Maximum likelihood estimation with Stata:
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
College Station, Tex.
Stata Press
2006
|
| Ausgabe: | 3. ed. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XX, 290 S. graph. Darst. |
| ISBN: | 1597180122 |
Internformat
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| 250 | |a 3. ed. | ||
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Datensatz im Suchindex
| _version_ | 1804134591167987712 |
|---|---|
| adam_text | Titel: Maximum likelihood estimation with Stata
Autor: Gould, William
Jahr: 2006
Contents
Preface xv
Versions of Stata xvii
Notation and Typography xix
1 Theory and practice 1
1.1 The likelihood-maximization problem................... 2
1.2 Likelihood theory.............................. 4
1.2.1 All results are asymptotic..................... 8
1.2.2 Variance estimates and hypothesis tests............. 9
1.2.3 Likelihood-ratio tests and Wald tests.............. 9
1.2.4 The outer product of gradients variance estimator....... 10
1.2.5 Robust variance estimates .................... 12
1.3 The maximization problem......................... 13
1.3.1 Numerical root finding...................... 14
Newton s method......................... 14
The Newton-Raphson algorithm................. 16
1.3.2 Quasi-Newton methods...................... 18
The BHHH algorithm....................... 19
The DFP and BFGS algorithms................. 19
1.3.3 Numerical maximization..................... 20
1.3.4 Numerical derivatives....................... 21
1.3.5 Numerical second derivatives................... 25
1.4 Monitoring convergence........................... 26
vi Contents
2 Overview of ml 29
2.1 The jargon of ml .............................. 29
2.2 Equations in ml............................... 30
2.3 Likelihood-evaluator methods....................... 38
2.4 Tools for the ml programmer........................ 39
2.5 Common ml options............................. 39
2.5.1 Subsamples............................. 39
2.5.2 Weights............................... 40
2.5.3 OPG estimates of variance.................... 41
2.5.4 Robust estimates of variance................... 42
2.5.5 Survey data............................ 43
2.5.6 Constraints............................. 44
2.5.7 Choosing among the optimization algorithms.......... 45
2.6 Maximizing your own likelihood functions ................ 48
3 Method If 51
3.1 The linear-form restrictions ........................ 52
3.2 Examples................................... 53
3.2.1 The probit model......................... 53
3.2.2 The normal model: linear regression............... 54
3.2.3 The Weibull model........................ 57
3.3 The importance of generating temporary variables as doubles..... 58
3.4 Problems you can safely ignore....................... 60
3.5 Nonlinear specifications........................... 61
3.6 The advantages of If in terms of execution speed............. 62
3.7 The advantages of If in terms of accuracy................. 64
4 Methods dO, dl, and d2 67
4.1 Comparing these methods......................... 67
4.2 Outline of method dO, dl, and d2 evaluators............... 68
4.2.1 The todo argument........................ 69
Contents vii
4.2.2 The b argument.......................... 69
Using mleval to obtain values from each equation....... 70
4.2.3 The lnf argument......................... 72
Using lnf to indicate that the likelihood cannot be calculated . 74
Using mlsum to define lnf..................... 74
4.2.4 The g argument.......................... 76
Using mlvecsum to define g.................... 77
Scores for robust and OPG variance estimates (optional) ... 79
4.2.5 The negH argument........................ 81
Using mlmatsum to define negH................. 81
4.2.6 Aside: Stata s scalars....................... 83
4.3 Summary of methods dO, dl, and d2 ................... 86
4.3.1 Method dO............................. 86
4.3.2 Method dl............................. 89
4.3.3 Method d2............................. 92
4.4 Linear-form examples............................ 95
4.4.1 The probit model......................... 95
4.4.2 The normal model: linear regression............... 98
4.4.3 The Weibull model........................ 104
4.5 Panel-data likelihoods............................ 108
4.5.1 Calculating lnf........................... 110
4.5.2 Calculating g............................ 113
4.5.3 Calculating negH......................... 117
Using mlmatbysum to help define negH............. 117
4.6 Likelihoods other than linear form..................... 124
5 Debugging likelihood evaluators 131
5.1 ml check................................... 131
5.2 Using methods dldebug and d2debug................... 133
5.2.1 Method dldebug.......................... 135
viii Contents
5.2.2 Method d2debug.......................... 141
5.3 ml trace................................... 144
6 Setting initial values 147
6.1 ml search................................... 148
6.2 ml plot.................................... 150
6.3 mlinit.................................... 153
7 Interactive maximization 155
7.1 The iteration log .............................. 155
7.2 - Pressing the Break key........................... 156
7.3 Maximizing difficult likelihood functions................. 158
8 Final results 161
8.1 Graphing convergence............................ 161
8.2 Redisplaying output............................. 162
9 Writing do-files to maximize likelihoods 165
9.1 The structure of a do-file.......................... 165
9.2 Putting the do-file into production .................... 166
10 Writing ado-files to maximize likelihoods 169
10.1 Writing estimation commands....................... 169
10.2 The standard estimation-command outline................ 171
10.3 Outline for estimation commands using ml................ 172
10.4 Using ml in noninteractive mode...................... 173
10.5 Advice . ................................... 174
10.5.1 Syntax............................... 175
10.5.2 Estimation subsample....................... 177
10.5.3 Parsing with help from mlopts.................. 180
10.5.4 Weights............................... 183
10.5.5 Constant-only model ....................... 184
Contents ix
10.5.6 Initial values............................ 188
10.5.7 Saving results in e()........................ 191
10.5.8 Displaying ancillary parameters................. 191
10.5.9 Exponentiated coefficients .................... 193
10.5.10 Offsetting linear equations.................... 195
10.5.11 Program properties........................ 197
11 Writing ado-flies for survey data analysis 201
11.1 Program properties............................. 201
11.2 Writing your own predict command.................... 204
12 Other examples 207
12.1 The logit model............................... 207
12.2 The probit model.............................. 209
12.3 The normal model: linear regression.................... 211
12.4 The Weibull model............................. 214
12.5 The Cox proportional hazards model................... 217
12.6 The random-effects regression model ................... 220
12.7 The seemingly unrelated regression model................ 223
A Syntax of ml 235
 Likelihood evaluator checklists 253
B.I Method If.................................. 253
B.2 Method dO.................................. 254
B.3 Method dl.................................. 255
B.4 Method d2.................................. 257
C Listing of estimation commands 261
C.I The logit model............................... 261
C.2 The probit model.............................. 264
C.3 The normal model.............................. 265
Contents
C.4 The Weibull model............................. 268
C.5 The Cox proportional hazards model................... 270
C.6 The random-effects regression model ................... 272
C.7 The seemingly unrelated regression model ................ 275
References 283
Author index 285
Subject index 287
|
| adam_txt |
Titel: Maximum likelihood estimation with Stata
Autor: Gould, William
Jahr: 2006
Contents
Preface xv
Versions of Stata xvii
Notation and Typography xix
1 Theory and practice 1
1.1 The likelihood-maximization problem. 2
1.2 Likelihood theory. 4
1.2.1 All results are asymptotic. 8
1.2.2 Variance estimates and hypothesis tests. 9
1.2.3 Likelihood-ratio tests and Wald tests. 9
1.2.4 The outer product of gradients variance estimator. 10
1.2.5 Robust variance estimates . 12
1.3 The maximization problem. 13
1.3.1 Numerical root finding. 14
Newton's method. 14
The Newton-Raphson algorithm. 16
1.3.2 Quasi-Newton methods. 18
The BHHH algorithm. 19
The DFP and BFGS algorithms. 19
1.3.3 Numerical maximization. 20
1.3.4 Numerical derivatives. 21
1.3.5 Numerical second derivatives. 25
1.4 Monitoring convergence. 26
vi Contents
2 Overview of ml 29
2.1 The jargon of ml . 29
2.2 Equations in ml. 30
2.3 Likelihood-evaluator methods. 38
2.4 Tools for the ml programmer. 39
2.5 Common ml options. 39
2.5.1 Subsamples. 39
2.5.2 Weights. 40
2.5.3 OPG estimates of variance. 41
2.5.4 Robust estimates of variance. 42
2.5.5 Survey data. 43
2.5.6 Constraints. 44
2.5.7 Choosing among the optimization algorithms. 45
2.6 Maximizing your own likelihood functions . 48
3 Method If 51
3.1 The linear-form restrictions . 52
3.2 Examples. 53
3.2.1 The probit model. 53
3.2.2 The normal model: linear regression. 54
3.2.3 The Weibull model. 57
3.3 The importance of generating temporary variables as doubles. 58
3.4 Problems you can safely ignore. 60
3.5 Nonlinear specifications. 61
3.6 The advantages of If in terms of execution speed. 62
3.7 The advantages of If in terms of accuracy. 64
4 Methods dO, dl, and d2 67
4.1 Comparing these methods. 67
4.2 Outline of method dO, dl, and d2 evaluators. 68
4.2.1 The todo argument. 69
Contents vii
4.2.2 The b argument. 69
Using mleval to obtain values from each equation. 70
4.2.3 The lnf argument. 72
Using lnf to indicate that the likelihood cannot be calculated . 74
Using mlsum to define lnf. 74
4.2.4 The g argument. 76
Using mlvecsum to define g. 77
Scores for robust and OPG variance estimates (optional) . 79
4.2.5 The negH argument. 81
Using mlmatsum to define negH. 81
4.2.6 Aside: Stata's scalars. 83
4.3 Summary of methods dO, dl, and d2 . 86
4.3.1 Method dO. 86
4.3.2 Method dl. 89
4.3.3 Method d2. 92
4.4 Linear-form examples. 95
4.4.1 The probit model. 95
4.4.2 The normal model: linear regression. 98
4.4.3 The Weibull model. 104
4.5 Panel-data likelihoods. 108
4.5.1 Calculating lnf. 110
4.5.2 Calculating g. 113
4.5.3 Calculating negH. 117
Using mlmatbysum to help define negH. 117
4.6 Likelihoods other than linear form. 124
5 Debugging likelihood evaluators 131
5.1 ml check. 131
5.2 Using methods dldebug and d2debug. 133
5.2.1 Method dldebug. 135
viii Contents
5.2.2 Method d2debug. 141
5.3 ml trace. 144
6 Setting initial values 147
6.1 ml search. 148
6.2 ml plot. 150
6.3 mlinit. 153
7 Interactive maximization 155
7.1 The iteration log . 155
7.2 - Pressing the Break key. 156
7.3 Maximizing difficult likelihood functions. 158
8 Final results 161
8.1 Graphing convergence. 161
8.2 Redisplaying output. 162
9 Writing do-files to maximize likelihoods 165
9.1 The structure of a do-file. 165
9.2 Putting the do-file into production . 166
10 Writing ado-files to maximize likelihoods 169
10.1 Writing estimation commands. 169
10.2 The standard estimation-command outline. 171
10.3 Outline for estimation commands using ml. 172
10.4 Using ml in noninteractive mode. 173
10.5 Advice . . 174
10.5.1 Syntax. 175
10.5.2 Estimation subsample. 177
10.5.3 Parsing with help from mlopts. 180
10.5.4 Weights. 183
10.5.5 Constant-only model . 184
Contents ix
10.5.6 Initial values. 188
10.5.7 Saving results in e(). 191
10.5.8 Displaying ancillary parameters. 191
10.5.9 Exponentiated coefficients . 193
10.5.10 Offsetting linear equations. 195
10.5.11 Program properties. 197
11 Writing ado-flies for survey data analysis 201
11.1 Program properties. 201
11.2 Writing your own predict command. 204
12 Other examples 207
12.1 The logit model. 207
12.2 The probit model. 209
12.3 The normal model: linear regression. 211
12.4 The Weibull model. 214
12.5 The Cox proportional hazards model. 217
12.6 The random-effects regression model . 220
12.7 The seemingly unrelated regression model. 223
A Syntax of ml 235
 Likelihood evaluator checklists 253
B.I Method If. 253
B.2 Method dO. 254
B.3 Method dl. 255
B.4 Method d2. 257
C Listing of estimation commands 261
C.I The logit model. 261
C.2 The probit model. 264
C.3 The normal model. 265
Contents
C.4 The Weibull model. 268
C.5 The Cox proportional hazards model. 270
C.6 The random-effects regression model . 272
C.7 The seemingly unrelated regression model . 275
References 283
Author index 285
Subject index 287 |
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| publisher | Stata Press |
| record_format | marc |
| spelling | Gould, William 1952- Verfasser (DE-588)1059407698 aut Maximum likelihood estimation with Stata William Gould ; Jeffrey Pitblado ; William Sribney 3. ed. College Station, Tex. Stata Press 2006 XX, 290 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Stata Sozialwissenschaften Social sciences Statistical methods Computer programs Statistische Analyse (DE-588)4116599-8 gnd rswk-swf Maximum-Likelihood-Schätzung (DE-588)4194624-8 gnd rswk-swf Stata (DE-588)4617285-3 gnd rswk-swf Statistische Analyse (DE-588)4116599-8 s Stata (DE-588)4617285-3 s DE-604 Maximum-Likelihood-Schätzung (DE-588)4194624-8 s Pitblado, Jeffrey Verfasser aut Sribney, William Verfasser aut HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014189067&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Gould, William 1952- Pitblado, Jeffrey Sribney, William Maximum likelihood estimation with Stata Stata Sozialwissenschaften Social sciences Statistical methods Computer programs Statistische Analyse (DE-588)4116599-8 gnd Maximum-Likelihood-Schätzung (DE-588)4194624-8 gnd Stata (DE-588)4617285-3 gnd |
| subject_GND | (DE-588)4116599-8 (DE-588)4194624-8 (DE-588)4617285-3 |
| title | Maximum likelihood estimation with Stata |
| title_auth | Maximum likelihood estimation with Stata |
| title_exact_search | Maximum likelihood estimation with Stata |
| title_exact_search_txtP | Maximum likelihood estimation with Stata |
| title_full | Maximum likelihood estimation with Stata William Gould ; Jeffrey Pitblado ; William Sribney |
| title_fullStr | Maximum likelihood estimation with Stata William Gould ; Jeffrey Pitblado ; William Sribney |
| title_full_unstemmed | Maximum likelihood estimation with Stata William Gould ; Jeffrey Pitblado ; William Sribney |
| title_short | Maximum likelihood estimation with Stata |
| title_sort | maximum likelihood estimation with stata |
| topic | Stata Sozialwissenschaften Social sciences Statistical methods Computer programs Statistische Analyse (DE-588)4116599-8 gnd Maximum-Likelihood-Schätzung (DE-588)4194624-8 gnd Stata (DE-588)4617285-3 gnd |
| topic_facet | Stata Sozialwissenschaften Social sciences Statistical methods Computer programs Statistische Analyse Maximum-Likelihood-Schätzung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014189067&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT gouldwilliam maximumlikelihoodestimationwithstata AT pitbladojeffrey maximumlikelihoodestimationwithstata AT sribneywilliam maximumlikelihoodestimationwithstata |