Essentials of econometrics:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boston, Mass. [u.a.]
McGraw-Hill
2010
|
| Ausgabe: | 4. ed., internat. ed. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Includes index |
| Beschreibung: | XXII, 554 S. graph. Darst. |
| ISBN: | 0071276076 9780071276078 |
Internformat
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| 245 | 1 | 0 | |a Essentials of econometrics |c Damodar N. Gujarati ; Dawn C. Porter |
| 250 | |a 4. ed., internat. ed. | ||
| 264 | 1 | |a Boston, Mass. [u.a.] |b McGraw-Hill |c 2010 | |
| 300 | |a XXII, 554 S. |b graph. Darst. | ||
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Datensatz im Suchindex
| _version_ | 1804143486871535616 |
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| adam_text | IMAGE 1
ESSENTIALS OF
ECONOMETRICS FOURTH EDITION ; DAMODAR N. GUJARATI
PROFESSOR EMERITUS OF ECONOMICS, UNITED STATES MILITARY ACADEMY, WEST
POINT DAWN C. PORTER
UNIVERSITY OF SOUTHERN CALIFORNIA
C 259614
ME GRAW HILL
BOSTON BURR RIDGE, IL DUBUQUE, IA MADISON, WL NEW YORK SAN FRANCISCO ST.
LOUIS BANGKOK BOGOTA CARACAS KUALA LUMPUR LISBON LONDON MADRID MEXICO
CITY MILAN MONTREAL NEW DELHI SANTIAGO SEOUL SINGAPORE SYDNEY TAIPEI
TORONTO
IMAGE 2
CONTENTS
PREFACE XIX
THE NATURE AND SCOPE OF ECONOMETRICS 1
1.1 WHAT IS ECONOMETRICS? 1
1.2 WHY STUDY ECONOMETRICS? 2
1.3 THE METHODOLOGY OF ECONOMETRICS . 3
CREATING A STATEMENT OF THEORY OR HYPOTHESIS 3
COLLECTING DATA 4
SPECIFYING THE MATHEMATICAL MODEL OF LABOR FORCE PARTICIPATION 5
SPECIFYING THE STATISTICAL, OR ECONOMETRIC, MODEL OF LABOR FORCE
PARTICIPATION 7
ESTIMATING THE PARAMETERS OF THE CHOSEN ECONOMETRIC MODEL 9 CHECKING FOR
MODEL ADEQUACY: MODEL SPECIFICATION TESTING 9
TESTING THE HYPOTHESIS DERIVED FROM THE MODEL 11
USING THE MODEL FOR PREDICTION OR FORECASTING 12
1.4 THE ROAD AHEAD 12
KEY TERMS AND CONCEPTS 13
QUESTIONS 14
PROBLEMS 14
APPENDIX 1A: ECONOMIC DATA ON
THE WORLD WIDE WEB 16
PART I THE LINEAR REGRESSION MODEL 19
2 BASIC IDEAS OF LINEAR REGRESSION: THE TWO-VARIABLE MODEL 21 2.1 THE
MEANING OF REGRESSION 21
2.2 THE POPULATION REGRESSION FUNCTION (PRF):
A HYPOTHETICAL EXAMPLE 22
VII
IMAGE 3
VIII CONTENTS
2.3 STATISTICAL OR STOCHASTIC SPECIFICATION
OF THE POPULATION REGRESSION FUNCTION 25 2.4 THE NATURE OF THE
STOCHASTIC ERROR TERM 27 2.5 THE SAMPLE REGRESSION FUNCTION (SRF) 28 2.6
THE SPECIAL MEANING OF THE TERM LINEAR
REGRESSION 31
LINEARITY IN THE VARIABLES 31
LINEARITY IN THE PARAMETERS 32
2.7 TWO-VARIABLE VERSUS MULTIPLE LINEAR REGRESSION 33
2.8 ESTIMATION OF PARAMETERS: THE METHOD OF ORDINARY LEAST SQUARES 33
THE METHFOD OF ORDINARY LEAST SQUARES 34
2.9 PUTTING IT ALL TOGETHER . 36
INTERPRETATION OF THE ESTIMATED MATH S.A.T. SCORE FUNCTION 37 2.10 SOME
ILLUSTRATIVE EXAMPLES 38
2.11 SUMMARY 43
KEY TERMS AND CONCEPTS 44
QUESTIONS 44
PROBLEMS 45
OPTIONAL QUESTIONS 51
APPENDIX 2A: DERIVATION OF LEAST-SQUARES ESTIMATES * 52
THE TWO-VARIABLE MODEL: HYPOTHESIS TESTING 53
3.1 THE CLASSICAL LINEAR REGRESSION MODEL 54 3.2 VARIANCES AND STANDARD
ERRORS OF ORDINARY LEAST SQUARES ESTIMATORS 57 VARIANCES AND STANDARD
ERRORS OF THE MATH S.A.T. SCORE EXAMPLE 59
SUMMARY OF THE MATH S.A.T. SCORE FUNCTION 59
3.3 WHY OLS? THE PROPERTIES OF OLS ESTIMATORS 60 MONTE CARLO EXPERIMENT
61
3.4 THE SAMPLING, OR PROBABILITY, DISTRIBUTIONS OF OLS ESTIMATORS 62
3.5 HYPOTHESIS TESTING 64
TESTING H 0:B2 = 0 VERSUS H- :S2 # 0: THE CONFIDENCE INTERVAL APPROACH
66
THE TEST OF SIGNIFICANCE APPROACH TO HYPOTHESIS TESTING 68 MATH S.A.T.
EXAMPLE CONTINUED 69
3.6 HOW GOOD IS THE FITTED REGRESSION LINE:
THE COEFFICIENT OF DETERMINATION, R 2 71
FORMULAS TO COMPUTE R 2 73
T 2 FOR THE MATH S.A.T. EXAMPLE 74
THE COEFFICIENT OF CORRELATION, R 74
3.7 REPORTING THE RESULTS OF REGRESSION ANALYSIS 75
IMAGE 4
CONTENTS IX
3.8 COMPUTER OUTPUT OF THE MATH S.A.T. SCORE
. EXAMPLE 76
3.9 NORMALITY TESTS 77
HISTOGRAMS OF RESIDUALS 77
NORMAL PROBABILITY PLOT 78
JARQUE-BERA TEST 78
3.10 A CONCLUDING EXAMPLE: RELATIONSHIP BETWEEN WAGES AND PRODUCTIVITY
IN THE U.S. BUSINESS SECTOR, 1959-2006 79
3.11 A WORD ABOUT FORECASTING 82
3.12 SUMMARY 85
KEY TERMS AND CONCEPTS 86
QUESTIONS 86
PROBLEMS 88
MULTIPLE REGRESSION: ESTIMATION AND HYPOTHESIS TESTING 93 4.1 THE
THREE-VARIABLE LINEAR REGRESSION MODEL 94
THE MEANING OF PARTIAL REGRESSION COEFFICIENT 95
4.2 ASSUMPTIONS OF THE MULTIPLE LINEAR REGRESSION MODEL 97
4.3 ESTIMATION OF THE PARAMETERS OF MULTIPLE REGRESSION 99
ORDINARY LEAST SQUARES ESTIMATORS 99
VARIANCE AND STANDARD ERRORS OF OLS ESTIMATORS 100 PROPERTIES OF OLS
ESTIMATORS OF MULTIPLE REGRESSION 102 4.4 GOODNESS OF FIT OF ESTIMATED
MULTIPLE REGRESSION: MULTIPLE COEFFICIENT OF
DETERMINATION, R 2 102
4.5 ANTIQUE CLOCK AUCTION PRICES REVISITED 103 INTERPRETATION OF THE
REGRESSION RESULTS 103
4.6 HYPOTHESIS TESTING IN A MULTIPLE REGRESSION: GENERAL COMMENTS 104
4.7 TESTING HYPOTHESES ABOUT INDIVIDUAL PARTIAL^ REGRESSION COEFFICIENTS
105
THE TEST OF SIGNIFICANCE APPROACH 105
THE CONFIDENCE INTERVAL APPROACH TO HYPOTHESIS TESTING 106 4.8 TESTING
THE JOINT HYPOTHESIS THAT E 2 = E3 = O O R R 2 = O 107
AN IMPORTANT RELATIONSHIP BETWEEN FAND R 2 111
4.9 TWO-VARIABLE REGRESSION IN THE CONTEXT OF MULTIPLE REGRESSION:
INTRODUCTION TO SPECIFICATION BIAS 112
4.10 COMPARING TWO R 2 VALUES: THE ADJUSTED R 2 113
IMAGE 5
X CONTENTS
4.11 WHEN TO ADD AN ADDITIONAL EXPLANATORY
VARIABLE TO A MODEL 114
4.12 RESTRICTED LEAST SQUARES 116
4.13 ILLUSTRATIVE EXAMPLES 117
DISCUSSION OF REGRESSION RESULTS 118
4.14 SUMMARY 122
KEY TERMS AND CONCEPTS 123
QUESTIONS 123
PROBLEMS _ 125
APPENDIX 4A;1: DERIVATIONS OF OLS ESTIMATORS GIVEN IN EQUATIONS
(4.20)TO (4.22) 129
APPENDIX 4A.2: DERIVATION OF EQUATION (4.31) 129 APPENDIX 4A.3:
DERIVATION OF EQUATION (4.50) 130 APPENDIX 4A.4: EVIEWS OUTPUT OF THE
CLOCK AUCTION PRICE EXAMPLE 131
FUNCTIONAL FORMS OF REGRESSION MODELS 132
5.1 HOW TO MEASURE ELASTICITY: THE LOG-LINEAR MODEL 133
HYPOTHESIS TESTING IN LOG-LINEAR MODELS 137
5.2 COMPARING LINEAR AND LOG-LINEAR REGRESSION MODELS 138
5.3 MULTIPLE LOG-LINEAR REGRESSION MODELS . 140 5.4 HOW TO MEASURE THE
GROWTH RATE: THE SEMILOG MODEL 144
INSTANTANEOUS VERSUS COMPOUND RATE OF GROWTH 147 THE LINEAR TREND MODEL
148
5.5 THE LIN-LOG MODEL: WHEN THE EXPLANATORY VARIABLE IS LOGARITHMIC 149
5.6 RECIPROCAL MODELS 150
5.7 POLYNOMIAL REGRESSION MODELS 156
5.8 REGRESSION THROUGH THE ORIGIN 158
5.9 A NOTE ON SCALING AND UNITS OF MEASUREMENT 160 5.10 REGRESSION ON
STANDARDIZED VARIABLES 161 5.11 SUMMARY OF FUNCTIONAL FORMS 163
5.12 SUMMARY 164
KEY TERMS AND CONCEPTS 165
QUESTIONS 166
PROBLEMS 167
APPENDIX 5A: LOGARITHMS 175
DUMMY VARIABLE REGRESSION MODELS 178
6.1 THE NATURE OF DUMMY VARIABLES 178
6.2 ANCOVA MODELS: REGRESSION ON ONE QUANTITATIVE VARIABLE AND ONE
QUALITATIVE VARIABLE WITH TWO CATEGORIES: EXAMPLE 6.1 REVISITED 185
IMAGE 6
CONTENTS XI
6.3 REGRESSION ON ONE QUANTITATIVE
VARIABLE AND ONE QUALITATIVE VARIABLE WITH MORE THAN TWO CLASSES OR
CATEGORIES * 187
6.4 REGRESSION ON ONE QUANTIATIVE EXPLANATORY VARIABLE AND MORE THAN ONE
QUALITATIVE VARIABLE 190
INTERACTION EFFECTS 191
A GENERALIZATION 192
6.5 COMPARING TWO REGESSIONS 193
6.6 THE USE OF DUMMY VARIABLES IN SEASONAL ANALYSIS 198
6.7 WHAT HAPPENS IF THE DEPENDENT VARIABLE IS ALSO A DUMMY VARIABLE? THE
LINEAR PROBABILITY MODEL (LPM) 201
6.8 SUMMARY 204
KEY TERMS AND CONCEPTS 205
QUESTIONS 206
PROBLEMS 207
PART II REGRESSION ANALYSIS IN PRACTICE 217
7 MODEL SELECTION: CRITERIA AND TESTS 219
7.1 THE ATTRIBUTES OF A GOOD MODEL 220
7.2 TYPES OF SPECIFICATION ERRORS 221
7.3 OMISSON OF RELEVANT VARIABLE BIAS: UNDERFITTING AMODEL 221
7.4 INCLUSION OF IRRELEVANT VARIABLES: OVERFITTING AMODEL 225
7.5 INCORRECT FUNCTIONAL FORM 227
7.6 ERRORS OF MEASUREMENT . 229
ERRORS OF MEASUREMENT IN THE DEPENDENT VARIABLE 229 ERRORS OF
MEASUREMENT IN THE EXPLANATORY VARIABLE(S) 229 7.7 DETECTING
SPECIFICATION ERRORS: TESTS OF SPECIFICATION ERRORS 230
DETECTING THE PRESENCE OF UNNECESSARY VARIABLES 230 TESTS FOR OMITTED
VARIABLES AND INCORRECT FUNCTIONAL FORMS 233 CHOOSING BETWEEN LINEAR AND
LOG-LINEAR REGRESSION MODELS:
THE MWD TEST 235
REGRESSION ERROR SPECIFICATION TEST: RESET 237
7.8 SUMMARY 239
KEY TERMS AND CONCEPTS 240
QUESTIONS 240
PROBLEMS 241
IMAGE 7
XII CONTENTS
MULTICOLLINEARITY: WHAT HAPPENS IF EXPLANATORY
VARIABLES ARE CORRELATED? 245
8.1 THE NATURE OF MULTICOLLINEARITY: THE CASE OF PERFECT
MULTICOLLINEARITY 246 8.2 THE CASE OF NEAR, OR IMPERFECT,
MULTICOLLINEARITY 248
8.3 THEORETICAL CONSEQUENCES OF MULTICOLLINEARITY 250
8.4 PRACTICAL CONSEQUENCES OF MULTICOLLINEARITY 251 8.5 DETECTION OF
MULTICOLLINEARITY 253
8.6 IS-MULTICOLLINEARITY NECESSARILY BAD? 258 8.7 AN EXTENDED EXAMPLE:
THE DEMAND FOR CHICKENS IN THE UNITED STATES, 1960 TO 1982 259
COLLINEARITY DIAGNOSTICS FOR THE DEMAND FUNCTION FOR
CHICK4NS (EQUATION [8.15]) 260
8.8 WHAT TO DO WITH MULTICOLLINEARITY: REMEDIAL MEASURES 261
DROPPING A VARIABLE(S) FROM THE MODEL 262
ACQUIRING ADDITIONAL DATA OR A NEW SAMPLE 262
RETHINKING THE MODEL 263
PRIOR INFORMATION ABOUT SOME PARAMETERS 264
TRANSFORMATION OF VARIABLES 265
OTHER REMEDIES * 266
8.9 SUMMARY 266
KEY TERMS AND CONCEPTS 267
QUESTIONS 267
PROBLEMS 268
HETEROSCEDASTICITY: WHAT HAPPENS IF THE ERROR VARIANCE IS NONCONSTANT?
274
9.1 THE NATURE OF HETEROSCEDASTICITY 274
9.2 CONSEQUENCES OF HETEROSCEDASTICITY 280 9.3 DETECTION OF
HETEROSCEDASTICITY: HOW DO WE KNOW WHEN THERE IS A HETEROSCEDASTICITY
PROBLEM? 282
NATURE OF THE PROBLEM 283
GRAPHICAL EXAMINATION OF RESIDUALS 283
PARK TEST 285
GLEJSERTEST 287
WHITE S GENERAL HETEROSCEDASTICITY TEST 289
OTHER TESTS OF HETEROSCEDASTICITY 290
9.4 WHAT TO DO IF HETEROSCEDASTICITY IS OBSERVED: REMEDIAL MEASURES 291
WHEN A 2 . IS KNOWN: THE METHOD OF WEIGHTED LEAST SQUARES (WLS) 291 WHEN
TRUE A 2 IS UNKNOWN 292
RESPECIFICATION OF THE MODEL 297
IMAGE 8
CONTENTS XIII
9.5 WHITE S HETEROSCEDASTICITY-CORRECTED
STANDARD ERRORS AND T STATISTICS 298
9.6 SOME CONCRETE EXAMPLES OF HETEROSCEDASTICITY 299
9.7 SUMMARY 302
KEY TERMS AND CONCEPTS 303
QUESTIONS 304
PROBLEMS 304
10 AUTOCORRELATION: WHAT HAPPENS IF ERROR TERMS ARE CORRELATED? 312 10.1
THE NATURE OF AUTOCORRELATION 313
* INERTIA - 314
MODEL SPECIFICATION ERROR(S) 315
THE COBWEB PHENOMENON 315
DATA MANIPULATION 315
10.2 CONSEQUENCES OF AUTOCORRELATION 316
10.3 DETECTING AUTOCORRELATION, 317
THE GRAPHICAL METHOD 318
THE DURBIN-WATSON DTEST 320
10.4 REMEDIAL MEASURES 325
10.5 HOW TO ESTIMATE P 327
P = 1: THE FIRST DIFFERENCE METHOD 327
P ESTIMATED FROM DURBIN-WATSON D STATISTIC 327
P ESTIMATED FROM OLS RESIDUALS, ET 328
OTHER METHODS OF ESTIMATING P 328
10.6 A LARGE SAMPLE METHOD OF CORRECTING OLS STANDARD ERRORS: THE
NEWEY-WEST (NW) METHOD 332
10.7 SUMMARY 334
KEY TERMS AND CONCEPTS 335
QUESTIONS 335
PROBLEMS 336
APPENDIX 10A: THE RUNS TEST 341
SWED-EISENHART CRITICAL RUNS TEST 342
DECISION RULE 342
APPENDIX 10B: A GENERAL TEST OF
AUTOCORRELATION: THE BREUSCH-GODFREY (BG) TEST 343
PART III ADVANCED TOPICS IN ECONOMETRICS 345
11 SIMULTANEOUS EQUATION MODELS 347
11.1 THE NATURE OF SIMULTANEOUS EQUATION MODELS 348 11.2 THE
SIMULTANEOUS EQUATION BIAS:
INCONSISTENCY OF OLS ESTIMATORS 350
IMAGE 9
XIV CONTENTS
11.3 THE METHOD OF INDIRECT LEAST SQUARES (ILS) 352
11.4 INDIRECT LEAST SQUARES: AN ILLUSTRATIVE EXAMPLE 353
11.5 THE IDENTIFICATION PROBLEM: A ROSE BY ANY OTHER NAME MAY NOT BE A
ROSE 355
UNDERIDENTIFICATION 356
JUST OR EXACT IDENTIFICATION 357
OVERIDENTIFICATION 359
11.6 RULES FOR IDENTIFICATION: THE ORDER CONDITION OF IDENTIFICATION 361
11.7 ESTIMATION OF AN OVERIDENTIFIED EQUATION: THE METHOD OF TWO-STAGE
LEAST SQUARES 362 11.8 2SLS: A NUMERICAL EXAMPLE 364
11.9 SUMMARY 365
KEY TERMS AND CONCEPTS 366
QUESTIONS 367
PROBLEMS 367
APPENDIX 11 A: INCONSISTENCY OF OLS ESTIMATORS 369
12 SELECTED TOPICS IN SINGLE EQUATION REGRESSION MODELS 371 12.1 DYNAMIC
ECONOMIC MODELS: AUTOREGRESSIVE AND DISTRIBUTED LAG MODELS 371
REASONS FOR LAG . 372
ESTIMATION OF DISTRIBUTED LAG MODELS 374
THE KOYCK, ADAPTIVE EXPECTATIONS, AND STOCK ADJUSTMENT MODELS APPROACH
TO ESTIMATING DISTRIBUTED LAG MODELS 377 I 12.2 THE PHENOMENON OF
SPURIOUS REGRESSION: NONSTATIONARY TIME SERIES 380
12.3 TESTS OF STATIONARITY 382
12.4 COINTEGRATED TIME SERIES 383
12.5 THE RANDOM WALK MODEL 384
12.6 THE LOGIT MODEL 386
ESTIMATION OF THE LOGIT MODEL 390
12.7 SUMMARY 396
KEY TERMS AND CONCEPTS 397
QUESTIONS 397
PROBLEMS 398
INTRODUCTION TO APPENDIXES A, B, C, AND D: BASICS OF PROBABILITY AND
STATISTICS 403
APPENDIX A: REVIEW OF STATISTICS: PROBABILITY AND PROBABILITY
DISTRIBUTIONS 405
A.1 SOME NOTATION 405
THE SUMMATION NOTATION 405
PROPERTIES OF THE SUMMATION OPERATOR 406
IMAGE 10
CONTENTS XV
A.2 EXPERIMENT, SAMPLE SPACE, SAMPLE POINT,
. AND EVENTS 407
EXPERIMENT 407
SAMPLE SPACE OR POPULATION 407
SAMPLE POINT 408
EVENTS 408
VENN DIAGRAMS 408
A.3 RANDOM VARIABLES 409
A.4 PROBABILITY. 410
PROBABILITY OF AN EVENT: THE CLASSICAL OR A PRIORI DEFINITION 410
RELATIVE FREQUENCY OR EMPIRICAL DEFINITION OF PROBABILITY 411
PROBABILITY OF RANDOM VARIABLES 417
A.5 RANDOM VARIABLES AND THEIR PROBABILITY DISTRIBUTIONS 417
PROBABILITY DISTRIBUTION OF A DISCRETE RANDOM VARIABLE 417 PROBABILITY
DISTRIBUTION OF A CONTINUOUS RANDOM VARIABLE 419 CUMULATIVE DISTRIBUTION
FUNCTION (CDF) 420
A.6 MULTIVARIATE PROBABILITY DENSITY FUNCTIONS 422 MARGINAL PROBABILITY
FUNCTIONS 424
CONDITIONAL PROBABILITY FUNCTIONS 425
STATISTICAL INDEPENDENCE 427
A.7 SUMMARY AND CONCLUSIONS 428
. KEY TERMS AND CONCEPTS * 428
REFERENCES 429
QUESTIONS 429
PROBLEMS 430
APPENDIX B: CHARACTERISTICS OF PROBABILITY DISTRIBUTIONS 434 B.1
EXPECTED VALUE: A MEASURE OF CENTRAL TENDENCY 434
PROPERTIES OF EXPECTED VALUE 436
EXPECTED VALUE OF MULTIVARIATE PROBABILITY DISTRIBUTIONS 437 B.2
VARIANCE: A MEASURE OF DISPERSION 438
PROPERTIES OF VARIANCE 439
CHEBYSHEV S INEQUALITY 441
COEFFICIENT OF VARIATION 442
B.3 COVARIANCE 443
PROPERTIES OF COVARIANCE 444
B.4 CORRELATION COEFFICIENT 445
PROPERTIES OF CORRELATION COEFFICIENT 445
VARIANCES OF CORRELATED VARIABLES 447
B.5 CONDITIONAL EXPECTATION 447
CONDITIONAL VARIANCE 449
B.6 SKEWNESSANDKURTOSIS 449
B.7 FROM THE POPULATION TO THE SAMPLE 452
SAMPLE MEAN 452
IMAGE 11
XVI CONTENTS
SAMPLE VARIANCE 453
SAMPLE COVARIANCE 454
SAMPLE CORRELATION COEFFICIENT 455
SAMPLE SKEWNESS AND KURTOSIS 456
B.8 SUMMARY 456
KEY TERMS AND CONCEPTS 457
QUESTIONS 457
PROBLEMS 458
OPTIONALI.EXERCISES 460
APPENDIX C: SOME IMPORTANT PROBABILITY DISTRIBUTIONS 461 C.1 THE NORMAL
DISTRIBUTION 462
PROPERTIES OF THE NORMAL DISTRIBUTION 462
THE STANDARD NORMAL DISTRIBUTION 464
RANDOM SAMPLING FROM A NORMAL POPULATION 468
THE SAMPLING OR PROBABILITY DISTRIBUTION OF THE SAMPLE MEAN X 468 THE
CENTRAL LIMIT THEOREM (CLT) R 472
C.2 THE T DISTRIBUTION 473
PROPERTIES OF THE T DISTRIBUTION 474
C.3 THE CHI-SQUARE (X 2 ) PROBABILITY DISTRIBUTION 477 PROPERTIES OF THE
CHI-SQUARE DISTRIBUTION 478
C.4 THE F DISTRIBUTION 480
PROPERTIES OF THE F DISTRIBUTION 481
C.5 SUMMARY 483
KEY TERMS AND CONCEPTS 483
QUESTIONS 484
PROBLEMS 484
APPENDIX D: STATISTICAL INFERENCE: ESTIMATION AND HYPOTHESIS TESTING 487
D.1 THE MEANING OF STATISTICAL INFERENCE 487 D.2 ESTIMATION AND
HYPOTHESIS TESTING: - TWIN BRANCHES OF STATISTICAL INFERENCE 489 D.3
ESTIMATION OF PARAMETERS 490
D.4 PROPERTIES OF POINT ESTIMATORS 493
LINEARITY 494
UNBIASEDNESS 494
MINIMUM VARIANCE 495
EFFICIENCY 496
BEST LINEAR UNBIASED ESTIMATOR (BLUE) 497
CONSISTENCY 497
D.5 STATISTICAL INFERENCE: HYPOTHESIS TESTING 498 THE CONFIDENCE
INTERVAL APPROACH TO HYPOTHESIS TESTING 499 TYPE I AND TYPE II ERRORS: A
DIGRESSION 500
THE TEST OF SIGNIFICANCE APPROACH TO HYPOTHESIS TESTING 503
IMAGE 12
CONTENTS XVII
A WORD ON CHOOSING THE LEVEL OF SIGNIFICANCE, A, AND THE P VALUE 506
THE X 2 AND FTESTS OF SIGNIFICANCE 507
D.6 SUMMARY 510
KEY TERMS AND CONCEPTS 510
QUESTIONS 511
PROBLEMS 512
APPENDIX E: STATISTICAL TABLES 515
APPENDIX F: COMPUTER OUTPUT OF EVIEWS, MINITAB, EXCEL/AND STATA 534
SELECTED BIBLIOGRAPHY 541
INDEXES * 545
NAME INDEX 545
SUBJECT INDEX , 547
|
| any_adam_object | 1 |
| author | Gujarati, Damodar N. Porter, Dawn C. |
| author_GND | (DE-588)129300462 |
| author_facet | Gujarati, Damodar N. Porter, Dawn C. |
| author_role | aut aut |
| author_sort | Gujarati, Damodar N. |
| author_variant | d n g dn dng d c p dc dcp |
| building | Verbundindex |
| bvnumber | BV036796644 |
| classification_rvk | SK 980 |
| ctrlnum | (OCoLC)698893936 (DE-599)GBV594666856 |
| discipline | Mathematik |
| edition | 4. ed., internat. ed. |
| format | Book |
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| id | DE-604.BV036796644 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T22:48:26Z |
| institution | BVB |
| isbn | 0071276076 9780071276078 |
| language | English |
| lccn | 2009010482 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-020712943 |
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| physical | XXII, 554 S. graph. Darst. |
| publishDate | 2010 |
| publishDateSearch | 2010 |
| publishDateSort | 2010 |
| publisher | McGraw-Hill |
| record_format | marc |
| spelling | Gujarati, Damodar N. Verfasser (DE-588)129300462 aut Essentials of econometrics Damodar N. Gujarati ; Dawn C. Porter 4. ed., internat. ed. Boston, Mass. [u.a.] McGraw-Hill 2010 XXII, 554 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Includes index Diskette (DE-588)4122115-1 gnd rswk-swf Ökonometrie (DE-588)4132280-0 gnd rswk-swf (DE-588)4123623-3 Lehrbuch gnd-content Ökonometrie (DE-588)4132280-0 s Diskette (DE-588)4122115-1 s 1\p DE-604 Porter, Dawn C. Verfasser aut GBV Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020712943&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Gujarati, Damodar N. Porter, Dawn C. Essentials of econometrics Diskette (DE-588)4122115-1 gnd Ökonometrie (DE-588)4132280-0 gnd |
| subject_GND | (DE-588)4122115-1 (DE-588)4132280-0 (DE-588)4123623-3 |
| title | Essentials of econometrics |
| title_auth | Essentials of econometrics |
| title_exact_search | Essentials of econometrics |
| title_full | Essentials of econometrics Damodar N. Gujarati ; Dawn C. Porter |
| title_fullStr | Essentials of econometrics Damodar N. Gujarati ; Dawn C. Porter |
| title_full_unstemmed | Essentials of econometrics Damodar N. Gujarati ; Dawn C. Porter |
| title_short | Essentials of econometrics |
| title_sort | essentials of econometrics |
| topic | Diskette (DE-588)4122115-1 gnd Ökonometrie (DE-588)4132280-0 gnd |
| topic_facet | Diskette Ökonometrie Lehrbuch |
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