Using SAS for econometrics:
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| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
New York, NY [u.a.]
Wiley
2012
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Includes index |
| Physical Description: | XV, 574 S. graph. Darst. 28 cm |
| ISBN: | 9781118032091 |
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MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV041101255 | ||
| 003 | DE-604 | ||
| 005 | 20130709 | ||
| 007 | t | ||
| 008 | 130621s2012 d||| |||| 00||| eng d | ||
| 020 | |a 9781118032091 |c pbk. |9 978-1-11-803209-1 | ||
| 035 | |a (OCoLC)840035509 | ||
| 035 | |a (DE-599)BSZ380238322 | ||
| 040 | |a DE-604 |b ger |e rakwb | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-188 | ||
| 082 | 0 | |a 005.133 | |
| 100 | 1 | |a Hill, Rufus Carter |e Verfasser |0 (DE-588)170255018 |4 aut | |
| 245 | 1 | 0 | |a Using SAS for econometrics |c R. Carter Hill ; Randall C. Campbell |
| 264 | 1 | |a New York, NY [u.a.] |b Wiley |c 2012 | |
| 300 | |a XV, 574 S. |b graph. Darst. |c 28 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Includes index | ||
| 650 | 0 | 7 | |a Ökonometrie |0 (DE-588)4132280-0 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a SAS |g Programm |0 (DE-588)4195685-0 |2 gnd |9 rswk-swf |
| 653 | |a SAS (Computer file) | ||
| 653 | |a SAS (Computer program language) | ||
| 653 | |a Econometrics / Computer programs | ||
| 689 | 0 | 0 | |a Ökonometrie |0 (DE-588)4132280-0 |D s |
| 689 | 0 | 1 | |a SAS |g Programm |0 (DE-588)4195685-0 |D s |
| 689 | 0 | |5 DE-188 | |
| 700 | 1 | |a Campbell, Randall C. |e Verfasser |0 (DE-588)17181519X |4 aut | |
| 856 | 4 | 2 | |m SWB Datenaustausch |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026077641&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-026077641 | ||
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| _version_ | 1804150483187662848 |
|---|---|
| adam_text | IMAGE 1
CONTENTS
.1
1.2 .3 .4 .5
1.6 1.7
1.8
1.9
1.10
1.11
1.12
1.13
INTRODUCING SAS 1 THE SAS SYSTEM 1 STARTING SAS 1 THE OPENING DISPLAY 1
EXITING SAS 3 USING PRINCIPLES OF ECONOMETRICS, 4E DATA FILES 3 1.5.1
DATA DEFINITION FILES 4 A WORKING ENVIRONMENT 4
SAS PROGRAM STRUCTURE 6 1.7.1 SAS COMMENT STATEMENTS 6 1.7.2 CREATING A
SAS PROGRAM 7 1.7.3 SAVING A SAS PROGRAM 8
1.7.4 RUNNING A SAS PROGRAM 9 1.7.5 PRINTING DATA WITH PROC PRINT 10
1.7.6 SAVING SAS OUTPUT 12 1.7.7 OPENING SAS PROGRAMS 15 SUMMARY
STATISTICS USING PROC MEANS 15 MAKING ERRORS IN SAS PROGRAMS 17
1.9.1 TYPING ERRORS 18 1.9.2 THE SAS SEMI-COLON ; 18 SAS GRAPHICS: A
SCATTER DIAGRAM 19 .10.1 PROC PLOT 19
.10.2 PROCGPLOT 20 CREATING OR MODIFYING DATA SETS 22 .11.1 THE SET
STATEMENT 22
. 11.2 USING DROP AND KEEP 22 CREATING NEW VARIABLES 23 .12.1 ARITHMETIC
OPERATORS 23 .12.2 COMPARISON OPERATORS 24
.12.3 LOGICAL OPERATORS 25 .12.4 USING SAS FUNCTIONS 25 .12.5 MISSING
VALUES 26 .12.6 USING IF-THEN TO RECODE
VARIABLES 26
1.12.7 CREATING A DATA SUBSET 27 1.12.8 USING SET TO COMBINE DATA SETS
27 USING SET TO OPEN SAS DATA SETS 28
1.13.1 USING SAS SYSTEM OPTIONS 29
1.13.2 ADDING LABELS 30 1.14 USING PROC SORT 31 1.14.1 PROC PRINT WITH
BY 31
1.14.2 PROC MEANS WITH BY 32 1.14.3 PROC SORT ON TWO VARIABLES 32 1.14.4
SORT IN DESCENDING ORDER 33 1.15 MERGING DATA SETS 33 APPENDIX 1A A
GUIDE TO SAS HELP AND
ONLINE DOCUMENTATION 34 1A.1 SAS COMMAND LINE 35 1A.2 SAS HELP 35
1 A.3 SAS ONLINE DOCUMENTATION 37 1A.4 SAS ONLINE EXAMPLES 40 1A.5 OTHER
RESOURCES 40 APPENDIX IB IMPORTING DATA INTO SAS 41
1B.1 READING ASCII DATA 41 1 B.2 READING AN EXTERNAL ASCII FILE 42 1 B.3
IMPORTING DATA IN EXCEL FORMAT
47
2. THE SIMPLE LINEAR REGRESSION MODEL 50 2.1 ECONOMETRIC MODEL AND
ESTIMATORS 50 2.2 EXAMPLE: THE FOOD EXPENDITURE DATA 52 2.3 SCATTER
DIAGRAM USING PROC GPLOT
53
2.4 USING PROC REG FOR SIMPLE REGRESSION 54 2.4.1 ANALYSIS OF VARIANCE
TABLE 54 2.4.2 ANOVA AUXILIARY INFORMATION
56
2.4.3 PROC MEANS OPTIONS 56 2.5 PROC REG OPTIONS 57 2.5.1 COVARIANCE
MATRIX 57 2.5.2 THE LEAST SQUARES RESIDUALS
58
2.5.3 OUTPUT RESIDUALS 58 2.5.4 PROC UNIVARIATE ANALYSIS OF RESIDUALS 59
2.6 PREDICTION WITH PROC REG 60 2.6.1 DELETING MISSING VALUES FROM
DATA SET 62
2.6.2 PLOTTING A FITTED LINE USING PROCGPLOT 63 2.7 CREATING PLOTS USING
PROC REG 63 2.8 SAS ODS GRAPHICS 64
IMAGE 2
2.9 FITTING NONLINEAR RELATIONSHIPS 66
2.10 USING INDICATOR VARIABLES 70 APPENDIX 2 A CALCULATION OF LEAST
SQUARES ESTIMATES: DETAILS 71 APPENDIX 2B MONTE CARLO SIMULATION 75
2B. 1 THE TRUE ESTIMATOR VARIANCE 76 2B.2 REGRESSION ON ARTIFICIAL DATA
77 2B.3 OUTEST FROM PROC REG
78
2B.4 SIMULATING SAMPLES USING DO-LOOPS 79 2B.5 SUMMARIZING PARAMETER
ESTIMATES 79
3. INTERVAL ESTIMATION AND HYPOTHESIS TESTING 82 3.1 INTERVAL ESTIMATION
82 3.1.1 INTERVAL ESTIMATION DETAILS 84 3.2 HYPOTHESIS TESTING THEORY 85
3.2.1 RIGHT TAIL T-TESTS 86 3.2.2 LEFT TAIL T-TESTS 86
3.2.3 TWO-TAIL T-TESTS 86 3.2.4 THE P-VALUE FOR T-TESTS 87 3.3
HYPOTHESIS TESTING EXAMPLES 87 3.3.1 RIGHT TAIL TEST OF SIGNIFICANCE
87
3.3.2 RIGHT TAIL TEST FOR AN ECONOMIC HYPOTHESIS 88 3.3.3 LEFT TAIL TEST
OF AN ECONOMIC HYPOTHESIS 89 3.3.4 TWO TAIL TEST OF AN ECONOMIC
HYPOTHESIS 90
3.3.5 TWO TAIL TEST OF SIGNIFICANCE 91 3.4 TESTING AND ESTIMATING LINEAR
COMBINATIONS 91
3.4.1 PROC MODEL 92 APPENDIX 3A MONTE CARLO SIMULATION 94 3 A. 1
SUMMARIZING INTERVAL ESTIMATES 94
3A.2 SUMMARIZING T-TESTS 96 3A.3 ILLUSTRATING THE CENTRAL LIMIT THEOREM
97 3A.4 MONTE CARLO EXPERIMENT WITH
TRIANGULAR ERRORS 99
4. PREDICTION, GOODNESS-OF-FIT, AND MODELING ISSUES 103 4. 1 LEAST
SQUARES PREDICTION THEORY 103 4.2 LEAST SQUARES PREDICTION EXAMPLE 104
4.3 MEASURING GOODNESS-OF-FIT 108 4.4 RESIDUAL ANALYSIS 109
4.4.1 USING PROC AUTOREG 112
4.5 SAS ODS GRAPHICS 113 4.5.1 THE SAS IMAGE EDITOR 114 4.5.2 ODS PLOTS
115
4.6 NONLINEAR RELATIONSHIPS 118 4.7 LOG-LINEAR MODELS 122 4.7.1 A GROWTH
MODEL 122 4.7.2 A WAGE EQUATION 124
4.7.3 PREDICTION IN THE LOG-LINEAR MODEL 127
5. THE MULTIPLE REGRESSION MODEL 130 5.1 MULTIPLE REGRESSION THEORY AND
METHODS 130 5.2 MULTIPLE REGRESSION EXAMPLE 132
5.2.1 USING PROC REG 133 5.2.2 USING PROC AUTOREG 136 5.2.3 USING PROC
MODEL 136 5.3 POLYNOMIAL MODELS 137
5.3.1 USING PROC REG 138 5.3.2 USING PROC MODEL 138 5.4 LOG-LINEAR
MODELS 139 5.4.1 USING PROC REG 140
5.4.2 USING PROC MODEL 141 APPENDIX 5A THE DELTA METHOD IN PROC MODEL
142
5A. 1 MONTE CARLO STUDY OF DELTA METHOD 143 APPENDIX 5B MATRIX
OPERATIONS 147 5B.1 VECTOR CONCEPTS 148
5B.2 MATRIX CONCEPTS 149 APPENDIX 5C REGRESSION CALCULATIONS IN MATRIX
NOTATION 154 5C. 1 SAS/IML MODULE FOR MULTIPLE
REGRESSION 155
5C.2 ESTIMATING A LINEAR COMBINATION OF PARAMETERS 158
IMAGE 3
5C.3
5C.4 5C.5
TESTING A SINGLE LINEAR HYPOTHESIS 158 ILLUSTRATING COMPUTATIONS 159
DELTA METHOD 160
6. FURTHER INFERENCE IN THE MULTIPLE REGRESSION MODEL 162 6.1 JOINT
HYPOTHESIS TESTS 162 6.1.1
6.1.2
6.1.3
6.1.4
6.1.5
6.1.6
AN EXAMPLE 163 PROC REG TEST STATEMENT 165 F-TEST OF MODEL SIGNIFICANCE
167 TESTING IN PROC AUTOREG 167 PROC AUTOREG FIT STATISTICS
168 TESTING IN PROC MODEL 169
6.2 RESTRICTED ESTIMATION 170 6.3 MODEL
6.3.1
SPECIFICATION ISSUES 172 THE RESET TEST 174
6.4 COLLINEARITY 175 6.4.1
6.4.2 6.4.3
CONSEQUENCES OF COLLINEARITY 176 DIAGNOSING COLLINEARITY 176 CONDITION
INDEXES 178
6.5 PREDICTION IN MULTIPLE REGRESSION 179 APPENDIX 6A
6 A.I
6A.2
6A.3
6A.4
EXTENDING THE MATRIX APPROACH 180 ANOVA FOR OLS MODULE 180 PREDICTION
AND PREDICTION
INTERVAL 183 TESTS OF A JOINT HYPOTHESIS 184
COLLINEARITY DIAGNOSTICS 187
7. USING INDICATOR VARIABLES 190 7.1 INDICATOR VARIABLES 190 7.1.1 SLOPE
AND INTERCEPT EFFECTS 190
7.1.2 THE CHOW TEST 192 7.2 USING PROC MODEL FOR LOG-LINEAR REGRESSION
195 7.3 THE LINEAR PROBABILITY MODEL 197
7.4 TREATMENT EFFECTS 198
7.5 DIFFERENCES-IN-DIFFERENCES ESTIMATION 202
8. HETEROSKEDASTICITY 207 8.1 THE NATURE OF HETEROSKEDASTICITY 207 8.2
PLOTTING THE LEAST SQUARES RESIDUALS 207
8.3 LEAST SQUARES WITH ROBUST STANDARD ERRORS 209 8.4 GENERALIZED LEAST
SQUARES ESTIMATION 211
8.4.1 APPLYING GLS USING TRANSFORMED DATA 212 8.4.2 USING PROC REG WITH
A WEIGHT STATEMENT 213 8.5 ESTIMATING THE VARIANCE FUNCTION 213
8.5.1 MODEL OF MULTIPLICATIVE HETEROSKEDASTICITY 213 8.5.2 A CONVENIENT
SPECIAL CASE 214 8.5.3 TWO-STEP ESTIMATOR FOR
MULTIPLICATIVE HETEROSKEDASTICITY 214
8.6 LAGRANGE MULTIPLIER (LM) TEST FOR HETEROSKEDASTICITY 216 8.7
GOLDFELD-QUANDT TEST FOR HETEROSKEDASTICITY 218 8.8 A HETEROSKEDASTIC
PARTITION 221
8.8.1 THE GOLDFELD-QUANDT TEST 222 8.8.2 GENERALIZED LEAST SQUARES
ESTIMATION 224 8.9 USING PROC AUTOREG FOR
HETEROSKEDASTICITY 225 8.9.1 PROC AUTOREG FOR A HETEROSKEDASTIC
PARTITION 226 8.9.2 AN EXTENDED HETEROSKEDASTICITY
MODEL 227
8.10 USING SAS ODS GRAPHICS 228 8.11 USING PROC MODEL FOR
HETEROSKEDASTIC DATA 229 APPENDIX 8A MONTE CARLO SIMULATIONS 231
8A.1 SIMULATING HETEROSKEDASTIC DATA 231 8A.2 HETEROSKEDASTIC DATA MONTE
CARLO EXPERIMENT 233
8A.3 USING PROC IML TO COMPUTE TRUE VARIANCES 238
XI
IMAGE 4
8A.4
APPENDIX 8B 8B.1
8B.2
8B.3
APPENDIX 8C
8C.1
8C.2
8C.3
APPENDIX 8D
8D.1
8D.2
8D.3
8D.4
8D.5
WHITE HCE MONTE CARLO EXPERIMENT 242 TWO-STEP ESTIMATION 245 SIMULATING
HETEROSKEDASTIC DATA 245 FEASIBLE GLS IN MULTIPLICATIVE MODEL 246
FEASIBLE GLS IN PROC 1ML 247 MULTIPLICATIVE MODEL MONTE CARLO 249
SIMULATING HETEROSKEDASTIC DATA 249 THE LEAST SQUARES ESTIMATOR 250
MAXIMUM LIKELIHOOD ESTIMATION 251 MULTIPLICATIVE MODEL MLE 253 USING
PROC AUTOREG 253 NUMERICAL OPTIMIZATION IN THE MULTIPLICATIVE MODEL 254
MLE BASED TESTS FOR HETEROSKEDASTICITY 257 MLE USING ANALYTIC
DERIVATIVES 258
MLE USING METHOD OF SCORING 260
9.6
9.7
9.8
9.5.1
9.5.2 9.5.3
LEAST SQUARES AND HAC STANDARD ERRORS 277 NONLINEAR LEAST SQUARES 278
ESTIMATING A MORE GENERAL
MODEL 280
AUTOREGRESSIVE DISTRIBUTED LAG MODELS 282 9.6.1 9.6.2
9.6.3
THE PHILLIPS CURVE 282 OKUN S LAW 284 AUTOREGRESSIVE MODELS 285
FORECASTING 285 9.7.1
9.7.2
FORECASTING WITH AN AR MODEL 286 EXPONENTIAL SMOOTHING 287 MULTIPLIER
ANALYSIS 289 APPENDIX 9A
9A.1
9A.2
9A.3
9A.4
APPENDIX 9B
ESTIMATION AND FORECASTING WITH PROC ARIMA 291 FINITE DISTRIBUTED LAG
MODELS IN
PROC ARIMA 291 SERIALLY CORRELATED ERROR MODELS IN PROC ARIMA 293
AUTOREGRESSIVE DISTRIBUTED LAG MODELS IN PROC ARIMA 295
AUTOREGRESSIVE MODELS AND FORECASTING IN PROC ARIMA 297 GLS ESTIMATION
OF AR(1) ERROR MODEL 299
CHAPTER 9 REGRESSION WITH TIME-SERIES DATA: STATIONARY VARIABLES 264 9.1
TIME-SERIES DATA 264 9.2 FINITE DISTRIBUTED LAGS 264
9.2.1 LAG AND DIFFERENCE OPERATORS 265 9.2.2 TIME-SERIES PLOTS 267 9.2.3
MODEL ESTIMATION 268 9.3 SERIAL CORRELATION 269
9.3.1 RESIDUAL CORRELOGRAM 272 9.4 TESTING FOR SERIALLY CORRELATED
ERRORS 274
9.4.1 A LAGRANGE MULTIPLER (LM) TEST 274 9.4.2 DURBIN-WATSON TEST 276
9.5 ESTIMATION WITH SERIALLY CORRELATED ERRORS
277
10. RANDOM REGRESSORS AND MOMENT-BASED ESTIMATION 304 1 0. 1 THE
CONSEQUENCES OF RANDOM REGRESSORS 304
10.2 INSTRUMENTAL VARIABLES ESTIMATION 305 10.2.1 TWO-STAGE LEAST
SQUARES ESTIMATION 306
10.3 AN ILLUSTRATION USING SIMULATED DATA 307 10.3.1 USING TWO-STAGE
LEAST SQUARES 309
10.3.2 SPECIFICATION TESTING 310 10.4 A WAGE EQUATION 315 10.4.1 ROBUST
SPECIFICATION TESTS 319 10.5 USING PROC MODEL 321
10.5.1 ROBUST 2SLS ESTIMATION 321
XN
IMAGE 5
10.5.2
APPENDIX 10A
10A.1 10A.2
APPENDIX 10B
10B.1
10B.2
APPENDIX IOC
APPENDIX 10D 10D.1
10D.2
USING THE HAUSMAN TEST COMMAND 321 SIMULATING ENDOGENOUS REGRESSORS 323
SIMULATING THE DATA 323 THE CHOLESKY DECOMPOSITION 326
USING PROC 1ML FOR 2SLS 328 THE MODEL, ESTIMATORS AND TESTS 328
PROC IML COMMANDS 330 THE REPEATED SAMPLING PROPERTIES OF IV/2SLS 336
ROBUST 2SLS AND GMM 342
THE MODEL, ESTIMATORS AND TESTS 342 USING PROC MODEL AND
IML 342
11. SIMULTANEOUS EQUATIONS MODELS 346 11.1 SIMULTANEOUS EQUATIONS 346
11.1.1 STRUCTURAL EQUATIONS 346 11.1.2 REDUCED FORM EQUATIONS 347
11.1.3 WHY LEAST SQUARES FAILS 347 11.1.4 TWO-STAGE LEAST SQUARES
ESTIMATION 348 11.2 TRUFFLE SUPPLY AND DEMAND 348 11.2.1 THE REDUCED
FORM EQUATIONS
349
11.2.2 TWO-STAGE LEAST SQUARES ESTIMATION 351 11.2.3 2SLS USING PROC
SYSLIN 351 11.3 LIMITED INFORMATION MAXIMUM
LIKELIHOOD (LIML) 352 11.3.1 LIML MODIFICATIONS 353 11.4 SYSTEM
ESTIMATION METHODS 354 11.4.1 THREE STAGE LEAST SQUARES
(3SLS) 354
11.4.2 ITERATED THREE STAGE LEAST SQUARES 354 11.4.3 FULL INFORMATION
MAXIMUM LIKELIHOOD (F1ML) 355
11.4.4 POSTSCRIPT 356 APPENDIX 11A ALTERNATIVES TO TWO-STAGE LEAST
SQUARES 356 11A.1 THE LIML ESTIMATOR 357
11A.2 11A.3 11A.4
11A.5
11A.6
APPENDIX 1 IB
FULLER S MODIFIED LIML 357 ADVANTAGES OF LIML 358 STOCK-YOGO WEAK IV
TESTS FOR
LIML 358 LIML AND K-CLASS ALGEBRA 358
PROC IML FOR LIML AND K- CLASS 359 MONTE CARLO SIMULATION 364
12. REGRESSION WITH TIME-SERIES DATA: NONSTATIONARY VARIABLES 369 12.1
STATIONARY AND NONSTATIONARY VARIABLES 369
12.1.1 THE FIRST-ORDER AUTOREGRESSIVE MODEL 375 12.1.2 RANDOM WALK
MODELS 375 12.2 SPURIOUS REGRESSIONS 375 12.3 UNIT ROOT TESTS FOR
STATIONARITY 378
12.3.1 THE DICKEY-FULLER TESTS: AN EXAMPLE 380 12.3.2 ORDER OF
INTEGRATION 382 12.4 COINTEGRATION 385
12.4.1 AN EXAMPLE OF A COINTEGRATION TEST 386 12.4.2 THE ERROR
CORRECTION MODEL 388
13. VECTOR ERROR CORRECTION AND VECTOR AUTOREGRESSIVE MODELS 390 13.1
VEC AND VAR MODELS 390 13.2 ESTIMATING A VECTOR ERROR CORRECTION
MODEL 391
13.3 ESTIMATING A VAR MODEL 397 13.4 IMPULSE RESPONSES AND VARIANCE
DECOMPOSITIONS 403
14. TIME-VARYING VOLATILITY AND ARCH MODELS 406 14.1 TIME-VARYING
VOLATILITY 406 14.2 TESTING, ESTIMATING AND FORECASTING 411
14.2.1 TESTING FOR ARCH EFFECTS 412 14.2.2 ESTIMATING AN ARCH MODEL 415
14.2.3 FORECASTING VOLATILITY 417 14.3 EXTENSIONS 418
XIII
IMAGE 6
14.3.1 THE GARCH M O D E L-
GENERALIZED ARCH 418 14.3.2 ALLOWING FOR AN ASYMMETRIC EFFECT-THRESHOLD
GARCH 421
14.3.3 GARCH-IN-MEAN AND TIME- VARYING RISK PREMIUM 424
15. PANEL DATA MODELS 428 15.1 A MICROECONOMETRIC PANEL 428 15.2 A
POOLED MODEL 429 15.2.1 CLUSTER-ROBUST STANDARD ERRORS
430
15.3 THE FIXED EFFECTS MODEL 432 15.3.1 THE FIXED EFFECTS ESTIMATOR 435
15.3.2 THE FIXED EFFECTS ESTIMATOR
USING PROC PANEL 438 15.3.3 FIXED EFFECTS USING THE COMPLETE PANEL 439
15.4 RANDOM EFFECTS ESTIMATION 440
15.4.1 THE BREUSCH-PAGAN TEST 442 15.4.2 THE HAUSMAN TEST 442 15.5 SETS
OF REGRESSION EQUATIONS 444 15.5.1 SEEMINGLY UNRELATED
REGRESSIONS 447
15.5.2 USING PROC MODEL FOR SUR 449 APPENDIX 15A POOLED OLS ROBUST
COVARIANCE MATRIX 451
15A.I NLS EXAMPLES 452 15A.2 USING PROC IML 453 APPENDIX 15B PANEL DATA
ESTIMATION DETAILS 454
15B.1 ESTIMATING VARIANCE COMPONENTS 454 15B.2 USING PROC PANEL 456
15B.3 USING PROC IML 458 APPENDIX 15C ROBUST FIXED EFFECTS ESTIMATION
461
APPENDIX 15D THE HAUSMAN-TAYLOR ESTIMATOR 464
16. QUALITATIVE AND LIMITED DEPENDENT VARIABLE MODELS 468 16.1 MODELS
WITH BINARY DEPENDENT VARIABLES 468
16.1.1 THE LINEAR PROBABILITY MODEL 469 16.1.2 THE PROBIT MODEL 472
16.1.3 THE LOGIT MODEL 476
16.1.4 A LABOR FORCE PARTICIPATION MODEL 476 16.2 PROBIT FOR CONSUMER
CHOICE 479 16.2.1 WALD TESTS 482
16.2.2 LIKELIHOOD RATIO TESTS 483 16.3 MULTINOMIAL LOGIT 484 16.3.1
EXAMPLE: POST-SECONDARY EDUCATION CHOICE 485 16.4 CONDITIONAL LOGIT 491
16.4.1 MARGINAL EFFECTS 496 16.4.2 TESTING THE IIA ASSUMPTION 497 16.5
ORDERED CHOICE MODELS 501 16.6 MODELS FOR COUNT DATA 504 16.7 LIMITED
DEPENDENT VARIABLE MODELS 507
16.7.1 CENSORED VARIABLE MODELS 508 16.7.2 SAMPLE SELECTION MODEL 512
APPENDIX 16A PROBIT MAXIMUM LIKELIHOOD
ESTIMATION 515
16A.1 PROBIT ESTIMATION 515 16A.2 PREDICTED PROBABILITIES 516 16A.3
MARGINAL EFFECTS 517 16A.4 SAS/IML CODE FOR PROBIT
517
APPENDIX A. MATH FUNCTIONS 522 A.I SAS MATH AND LOGICAL OPERATORS 522
A.2 MATH FUNCTIONS 523 A.3 MATRIX MANIPULATION 525
APPENDIX B. PROBABILITY 528 B.I PROBABILITY CALCULATIONS 528 B.2
QUANTILES 531 B.3 PLOTTING PROBABILITY DENSITY FUNCTIONS
532 B.3.1 NORMAL DISTRIBUTION 533 B.3.2 T-DISTRIBUTION 535 B.3.3
CHI-SQUARE DISTRIBUTION 536 B.3.4 F-DISTRIBUTION 537 B.4 RANDOM NUMBERS
538
XIV
IMAGE 7
APPENDIX C. REVIEW OF STATISTICAL INFERENCE
541 C.I HISTOGRAM 541
C.2 SUMMARY STATISTICS 542 C.2.1 ESTIMATING HIGHER MOMENTS 544 C.2.2
JARQUE-BERA NORMALITY TEST
545
C.3 CONFIDENCE INTERVAL FOR THE MEAN 546 C.4 TESTING THE POPULATION MEAN
546 C.4.1 A RIGHT-TAIL TEST 547 C.4.2 A TWO-TAIL TEST 547
C.4.3 AUTOMATIC TESTS USING PROC TTEST 548 C.5 MAXIMUM LIKELIHOOD
ESTIMATION: ONE PARAMETER 549
C.5.1 A COIN FLIP EXAMPLE 549 C.5.2 STATISTICAL INFERENCE 551 C.5.3
INFERENCE IN THE COIN FLIP EXAMPLE 553
C.6 MAXIMUM LIKELIHOOD ESTIMATION 554 C.6.1 EXPONENTIAL DISTRIBUTION
EXAMPLE 556 C.6.2 GAMMA DISTRIBUTION EXAMPLE
558
C.6.3 TESTING THE GAMMA DISTRIBUTION 559 C.7 EXPONENTIAL MODEL USING
SAS/IML 560
C.7.1 DIRECT MAXIMIZATION 561 C.7.2 USING SAS OPTIMIZERS 562 C.7.3
MAXIMUM LIKELIHOOD ESTIMATION OF GAMMA MODEL
565
C.7.4 TESTING THE GAMMA MODEL 567
INDEX 571
XV
|
| any_adam_object | 1 |
| author | Hill, Rufus Carter Campbell, Randall C. |
| author_GND | (DE-588)170255018 (DE-588)17181519X |
| author_facet | Hill, Rufus Carter Campbell, Randall C. |
| author_role | aut aut |
| author_sort | Hill, Rufus Carter |
| author_variant | r c h rc rch r c c rc rcc |
| building | Verbundindex |
| bvnumber | BV041101255 |
| ctrlnum | (OCoLC)840035509 (DE-599)BSZ380238322 |
| dewey-full | 005.133 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 005 - Computer programming, programs, data, security |
| dewey-raw | 005.133 |
| dewey-search | 005.133 |
| dewey-sort | 15.133 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Book |
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| id | DE-604.BV041101255 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T00:39:38Z |
| institution | BVB |
| isbn | 9781118032091 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-026077641 |
| oclc_num | 840035509 |
| open_access_boolean | |
| owner | DE-188 |
| owner_facet | DE-188 |
| physical | XV, 574 S. graph. Darst. 28 cm |
| publishDate | 2012 |
| publishDateSearch | 2012 |
| publishDateSort | 2012 |
| publisher | Wiley |
| record_format | marc |
| spelling | Hill, Rufus Carter Verfasser (DE-588)170255018 aut Using SAS for econometrics R. Carter Hill ; Randall C. Campbell New York, NY [u.a.] Wiley 2012 XV, 574 S. graph. Darst. 28 cm txt rdacontent n rdamedia nc rdacarrier Includes index Ökonometrie (DE-588)4132280-0 gnd rswk-swf SAS Programm (DE-588)4195685-0 gnd rswk-swf SAS (Computer file) SAS (Computer program language) Econometrics / Computer programs Ökonometrie (DE-588)4132280-0 s SAS Programm (DE-588)4195685-0 s DE-188 Campbell, Randall C. Verfasser (DE-588)17181519X aut SWB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026077641&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Hill, Rufus Carter Campbell, Randall C. Using SAS for econometrics Ökonometrie (DE-588)4132280-0 gnd SAS Programm (DE-588)4195685-0 gnd |
| subject_GND | (DE-588)4132280-0 (DE-588)4195685-0 |
| title | Using SAS for econometrics |
| title_auth | Using SAS for econometrics |
| title_exact_search | Using SAS for econometrics |
| title_full | Using SAS for econometrics R. Carter Hill ; Randall C. Campbell |
| title_fullStr | Using SAS for econometrics R. Carter Hill ; Randall C. Campbell |
| title_full_unstemmed | Using SAS for econometrics R. Carter Hill ; Randall C. Campbell |
| title_short | Using SAS for econometrics |
| title_sort | using sas for econometrics |
| topic | Ökonometrie (DE-588)4132280-0 gnd SAS Programm (DE-588)4195685-0 gnd |
| topic_facet | Ökonometrie SAS Programm |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026077641&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT hillrufuscarter usingsasforeconometrics AT campbellrandallc usingsasforeconometrics |