Essentials of econometrics:
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| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boston [u.a.]
McGraw-Hill/Irwin
2006
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| Edition: | 3. ed., internat. ed. |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Literaturverz. S. 541 - 544 |
| Physical Description: | XXI, 553 S. graph. Darst. 1 CD-ROM (12 cm) |
| ISBN: | 0071244484 9780071244480 |
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| 245 | 1 | 0 | |a Essentials of econometrics |c Damodar N. Gujarati |
| 250 | |a 3. ed., internat. ed. | ||
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| 500 | |a Literaturverz. S. 541 - 544 | ||
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| adam_text | CONTENTS PREFACE XVIII 1 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 8 CHECKING FOR MODEL ADEQUACY: MODEL SPECIFICATION TESTING 9
TESTING THE HYPOTHESIS DERIVED FROM THE MODEL 11 USING THE MODEL FOR
PREDICTION OR FORECASTING 11 1.4 THE ROAD AHEAD 12 KEY TERMS AND
CONCEPTS 13 QUESTIONS 13 PROBLEMS 14 APPENDIX 1 A: ECONOMIC DATA ON THE
WORLD WIDE WEB 15 PART I BASICS OF PROBABILITY AND STATISTICS 19 2
REVIEW OF STATISTICS I: PROBABILITY AND PROBABILITY DISTRIBUTIONS 21 2.1
SOME NOTATION 21 THE SUMMATION NOTATION 21 PROPERTIES OF THE SUMMATION
OPERATOR 22 VII ONTENTS 2.2 EXPERIMENT, SAMPLE SPACE, SAMPLE POINT, AND
EVENTS 23 EXPERIMENT 23 SAMPLE SPACE OR POPULATION 23 SAMPLE POINT 24
EVENTS 24 VENN DIAGRAMS 24 2.3 RANDOM VARIABLES 25 2.4 PROBABILITY 26
PROBABILITY OF AN EVENT: THE CLASSICAL OR A PRIORI DEFINITION 26
RELATIVE FREQUENCY OR EMPIRICAL DEFINITION OF PROBABILITY 27 PROBABILITY
OF RANDOM VARIABLES 33 2.5 RANDOM VARIABLES AND THEIR PROBABILITY
DISTRIBUTIONS 33 PROBABILITY DISTRIBUTION OF A DISCRETE RANDOM VARIABLE
33 PROBABILITY DISTRIBUTION OF A CONTINUOUS RANDOM VARIABLE 35
CUMULATIVE DISTRIBUTION FUNCTION (CDF) 36 2.6 MULTIVARIATE PROBABILITY
DENSITY FUNCTIONS 38 MARGINAL PROBABILITY FUNCTIONS 40 CONDITIONAL
PROBABILITY FUNCTIONS 41 STATISTICAL INDEPENDENCE 43 2.7 SUMMARY AND
CONCLUSIONS 44 KEY TERMS AND CONCEPTS 44 REFERENCES 45 QUESTIONS 45
PROBLEMS 46 CHARACTERISTICS OF PROBABILITY DISTRIBUTIONS 50 3.1 EXPECTED
VALUE: A MEASURE OF CENTRAL TENDENCY 50 PROPERTIES OF EXPECTED VALUE 52
EXPECTED VALUE OF MULTIVARIATE PROBABILITY DISTRIBUTIONS 53 3.2
VARIANCE: A MEASURE OF DISPERSION 54 PROPERTIES OF VARIANCE 55
CHEBYSHEV S INEQUALITY 57 COEFFICIENT OF VARIATION 58 3.3 COVARIANCE 59
PROPERTIES OF COVARIANCE 60 3.4 CORRELATION COEFFICIENT 61 PROPERTIES OF
CORRELATION COEFFICIENT 61 VARIANCES OF CORRELATED VARIABLES 63 3.5
CONDITIONAL EXPECTATION 63 CONDITIONAL VARIANCE 65 3.6
SKEWNESSANDKURTOSIS 65 CONTENTS IX 3.7 FROM THE POPULATION TO THE SAMPLE
68 SAMPLE MEAN 68 SAMPLE VARIANCE 69 SAMPLE COVARIANCE 70 SAMPLE
CORRELATION COEFFICIENT 71 SAMPLE SKEWNESS AND KURTOSIS 72 3.8 SUMMARY
72 KEY TERMS AND CONCEPTS 73 QUESTIONS 73 PROBLEMS 74 OPTIONAL EXERCISES
76 SOME IMPORTANT PROBABILITY DISTRIBUTIONS 77 4.1 THE NORMAL
DISTRIBUTION 78 PROPERTIES OF THE NORMAL DISTRIBUTION 78 THE STANDARD
NORMAL DISTRIBUTION 80 RANDOM SAMPLING FROM A NORMAL POPULATION 84 THE
SAMPLING OR PROBABILITY DISTRIBUTION OF THE SAMPLE MEANX 84 THE CENTRAL
LIMIT THEOREM (CLT) 88 4.2 THE T DISTRIBUTION 89 PROPERTIES OF THE T
DISTRIBUTION 90 4.3 THE CHI-SQUARE (X 2 ) PROBABILITY DISTRIBUTION 93
PROPERTIES OF THE CHI-SQUARE DISTRIBUTION 94 4.4 THE F DISTRIBUTION 96
PROPERTIES OF THE F DISTRIBUTION 97 4.5 SUMMARY 99 KEY TERMS AND
CONCEPTS 99 QUESTIONS 100 PROBLEMS 100 STATISTICAL INFERENCE: ESTIMATION
AND HYPOTHESIS TESTING 103 5.1 THE MEANING OF STATISTICAL INFERENCE 103
5.2 ESTIMATION AND HYPOTHESIS TESTING: TWIN BRANCHES OF STATISTICAL
INFERENCE 105 5.3 ESTIMATION OF PARAMETERS 106 5.4 PROPERTIES OF POINT
ESTIMATORS 109 LINEARITY 110 UNBIASEDNESS 110 MINIMUM VARIANCE 111
EFFICIENCY 112 BEST LINEAR UNBIASED ESTIMATOR (BLUE) 113 CONSISTENCY 1
13 5.5 STATISTICAL INFERENCE: HYPOTHESIS TESTING 114 THE CONFIDENCE
INTERVAL APPROACH TO HYPOTHESIS TESTING 115 TYPE I AND TYPE II ERRORS: A
DIGRESSION 116 NTENTS THE TEST OF SIGNIFICANCE APPROACH TO HYPOTHESIS
TESTING 119 A WORD ON CHOOSING THE LEVEL OF SIGNIFICANCE, A, AND THE P
VALUE 122 THE X 2 AND F TESTS OF SIGNIFICANCE 123 5.6 SUMMARY 126 KEY
TERMS AND CONCEPTS 126 QUESTIONS 127 PROBLEMS 128 PART II THE LINEAR
REGRESSION MODEL 131 6 BASIC IDEAS OF LINEAR REGRESSION: THE
TWO-VARIABLE MODEL 133 6.1 THE MEANING OF REGRESSION 133 6.2 THE
POPULATION REGRESSION FUNCTION (PRF): A HYPOTHETICAL EXAMPLE 134 6.3
STATISTICAL OR STOCHASTIC SPECIFICATION OF THE POPULATION REGRESSION
FUNCTION 137 6.4 THE NATURE OF THE STOCHASTIC ERROR TERM 139 6.5 THE
SAMPLE REGRESSION FUNCTION (SRF) 140 6.6 THE SPECIAL MEANING OF THE TERM
LINEAR REGRESSION 143 LINEARITY IN THE VARIABLES 143 LINEARITY IN THE
PARAMETERS 144 6.7 TWO-VARIABLE VERSUS MULTIPLE LINEAR REGRESSION 145
6.8 ESTIMATION OF PARAMETERS: THE METHOD OF ORDINARY LEAST SQUARES 145
THE METHOD OF ORDINARY LEAST SQUARES 146 6.9 PUTTING IT ALL TOGETHER 148
INTERPRETATION OF THE ESTIMATED LOTTO EXPENDITURE FUNCTION 148 6.10 SOME
ILLUSTRATIVE EXAMPLES 150 6.11 SUMMARY 155 KEY TERMS AND CONCEPTS 156
QUESTIONS 156 PROBLEMS 157 OPTIONAL QUESTIONS 166 APPENDIX 6A:
DERIVATION OF LEAST-SQUARES ESTIMATES 166 7 THE TWO-VARIABLE MODEL:
HYPOTHESIS TESTING 167 7.1 THE CLASSICAL LINEAR REGRESSION MODEL 168 7.2
VARIANCES AND STANDARD ERRORS OF ORDINARY LEAST SQUARES ESTIMATORS 171
VARIANCES AND STANDARD ERRORS OF THE LOTTO EXAMPLE 173 SUMMARY OF THE
LOTTO EXPENDITURE FUNCTION 173 CONTENTS XI 7.3 WHY OLS? THE PROPERTIES
OF OLS ESTIMATORS 174 MONTE CARLO EXPERIMENT 175 7.4 THE SAMPLING, OR
PROBABILITY, DISTRIBUTIONS OF OLS ESTIMATORS 176 7.5 HYPOTHESIS TESTING
178 TESTING H 0 :B 2 * 0 VERSUS H,:B 2 ^ 0: THE CONFIDENCE INTERVAL
APPROACH 180 THE TEST OF SIGNIFICANCE APPROACH TO HYPOTHESIS TESTING 182
LOTTO EXAMPLE CONTINUED 183 7.6 HOW GOOD IS THE FITTED REGRESSION LINE:
THE COEFFICIENT OF DETERMINATION, R 2 185 FORMULAS TO COMPUTE R 2 187 R
2 FOR THE LOTTO EXAMPLE 188 THE COEFFICIENT OF CORRELATION, R 188 7.7
REPORTING THE RESULTS OF REGESSION ANALYSIS 189 7.8 COMPUTER OUTPUT OF
THE LOTTO EXAMPLE 190 7.9 NORMALITY TESTS 191 HISTOGRAMS OF RESIDUALS
191 NORMAL PROBABILITY PLOT 192 JARQUE-BERA TEST 192 7.10 A CONCLUDING
EXAMPLE: RELATIONSHIP BETWEEN WAGES AND PRODUCTIVITY IN THE U.S.
BUSINESS SECTOR, 1959 TO 2000 193 7.11 A WORD ABOUT FORECASTING 196 7.12
SUMMARY 200 KEY TERMS AND CONCEPTS 200 QUESTIONS 201 PROBLEMS 202 8
MULTIPLE REGRESSION: ESTIMATION AND HYPOTHESIS TESTING 208 8.1 THE
THREE-VARIABLE LINEAR REGRESSION MODEL 209 THE MEANING OF PARTIAL
REGRESSION COEFFICIENT 210 8.2 ASSUMPTIONS OF MULTIPLE LINEAR REGRESSION
MODEL 212 8.3 ESTIMATION OF PARAMETERS OF MULTIPLE REGRESSION 214
ORDINARY LEAST SQUARES ESTIMATORS 214 VARIANCE AND STANDARD ERRORS OF
OLS ESTIMATORS 215 PROPERTIES OF OLS ESTIMATORS OF MULTIPLE REGRESSION
217 8.4 GOODNESS OF FIT OF ESTIMATED MULTIPLE REGRESSION: MULTIPLE
COEFFICIENT OF DETERMINATION, R 2 217 8.5 ANTIQUE CLOCK AUCTION PRICES
REVISITED 218 INTERPRETATION OF THE REGRESSION RESULTS 218 ONTENTS 8.6
HYPOTHESIS TESTING IN A MULTIPLE REGRESSION: GENERAL COMMENTS 219 8.7
TESTING HYPOTHESES ABOUT INDIVIDUAL PARTIAL REGRESSION COEFFICIENTS 220
THE TEST OF SIGNIFICANCE APPROACH 220 THE CONFIDENCE INTERVAL APPROACH
TO HYPOTHESIS TESTING 221 8.8 TESTING THE JOINT HYPOTHESIS THAT S 2 = B
3 = 0 OR R 2 = 0 222 AN IMPORTANT RELATIONSHIP BETWEEN FAND R 2 226 8.9
TWO-VARIABLE REGRESSION IN THE CONTEXT OF MULTIPLE REGRESSION:
INTRODUCTION TO SPECIFICATION BIAS 227 8.10 COMPARING TWO FL 2 VALUES:
THE ADJUSTED FL 2 228 8.11 WHEN TO ADD AN ADDITIONAL EXPLANATORY
VARIABLE TO A MODEL 229 8.12 RESTRICTED LEAST SQUARES 231 8.13
ILLUSTRATIVE EXAMPLES 232 DISCUSSION OF REGRESSION RESULTS 233 8.14
SUMMARY 236 KEY TERMS AND CONCEPTS 237 QUESTIONS 238 PROBLEMS 239
APPENDIX 8A.1: DERIVATIONS OF OLS ESTIMATORS GIVEN IN EQUATIONS (8.20)
TO (8.22) 243 APPENDIX 8A.2: DERIVATION OF EQUATION (8.31) 244 APPENDIX
8A.3: DERIVATION OF EQUATION (8.50) 244 APPENDIX 8A.4: EVIEWS OUTPUT OF
THE CLOCK AUCTION PRICE EXAMPLE 245 FUNCTIONAL FORMS OF REGRESSION
MODELS 246 9.1 HOW TO MEASURE ELASTICITY: THE LOG-LINEAR MODEL 247
HYPOTHESIS TESTING IN LOG-LINEAR MODELS 251 9.2 COMPARING LINEAR AND
LOG-LINEAR REGRESSION MODELS 252 9.3 MULTIPLE LOG-LINEAR REGRESSION
MODELS 254 9.4 HOW TO MEASURE THE GROWTH RATE: THE SEMILOG MODEL 258
INSTANTANEOUS VERSUS COMPOUND RATE OF GROWTH 261 THE LINEAR TREND MODEL
262 9.5 THE LIN-LOG MODEL: WHEN THE EXPLANATORY VARIABLE IS LOGARITHMIC
263 9.6 RECIPROCAL MODELS 265 9.7 POLYNOMIAL REGRESSION MODELS 270 9.8
REGRESSION THROUGH THE ORIGIN 274 CONTENTS XIII 9.9 A NOTE ON SCALING
AND UNITS OF MEASUREMENT 275 9.10 SUMMARY OF FUNCTIONAL FORMS 277 9.11
SUMMARY 277 KEY TERMS AND CONCEPTS 278 QUESTIONS 279 PROBLEMS 280
APPENDIX 9A: LOGARITHMS 287 10 DUMMY VARIABLE REGRESSION MODELS 290 10.1
THE NATURE OF DUMMY VARIABLES 290 10.2 ANCOVA MODELS: REGRESSION ON ONE
QUANTITATIVE VARIABLE AND ONE QUALITATIVE VARIABLE WITH TWO CATEGORIES:
EXAMPLE 10.1 REVISITED 297 10.3 REGRESSION ON ONE QUANTITATIVE VARIABLE
AND ONE QUALITATIVE VARIABLE WITH MORE THAN TWO CLASSES OR CATEGORIES
299 10.4 REGRESSION ON ONE QUANTITATIVE EXPLANATORY VARIABLE AND MORE
THAN ONE QUALITATIVE VARIABLE 303 INTERACTION EFFECTS 304 A
GENERALIZATION 305 10.5 COMPARING TWO REGRESSIONS 306 10.6 THE USE OF
DUMMY VARIABLES IN SEASONAL ANALYSIS 311 10.7 WHAT HAPPENS IF THE
DEPENDENT VARIABLE IS ALSO A DUMMY VARIABLE? THE LINEAR PROBABILITY
MODEL (LPM) 315 10.8 SUMMARY 319 KEY TERMS AND CONCEPTS 320 QUESTIONS
321 PROBLEMS 321 PART III REGRESSION ANALYSIS IN PRACTICE 333 11 MODEL
SELECTION: CRITERIA AND TESTS 335 11.1 THE ATTRIBUTES OF A GOOD MODEL
336 11.2 TYPES OF SPECIFICATION ERRORS 337 11.3 OMISSON OF RELEVANT
VARIABLE BIAS: UNDERFITTING A MODEL 337 11.4 INCLUSION OF IRRELEVANT
VARIABLES: OVERFITTING A MODEL 341 11.5 INCORRECT FUNCTIONAL FORM 343
11.6 ERRORS OF MEASUREMENT 345 ERRORS OF MEASUREMENT IN THE DEPENDENT
VARIABLE 346 ERRORS OF MEASUREMENT IN THE EXPLANATORY VARIABLE(S) 346
CONTENTS 11.7 DETECTING SPECIFICATION ERRORS: TESTS OF SPECIFICATION
ERRORS 347 DETECTING THE PRESENCE OF UNNECESSARY VARIABLES 347 TESTS FOR
OMITTED VARIABLES AND INCORRECT FUNCTIONAL FORMS 350 CHOOSING BETWEEN
LINEAR AND LOG-LINEAR REGRESSION MODELS:THE MWD TEST 352 REGRESSION
ERROR SPECIFICATION TEST: RESET 353 11.8 SUMMARY 356 KEY TERMS AND
CONCEPTS 357 QUESTIONS 357 PROBLEMS 357 12 MULTICOLLINEARITY: WHAT
HAPPENS IF EXPLANATORY VARIABLES ARE CORRELATED? 363 12.1 THE NATURE OF
MULTICOLLINEARITY: THE CASE OF PERFECT MULTICOLLINEARITY 364 12.2 THE
CASE OF NEAR, OR IMPERFECT, MULTICOLLINEARITY 366 12.3 THEORETICAL
CONSEQUENCES OF MULTICOLLINEARITY 368 12.4 PRACTICAL CONSEQUENCES OF
MULTICOLLINEARITY 369 12.5 DETECTION OF MULTICOLLINEARITY 371 12.6 IS
MULTICOLLINEARITY NECESSARILY BAD? 376 12.7 AN EXTENDED EXAMPLE: THE
DEMAND FOR CHICKENS IN THE UNITED STATES, 1960 TO 1982 377 COLLINEARITY
DIAGNOSTICS FOR THE DEMAND FUNCTION FOR CHICKENS [EQUATION (12.15)] 378
12.8 WHAT TO DO WITH MULTICOLLINEARITY: REMEDIAL MEASURES 379 DROPPING A
VARIABLE FROM THE MODEL 380 ACQUIRING ADDITIONAL DATA OR A NEW SAMPLE
380 RETHINKING THE MODEL 381 PRIOR INFORMATION ABOUT SOME PARAMETERS 382
TRANSFORMATION OF VARIABLES 383 OTHER REMEDIES 384 12.9 SUMMARY 384 KEY
TERMS AND CONCEPTS 385 QUESTIONS 385 PROBLEMS 386 CONTENTS XV 13
HETEROSCEDASTICITY: WHAT HAPPENS IF THE ERROR VARIANCE IS NONCONSTANT?
390 13.1 THE NATURE OF HETEROSCEDASTICITY 390 13.2 CONSEQUENCES OF
HETEROSCEDASTICITY 396 13.3 DETECTION OF HETEROSCEDASTICITY: HOW DO WE
KNOW WHEN THERE IS A HETEROSCEDASTICITY PROBLEM? 398 NATURE OF THE
PROBLEM 399 GRAPHICAL EXAMINATION OF RESIDUALS 400 PARK TEST 402 GLEJSER
TEST 404 WHITE S GENERAL HETEROSCEDASTICITY TEST 405 OTHER TESTS OF
HETEROSCEDASTICITY 407 13.4 WHAT TO DO IF HETEROSCEDASTICITY IS
OBSERVED: REMEDIAL MEASURES 407 WHEN UF IS KNOWN: THE METHOD OF WEIGHTED
LEAST SQUARES (WLS) 407 WHEN TRUE O, 2 IS UNKNOWN 409 RESPECIFICATION OF
THE MODEL 412 13.5 WHITE S HETEROSCEDASTICITY-CORRECTED STANDARD ERRORS
AND T STATISTICS 414 13.6 SOME CONCRETE EXAMPLES OF HETEROSCEDASTICITY
414 13.7 SUMMARY 417 KEY TERMS AND CONCEPTS 417 QUESTIONS 418 PROBLEMS
418 14 AUTOCORRELATION: WHAT HAPPENS IF ERROR TERMS ARE CORRELATED? 427
14.1 THE NATURE OF AUTOCORRELATION 428 INERTIA 429 MODEL SPECIFICATION
ERROR(S) 430 THE COBWEB PHENOMENON 430 DATA MANIPULATION 430 14.2
CONSEQUENCES OF AUTOCORRELATION 431 14.3 DETECTING AUTOCORRELATION 432
THE GRAPHICAL METHOD 434 THE DURBIN-WATSON DTEST 434 14.4 REMEDIAL
MEASURES 440 14.5 HOW TO ESTIMATE 442 P = 1: THE FIRST DIFFERENCE METHOD
442 P ESTIMATED FROM DURBIN-WATSON D STATISTIC 443 P ESTIMATED FROM OLS
RESIDUALS, E, 444 OTHER METHODS OF ESTIMATING P 444 CONTENTS 14.6
SUMMARY 448 KEY TERMS AND CONCEPTS 448 QUESTIONS 449 PROBLEMS 450
APPENDIX 14A: THE RUNS TEST 455 PART IV ADVANCED TOPICS IN ECONOMETRICS
457 15 SIMULTANEOUS EQUATION MODELS 459 15.1 THE NATURE OF SIMULTANEOUS
EQUATION MODELS 460 15.2 THE SIMULTANEOUS EQUATION BIAS: INCONSISTENCY
OF OLS ESTIMATORS 462 15.3 THE METHOD OF INDIRECT LEAST SQUARES 464 15.4
INDIRECT LEAST SQUARES: AN ILLUSTRATIVE EXAMPLE 465 15.5 THE
IDENTIFICATION PROBLEM: A ROSE BY ANY OTHER NAME MAY NOT BE A ROSE 467
UNDERIDENTIFICATION 469 JUST OR EXACT IDENTIFICATION 470
OVERIDENTIFICATION 472 15.6 RULES FOR IDENTIFICATION: THE ORDER
CONDITION OF IDENTIFICATION 474 15.7 ESTIMATION OF AN OVERIDENTIFIED
EQUATION: THE METHOD OF TWO-STAGE LEAST SQUARES 475 15.8 2SLS: A
NUMERICAL EXAMPLE 476 15.9 SUMMARY 478 KEY TERMS AND CONCEPTS 479
QUESTIONS 479 PROBLEMS 480 APPENDIX 15A: INCONSISTENCY OF OLS ESTIMATORS
482 16 SELECTED TOPICS IN SINGLE EQUATION REGRESSION MODELS 484 16.1
DYNAMIC ECONOMIC MODELS: AUTOREGRESSIVE AND DISTRIBUTED LAG MODELS 484
REASONS FOR LAG 485 ESTIMATION OF DISTRIBUTED LAG MODELS 487 THE KOYCK,
ADAPTIVE EXPECTATIONS, AND STOCK ADJUSTMENT MODELS APPROACHES TO
ESTIMATING DISTRIBUTED LAG MODELS 490 16.2 THE PHENOMENON OF SPURIOUS
REGRESSION: NONSTATIONARY TIME SERIES 493 16.3 TESTS OF STATIONARITY 497
16.4 COINTEGRATED TIME SERIES 498 CONTENTS XVN 16.5 THE RANDOM WALK
MODEL 499 16.6 THE LOGIT MODEL 501 ESTIMATION OF THE LOGIT MODEL 505
16.7 SUMMARY 508 KEY TERMS AND CONCEPTS 510 QUESTIONS 510 PROBLEMS 511
APPENDIX A: STATISTICAL TABLES 515 APPENDIX B: COMPUTER OUTPUT OF
EVIEWS, MINITAB, EXCEL, AND STATA 534 SELECTED BIBLIOGRAPHY 541 INDEXES
545 NAME INDEX 545 SUBJECT INDEX 547
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| adam_txt |
CONTENTS PREFACE XVIII 1 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 8 CHECKING FOR MODEL ADEQUACY: MODEL SPECIFICATION TESTING 9
TESTING THE HYPOTHESIS DERIVED FROM THE MODEL 11 USING THE MODEL FOR
PREDICTION OR FORECASTING 11 1.4 THE ROAD AHEAD 12 KEY TERMS AND
CONCEPTS 13 QUESTIONS 13 PROBLEMS 14 APPENDIX 1 A: ECONOMIC DATA ON THE
WORLD WIDE WEB 15 PART I BASICS OF PROBABILITY AND STATISTICS 19 2
REVIEW OF STATISTICS I: PROBABILITY AND PROBABILITY DISTRIBUTIONS 21 2.1
SOME NOTATION 21 THE SUMMATION NOTATION 21 PROPERTIES OF THE SUMMATION
OPERATOR 22 VII ONTENTS 2.2 EXPERIMENT, SAMPLE SPACE, SAMPLE POINT, AND
EVENTS 23 EXPERIMENT 23 SAMPLE SPACE OR POPULATION 23 SAMPLE POINT 24
EVENTS 24 VENN DIAGRAMS 24 2.3 RANDOM VARIABLES 25 2.4 PROBABILITY 26
PROBABILITY OF AN EVENT: THE CLASSICAL OR A PRIORI DEFINITION 26
RELATIVE FREQUENCY OR EMPIRICAL DEFINITION OF PROBABILITY 27 PROBABILITY
OF RANDOM VARIABLES 33 2.5 RANDOM VARIABLES AND THEIR PROBABILITY
DISTRIBUTIONS 33 PROBABILITY DISTRIBUTION OF A DISCRETE RANDOM VARIABLE
33 PROBABILITY DISTRIBUTION OF A CONTINUOUS RANDOM VARIABLE 35
CUMULATIVE DISTRIBUTION FUNCTION (CDF) 36 2.6 MULTIVARIATE PROBABILITY
DENSITY FUNCTIONS 38 MARGINAL PROBABILITY FUNCTIONS 40 CONDITIONAL
PROBABILITY FUNCTIONS 41 STATISTICAL INDEPENDENCE 43 2.7 SUMMARY AND
CONCLUSIONS 44 KEY TERMS AND CONCEPTS 44 REFERENCES 45 QUESTIONS 45
PROBLEMS 46 CHARACTERISTICS OF PROBABILITY DISTRIBUTIONS 50 3.1 EXPECTED
VALUE: A MEASURE OF CENTRAL TENDENCY 50 PROPERTIES OF EXPECTED VALUE 52
EXPECTED VALUE OF MULTIVARIATE PROBABILITY DISTRIBUTIONS 53 3.2
VARIANCE: A MEASURE OF DISPERSION 54 PROPERTIES OF VARIANCE 55
CHEBYSHEV'S INEQUALITY 57 COEFFICIENT OF VARIATION 58 3.3 COVARIANCE 59
PROPERTIES OF COVARIANCE 60 3.4 CORRELATION COEFFICIENT 61 PROPERTIES OF
CORRELATION COEFFICIENT 61 VARIANCES OF CORRELATED VARIABLES 63 3.5
CONDITIONAL EXPECTATION 63 CONDITIONAL VARIANCE 65 3.6
SKEWNESSANDKURTOSIS 65 CONTENTS IX 3.7 FROM THE POPULATION TO THE SAMPLE
68 SAMPLE MEAN 68 SAMPLE VARIANCE 69 SAMPLE COVARIANCE 70 SAMPLE
CORRELATION COEFFICIENT 71 SAMPLE SKEWNESS AND KURTOSIS 72 3.8 SUMMARY
72 KEY TERMS AND CONCEPTS 73 QUESTIONS 73 PROBLEMS 74 OPTIONAL EXERCISES
76 SOME IMPORTANT PROBABILITY DISTRIBUTIONS 77 4.1 THE NORMAL
DISTRIBUTION 78 PROPERTIES OF THE NORMAL DISTRIBUTION 78 THE STANDARD
NORMAL DISTRIBUTION 80 RANDOM SAMPLING FROM A NORMAL POPULATION 84 THE
SAMPLING OR PROBABILITY DISTRIBUTION OF THE SAMPLE MEANX 84 THE CENTRAL
LIMIT THEOREM (CLT) 88 4.2 THE T DISTRIBUTION 89 PROPERTIES OF THE T
DISTRIBUTION 90 4.3 THE CHI-SQUARE (X 2 ) PROBABILITY DISTRIBUTION 93
PROPERTIES OF THE CHI-SQUARE DISTRIBUTION 94 4.4 THE F DISTRIBUTION 96
PROPERTIES OF THE F DISTRIBUTION 97 4.5 SUMMARY 99 KEY TERMS AND
CONCEPTS 99 QUESTIONS 100 PROBLEMS 100 STATISTICAL INFERENCE: ESTIMATION
AND HYPOTHESIS TESTING 103 5.1 THE MEANING OF STATISTICAL INFERENCE 103
5.2 ESTIMATION AND HYPOTHESIS TESTING: TWIN BRANCHES OF STATISTICAL
INFERENCE 105 5.3 ESTIMATION OF PARAMETERS 106 5.4 PROPERTIES OF POINT
ESTIMATORS 109 LINEARITY 110 UNBIASEDNESS 110 MINIMUM VARIANCE 111
EFFICIENCY 112 BEST LINEAR UNBIASED ESTIMATOR (BLUE) 113 CONSISTENCY 1
"13 5.5 STATISTICAL INFERENCE: HYPOTHESIS TESTING 114 THE CONFIDENCE
INTERVAL APPROACH TO HYPOTHESIS TESTING 115 TYPE I AND TYPE II ERRORS: A
DIGRESSION 116 'NTENTS THE TEST OF SIGNIFICANCE APPROACH TO HYPOTHESIS
TESTING 119 A WORD ON CHOOSING THE LEVEL OF SIGNIFICANCE, A, AND THE P
VALUE 122 THE X 2 AND F TESTS OF SIGNIFICANCE 123 5.6 SUMMARY 126 KEY
TERMS AND CONCEPTS 126 QUESTIONS 127 PROBLEMS 128 PART II THE LINEAR
REGRESSION MODEL 131 6 BASIC IDEAS OF LINEAR REGRESSION: THE
TWO-VARIABLE MODEL 133 6.1 THE MEANING OF REGRESSION 133 6.2 THE
POPULATION REGRESSION FUNCTION (PRF): A HYPOTHETICAL EXAMPLE 134 6.3
STATISTICAL OR STOCHASTIC SPECIFICATION OF THE POPULATION REGRESSION
FUNCTION 137 6.4 THE NATURE OF THE STOCHASTIC ERROR TERM 139 6.5 THE
SAMPLE REGRESSION FUNCTION (SRF) 140 6.6 THE SPECIAL MEANING OF THE TERM
"LINEAR" REGRESSION 143 LINEARITY IN THE VARIABLES 143 LINEARITY IN THE
PARAMETERS 144 6.7 TWO-VARIABLE VERSUS MULTIPLE LINEAR REGRESSION 145
6.8 ESTIMATION OF PARAMETERS: THE METHOD OF ORDINARY LEAST SQUARES 145
THE METHOD OF ORDINARY LEAST SQUARES 146 6.9 PUTTING IT ALL TOGETHER 148
INTERPRETATION OF THE ESTIMATED LOTTO EXPENDITURE FUNCTION 148 6.10 SOME
ILLUSTRATIVE EXAMPLES 150 6.11 SUMMARY 155 KEY TERMS AND CONCEPTS 156
QUESTIONS 156 PROBLEMS 157 OPTIONAL QUESTIONS 166 APPENDIX 6A:
DERIVATION OF LEAST-SQUARES ESTIMATES 166 7 THE TWO-VARIABLE MODEL:
HYPOTHESIS TESTING 167 7.1 THE CLASSICAL LINEAR REGRESSION MODEL 168 7.2
VARIANCES AND STANDARD ERRORS OF ORDINARY LEAST SQUARES ESTIMATORS 171
VARIANCES AND STANDARD ERRORS OF THE LOTTO EXAMPLE 173 SUMMARY OF THE
LOTTO EXPENDITURE FUNCTION 173 CONTENTS XI 7.3 WHY OLS? THE PROPERTIES
OF OLS ESTIMATORS 174 MONTE CARLO EXPERIMENT 175 7.4 THE SAMPLING, OR
PROBABILITY, DISTRIBUTIONS OF OLS ESTIMATORS 176 7.5 HYPOTHESIS TESTING
178 TESTING H 0 :B 2 * 0 VERSUS H,:B 2 ^ 0: THE CONFIDENCE INTERVAL
APPROACH 180 THE TEST OF SIGNIFICANCE APPROACH TO HYPOTHESIS TESTING 182
LOTTO EXAMPLE CONTINUED 183 7.6 HOW GOOD IS THE FITTED REGRESSION LINE:
THE COEFFICIENT OF DETERMINATION, R 2 185 FORMULAS TO COMPUTE R 2 187 R
2 FOR THE LOTTO EXAMPLE 188 THE COEFFICIENT OF CORRELATION, R 188 7.7
REPORTING THE RESULTS OF REGESSION ANALYSIS 189 7.8 COMPUTER OUTPUT OF
THE LOTTO EXAMPLE 190 7.9 NORMALITY TESTS 191 HISTOGRAMS OF RESIDUALS
191 NORMAL PROBABILITY PLOT 192 JARQUE-BERA TEST 192 7.10 A CONCLUDING
EXAMPLE: RELATIONSHIP BETWEEN WAGES AND PRODUCTIVITY IN THE U.S.
BUSINESS SECTOR, 1959 TO 2000 193 7.11 A WORD ABOUT FORECASTING 196 7.12
SUMMARY 200 KEY TERMS AND CONCEPTS 200 QUESTIONS 201 PROBLEMS 202 8
MULTIPLE REGRESSION: ESTIMATION AND HYPOTHESIS TESTING 208 8.1 THE
THREE-VARIABLE LINEAR REGRESSION MODEL 209 THE MEANING OF PARTIAL
REGRESSION COEFFICIENT 210 8.2 ASSUMPTIONS OF MULTIPLE LINEAR REGRESSION
MODEL 212 8.3 ESTIMATION OF PARAMETERS OF MULTIPLE REGRESSION 214
ORDINARY LEAST SQUARES ESTIMATORS 214 VARIANCE AND STANDARD ERRORS OF
OLS ESTIMATORS 215 PROPERTIES OF OLS ESTIMATORS OF MULTIPLE REGRESSION
217 8.4 GOODNESS OF FIT OF ESTIMATED MULTIPLE REGRESSION: MULTIPLE
COEFFICIENT OF DETERMINATION, R 2 217 8.5 ANTIQUE CLOCK AUCTION PRICES
REVISITED 218 INTERPRETATION OF THE REGRESSION RESULTS 218 ONTENTS 8.6
HYPOTHESIS TESTING IN A MULTIPLE REGRESSION: GENERAL COMMENTS 219 8.7
TESTING HYPOTHESES ABOUT INDIVIDUAL PARTIAL REGRESSION COEFFICIENTS 220
THE TEST OF SIGNIFICANCE APPROACH 220 THE CONFIDENCE INTERVAL APPROACH
TO HYPOTHESIS TESTING 221 8.8 TESTING THE JOINT HYPOTHESIS THAT S 2 = B
3 = 0 OR R 2 = 0 222 AN IMPORTANT RELATIONSHIP BETWEEN FAND R 2 226 8.9
TWO-VARIABLE REGRESSION IN THE CONTEXT OF MULTIPLE REGRESSION:
INTRODUCTION TO SPECIFICATION BIAS 227 8.10 COMPARING TWO FL 2 VALUES:
THE ADJUSTED FL 2 228 8.11 WHEN TO ADD AN ADDITIONAL EXPLANATORY
VARIABLE TO A MODEL 229 8.12 RESTRICTED LEAST SQUARES 231 8.13
ILLUSTRATIVE EXAMPLES 232 DISCUSSION OF REGRESSION RESULTS 233 8.14
SUMMARY 236 KEY TERMS AND CONCEPTS 237 QUESTIONS 238 PROBLEMS 239
APPENDIX 8A.1: DERIVATIONS OF OLS ESTIMATORS GIVEN IN EQUATIONS (8.20)
TO (8.22) 243 APPENDIX 8A.2: DERIVATION OF EQUATION (8.31) 244 APPENDIX
8A.3: DERIVATION OF EQUATION (8.50) 244 APPENDIX 8A.4: EVIEWS OUTPUT OF
THE CLOCK AUCTION PRICE EXAMPLE 245 FUNCTIONAL FORMS OF REGRESSION
MODELS 246 9.1 HOW TO MEASURE ELASTICITY: THE LOG-LINEAR MODEL 247
HYPOTHESIS TESTING IN LOG-LINEAR MODELS 251 9.2 COMPARING LINEAR AND
LOG-LINEAR REGRESSION MODELS 252 9.3 MULTIPLE LOG-LINEAR REGRESSION
MODELS 254 9.4 HOW TO MEASURE THE GROWTH RATE: THE SEMILOG MODEL 258
INSTANTANEOUS VERSUS COMPOUND RATE OF GROWTH 261 THE LINEAR TREND MODEL
262 9.5 THE LIN-LOG MODEL: WHEN THE EXPLANATORY VARIABLE IS LOGARITHMIC
263 9.6 RECIPROCAL MODELS 265 9.7 POLYNOMIAL REGRESSION MODELS 270 9.8
REGRESSION THROUGH THE ORIGIN 274 CONTENTS XIII 9.9 A NOTE ON SCALING
AND UNITS OF MEASUREMENT 275 9.10 SUMMARY OF FUNCTIONAL FORMS 277 9.11
SUMMARY 277 KEY TERMS AND CONCEPTS 278 QUESTIONS 279 PROBLEMS 280
APPENDIX 9A: LOGARITHMS 287 10 DUMMY VARIABLE REGRESSION MODELS 290 10.1
THE NATURE OF DUMMY VARIABLES 290 10.2 ANCOVA MODELS: REGRESSION ON ONE
QUANTITATIVE VARIABLE AND ONE QUALITATIVE VARIABLE WITH TWO CATEGORIES:
EXAMPLE 10.1 REVISITED 297 10.3 REGRESSION ON ONE QUANTITATIVE VARIABLE
AND ONE QUALITATIVE VARIABLE WITH MORE THAN TWO CLASSES OR CATEGORIES
299 10.4 REGRESSION ON ONE QUANTITATIVE EXPLANATORY VARIABLE AND MORE
THAN ONE QUALITATIVE VARIABLE 303 INTERACTION EFFECTS 304 A
GENERALIZATION 305 10.5 COMPARING TWO REGRESSIONS 306 10.6 THE USE OF
DUMMY VARIABLES IN SEASONAL ANALYSIS 311 10.7 WHAT HAPPENS IF THE
DEPENDENT VARIABLE IS ALSO A DUMMY VARIABLE? THE LINEAR PROBABILITY
MODEL (LPM) 315 10.8 SUMMARY 319 KEY TERMS AND CONCEPTS 320 QUESTIONS
321 PROBLEMS 321 PART III REGRESSION ANALYSIS IN PRACTICE 333 11 MODEL
SELECTION: CRITERIA AND TESTS 335 11.1 THE ATTRIBUTES OF A GOOD MODEL
336 11.2 TYPES OF SPECIFICATION ERRORS 337 11.3 OMISSON OF RELEVANT
VARIABLE BIAS: "UNDERFITTING" A MODEL 337 11.4 INCLUSION OF IRRELEVANT
VARIABLES: "OVERFITTING" A MODEL 341 11.5 INCORRECT FUNCTIONAL FORM 343
11.6 ERRORS OF MEASUREMENT 345 ERRORS OF MEASUREMENT IN THE DEPENDENT
VARIABLE 346 ERRORS OF MEASUREMENT IN THE EXPLANATORY VARIABLE(S) 346
CONTENTS 11.7 DETECTING SPECIFICATION ERRORS: TESTS OF SPECIFICATION
ERRORS 347 DETECTING THE PRESENCE OF UNNECESSARY VARIABLES 347 TESTS FOR
OMITTED VARIABLES AND INCORRECT FUNCTIONAL FORMS 350 CHOOSING BETWEEN
LINEAR AND LOG-LINEAR REGRESSION MODELS:THE MWD TEST 352 REGRESSION
ERROR SPECIFICATION TEST: RESET 353 11.8 SUMMARY 356 KEY TERMS AND
CONCEPTS 357 QUESTIONS 357 PROBLEMS 357 12 MULTICOLLINEARITY: WHAT
HAPPENS IF EXPLANATORY VARIABLES ARE CORRELATED? 363 12.1 THE NATURE OF
MULTICOLLINEARITY: THE CASE OF PERFECT MULTICOLLINEARITY 364 12.2 THE
CASE OF NEAR, OR IMPERFECT, MULTICOLLINEARITY 366 12.3 THEORETICAL
CONSEQUENCES OF MULTICOLLINEARITY 368 12.4 PRACTICAL CONSEQUENCES OF
MULTICOLLINEARITY 369 12.5 DETECTION OF MULTICOLLINEARITY 371 12.6 IS
MULTICOLLINEARITY NECESSARILY BAD? 376 12.7 AN EXTENDED EXAMPLE: THE
DEMAND FOR CHICKENS IN THE UNITED STATES, 1960 TO 1982 377 COLLINEARITY
DIAGNOSTICS FOR THE DEMAND FUNCTION FOR CHICKENS [EQUATION (12.15)] 378
12.8 WHAT TO DO WITH MULTICOLLINEARITY: REMEDIAL MEASURES 379 DROPPING A
VARIABLE FROM THE MODEL 380 ACQUIRING ADDITIONAL DATA OR A NEW SAMPLE
380 RETHINKING THE MODEL 381 PRIOR INFORMATION ABOUT SOME PARAMETERS 382
TRANSFORMATION OF VARIABLES 383 OTHER REMEDIES 384 12.9 SUMMARY 384 KEY
TERMS AND CONCEPTS 385 QUESTIONS 385 PROBLEMS 386 CONTENTS XV 13
HETEROSCEDASTICITY: WHAT HAPPENS IF THE ERROR VARIANCE IS NONCONSTANT?
390 13.1 THE NATURE OF HETEROSCEDASTICITY 390 13.2 CONSEQUENCES OF
HETEROSCEDASTICITY 396 13.3 DETECTION OF HETEROSCEDASTICITY: HOW DO WE
KNOW WHEN THERE IS A HETEROSCEDASTICITY PROBLEM? 398 NATURE OF THE
PROBLEM 399 GRAPHICAL EXAMINATION OF RESIDUALS 400 PARK TEST 402 GLEJSER
TEST 404 WHITE'S GENERAL HETEROSCEDASTICITY TEST 405 OTHER TESTS OF
HETEROSCEDASTICITY 407 13.4 WHAT TO DO IF HETEROSCEDASTICITY IS
OBSERVED: REMEDIAL MEASURES 407 WHEN UF IS KNOWN: THE METHOD OF WEIGHTED
LEAST SQUARES (WLS) 407 WHEN TRUE O, 2 IS UNKNOWN 409 RESPECIFICATION OF
THE MODEL 412 13.5 WHITE'S HETEROSCEDASTICITY-CORRECTED STANDARD ERRORS
AND T STATISTICS 414 13.6 SOME CONCRETE EXAMPLES OF HETEROSCEDASTICITY
414 13.7 SUMMARY 417 KEY TERMS AND CONCEPTS 417 QUESTIONS 418 PROBLEMS
418 14 AUTOCORRELATION: WHAT HAPPENS IF ERROR TERMS ARE CORRELATED? 427
14.1 THE NATURE OF AUTOCORRELATION 428 INERTIA 429 MODEL SPECIFICATION
ERROR(S) 430 THE COBWEB PHENOMENON 430 DATA MANIPULATION 430 14.2
CONSEQUENCES OF AUTOCORRELATION 431 14.3 DETECTING AUTOCORRELATION 432
THE GRAPHICAL METHOD 434 THE DURBIN-WATSON DTEST 434 14.4 REMEDIAL
MEASURES 440 14.5 HOW TO ESTIMATE 442 P = 1: THE FIRST DIFFERENCE METHOD
442 P ESTIMATED FROM DURBIN-WATSON D STATISTIC 443 P ESTIMATED FROM OLS
RESIDUALS, E, 444 OTHER METHODS OF ESTIMATING P 444 CONTENTS 14.6
SUMMARY 448 KEY TERMS AND CONCEPTS 448 QUESTIONS 449 PROBLEMS 450
APPENDIX 14A: THE RUNS TEST 455 PART IV ADVANCED TOPICS IN ECONOMETRICS
457 15 SIMULTANEOUS EQUATION MODELS 459 15.1 THE NATURE OF SIMULTANEOUS
EQUATION MODELS 460 15.2 THE SIMULTANEOUS EQUATION BIAS: INCONSISTENCY
OF OLS ESTIMATORS 462 15.3 THE METHOD OF INDIRECT LEAST SQUARES 464 15.4
INDIRECT LEAST SQUARES: AN ILLUSTRATIVE EXAMPLE 465 15.5 THE
IDENTIFICATION PROBLEM: A ROSE BY ANY OTHER NAME MAY NOT BE A ROSE 467
UNDERIDENTIFICATION 469 JUST OR EXACT IDENTIFICATION 470
OVERIDENTIFICATION 472 15.6 RULES FOR IDENTIFICATION: THE ORDER
CONDITION OF IDENTIFICATION 474 15.7 ESTIMATION OF AN OVERIDENTIFIED
EQUATION: THE METHOD OF TWO-STAGE LEAST SQUARES 475 15.8 2SLS: A
NUMERICAL EXAMPLE 476 15.9 SUMMARY 478 KEY TERMS AND CONCEPTS 479
QUESTIONS 479 PROBLEMS 480 APPENDIX 15A: INCONSISTENCY OF OLS ESTIMATORS
482 16 SELECTED TOPICS IN SINGLE EQUATION REGRESSION MODELS 484 16.1
DYNAMIC ECONOMIC MODELS: AUTOREGRESSIVE AND DISTRIBUTED LAG MODELS 484
REASONS FOR LAG 485 ESTIMATION OF DISTRIBUTED LAG MODELS 487 THE KOYCK,
ADAPTIVE EXPECTATIONS, AND STOCK ADJUSTMENT MODELS APPROACHES TO
ESTIMATING DISTRIBUTED LAG MODELS 490 16.2 THE PHENOMENON OF SPURIOUS
REGRESSION: NONSTATIONARY TIME SERIES 493 16.3 TESTS OF STATIONARITY 497
16.4 COINTEGRATED TIME SERIES 498 CONTENTS XVN 16.5 THE RANDOM WALK
MODEL 499 16.6 THE LOGIT MODEL 501 ESTIMATION OF THE LOGIT MODEL 505
16.7 SUMMARY 508 KEY TERMS AND CONCEPTS 510 QUESTIONS 510 PROBLEMS 511
APPENDIX A: STATISTICAL TABLES 515 APPENDIX B: COMPUTER OUTPUT OF
EVIEWS, MINITAB, EXCEL, AND STATA 534 SELECTED BIBLIOGRAPHY 541 INDEXES
545 NAME INDEX 545 SUBJECT INDEX 547 |
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| index_date | 2024-07-02T19:06:14Z |
| indexdate | 2024-07-09T21:08:43Z |
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| spelling | Gujarati, Damodar N. Verfasser (DE-588)129300462 aut Essentials of econometrics Damodar N. Gujarati 3. ed., internat. ed. Boston [u.a.] McGraw-Hill/Irwin 2006 XXI, 553 S. graph. Darst. 1 CD-ROM (12 cm) txt rdacontent n rdamedia nc rdacarrier Literaturverz. S. 541 - 544 Wirtschaft Econometrics Economics Statistical methods Ökonometrie (DE-588)4132280-0 gnd rswk-swf Diskette (DE-588)4122115-1 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 SWB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016169230&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. Essentials of econometrics Wirtschaft Econometrics Economics Statistical methods Ökonometrie (DE-588)4132280-0 gnd Diskette (DE-588)4122115-1 gnd |
| subject_GND | (DE-588)4132280-0 (DE-588)4122115-1 (DE-588)4123623-3 |
| title | Essentials of econometrics |
| title_auth | Essentials of econometrics |
| title_exact_search | Essentials of econometrics |
| title_exact_search_txtP | Essentials of econometrics |
| title_full | Essentials of econometrics Damodar N. Gujarati |
| title_fullStr | Essentials of econometrics Damodar N. Gujarati |
| title_full_unstemmed | Essentials of econometrics Damodar N. Gujarati |
| title_short | Essentials of econometrics |
| title_sort | essentials of econometrics |
| topic | Wirtschaft Econometrics Economics Statistical methods Ökonometrie (DE-588)4132280-0 gnd Diskette (DE-588)4122115-1 gnd |
| topic_facet | Wirtschaft Econometrics Economics Statistical methods Ökonometrie Diskette Lehrbuch |
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