Verfügbarkeit: A guide to modern econometrics

A guide to modern econometrics:

This_highly successful text focuses on exploring alternative techniques, combined with a practical emphasis, A guide to alternative techniques with the emphasis on the intuition behind the approaches and their practical reference, this new edition builds on the strengths of the second edition and br...

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Bibliographische Detailangaben
1. Verfasser:	Verbeek, Marno 1965- (VerfasserIn)
Format:	Buch
Sprache:	English
Veröffentlicht:	Chichester [u.a.] Wiley 2008
Ausgabe:	3. ed., repr.
Schlagworte:	Regression Ökonometrie Econometrics Regression analysis Lehrbuch
Online-Zugang:	Inhaltsverzeichnis
Zusammenfassung:	This_highly successful text focuses on exploring alternative techniques, combined with a practical emphasis, A guide to alternative techniques with the emphasis on the intuition behind the approaches and their practical reference, this new edition builds on the strengths of the second edition and brings the text completely up-to-date.
Beschreibung:	Literaturverz. S. [437] - 449
Beschreibung:	XV, 472 S. graph. Darst.
ISBN:	9780470517697

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adam_text	CONTENTS PREFACE XIII 1 INTRODUCTION 1 1.1 ABOUT ECONOMETRICS 1 1.2 THE STRUCTURE OF THIS BOOK 3 1.3 ILLUSTRATIONS AND EXERCISES 4 2 AN INTRODUCTION TO LINEAR REGRESSION 7 2.1 ORDINARY LEAST SQUARES AS AN ALGEBRAIC TOOL 8 2.1.1 ORDINARY LEAST SQUARES . 8 2.1.2 SIMPLE LINEAR REGRESSION 10 2.1.3 EXAMPLE: INDIVIDUAL WAGES 11 2.1.4 MATRIX NOTATION 12 2.2 THE LINEAR REGRESSION MODEL 13 2.3 SMALL SAMPLE PROPERTIES OF THE OLS ESTIMATOR 16 2.3.1 THE GAUSS-MARKOV ASSUMPTIONS 16 2.3.2 PROPERTIES OF THE OLS ESTIMATOR 17 2.3.3 EXAMPLE: INDIVIDUAL WAGES (CONTINUED) 20 2.4 GOODNESS-OF-FIT , 21 2.5 HYPOTHESIS TESTING 23 2.5.1 A SIMPLE F-TEST 24 2.5.2 EXAMPLE: INDIVIDUAL WAGES (CONTINUED) 26 2.5.3 TESTING ONE LINEAR RESTRICTION 26 2.5.4 A JOINT TEST OF SIGNIFICANCE OF REGRESSION COEFFICIENTS 27 2.5.5 EXAMPLE: INDIVIDUAL WAGES (CONTINUED) , 29 2.5.6 THE GENERAL CASE 30 2.5.7 SIZE, POWER AND P-VALUES T 31 2.6 ASYMPTOTIC PROPERTIES OF THE OLS ESTIMATOR 32 VI CONTENTS 2.6.1 CONSISTENCY 33 2.6.2 ASYMPTOTIC NORMALITY 35 2.6.3 SMALL SAMPLES AND ASYMPTOTIC THEORY 36 2.7 ILLUSTRATION: THE CAPITAL ASSET PRICING MODEL 38 2.7.1 THE CAPM AS A REGRESSION MODEL 39 2.7.2 ESTIMATING AND TESTING THE CAPM 40 2.8 MULTICOLLINEARITY 43 2.8.1 EXAMPLE: INDIVIDUAL WAGES (CONTINUED) 45 2.9 PREDICTION 46 EXERCISES 47 3 INTERPRETING AND COMPARING REGRESSION MODELS 53 3.1 INTERPRETING THE LINEAR MODEL 53 3.2 SELECTING THE SET OF REGRESSORS 57 3.2.1 MISSPECIFYING THE SET OF REGRESSORS 57 3.2.2 SELECTING REGRESSORS 58 3.2.3 COMPARING NON-NESTED MODELS 62 3.3 MISSPECIFYING THE FUNCTIONAL FORM 64 3.3.1 NONLINEAR MODELS 65 3.3.2 TESTING THE FUNCTIONAL FORM 66 3.3.3 TESTING FOR A STRUCTURAL BREAK 66 3.4 ILLUSTRATION: EXPLAINING HOUSE PRICES 67 3.5 ILLUSTRATION: PREDICTING STOCK INDEX RETURNS 71 3.6 ILLUSTRATION: EXPLAINING INDIVIDUAL WAGES 75 3.6.1 LINEAR MODELS 75 3.6.2 LOGLINEAR MODELS 78 3.6.3 THE EFFECTS OF GENDER 81 3.6.4 SOME WORDS OF WARNING 83 EXERCISES 83 4 HETEROSKEDASTICITY AND AUTOCORRELATION 85 4.1 CONSEQUENCES FOR THE OLS ESTIMATOR 85 4.2 DERIVING AN ALTERNATIVE ESTIMATOR 87 4.3 HETEROSKEDASTICITY 88 4.3.1 INTRODUCTION 88 4.3.2 ESTIMATOR PROPERTIES AND HYPOTHESIS TESTING 91 4.3.3 WHEN THE VARIANCES ARE UNKNOWN 92 4.3.4 HETEROSKEDASTICITY-CONSISTENT STANDARD ERRORS FOR OLS 93 4.3.5 A MODEL WITH TWO UNKNOWN VARIANCES 95 4.3.6 MULTIPLICATIVE HETEROSKEDASTICITY 96 4.3.7 WEIGHTED LEAST SQUARES WITH ARBITRARY WEIGHTS 97 4.4 TESTING FOR HETEROSKEDASTICITY 97 4.4.1 TESTING EQUALITY OF TWO UNKNOWN VARIANCES 98 4.4.2 TESTING FOR MULTIPLICATIVE HETEROSKEDASTICITY 98 4.4.3 THE BREUSCH-PAGAN TEST 99 4.4.4 THE WHITE TEST 99 CONTENTS VII 0 4.4.5 WHICH TEST? 100 4.5 ILLUSTRATION: EXPLAINING LABOUR DEMAND 100 4.6 AUTOCORRELATION 104 4.6.1 FIRST-ORDER AUTOCORRELATION 105 4.6.2 UNKNOWN P 108 4.7 TESTING FOR FIRST-ORDER AUTOCORRELATION 109 4.7.1 ASYMPTOTIC TESTS . , 1 0 9 4.7.2 THE DURBIN-WATSON TEST 110 4.8 ILLUSTRATION: THE DEMAND FOR ICE CREAM 111 4.9 ALTERNATIVE AUTOCORRELATION PATTERNS 114 4.9.1 HIGHER-ORDER AUTOCORRELATION 114 4.9.2 MOVING AVERAGE ERRORS 115 4.10 WHAT TO DO WHEN YOU FIND AUTOCORRELATION? 116 4.10.1 MISSPECIFICATION 116 4.10.2 HETEROSKEDASTICITY-AND-AUTOCORRELATION-CONSISTENT STANDARD ERRORS FOR OLS 118 4.11 ILLUSTRATION: RISK PREMIA IN FOREIGN EXCHANGE MARKETS 120 4.11.1 NOTATION 120 4.11.2 TESTS FOR RISK PREMIA IN THE 1 MONTH MARKET 121 4.11.3 TESTS FOR RISK PREMIA USING OVERLAPPING SAMPLES 124 EXERCISES 127 5 ENDOGENEITY, INSTRUMENTAL VARIABLES AND GMM 129 5.1 A REVIEW OF THE PROPERTIES OF THE OLS ESTIMATOR 130 5.2 CASES WHERE THE OLS ESTIMATOR CANNOT BE SAVED , 133 5.2.1 AUTOCORRELATION WITH A LAGGED DEPENDENT VARIABLE 134 5.2.2 AN EXAMPLE WITH MEASUREMENT ERROR - 134 5.2.3 ENDOGENEITY AND OMITTED VARIABLE BIAS 137 5.2.4 SIMULTANEITY AND REVERSE CAUSALITY 138 5.3 THE INSTRUMENTAL VARIABLES ESTIMATOR 140 5.3.1 ESTIMATION WITH A SINGLE ENDOGENOUS REGRESSOR AND A SINGLE INSTRUMENT * 140 5.3.2 BACK TO THE KEYNESIAN MODEL 144 5.3.3 BACK TO THE MEASUREMENT ERROR PROBLEM 145 5.3.4 MULTIPLE ENDOGENOUS REGRESSORS 145 5.4 ILLUSTRATION: ESTIMATING THE RETURNS TO SCHOOLING 146 5.5 THE GENERALIZED INSTRUMENTAL VARIABLES ESTIMATOR 150 5.5.1 MULTIPLE ENDOGENOUS REGRESSORS WITH AN ARBITRARY NUMBER OF INSTRUMENTS 151 5.5.2 TWO-STAGE LEAST SQUARES AND THE KEYNESIAN MODEL AGAIN 154 5.5.3 SPECIFICATION TESTS 155 5.5.4 WEAK INSTRUMENTS 156 5.6 THE GENERALIZED METHOD OF MOMENTS 157 5.6.1 EXAMPLE 158 5.6.2 THE GENERALIZED METHOD OF MOMENTS 159 5.6.3 SOME SIMPLE EXAMPLES 162 VIII , CONTENTS 5.6A WEAK IDENTIFICATION 163 5.7 ILLUSTRATION: ESTIMATING INTERTEMPORAL ASSET PRICING MODELS 164 5.8 CONCLUDING REMARKS 167 EXERCISES 167 6 MAXIMUM LIKELIHOOD ESTIMATION AND SPECIFICATION TESTS 171 6.1 AN INTRODUCTION TO MAXIMUM LIKELIHOOD , 172 6.1.1 SOME EXAMPLES 172 6.1.2 GENERAL PROPERTIES 176 6.1.3 AN EXAMPLE (CONTINUED) 179 6.1.4 THE NORMAL LINEAR REGRESSION MODEL 180 6.2 SPECIFICATION TESTS 181 6.2.1 THREE TEST PRINCIPLES 181 6.2.2 LAGRANGE MULTIPLIER TESTS 183 6.2.3 AN EXAMPLE (CONTINUED) 187 6.3 TESTS IN THE NORMAL LINEAR REGRESSION MODEL 188 6.3.1 TESTING FOR OMITTED VARIABLES 188 6.3.2 TESTING FOR HETEROSKEDASTICITY 189 6.3.3 TESTING FOR AUTOCORRELATION 191 6.4 QUASI-MAXIMUM LIKELIHOOD AND MOMENT CONDITIONS TESTS 192 6.4.1 QUASI-MAXIMUM LIKELIHOOD , 192 6.4.2 CONDITIONAL MOMENT TESTS 194 6.4.3 TESTING FOR NORMALITY 195 EXERCISES 195 7 MODELS WITH LIMITED DEPENDENT VARIABLES 199 7.1 BINARY CHOICE MODELS 200 7.1.1 USING LINEAR REGRESSION? 200 7.1.2 INTRODUCING BINARY CHOICE MODELS 200 7.1.3 AN UNDERLYING LATENT MODEL 202 7.1.4 ESTIMATION 203 7.1.5 GOODNESS-OF-FIT 205 7.1.6 ILLUSTRATION: THE IMPACT OF UNEMPLOYMENT BENEFITS ON RECIPIENCY 207 7.1.7 SPECIFICATION TESTS IN BINARY CHOICE MODELS 210 7.1.8 RELAXING SOME ASSUMPTIONS IN BINARY CHOICE MODELS 212 J 7.2 MULTIRESPONSE MODELS 213 7.2.1 ORDERED RESPONSE MODELS 213 7.2.2 ABOUT NORMALIZATION 214 7.2.3 ILLUSTRATION: EXPLAINING FIRMS CREDIT RATINGS 215 7.2.4 ILLUSTRATION: WILLINGNESS TO PAY FOR NATURAL AREAS 217 7.2.5 MULTINOMIAL MODELS 220 7.3 MODELS FOR COUNT DATA 223 7.3.1 THE POISSON AND NEGATIVE BINOMIAL MODELS 224 7.3.2 ILLUSTRATION: PATENTS AND R&D EXPENDITURES 228 CONTENTS IX 7.4 TOBIT MODELS 230 7.4.1 THE STANDARD TOBIT MODEL 231 7.4.2 ESTIMATION 233 7.4.3 ILLUSTRATION: EXPENDITURES ON ALCOHOL AND TOBACCO (PART 1) 235 7.4.4 SPECIFICATION TESTS IN THE TOBIT MODEL 238 7.5 EXTENSIONS OF TOBIT MODELS 240 7.5.1 THE TOBIT II MODEL 240 7.5.2 ESTIMATION 243 7.5.3 FURTHER EXTENSIONS 245 7.5.4 ILLUSTRATION: EXPENDITURES ON ALCOHOL AND TOBACCO (PART 2) 245 7.6 SAMPLE SELECTION BIAS 249 7.6.1 THE NATURE OF THE SELECTION PROBLEM 250 7.6.2 SEMI-PARAMETRIC ESTIMATION OF THE SAMPLE-SELECTION MODEL 252 7.7 ESTIMATING TREATMENT EFFECTS 253 7.8 DURATION MODELS 257 7.8.1 HAZARD RATES AND SURVIVAL FUNCTIONS 257 7.8.2 SAMPLES AND MODEL ESTIMATION 260 7.8.3 ILLUSTRATION: DURATION OF BANK RELATIONSHIPS 262 EXERCISES 264 8 UNIVARIATE TIME SERIES MODELS 269 8.1 INTRODUCTION 270 8.1.1 SOME EXAMPLES 270 8.1.2 STATIONARITY AND THE AUTOCORRELATION FUNCTION * 272 8.2 GENERAL ARM A PROCESSES 275 8.2.1 FORMULATING ARMA PROCESSES 275 8.2.2 INVERTIBILITY OF LAG POLYNOMIALS 278 8.2.3 COMMON ROOTS 279 8.3 STATIONARITY AND UNIT ROOTS 280 8.4 TESTING FOR UNIT ROOTS 283 8.4.1 TESTING FOR UNIT ROOTS IN A FIRST-ORDER AUTOREGRESSIVE MODEL 283 8.4.2 TESTING FOR UNIT ROOTS IN HIGHER-ORDER AUTOREGRESSIVE MODELS 286 8.4.3 EXTENSIONS 287 8.4.4 ILLUSTRATION: ANNUAL PRICE/EARNINGS RATIO 288 8.5 ILLUSTRATION: LONG-RUN PURCHASING POWER PARITY (PART 1) 290 8.6 ESTIMATION OF ARMA MODELS 293 8.6.1 LEAST SQUARES 293 8.6.2 MAXIMUM LIKELIHOOD 294 8.7 CHOOSING A MODEL 295 8.7.1 THE AUTOCORRELATION FUNCTION 296 8.7.2 THE PARTIAL AUTOCORRELATION FUNCTION 298 8.7.3 DIAGNOSTIC CHECKING 299 X CONTENTS 8.7.4 CRITERIA FOR MODEL SELECTION 299 8.7.5 ILLUSTRATION: MODELLING THE PRICE/EARNINGS RATIO 300 8.8 PREDICTING WITH ARMA MODELS 302 8.8.1 THE OPTIMAL PREDICTOR 303 8.8.2 PREDICTION ACCURACY 305 8.9 ILLUSTRATION: THE EXPECTATIONS THEORY OF THE TERM STRUCTURE 307 8.10 AUTOREGRESSIVE CONDITIONAL HETEROSKEDASTICITY 311 8.10.1 ARCH AND GARCH MODELS 312 8.10.2 ESTIMATION AND PREDICTION 315 8.10.3 ILLUSTRATION: VOLATILITY IN DAILY EXCHANGE RATES 317 8.11 WHAT ABOUT MULTIVARIATE MODELS? 319 EXERCISES 320 9 MULTIVARIATE TIME SERIES MODELS 323 9.1 DYNAMIC MODELS WITH STATIONARY VARIABLES 324 9.2 MODELS WITH NONSTATIONARY VARIABLES 327 9.2.1 SPURIOUS REGRESSIONS 327 9.2.2 COINTEGRATION 328 9.2.3 COINTEGRATION AND ERROR-CORRECTION MECHANISMS 332 9.3 ILLUSTRATION: LONG-RUN PURCHASING POWER PARITY (PART 2) 333 9.4 VECTOR AUTOREGRESSIVE MODELS 335 9.5 COINTEGRATION: THE MULTIVARIATE CASE 338 9.5.1 COINTEGRATION IN A VAR 338 9.5.2 EXAMPLE: COINTEGRATION IN A BIVARIATE VAR 341 9.5.3 TESTING FOR COINTEGRATION 342 9.5.4 ILLUSTRATION: LONG-RUN PURCHASING POWER PARITY (PART 3) 345 9.6 ILLUSTRATION: MONEY DEMAND AND INFLATION 347 9.7 CONCLUDING REMARKS 353 EXERCISES 353 10 MODELS BASED ON PANEL DATA 355 10.1 INTRODUCTION TO PANEL DATA MODELING 356 10.1.1 EFFICIENCY OF PARAMETER ESTIMATORS 357 10.1.2 IDENTIFICATION OF PARAMETERS 358 10.2 THE STATIC LINEAR MODEL 359 10.2.1 THE FIXED EFFECTS MODEL 359 10.2.2 THE FIRST-DIFFERENCE ESTIMATOR 362 , 10.2.3 THE RANDOM EFFECTS MODEL 364 10.2.4 FIXED EFFECTS OR RANDOM EFFECTS? 367 10.2.5 GOODNESS-OF-FIT 369 10.2.6 ALTERNATIVE INSTRUMENTAL VARIABLES ESTIMATORS 370 10.2.6 ROBUST INFERENCE 372 10.2.7 TESTING FOR HETEROSKEDASTICITY AND AUTOCORRELATION 373 10.3 ILLUSTRATION: EXPLAINING INDIVIDUAL WAGES 375 10.4 DYNAMIC LINEAR MODELS 377 10.4.1 AN AUTOREGRESSIVE PANEL DATA MODEL 377 CONTENTS XI 10.4.2 DYNAMIC MODELS WITH EXOGENOUS VARIABLES 382 10.5 ILLUSTRATION: EXPLAINING CAPITAL STRUCTURE 383 10.6 NONSTATIONARITY, UNIT ROOTS AND COINTEGRATION 389 10.6.1 PANEL DATA UNIT ROOT TESTS 390 10.6.2 PANEL DATA COINTEGRATION TESTS 392 10.7 MODELS WITH LIMITED DEPENDENT VARIABLES 393 10.7.1 BINARY CHOICE MODELS 394 10.7.2 THE FIXED EFFECTS LOGIT MODEL 395 10.7.3 THE RANDOM EFFECTS PROBIT MODEL 396 10.7.4 TOBIT MODELS 398 10.7.5 DYNAMICS AND THE PROBLEM OF INITIAL CONDITIONS 398 10.7.6 SEMI-PARAMETRIC ALTERNATIVES 400 10.8 INCOMPLETE PANELS AND SELECTION BIAS 401 10.8.1 ESTIMATION WITH RANDOMLY MISSING DATA 402 10.8.2 SELECTION BIAS AND SOME SIMPLE TESTS 403 10.8.3 ESTIMATION WITH NONRANDOMLY MISSING DATA 405 10.9 PSEUDO PANELS AND REPEATED CROSS-SECTIONS 406 10.9.1 THE FIXED EFFECTS MODEL . 407 10.9.2 AN INSTRUMENTAL VARIABLES INTERPRETATION 409 10.9.3 DYNAMIC MODELS 410 EXERCISES , 411 A VECTORS AND MATRICES 417 A.I TERMINOLOGY 417 A.2 MATRIX MANIPULATIONS 418 A.3 PROPERTIES OF MATRICES AND VECTORS 419 A.4 INVERSE MATRICES . 420 A.5 IDEMPOTENT MATRICES 421 A. 6 EIGENVALUES AND EIGENVECTORS 421 A.7 DIFFERENTIATION 422 A.8 SOME LEAST SQUARES MANIPULATIONS . 423 B STATISTICAL AND DISTRIBUTION THEORY 425 B.I DISCRETE RANDOM VARIABLES 425 B.2 CONTINUOUS RANDOM VARIABLES 426 B.3 EXPECTATIONS AND MOMENTS 427 B.4 MULTIVARIATE DISTRIBUTIONS 428 B.5 CONDITIONAL DISTRIBUTIONS 429 B.6 THE NORMAL DISTRIBUTION 431 B.7 RELATED DISTRIBUTIONS 433 BIBLIOGRAPHY 437 INDEX 451
adam_txt	CONTENTS PREFACE XIII 1 INTRODUCTION 1 1.1 ABOUT ECONOMETRICS 1 1.2 THE STRUCTURE OF THIS BOOK ' 3 1.3 ILLUSTRATIONS AND EXERCISES 4 2 AN INTRODUCTION TO LINEAR REGRESSION 7 2.1 ORDINARY LEAST SQUARES AS AN ALGEBRAIC TOOL 8 2.1.1 ORDINARY LEAST SQUARES . 8 2.1.2 SIMPLE LINEAR REGRESSION 10 2.1.3 EXAMPLE: INDIVIDUAL WAGES 11 2.1.4 MATRIX NOTATION 12 2.2 THE LINEAR REGRESSION MODEL 13 2.3 SMALL SAMPLE PROPERTIES OF THE OLS ESTIMATOR 16 2.3.1 THE GAUSS-MARKOV ASSUMPTIONS 16 2.3.2 PROPERTIES OF THE OLS ESTIMATOR 17 2.3.3 EXAMPLE: INDIVIDUAL WAGES (CONTINUED) 20 2.4 GOODNESS-OF-FIT , 21 2.5 HYPOTHESIS TESTING 23 2.5.1 A SIMPLE F-TEST 24 2.5.2 EXAMPLE: INDIVIDUAL WAGES (CONTINUED) 26 2.5.3 TESTING ONE LINEAR RESTRICTION 26 2.5.4 A JOINT TEST OF SIGNIFICANCE OF REGRESSION COEFFICIENTS 27 2.5.5 EXAMPLE: INDIVIDUAL WAGES (CONTINUED) , 29 2.5.6 THE GENERAL CASE 30 2.5.7 SIZE, POWER AND P-VALUES T 31 2.6 ASYMPTOTIC PROPERTIES OF THE OLS ESTIMATOR 32 VI CONTENTS 2.6.1 CONSISTENCY 33 2.6.2 ASYMPTOTIC NORMALITY 35 2.6.3 SMALL SAMPLES AND ASYMPTOTIC THEORY 36 2.7 ILLUSTRATION: THE CAPITAL ASSET PRICING MODEL 38 2.7.1 THE CAPM AS A REGRESSION MODEL 39 2.7.2 ESTIMATING AND TESTING THE CAPM 40 2.8 MULTICOLLINEARITY 43 2.8.1 EXAMPLE: INDIVIDUAL WAGES (CONTINUED) 45 2.9 PREDICTION 46 EXERCISES 47 3 INTERPRETING AND COMPARING REGRESSION MODELS 53 3.1 INTERPRETING THE LINEAR MODEL 53 3.2 SELECTING THE SET OF REGRESSORS 57 3.2.1 MISSPECIFYING THE SET OF REGRESSORS 57 3.2.2 SELECTING REGRESSORS 58 3.2.3 COMPARING NON-NESTED MODELS 62 3.3 MISSPECIFYING THE FUNCTIONAL FORM 64 3.3.1 NONLINEAR MODELS 65 3.3.2 TESTING THE FUNCTIONAL FORM 66 3.3.3 TESTING FOR A STRUCTURAL BREAK 66 3.4 ILLUSTRATION: EXPLAINING HOUSE PRICES 67 3.5 ILLUSTRATION: PREDICTING STOCK INDEX RETURNS 71 3.6 ILLUSTRATION: EXPLAINING INDIVIDUAL WAGES 75 3.6.1 LINEAR MODELS 75 3.6.2 LOGLINEAR MODELS 78 3.6.3 THE EFFECTS OF GENDER 81 3.6.4 SOME WORDS OF WARNING 83 EXERCISES 83 4 HETEROSKEDASTICITY AND AUTOCORRELATION 85 4.1 CONSEQUENCES FOR THE OLS ESTIMATOR 85 4.2 DERIVING AN ALTERNATIVE ESTIMATOR 87 4.3 HETEROSKEDASTICITY 88 4.3.1 INTRODUCTION 88 4.3.2 ESTIMATOR PROPERTIES AND HYPOTHESIS TESTING 91 4.3.3 WHEN THE VARIANCES ARE UNKNOWN 92 4.3.4 HETEROSKEDASTICITY-CONSISTENT STANDARD ERRORS FOR OLS 93 4.3.5 A MODEL WITH TWO UNKNOWN VARIANCES 95 4.3.6 MULTIPLICATIVE HETEROSKEDASTICITY 96 4.3.7 WEIGHTED LEAST SQUARES WITH ARBITRARY WEIGHTS 97 4.4 TESTING FOR HETEROSKEDASTICITY 97 4.4.1 TESTING EQUALITY OF TWO UNKNOWN VARIANCES 98 4.4.2 TESTING FOR MULTIPLICATIVE HETEROSKEDASTICITY 98 4.4.3 THE BREUSCH-PAGAN TEST 99 4.4.4 THE WHITE TEST 99 CONTENTS VII 0 4.4.5 WHICH TEST? 100 4.5 ILLUSTRATION: EXPLAINING LABOUR DEMAND 100 4.6 AUTOCORRELATION 104 4.6.1 FIRST-ORDER AUTOCORRELATION 105 4.6.2 UNKNOWN P 108 4.7 TESTING FOR FIRST-ORDER AUTOCORRELATION 109 4.7.1 ASYMPTOTIC TESTS . , 1 0 9 4.7.2 THE DURBIN-WATSON TEST 110 4.8 ILLUSTRATION: THE DEMAND FOR ICE CREAM 111 4.9 ALTERNATIVE AUTOCORRELATION PATTERNS 114 4.9.1 HIGHER-ORDER AUTOCORRELATION 114 4.9.2 MOVING AVERAGE ERRORS 115 4.10 WHAT TO DO WHEN YOU FIND AUTOCORRELATION? 116 4.10.1 MISSPECIFICATION 116 4.10.2 HETEROSKEDASTICITY-AND-AUTOCORRELATION-CONSISTENT STANDARD ERRORS FOR OLS 118 4.11 ILLUSTRATION: RISK PREMIA IN FOREIGN EXCHANGE MARKETS 120 4.11.1 NOTATION 120 4.11.2 TESTS FOR RISK PREMIA IN THE 1 MONTH MARKET 121 4.11.3 TESTS FOR RISK PREMIA USING OVERLAPPING SAMPLES 124 EXERCISES 127 5 ENDOGENEITY, INSTRUMENTAL VARIABLES AND GMM 129 5.1 A REVIEW OF THE PROPERTIES OF THE OLS ESTIMATOR 130 5.2 CASES WHERE THE OLS ESTIMATOR CANNOT BE SAVED , 133 5.2.1 AUTOCORRELATION WITH A LAGGED DEPENDENT VARIABLE 134 5.2.2 AN EXAMPLE WITH MEASUREMENT ERROR - 134 5.2.3 ENDOGENEITY AND OMITTED VARIABLE BIAS 137 5.2.4 SIMULTANEITY AND REVERSE CAUSALITY 138 5.3 THE INSTRUMENTAL VARIABLES ESTIMATOR 140 5.3.1 ESTIMATION WITH A SINGLE ENDOGENOUS REGRESSOR AND A SINGLE INSTRUMENT * 140 5.3.2 BACK TO THE KEYNESIAN MODEL 144 5.3.3 BACK TO THE MEASUREMENT ERROR PROBLEM 145 5.3.4 MULTIPLE ENDOGENOUS REGRESSORS 145 5.4 ILLUSTRATION: ESTIMATING THE RETURNS TO SCHOOLING 146 5.5 THE GENERALIZED INSTRUMENTAL VARIABLES ESTIMATOR 150 5.5.1 MULTIPLE"ENDOGENOUS REGRESSORS WITH AN ARBITRARY NUMBER OF INSTRUMENTS 151 5.5.2 TWO-STAGE LEAST SQUARES AND THE KEYNESIAN MODEL AGAIN 154 5.5.3 SPECIFICATION TESTS ' 155 5.5.4 WEAK INSTRUMENTS 156 5.6 THE GENERALIZED METHOD OF MOMENTS 157 5.6.1 EXAMPLE 158 5.6.2 THE GENERALIZED METHOD OF MOMENTS 159 5.6.3 SOME SIMPLE EXAMPLES 162 VIII , CONTENTS 5.6A WEAK IDENTIFICATION 163 5.7 ILLUSTRATION: ESTIMATING INTERTEMPORAL ASSET PRICING MODELS ' 164 5.8 CONCLUDING REMARKS 167 EXERCISES 167 6 MAXIMUM LIKELIHOOD ESTIMATION AND SPECIFICATION TESTS 171 6.1 AN INTRODUCTION TO MAXIMUM LIKELIHOOD , 172 6.1.1 SOME EXAMPLES 172 6.1.2 GENERAL PROPERTIES 176 6.1.3 AN EXAMPLE (CONTINUED) 179 6.1.4 THE NORMAL LINEAR REGRESSION MODEL 180 6.2 SPECIFICATION TESTS 181 6.2.1 THREE TEST PRINCIPLES 181 6.2.2 LAGRANGE MULTIPLIER TESTS ' 183 6.2.3 AN EXAMPLE (CONTINUED) 187 6.3 TESTS IN THE NORMAL LINEAR REGRESSION MODEL 188 6.3.1 TESTING FOR OMITTED VARIABLES 188 6.3.2 TESTING FOR HETEROSKEDASTICITY 189 6.3.3 TESTING FOR AUTOCORRELATION 191 6.4 QUASI-MAXIMUM LIKELIHOOD AND MOMENT CONDITIONS TESTS 192 6.4.1 QUASI-MAXIMUM LIKELIHOOD , 192 6.4.2 CONDITIONAL MOMENT TESTS 194 6.4.3 TESTING FOR NORMALITY 195 EXERCISES 195 7 MODELS WITH LIMITED DEPENDENT VARIABLES 199 7.1 BINARY CHOICE MODELS 200 7.1.1 USING LINEAR REGRESSION? 200 7.1.2 INTRODUCING BINARY CHOICE MODELS 200 7.1.3 AN UNDERLYING LATENT MODEL 202 7.1.4 ESTIMATION 203 7.1.5 GOODNESS-OF-FIT 205 7.1.6 ILLUSTRATION: THE IMPACT OF UNEMPLOYMENT BENEFITS ON RECIPIENCY 207 7.1.7 SPECIFICATION TESTS IN BINARY CHOICE MODELS 210 7.1.8 RELAXING SOME ASSUMPTIONS IN BINARY CHOICE MODELS 212 J 7.2 MULTIRESPONSE MODELS 213 7.2.1 ORDERED RESPONSE MODELS 213 7.2.2 ABOUT NORMALIZATION 214 7.2.3 ILLUSTRATION: EXPLAINING FIRMS' CREDIT RATINGS 215 7.2.4 ILLUSTRATION: WILLINGNESS TO PAY FOR NATURAL AREAS 217 7.2.5 MULTINOMIAL MODELS 220 7.3 MODELS FOR COUNT DATA 223 7.3.1 THE POISSON AND NEGATIVE BINOMIAL MODELS 224 7.3.2 ILLUSTRATION: PATENTS AND R&D EXPENDITURES 228 CONTENTS IX 7.4 TOBIT MODELS 230 7.4.1 THE STANDARD TOBIT MODEL 231 7.4.2 ESTIMATION 233 7.4.3 ILLUSTRATION: EXPENDITURES ON ALCOHOL AND TOBACCO (PART 1) 235 7.4.4 SPECIFICATION TESTS IN THE TOBIT MODEL 238 7.5 EXTENSIONS OF TOBIT MODELS 240 7.5.1 THE TOBIT II MODEL 240 7.5.2 ESTIMATION 243 7.5.3 FURTHER EXTENSIONS 245 7.5.4 ILLUSTRATION: EXPENDITURES ON ALCOHOL AND TOBACCO (PART 2) 245 7.6 SAMPLE SELECTION BIAS 249 7.6.1 THE NATURE OF THE SELECTION PROBLEM 250 7.6.2 SEMI-PARAMETRIC ESTIMATION OF THE SAMPLE-SELECTION MODEL 252 7.7 ESTIMATING TREATMENT EFFECTS 253 7.8 DURATION MODELS 257 7.8.1 HAZARD RATES AND SURVIVAL FUNCTIONS 257 7.8.2 SAMPLES AND MODEL ESTIMATION 260 7.8.3 ILLUSTRATION: DURATION OF BANK RELATIONSHIPS 262 EXERCISES 264 8 UNIVARIATE TIME SERIES MODELS 269 8.1 INTRODUCTION 270 8.1.1 SOME EXAMPLES 270 8.1.2 STATIONARITY AND THE AUTOCORRELATION FUNCTION * 272 8.2 GENERAL ARM A PROCESSES 275 8.2.1 FORMULATING ARMA PROCESSES 275 8.2.2 INVERTIBILITY OF LAG POLYNOMIALS 278 8.2.3 COMMON ROOTS 279 8.3 STATIONARITY AND UNIT ROOTS '" 280 8.4 TESTING FOR UNIT ROOTS 283 8.4.1 TESTING FOR UNIT ROOTS IN A FIRST-ORDER AUTOREGRESSIVE MODEL 283 8.4.2 TESTING FOR UNIT ROOTS IN HIGHER-ORDER AUTOREGRESSIVE MODELS 286 8.4.3 EXTENSIONS 287 8.4.4 ILLUSTRATION: ANNUAL PRICE/EARNINGS RATIO 288 8.5 ILLUSTRATION: LONG-RUN PURCHASING POWER PARITY (PART 1) 290 8.6 ESTIMATION OF ARMA MODELS 293 8.6.1 LEAST SQUARES 293 8.6.2 MAXIMUM LIKELIHOOD 294 8.7 CHOOSING A MODEL 295 8.7.1 THE AUTOCORRELATION FUNCTION 296 8.7.2 THE PARTIAL AUTOCORRELATION FUNCTION 298 8.7.3 DIAGNOSTIC CHECKING 299 X CONTENTS 8.7.4 CRITERIA FOR MODEL SELECTION 299 8.7.5 ILLUSTRATION: MODELLING THE PRICE/EARNINGS RATIO 300 8.8 PREDICTING WITH ARMA MODELS 302 8.8.1 THE OPTIMAL PREDICTOR 303 8.8.2 PREDICTION ACCURACY 305 8.9 ILLUSTRATION: THE EXPECTATIONS THEORY OF THE TERM STRUCTURE 307 8.10 AUTOREGRESSIVE CONDITIONAL HETEROSKEDASTICITY 311 8.10.1 ARCH AND GARCH MODELS 312 8.10.2 ESTIMATION AND PREDICTION 315 8.10.3 ILLUSTRATION: VOLATILITY IN DAILY EXCHANGE RATES 317 8.11 WHAT ABOUT MULTIVARIATE MODELS? 319 EXERCISES 320 9 MULTIVARIATE TIME SERIES MODELS 323 9.1 DYNAMIC MODELS WITH STATIONARY VARIABLES 324 9.2 MODELS WITH NONSTATIONARY VARIABLES 327 9.2.1 SPURIOUS REGRESSIONS 327 9.2.2 COINTEGRATION 328 9.2.3 COINTEGRATION AND ERROR-CORRECTION MECHANISMS 332 9.3 ILLUSTRATION: LONG-RUN PURCHASING POWER PARITY (PART 2) 333 9.4 VECTOR AUTOREGRESSIVE MODELS 335 9.5 COINTEGRATION: THE MULTIVARIATE CASE 338 9.5.1 COINTEGRATION IN A VAR 338 9.5.2 EXAMPLE: COINTEGRATION IN A BIVARIATE VAR 341 9.5.3 TESTING FOR COINTEGRATION 342 9.5.4 ILLUSTRATION: LONG-RUN PURCHASING POWER PARITY (PART 3) 345 9.6 ILLUSTRATION: MONEY DEMAND AND INFLATION 347 9.7 CONCLUDING REMARKS 353 EXERCISES 353 10 MODELS BASED ON PANEL DATA 355 10.1 INTRODUCTION TO PANEL DATA MODELING 356 10.1.1 EFFICIENCY OF PARAMETER ESTIMATORS 357 10.1.2 IDENTIFICATION OF PARAMETERS 358 10.2 THE STATIC LINEAR MODEL 359 10.2.1 THE FIXED EFFECTS MODEL 359 10.2.2 THE FIRST-DIFFERENCE ESTIMATOR 362 , 10.2.3 THE RANDOM EFFECTS MODEL 364 10.2.4 FIXED EFFECTS OR RANDOM EFFECTS? 367 10.2.5 GOODNESS-OF-FIT 369 10.2.6 ALTERNATIVE INSTRUMENTAL VARIABLES ESTIMATORS 370 10.2.6 ROBUST INFERENCE 372 10.2.7 TESTING FOR HETEROSKEDASTICITY AND AUTOCORRELATION 373 10.3 ILLUSTRATION: EXPLAINING INDIVIDUAL WAGES 375 10.4 DYNAMIC LINEAR MODELS 377 10.4.1 AN AUTOREGRESSIVE PANEL DATA MODEL 377 CONTENTS XI 10.4.2 DYNAMIC MODELS WITH EXOGENOUS VARIABLES 382 10.5 ILLUSTRATION: EXPLAINING CAPITAL STRUCTURE 383 10.6 NONSTATIONARITY, UNIT ROOTS AND COINTEGRATION 389 10.6.1 PANEL DATA UNIT ROOT TESTS 390 10.6.2 PANEL DATA COINTEGRATION TESTS 392 10.7 MODELS WITH LIMITED DEPENDENT VARIABLES 393 10.7.1 BINARY CHOICE MODELS 394 10.7.2 THE FIXED EFFECTS LOGIT MODEL 395 10.7.3 THE RANDOM EFFECTS PROBIT MODEL 396 10.7.4 TOBIT MODELS 398 10.7.5 DYNAMICS AND THE PROBLEM OF INITIAL CONDITIONS 398 10.7.6 SEMI-PARAMETRIC ALTERNATIVES 400 10.8 INCOMPLETE PANELS AND SELECTION BIAS 401 10.8.1 ESTIMATION WITH RANDOMLY MISSING DATA 402 10.8.2 SELECTION BIAS AND SOME SIMPLE TESTS 403 10.8.3 ESTIMATION WITH NONRANDOMLY MISSING DATA 405 10.9 PSEUDO PANELS AND REPEATED CROSS-SECTIONS 406 10.9.1 THE FIXED EFFECTS MODEL . 407 10.9.2 AN INSTRUMENTAL VARIABLES INTERPRETATION 409 10.9.3 DYNAMIC MODELS 410 EXERCISES , 411 A VECTORS AND MATRICES 417 A.I TERMINOLOGY 417 A.2 MATRIX MANIPULATIONS 418 A.3 PROPERTIES OF MATRICES AND VECTORS 419 A.4 INVERSE MATRICES . 420 A.5 IDEMPOTENT MATRICES 421 A. 6 EIGENVALUES AND EIGENVECTORS 421 A.7 DIFFERENTIATION 422 A.8 SOME LEAST SQUARES MANIPULATIONS . 423 B STATISTICAL AND DISTRIBUTION THEORY 425 B.I DISCRETE RANDOM VARIABLES 425 B.2 CONTINUOUS RANDOM VARIABLES 426 B.3 EXPECTATIONS AND MOMENTS 427 B.4 MULTIVARIATE DISTRIBUTIONS 428 B.5 CONDITIONAL DISTRIBUTIONS 429 B.6 THE NORMAL DISTRIBUTION 431 B.7 RELATED DISTRIBUTIONS 433 BIBLIOGRAPHY 437 INDEX 451
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subject_GND	(DE-588)4132280-0 (DE-588)4123623-3
title	A guide to modern econometrics
title_auth	A guide to modern econometrics
title_exact_search	A guide to modern econometrics
title_exact_search_txtP	A guide to modern econometrics
title_full	A guide to modern econometrics Marno Verbeek
title_fullStr	A guide to modern econometrics Marno Verbeek
title_full_unstemmed	A guide to modern econometrics Marno Verbeek
title_short	A guide to modern econometrics
title_sort	a guide to modern econometrics
topic	Regression stw Ökonometrie Ökonometrie stw Econometrics Regression analysis Ökonometrie (DE-588)4132280-0 gnd
topic_facet	Regression Ökonometrie Econometrics Regression analysis Lehrbuch
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016318159&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
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