An introduction to modern econometrics using Stata:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
College Station, Tex.
Stata Press
2006
|
| Schriftenreihe: | A Stata Press publication
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XVIII, 341 S. graph. Darst. |
| ISBN: | 1597180130 9781597180139 |
Internformat
MARC
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| 100 | 1 | |a Baum, Christopher F. |d 1951- |e Verfasser |0 (DE-588)128792612 |4 aut | |
| 245 | 1 | 0 | |a An introduction to modern econometrics using Stata |c Christopher F. Baum |
| 264 | 1 | |a College Station, Tex. |b Stata Press |c 2006 | |
| 300 | |a XVIII, 341 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
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Datensatz im Suchindex
| _version_ | 1804135605247934464 |
|---|---|
| adam_text | Contents
Illustrations xv
Preface xvii
Notation and typography xix
1 Introduction 1
1.1 An overview of Stata s distinctive features 1
1.2 Installing the necessary software 4
1.3 Installing the support materials 5
2 Working with economic and financial data in Stata 7
2.1 The basics 7
2.1.1 The use command 7
2.1.2 Variable types 8
2.1.3 _n and _N 9
2.1.4 generate and replace 10
2.1.5 sort and gsort 10
2.1.6 if exp and in range 11
2.1.7 Using if exp with indicator variables 13
2.1.8 Using if exp versus by varlist: with statistical commands . . . 15
2.1.9 Labels and notes 17
2.1.10 The varlist 20
2.1.11 drop and keep 20
2.1.12 rename and renvars 21
2.1.13 The save command 21
2.1.14 insheet and infile 21
viii Contents
2.2 Common data transformations 22
2.2.1 The cond() function 22
2.2.2 Recoding discrete and continuous variables 23
2.2.3 Handling missing data 24
mvdecode and mvencode 25
2.2.4 String to numeric conversion and vice versa 26
2.2.5 Handling dates 27
2.2.6 Some useful functions for generate or replace 29
2.2.7 The egen command 30
Official egen functions 30
egen functions from the user community 31
2.2.8 Computation for by groups 33
2.2.9 Local macros 36
2.2.10 Looping over variables: forvalues and foreach 37
2.2.11 Scalars and matrices 39
2.2.12 Command syntax and return values 39
3 Organizing and handling economic data 43
3.1 Cross sectional data and identifier variables 43
3.2 Time series data 44
3.2.1 Time series operators 45
3.3 Pooled cross sectional time series data 45
3.4 Panel data 46
3.4.1 Operating on panel data 47
3.5 Tools for manipulating panel data 49
3.5.1 Unbalanced panels and data screening 50
3.5.2 Other transforms of panel data 53
3.5.3 Moving window summary statistics and correlations 53
3.6 Combining cross sectional and time series datasets 55
3.7 Creating long format datasets with append 56
3.7.1 Using merge to add aggregate characteristics 57
Contents ix
3.7.2 The dangers of many to many merges 58
3.8 The reshape command 58
3.8.1 The xpose command 62
3.9 Using Stata for reproducible research 62
3.9.1 Using do files 62
3.9.2 Data validation: assert and duplicates 63
4 Linear regression 69
4.1 Introduction 69
4.2 Computing linear regression estimates 70
4.2.1 Regression as a method of moments estimator 72
4.2.2 The sampling distribution of regression estimates 73
4.2.3 Efficiency of the regression estimator 74
4.2.4 Numerical identification of the regression estimates 75
4.3 Interpreting regression estimates 75
4.3.1 Research project: A study of single family housing prices ... 76
4.3.2 The ANOVA table: ANOVA F and R squared 77
4.3.3 Adjusted R squared 78
4.3.4 The coefficient estimates and beta coefficients 80
4.3.5 Regression without a constant term 81
4.3.6 Recovering estimation results 82
4.3.7 Detecting collinearity in regression 84
4.4 Presenting regression estimates 87
4.4.1 Presenting summary statistics and correlations 90
4.5 Hypothesis tests, linear restrictions, and constrained least squares ... 91
4.5.1 Wald tests with test 94
4.5.2 Wald tests involving linear combinations of parameters .... 96
4.5.3 Joint hypothesis tests 98
4.5.4 Testing nonlinear restrictions and forming nonlinear combi¬
nations 99
4.5.5 Testing competing (nonnested) models 100
x Contents
4.6 Computing residuals and predicted values 102
4.6.1 Computing interval predictions 103
4.7 Computing marginal effects 107
4.A Appendix: Regression as a least squares estimator 112
4.B Appendix: The large sample VCE for linear regression 113
5 Specifying the functional form 115
5.1 Introduction 115
5.2 Specification error 115
5.2.1 Omitting relevant variables from the model 116
Specifying dynamics in time series regression models 117
5.2.2 Graphically analyzing regression data 117
5.2.3 Added variable plots 119
5.2.4 Including irrelevant variables in the model 121
5.2.5 The asymmetry of specification error 121
5.2.6 Misspecification of the functional form 122
5.2.7 Ramsey s RESET 122
5.2.8 Specification plots 124
5.2.9 Specification and interaction terms 125
5.2.10 Outlier statistics and measures of leverage 126
The DFITS statistic 128
The DFBETA statistic 130
5.3 Endogeneity and measurement error 132
6 Regression with non i.i.d. errors 133
6.1 The generalized linear regression model 134
6.1.1 Types of deviations from i.i.d. errors 134
6.1.2 The robust estimator of the VCE 136
6.1.3 The cluster estimator of the VCE 138
6.1.4 The Newey West estimator of the VCE 139
6.1.5 The generalized least squares estimator 142
The FGLS estimator 143
Contents xi
6.2 Heteroskedasticity in the error distribution 143
6.2.1 Heteroskedasticity related to scale 144
Testing for heteroskedasticity related to scale 145
FGLS estimation 147
6.2.2 Heteroskedasticity between groups of observations 149
Testing for heteroskedasticity between groups of observations . 150
FGLS estimation 151
6.2.3 Heteroskedasticity in grouped data 152
FGLS estimation 153
6.3 Serial correlation in the error distribution 154
6.3.1 Testing for serial correlation 155
6.3.2 FGLS estimation with serial correlation 159
7 Regression with indicator variables 161
7.1 Testing for significance of a qualitative factor 161
7.1.1 Regression with one qualitative measure 162
7.1.2 Regression with two qualitative measures 165
Interaction effects 167
7.2 Regression with qualitative and quantitative factors 168
Testing for slope differences 170
7.3 Seasonal adjustment with indicator variables 174
7.4 Testing for structural stability and structural change 179
7.4.1 Constraints of continuity and differentiability 179
7.4.2 Structural change in a time series model 183
8 Instrumental variables estimators 185
8.1 Introduction 185
8.2 Endogeneity in economic relationships 185
8.3 2SLS 188
8.4 The ivreg command 189
8.5 Identification and tests of overidentifying restrictions 190
8.6 Computing IV estimates 192
xjj Contents
8.7 ivreg2 and GMM estimation 194
8.7.1 The GMM estimator 195
8.7.2 GMM in a homoskedastic context 196
8.7.3 GMM and heteroskedasticity consistent standard errors .... 197
8.7.4 GMM and clustering 198
8.7.5 GMM and HAC standard errors 199
8.8 Testing overidentifying restrictions in GMM 200
8.8.1 Testing a subset of the overidentifying restrictions in GMM . . 201
8.9 Testing for heteroskedasticity in the IV context 205
8.10 Testing the relevance of instruments 207
8.11 Durbin Wu Hausman tests for endogeneity in IV estimation 211
8.A Appendix: Omitted variables bias 216
8.B Appendix: Measurement error 216
8.B.1 Solving errors in variables problems 218
9 Panel data models 219
9.1 FE and RE models 220
9.1.1 One way FE 221
9.1.2 Time effects and two way FE 224
9.1.3 The between estimator 226
9.1.4 One way RE 227
9.1.5 Testing the appropriateness of RE 230
9.1.6 Prediction from one way FE and RE 231
9.2 IV models for panel data 232
9.3 Dynamic panel data models 232
9.4 Seemingly unrelated regression models 236
9.4.1 SUR with identical regressors 241
9.5 Moving window regression estimates 242
10 Models of discrete and limited dependent variables 247
10.1 Binomial logit and probit models 247
10.1.1 The latent variable approach 248
Contents xiii
10.1.2 Marginal effects and predictions 250
Binomial probit 251
Binomial logit and grouped logit 253
10.1.3 Evaluating specification and goodness of fit 254
10.2 Ordered logit and probit models 256
10.3 Truncated regression and tobit models 259
10.3.1 Truncation 259
10.3.2 Censoring 262
10.4 Incidental truncation and sample selection models 266
10.5 Bivariate probit and probit with selection 271
10.5.1 Binomial probit with selection 272
A Getting the data into Stata 277
A.I Inputting data from ASCII text files and spreadsheets 277
A.I.I Handling text files 278
Free format versus fixed format 278
The insheet command 280
A.1.2 Accessing data stored in spreadsheets 281
A.I.3 Fixed format data files 281
A.2 Importing data from other package formats 286
B The basics of Stata programming 289
B.I Local and global macros 290
B.I.I Global macros 293
B.1.2 Extended macro functions and list functions 293
B.2 Scalars 294
B.3 Loop constructs 295
B.3.1 foreach 297
B.4 Matrices 299
B.5 return and ereturn 301
B.5.1 ereturn list 305
xiv Contents
B.6 The program and syntax statements 307
B.7 Using Mata functions in Stata programs 313
References 321
Author index 329
Subject index 333
|
| adam_txt |
Contents
Illustrations xv
Preface xvii
Notation and typography xix
1 Introduction 1
1.1 An overview of Stata's distinctive features 1
1.2 Installing the necessary software 4
1.3 Installing the support materials 5
2 Working with economic and financial data in Stata 7
2.1 The basics 7
2.1.1 The use command 7
2.1.2 Variable types 8
2.1.3 _n and _N 9
2.1.4 generate and replace 10
2.1.5 sort and gsort 10
2.1.6 if exp and in range 11
2.1.7 Using if exp with indicator variables 13
2.1.8 Using if exp versus by varlist: with statistical commands . . . 15
2.1.9 Labels and notes 17
2.1.10 The varlist 20
2.1.11 drop and keep 20
2.1.12 rename and renvars 21
2.1.13 The save command 21
2.1.14 insheet and infile 21
viii Contents
2.2 Common data transformations 22
2.2.1 The cond() function 22
2.2.2 Recoding discrete and continuous variables 23
2.2.3 Handling missing data 24
mvdecode and mvencode 25
2.2.4 String to numeric conversion and vice versa 26
2.2.5 Handling dates 27
2.2.6 Some useful functions for generate or replace 29
2.2.7 The egen command 30
Official egen functions 30
egen functions from the user community 31
2.2.8 Computation for by groups 33
2.2.9 Local macros 36
2.2.10 Looping over variables: forvalues and foreach 37
2.2.11 Scalars and matrices 39
2.2.12 Command syntax and return values 39
3 Organizing and handling economic data 43
3.1 Cross sectional data and identifier variables 43
3.2 Time series data 44
3.2.1 Time series operators 45
3.3 Pooled cross sectional time series data 45
3.4 Panel data 46
3.4.1 Operating on panel data 47
3.5 Tools for manipulating panel data 49
3.5.1 Unbalanced panels and data screening 50
3.5.2 Other transforms of panel data 53
3.5.3 Moving window summary statistics and correlations 53
3.6 Combining cross sectional and time series datasets 55
3.7 Creating long format datasets with append 56
3.7.1 Using merge to add aggregate characteristics 57
Contents ix
3.7.2 The dangers of many to many merges 58
3.8 The reshape command 58
3.8.1 The xpose command 62
3.9 Using Stata for reproducible research 62
3.9.1 Using do files 62
3.9.2 Data validation: assert and duplicates 63
4 Linear regression 69
4.1 Introduction 69
4.2 Computing linear regression estimates 70
4.2.1 Regression as a method of moments estimator 72
4.2.2 The sampling distribution of regression estimates 73
4.2.3 Efficiency of the regression estimator 74
4.2.4 Numerical identification of the regression estimates 75
4.3 Interpreting regression estimates 75
4.3.1 Research project: A study of single family housing prices . 76
4.3.2 The ANOVA table: ANOVA F and R squared 77
4.3.3 Adjusted R squared 78
4.3.4 The coefficient estimates and beta coefficients 80
4.3.5 Regression without a constant term 81
4.3.6 Recovering estimation results 82
4.3.7 Detecting collinearity in regression 84
4.4 Presenting regression estimates 87
4.4.1 Presenting summary statistics and correlations 90
4.5 Hypothesis tests, linear restrictions, and constrained least squares . 91
4.5.1 Wald tests with test 94
4.5.2 Wald tests involving linear combinations of parameters . 96
4.5.3 Joint hypothesis tests 98
4.5.4 Testing nonlinear restrictions and forming nonlinear combi¬
nations 99
4.5.5 Testing competing (nonnested) models 100
x Contents
4.6 Computing residuals and predicted values 102
4.6.1 Computing interval predictions 103
4.7 Computing marginal effects 107
4.A Appendix: Regression as a least squares estimator 112
4.B Appendix: The large sample VCE for linear regression 113
5 Specifying the functional form 115
5.1 Introduction 115
5.2 Specification error 115
5.2.1 Omitting relevant variables from the model 116
Specifying dynamics in time series regression models 117
5.2.2 Graphically analyzing regression data 117
5.2.3 Added variable plots 119
5.2.4 Including irrelevant variables in the model 121
5.2.5 The asymmetry of specification error 121
5.2.6 Misspecification of the functional form 122
5.2.7 Ramsey's RESET 122
5.2.8 Specification plots 124
5.2.9 Specification and interaction terms 125
5.2.10 Outlier statistics and measures of leverage 126
The DFITS statistic 128
The DFBETA statistic 130
5.3 Endogeneity and measurement error 132
6 Regression with non i.i.d. errors 133
6.1 The generalized linear regression model 134
6.1.1 Types of deviations from i.i.d. errors 134
6.1.2 The robust estimator of the VCE 136
6.1.3 The cluster estimator of the VCE 138
6.1.4 The Newey West estimator of the VCE 139
6.1.5 The generalized least squares estimator 142
The FGLS estimator 143
Contents xi
6.2 Heteroskedasticity in the error distribution 143
6.2.1 Heteroskedasticity related to scale 144
Testing for heteroskedasticity related to scale 145
FGLS estimation 147
6.2.2 Heteroskedasticity between groups of observations 149
Testing for heteroskedasticity between groups of observations . 150
FGLS estimation 151
6.2.3 Heteroskedasticity in grouped data 152
FGLS estimation 153
6.3 Serial correlation in the error distribution 154
6.3.1 Testing for serial correlation 155
6.3.2 FGLS estimation with serial correlation 159
7 Regression with indicator variables 161
7.1 Testing for significance of a qualitative factor 161
7.1.1 Regression with one qualitative measure 162
7.1.2 Regression with two qualitative measures 165
Interaction effects 167
7.2 Regression with qualitative and quantitative factors 168
Testing for slope differences 170
7.3 Seasonal adjustment with indicator variables 174
7.4 Testing for structural stability and structural change 179
7.4.1 Constraints of continuity and differentiability 179
7.4.2 Structural change in a time series model 183
8 Instrumental variables estimators 185
8.1 Introduction 185
8.2 Endogeneity in economic relationships 185
8.3 2SLS 188
8.4 The ivreg command 189
8.5 Identification and tests of overidentifying restrictions 190
8.6 Computing IV estimates 192
xjj Contents
8.7 ivreg2 and GMM estimation 194
8.7.1 The GMM estimator 195
8.7.2 GMM in a homoskedastic context 196
8.7.3 GMM and heteroskedasticity consistent standard errors . 197
8.7.4 GMM and clustering 198
8.7.5 GMM and HAC standard errors 199
8.8 Testing overidentifying restrictions in GMM 200
8.8.1 Testing a subset of the overidentifying restrictions in GMM . . 201
8.9 Testing for heteroskedasticity in the IV context 205
8.10 Testing the relevance of instruments 207
8.11 Durbin Wu Hausman tests for endogeneity in IV estimation 211
8.A Appendix: Omitted variables bias 216
8.B Appendix: Measurement error 216
8.B.1 Solving errors in variables problems 218
9 Panel data models 219
9.1 FE and RE models 220
9.1.1 One way FE 221
9.1.2 Time effects and two way FE 224
9.1.3 The between estimator 226
9.1.4 One way RE 227
9.1.5 Testing the appropriateness of RE 230
9.1.6 Prediction from one way FE and RE 231
9.2 IV models for panel data 232
9.3 Dynamic panel data models 232
9.4 Seemingly unrelated regression models 236
9.4.1 SUR with identical regressors 241
9.5 Moving window regression estimates 242
10 Models of discrete and limited dependent variables 247
10.1 Binomial logit and probit models 247
10.1.1 The latent variable approach 248
Contents xiii
10.1.2 Marginal effects and predictions 250
Binomial probit 251
Binomial logit and grouped logit 253
10.1.3 Evaluating specification and goodness of fit 254
10.2 Ordered logit and probit models 256
10.3 Truncated regression and tobit models 259
10.3.1 Truncation 259
10.3.2 Censoring 262
10.4 Incidental truncation and sample selection models 266
10.5 Bivariate probit and probit with selection 271
10.5.1 Binomial probit with selection 272
A Getting the data into Stata 277
A.I Inputting data from ASCII text files and spreadsheets 277
A.I.I Handling text files 278
Free format versus fixed format 278
The insheet command 280
A.1.2 Accessing data stored in spreadsheets 281
A.I.3 Fixed format data files 281
A.2 Importing data from other package formats 286
B The basics of Stata programming 289
B.I Local and global macros 290
B.I.I Global macros 293
B.1.2 Extended macro functions and list functions 293
B.2 Scalars 294
B.3 Loop constructs 295
B.3.1 foreach 297
B.4 Matrices 299
B.5 return and ereturn 301
B.5.1 ereturn list 305
xiv Contents
B.6 The program and syntax statements 307
B.7 Using Mata functions in Stata programs 313
References 321
Author index 329
Subject index 333 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Baum, Christopher F. 1951- |
| author_GND | (DE-588)128792612 |
| author_facet | Baum, Christopher F. 1951- |
| author_role | aut |
| author_sort | Baum, Christopher F. 1951- |
| author_variant | c f b cf cfb |
| building | Verbundindex |
| bvnumber | BV021749134 |
| classification_rvk | MR 2100 QH 212 QH 310 |
| classification_tum | WIR 017f DAT 307f |
| ctrlnum | (OCoLC)255841824 (DE-599)BVBBV021749134 |
| discipline | Informatik Soziologie Wirtschaftswissenschaften |
| discipline_str_mv | Informatik Soziologie Wirtschaftswissenschaften |
| format | Book |
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| genre | (DE-588)4123623-3 Lehrbuch gnd-content Lehrbuch - Ökonometrie - Stata |
| genre_facet | Lehrbuch Lehrbuch - Ökonometrie - Stata |
| id | DE-604.BV021749134 |
| illustrated | Illustrated |
| index_date | 2024-07-02T15:31:42Z |
| indexdate | 2024-07-09T20:43:10Z |
| institution | BVB |
| isbn | 1597180130 9781597180139 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-014962370 |
| oclc_num | 255841824 |
| open_access_boolean | |
| owner | DE-19 DE-BY-UBM DE-N2 DE-473 DE-BY-UBG DE-1047 DE-824 DE-384 DE-703 DE-20 DE-M382 DE-91 DE-BY-TUM DE-91S DE-BY-TUM DE-11 DE-522 DE-188 DE-Re13 DE-BY-UBR DE-739 DE-521 DE-706 |
| owner_facet | DE-19 DE-BY-UBM DE-N2 DE-473 DE-BY-UBG DE-1047 DE-824 DE-384 DE-703 DE-20 DE-M382 DE-91 DE-BY-TUM DE-91S DE-BY-TUM DE-11 DE-522 DE-188 DE-Re13 DE-BY-UBR DE-739 DE-521 DE-706 |
| physical | XVIII, 341 S. graph. Darst. |
| publishDate | 2006 |
| publishDateSearch | 2006 |
| publishDateSort | 2006 |
| publisher | Stata Press |
| record_format | marc |
| series2 | A Stata Press publication |
| spelling | Baum, Christopher F. 1951- Verfasser (DE-588)128792612 aut An introduction to modern econometrics using Stata Christopher F. Baum College Station, Tex. Stata Press 2006 XVIII, 341 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier A Stata Press publication Ökonometrie Ökonometrie (DE-588)4132280-0 gnd rswk-swf Stata (DE-588)4617285-3 gnd rswk-swf (DE-588)4123623-3 Lehrbuch gnd-content Lehrbuch - Ökonometrie - Stata Ökonometrie (DE-588)4132280-0 s Stata (DE-588)4617285-3 s DE-604 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014962370&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Baum, Christopher F. 1951- An introduction to modern econometrics using Stata Ökonometrie Ökonometrie (DE-588)4132280-0 gnd Stata (DE-588)4617285-3 gnd |
| subject_GND | (DE-588)4132280-0 (DE-588)4617285-3 (DE-588)4123623-3 |
| title | An introduction to modern econometrics using Stata |
| title_auth | An introduction to modern econometrics using Stata |
| title_exact_search | An introduction to modern econometrics using Stata |
| title_exact_search_txtP | An introduction to modern econometrics using Stata |
| title_full | An introduction to modern econometrics using Stata Christopher F. Baum |
| title_fullStr | An introduction to modern econometrics using Stata Christopher F. Baum |
| title_full_unstemmed | An introduction to modern econometrics using Stata Christopher F. Baum |
| title_short | An introduction to modern econometrics using Stata |
| title_sort | an introduction to modern econometrics using stata |
| topic | Ökonometrie Ökonometrie (DE-588)4132280-0 gnd Stata (DE-588)4617285-3 gnd |
| topic_facet | Ökonometrie Stata Lehrbuch Lehrbuch - Ökonometrie - Stata |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014962370&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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