered logit model.371
7.16.3 (Advanced) Predictions without using factor-variable notation 374
\
Contents xv
7.16.4 The sequential logit model.378
7.17 Conclusion.382
8 Models for nominal outcomes 385
8.1 The multinomial logit model.386
8.1.1 Formal statement of the model.390
8.2 Estimation using the mlogit command.390
Weights and complex samples.391
Options .391
8.2.1 Example of MNLM.392
8.2.2 Selecting different base outcomes.395
8.2.3 Predicting perfectly.397
8.3 Hypothesis testing.398
8.3.1 mlogtest for tests of the MNLM.398
8.3.2 Testing the effects of the independent variables .399
8.3.3 Tests for combining alternatives.403
8.4 Independence of irrelevant alternatives.407
8.4.1 Hausman-McFadden test of IIA.408
8.4.2 Small-Hsiao test of IIA .409
8.5 Measures of fit .411
8.6 Overview of interpretation.411
8.7 Predicted probabilities with predict.412
8.8 Marginal effects.415
8.8.1 (Advanced) The distribution of marginal effects.420
8.9 Tables of predicted probabilities.423
8.9.1 (Advanced) Testing second differences.425
8.9.2 (Advanced) Predictions using local means and subsamples . 428
8.10 Graphing predicted probabilities .432
8.11 Odds ratios.435
8.11.1 Listing odds ratios with listcoef.435
8.11.2 Plotting odds ratios.436
xvi Contents
8.12 (Advanced) Additional models for nominal outcomes.444
8.12.1 Stereotype logistic regression.445
8.12.2 Conditional logit model .454
8.12.3 Multinomial probit model with IIA.465
8.12.4 Alternative-specific multinomial probit.469
8.12.5 Rank-ordered logit model .475
8.13 Conclusion.479
9 Models for count outcomes 481
9.1 The Poisson distribution.481
9.1.1 Fitting the Poisson distribution with the poisson command 483
9.1.2 Comparing observed and predicted counts with mgen . 484
9.2 The Poisson regression model.487
9.2.1 Estimation using poisson.488
Example of the PRM.489
9.2.2 Factor and percentage changes in E(y | x).490
Example of factor and percentage change.492
9.2.3 Marginal effects on E(y | x).493
Examples of marginal effects.495
9.2.4 Interpretation using predicted probabilities.496
Predicted probabilities using mtable and mchange.496
Treating a count independent variable as a factor variable . 498
Predicted probabilities using mgen.500
9.2.5 Comparing observed and predicted counts to evaluate model
specification.501
9.2.6 (Advanced) Exposure time.504
9.3 The negative binomial regression model.507
9.3.1 Estimation using nbreg.509
NB1 and NB2 variance functions.509
9.3.2 Example of NBRM.510
9.3.3 Testing for overdispersion.511
\
Contents xvii
9.3.4 Comparing the PRM and NBRM using estimates table . . . 511
9.3.5 Robust standard errors.512
9.3.6 Interpretation using E(y | x).514
9.3.7 Interpretation using predicted probabilities.516
9.4 Models for truncated counts.518
9.4.1 Estimation using tpoisson and tnbreg.521
Example of zero-truncated model.521
9.4.2 Interpretation using E(y | x).523
9.4.3 Predictions in the estimation sample.524
9.4.4 Interpretation using predicted rates and probabilities . 525
9.5 (Advanced) The hurdle regression model.527
9.5.1 Fitting the hurdle model.528
9.5.2 Predictions in the sample .531
9.5.3 Predictions at user-specified values.533
9.5.4 Warning regarding sample specification.534
9.6 Zero-inflated count models.535
9.6.1 Estimation using zinb and zip.538
9.6.2 Example of zero-inflated models.539
9.6.3 Interpretation of coefficients.540
9.6.4 Interpretation of predicted probabilities .541
Predicted probabilities with mtable.542
Plotting predicted probabilities with mgen.543
9.7 Comparisons among count models .544
9.7.1 Comparing mean probabilities. 545
9.7.2 Tests to compare count models. 547
9.7.3 Using countfit to compare count models. 551
9.8 Conclusion. 558
References 561
Author index 569
Subject index ' 573 |
| any_adam_object | 1 |
| author | Long, J. Scott Freese, Jeremy |
| author_GND | (DE-588)171773071 (DE-588)1017211698 |
| author_facet | Long, J. Scott Freese, Jeremy |
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| ctrlnum | (OCoLC)889992241 (DE-599)BVBBV042072735 |
| dewey-full | 519.536 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
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| dewey-search | 519.536 |
| dewey-sort | 3519.536 |
| dewey-tens | 510 - Mathematics |
| discipline | Informatik Politologie Soziologie Mathematik Wirtschaftswissenschaften |
| edition | Third edition |
| format | Book |
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| id | DE-604.BV042072735 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-20T07:45:24Z |
| institution | BVB |
| isbn | 9781597181112 1597181110 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027513965 |
| oclc_num | 889992241 |
| open_access_boolean | |
| owner | DE-11 DE-N2 DE-473 DE-BY-UBG DE-19 DE-BY-UBM DE-20 DE-Er8 DE-188 DE-706 DE-Aug4 DE-384 DE-739 DE-634 DE-1047 DE-824 |
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| physical | xxiii, 589 Seiten Diagramme |
| publishDate | 2014 |
| publishDateSearch | 2014 |
| publishDateSort | 2014 |
| publisher | Stata Press |
| record_format | marc |
| series2 | A Stata Press publication |
| spelling | Long, J. Scott Verfasser (DE-588)171773071 aut Regression models for categorical dependent variables using Stata J. Scott Long, Jeremy Freese Third edition College Station, Texas Stata Press 2014 xxiii, 589 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier A Stata Press publication Analisi della regressione - statistica sbt Lehrbuch / Textbook - 28 Regression / Schätztheorie / PC-Software / Programmiersprache / Theorie Regressionsanalyse - Stata Stata - analisi della regressione sbt Statistik Mathematical statistics Regression analysis Stata (Computer programs) Statistics Regressionsanalyse (DE-588)4129903-6 gnd rswk-swf Stata (DE-588)4617285-3 gnd rswk-swf Regressionsanalyse (DE-588)4129903-6 s Stata (DE-588)4617285-3 s DE-604 Freese, Jeremy Verfasser (DE-588)1017211698 aut HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027513965&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Long, J. Scott Freese, Jeremy Regression models for categorical dependent variables using Stata Analisi della regressione - statistica sbt Lehrbuch / Textbook - 28 Regression / Schätztheorie / PC-Software / Programmiersprache / Theorie Regressionsanalyse - Stata Stata - analisi della regressione sbt Statistik Mathematical statistics Regression analysis Stata (Computer programs) Statistics Regressionsanalyse (DE-588)4129903-6 gnd Stata (DE-588)4617285-3 gnd |
| subject_GND | (DE-588)4129903-6 (DE-588)4617285-3 |
| title | Regression models for categorical dependent variables using Stata |
| title_auth | Regression models for categorical dependent variables using Stata |
| title_exact_search | Regression models for categorical dependent variables using Stata |
| title_full | Regression models for categorical dependent variables using Stata J. Scott Long, Jeremy Freese |
| title_fullStr | Regression models for categorical dependent variables using Stata J. Scott Long, Jeremy Freese |
| title_full_unstemmed | Regression models for categorical dependent variables using Stata J. Scott Long, Jeremy Freese |
| title_short | Regression models for categorical dependent variables using Stata |
| title_sort | regression models for categorical dependent variables using stata |
| topic | Analisi della regressione - statistica sbt Lehrbuch / Textbook - 28 Regression / Schätztheorie / PC-Software / Programmiersprache / Theorie Regressionsanalyse - Stata Stata - analisi della regressione sbt Statistik Mathematical statistics Regression analysis Stata (Computer programs) Statistics Regressionsanalyse (DE-588)4129903-6 gnd Stata (DE-588)4617285-3 gnd |
| topic_facet | Analisi della regressione - statistica Lehrbuch / Textbook - 28 Regression / Schätztheorie / PC-Software / Programmiersprache / Theorie Regressionsanalyse - Stata Stata - analisi della regressione Statistik Mathematical statistics Regression analysis Stata (Computer programs) Statistics Regressionsanalyse Stata |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027513965&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT longjscott regressionmodelsforcategoricaldependentvariablesusingstata AT freesejeremy regressionmodelsforcategoricaldependentvariablesusingstata |