Verfügbarkeit: Introduction to econometrics | Technische Universität München

Introduction to econometrics:

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Bibliographische Detailangaben
Beteiligte Personen:	Stock, James H. (VerfasserIn), Watson, Mark W. 1952- (VerfasserIn)
Format:	Buch
Sprache:	Englisch
Veröffentlicht:	Boston, Harlow [u.a.] Pearson 2015
Ausgabe:	Updated 3. ed., global ed.
Schriftenreihe:	The Pearson series in economics Always learning
Schlagwörter:	Ökonometrie Lehrbuch
Links:	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027458833&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
Beschreibung:	Literaturverz. S. 811 - 815
Umfang:	836 S. graph. Darst.
ISBN:	9781292071312 1292071311 9780133486872

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Datensatz im Suchindex

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adam_text	Contents Preface 31 PART ONE Introduction and Review CHAPTER 1 Economic Questions and Data 47 1.1 Economic Questions We Examine 47 Question #1 : Does Reducing Class Size Improve Elementary School Education? Question #2: Is There Racial Discrimination in the Market for Home Loans? Question #3: How Much Do Cigarette Taxes Reduce Smoking? 49 Question #4: By How Much Will U.S. GDP Grow Next Year? 50 Quantitative Questions, Quantitative Answers 51 1.2 Causal Effects and Idealized Experiments 51 Estimation of Causal Effects 52 Forecasting and Causality 53 1.3 Data: Sources and Types 53 Experimental Versus Observational Data 53 Cross-Sectional Data 54 Time Series Data 55 Panel Data 57 48 49 CHAPTER 2 2.1 2.2 2.3 Review of Probability 60 Random Variables and Probability Distributions 61 Probabilities, the Sample Space, and Random Variables 61 Probability Distribution of a Discrete Random Variable 62 Probability Distribution of a Continuous Random Variable 65 Expected Values, Mean, and Variance 65 The Expected Value of a Random Variable 65 The Standard Deviation and Variance 67 Mean and Variance of a Linear Function of a Random Variable Other Measures of the Shape of a Distribution 69 Two Random Variables 72 Joint and Marginal Distributions 72 68 10 Contents Conditional Distributions 73 Independence 77 Covariance and Correlation 77 The Mean and Variance of Sums of Random Variables 78 2.4 The Normal, Chi-Squared, Student ŕ, and F Distributions 82 The Normal Distribution 82 The Chi-Squared Distribution 87 The Student f Distribution 87 The F Distribution 88 2.5 Random Sampling and the Distribution of the Sample Average 89 Random Sampling 89 The Sampling Distribution of the Sample Average 90 2.6 Large-Sample Approximations to Sampling Distributions 93 The Law of Large Numbers and Consistency 94 The Central Limit Theorem 96 APPENDIX 2.1 Derivation of Results in Key Concept 2.3 109 CHAPTER з Review of Statistics 1 Ί Ί 3.1 Estimation of the Population Mean 112 Estimators and Their Properties 112 Properties of Ϋ 114 The Importance of Random Sampling 11 б 3.2 Hypothesis Tests Concerning the Population Mean 117 Null and Alternative Hypotheses 117 Thep-Value 118 Calculating the p-Value When σγ Is Known 119 The Sample Variance, Sample Standard Deviation, and Standard Error 120 Calculating the p-Value When σγ Is Unknown 122 The f-Statistic 122 Hypothesis Testing with a Prespecified Significance Level 123 One-Sided Alternatives 125 3.3 Confidence Intervals for the Population Mean 126 3.4 Comparing Means from Different Populations 128 Hypothesis Tests for the Difference Between Two Means 128 Confidence Intervals for the Difference Between Two Population Means 130 Contents 11 3.5 Differences-of-Means Estimation of Causal Effects Using Experimental Data 130 The Causal Effect as a Difference of Conditional Expectations 131 Estimation of the Causal Effect Using Differences of Means 131 3.6 Using the f-Statistic When the Sample Size Is Small 133 The f-Statistic and the Student t Distribution 133 Use of the Student f Distribution in Practice 135 3.7 Scatterplots, the Sample Covariance, and the Sample Correlation 137 Scatterplots 137 Sample Covariance and Correlation 138 APPENDIX 3.1 The U.S. Current Population Survey 152 APPENDIX 3.2 Two Proofs That ľ Is the Least Squares Estimator of μγ 153 APPENDIX 3.3 A Proof That the Sample Variance Is Consistent 154 PART TWO Fundamentals of Regression Analysis CHAPTER 4 4.1 4.2 4.3 4.4 Linear Regression with One Regressor 155 The Linear Regression Model 155 Estimating the Coefficients of the Linear Regression Model 160 The Ordinary Least Squares Estimator 162 OLS Estimates of the Relationship Between Test Scores and the Student- Teacher Ratio 164 Why Use the OLS Estimator? 165 Measures of Fit 167 The/?2 167 The Standard Error of the Regression 168 Application to the Test Score Data 169 The Least Squares Assumptions 170 Assumption #1 : The Conditional Distribution of u¡ Given X¡ Has a Mean of Zero 170 Assumption #2: {X¡, Y¡), i = 1 ,. .., η, Are Independently and Identically Distributed 172 Assumption #3: Large Outliers Are Unlikely 173 Use of the Least Squares Assumptions 174 12 Contents 4.5 Sampling Distribution of the OLS Estimators 175 The Sampling Distribution of the OLS Estimators 176 4.6 Conclusion 179 APPENDIX 4.1 The California Test Score Data Set 187 APPENDIX 4.2 Derivation of the OLS Estimators 187 APPENDIX 43 Sampling Distribution of the OLS Estimator 188 CHAPTER 5 Regression with a Single Regressor: Hypothesis Tests and Confidence Intervals 192 5.1 Testing Hypotheses About One of the Regression Coefficients 192 Two-Sided Hypotheses Concerning βλ 193 One-Sided Hypotheses Concerning βλ 196 Testing Hypotheses About the Intercept β0 1 98 5.2 Confidence Intervals for a Regression Coefficient 199 5.3 Regression When λ Is a Binary Variable 201 Interpretation of the Regression Coefficients 201 5.4 Heteroskedasticity and Homoskedasticity 203 What Are Heteroskedasticity and Homoskedasticity? 204 Mathematical Implications of Homoskedasticity 206 What Does This Mean in Practice? 207 5.5 The Theoretical Foundations of Ordinary Least Squares 209 Linear Conditionally Unbiased Estimators and the Gauss-Markov Theorem 210 Regression Estimators Other Than OLS 211 5.6 Using the f-Statistic in Regression When the Sample Size Is Small 212 The f-Statistic and the Student f Distribution 212 Use of the Student f Distribution in Practice 213 5.7 Conclusion 214 APPENDIX 5.1 Formulas for OLS Standard Errors 223 APPENDIX 5.2 The Gauss-Markov Conditions and a Proof of the Gauss-Markov Theorem 224 Contents 13 CHAPTER 6 Linear Regression with Multiple Regressors 228 6.1 Omitted Variable Bias 228 Definition of Omitted Variable Bias 229 A Formula for Omitted Variable Bias 231 Addressing Omitted Variable Bias by Dividing the Data into Groups 233 6.2 The Multiple Regression Model 235 The Population Regression Line 235 The Population Multiple Regression Model 236 6.3 The OLS Estimator in Multiple Regression 238 The OLS Estimator 239 Application to Test Scores and the Student-Teacher Ratio 240 6.4 Measures of Fit in Multiple Regression 242 The Standard Error of the Regression [SER) 242 The R2 242 The Adjusted R2 243 Application to Test Scores 244 6.5 The Least Squares Assumptions in Multiple Regression 245 Assumption #1: The Conditional Distribution of u¡ Given ХџХц, ..., XtóHasa Mean of Zero 245 Assumption #2: {Xv, X2I, .... Х^ Ц, > = 1,..., n, Are i.i.d. 245 Assumption #3: Large Outliers Are Unlikely 245 Assumption #4: No Perfect Multicollinearity 246 6.6 The Distribution of the OLS Estimators in Multiple Regression 247 6.7 Multicollinearity 248 Examples of Perfect Multicollinearity 249 Imperfect Multicollinearity 251 6.8 Conclusion 252 APPENDIX 6.1 Derivation of Equation (6.1 ) 260 APPENDIX 6.2 Distribution of the OLS Estimators When There Are Two Regressors and Homoskedastic Errors 260 APPENDIX 6.3 The Frisch-Waugh Theorem 261 14 Contents CHAPTER 7 7.1 7.2 7.3 7.4 7.5 7.6 IJ CHAPTERS 8.1 8.2 Hypothesis Tests and Confidence Intervals in Multiple Regression 263 Hypothesis Tests and Confidence Intervals for a Single Coefficient 263 Standard Errors for the OLS Estimators 263 Hypothesis Tests for a Single Coefficient 264 Confidence Intervals for a Single Coefficient 265 Application to Test Scores and the Student-Teacher Ratio 266 Tests of Joint Hypotheses 268 Testing Hypotheses on Two or More Coefficients 268 The F-Statistic 270 Application to Test Scores and the Student-Teacher Ratio 272 The Homoskedasticity-Only F-Statistic 273 Testing Single Restrictions Involving Multiple Coefficients 275 Confidence Sets for Multiple Coefficients 277 Model Specification for Multiple Regression 278 Omitted Variable Bias in Multiple Regression 279 The Role of Control Variables in Multiple Regression 280 Mode! Specification in Theory and in Practice 282 Interpreting the R2 and the Adjusted R2 in Practice 283 Analysis of the Test Score Data Set 284 Conclusion 289 APPENDIX 7.1 The Bonferroni Test of a Joint Hypothesis 297 APPENDIX 7.2 Conditional Mean Independence 299 Nonlinear Regression Functions 302 A General Strategy for Modeling Nonlinear Regression Functions 304 Test Scores and District Income 304 The Effect on Y of a Change in X in Nonlinear Specifications 307 A General Approach to Modeling Nonlinearities Using Multiple Regression 312 Nonlinear Functions of a Single Independent Variable 312 Polynomials 313 Logarithms 315 Polynomial and Logarithmic Models of Test Scores and District Income 323 Contents 15 8.3 Interactions Between Independent Variables 324 Interactions Between Two Binary Variables 325 Interactions Between a Continuous and a Binary Variable 328 Interactions Between Two Continuous Variables 332 8.4 Nonlinear Effects on Test Scores of the Student-Teacher Ratio 339 Discussion of Regression Results 339 Summary of Findings 343 8.5 Conclusion 344 APPENDIX 8.1 Regression Functions That Are Nonlinear in the Parameters 355 APPENDIX 8.2 Slopes and Elasticities for Nonlinear Regression Functions 359 CHAPTER 9 Assessing Studies Based on Multiple Regression 361 9.1 Internal and External Validity 361 Threats to Internal Validity 362 Threats to External Validity 363 9.2 Threats to Internal Validity of Multiple Regression Analysis 365 Omitted Variable Bias 365 Misspecification of the Functional Form of the Regression Function 367 Measurement Error and Errors-in-Variables Bias 368 Missing Data and Sample Selection 371 Simultaneous Causality 372 Sources of Inconsistency of OLS Standard Errors 375 9.3 Internal and External Validity When the Regression Is Used for Forecasting 377 Using Regression Models for Forecasting 377 Assessing the Validity of Regression Models for Forecasting 378 9.4 Example: Test Scores and Class Size 378 External Validity 378 Internal Validity 385 Discussion and Implications 387 9.5 Conclusion 388 APPENDIX 9.1 The Massachusetts Elementary School Testing Data 395 16 Contents PART THREE Further Topics in Regression Analysis __________________ CHAPTER 10 Regression with Panel Data 396 10.1 Panel Data 397 Example: Traffic Deaths and Alcohol Taxes 398 10.2 Panel Data with Two Time Periods: Before and After Comparisons 400 10.3 Fixed Effects Regression 403 The Fixed Effects Regression Model 403 Estimation and Inference 405 Application to Traffic Deaths 407 10.4 Regression with Time Fixed Effects 407 Time Effects Only 408 Both Entity and Time Fixed Effects 409 10.5 The Fixed Effects Regression Assumptions and Standard Errors for Fixed Effects Regression 411 The Fixed Effects Regression Assumptions 411 Standard Errors for Fixed Effects Regression 413 10.6 Drunk Driving Laws and Traffic Deaths 414 10.7 Conclusion 418 APPENDIX 10.1 The State Traffic Fatality Data Set 426 APPENDIX 10.2 Standard Errors for Fixed Effects Regression 426 CHAPTER 11 Regression with a Binary Dependent Variable 431 11.1 Binary Dependent Variables and the Linear Probability Model 432 Binary Dependent Variables 432 The Linear Probability Model 434 11.2 Probit and Logit Regression 437 Probit Regression 437 Logit Regression 442 Comparing the Linear Probability, Probit, and Logit Models 444 11.3 Estimation and Inference in the Logit and Probit Models 444 Nonlinear Least Squares Estimation 445 Contents 17 Maximum Likelihood Estimation 446 Measures of Fit 447 1 1 A Application to the Boston HMDA Data 448 11.5 Conclusion 455 APPENDIX 11.1 The Boston HMDA Data Set 464 APPENDIX 11.2 Maximum Likelihood Estimation 464 APPENDIX 11.3 Other Limited Dependent Variable Models 467 CHAPTER 1 2 Instrumental Variables Regression 470 12.1 The IV Estimator with a Single Regressor and a Single Instrument 471 The IV Model and Assumptions 471 The Two Stage Least Squares Estimator 472 Why Does IV Regression Work? 473 The Sampling Distribution of the TSLS Estimator 477 Application to the Demand for Cigarettes 479 12.2 The General IV Regression Model 481 TSLS in the General IV Model 483 Instrument Relevance and Exogeneity in the General IV Model 484 The IV Regression Assumptions and Sampling Distribution of the TSLS Estimator 485 Inference Using the TSLS Estimator 486 Application to the Demand for Cigarettes 487 12.3 Checking Instrument Validity 488 Assumption #1 : Instrument Relevance 489 Assumption #2: Instrument Exogeneity 491 12.4 Application to the Demand for Cigarettes 494 12.5 Where Do Valid Instruments Come From? 499 Three Examples 500 12.6 Conclusion 504 APPENDIX 12.1 The Cigarette Consumption Panel Data Set 513 APPENDIX 12.2 Derivation of the Formula for the TSLS Estimator in Equation (12.4) 513 18 Contents APPENDIX 12.3 Large-Sample Distribution of theTSLS Estimator 514 APPENDIX 12.4 Large-Sample Distribution of theTSLS Estimator When the Instrument Is Not Valid 515 APPENDIX 12.5 Instrumental Variables Analysis with Weak Instruments 517 APPENDIX 12.6 TSLS with Control Variables 519 CHAPTER із Experiments and Quasi-Experiments 521 13.1 Potential Outcomes, Causal Effects, and Idealized Experiments 522 Potential Outcomes and the Average Causal Effect 522 Econometric Methods for Analyzing Experimental Data 524 13.2 Threats to Validity of Experiments 525 Threats to Internal Validity 525 Threats to External Validity 529 13.3 Experimental Estimates of the Effect of Class Size Reductions 530 Experimental Design 531 Analysis of the STAR Data 532 Comparison of the Observational and Experimental Estimates of Class Size Effects 537 13.4 Quasi-Experiments 539 Examples 540 The Differences-in-Differences Estimator 542 Instrumental Variables Estimators 545 Regression Discontinuity Estimators 546 13.5 Potential Problems with Quasi-Experiments 548 Threats to Internal Validity 548 Th reats to Externa I Va I id ity 550 13.6 Experimental and Quasi-Experimental Estimates in Heterogeneous Populations 550 OLS with Heterogeneous Causal Effects 551 IV Regression with Heterogeneous Causal Effects 552 Contents 19 13.7 Conclusion 555 APPENDIX 13.1 The Project STAR Data Set 564 APPENDIX 13.2 IV Estimation When the Causal Effect Varies Across Individuals 564 APPENDIX 13.3 The Potential Outcomes Framework for Analyzing Data from Experiments 566 PART FOUR Regression Analysis of Economic Time Series Data _______ CHAPTER 14 Introduction to Time Series Regression and Forecasting 568 14.1 Using Regression Models for Forecasting 569 14.2 Introduction to Time Series Data and Serial Correlation 570 Real GDP in the United States 570 Lags, First Differences, Logarithms, and Growth Rates 571 Autocorrelation 574 Other Examples of Economic Time Series 575 14.3 Autoregressions 577 The First-Order Autoregressive Model 577 The pth-Order Autoregressive Model 580 14.4 Time Series Regression with Additional Predictors and the Autoregressive Distributed Lag Model 583 Forecasting GDP Growth Using the Term Spread 583 Stationarity 586 Time Series Regression with Multiple Predictors 587 Forecast Uncertainty and Forecast Intervals 590 14.5 Lag Length Selection Using Information Criteria 593 Determining the Order of an Autoregression 593 Lag Length Selection in Time Series Regression with Multiple Predictors 596 14.6 Nonstationarity I: Trends 597 What Is a Trend? 597 Problems Caused by Stochastic Trends 600 Detecting Stochastic Trends: Testing for a Unit AR Root 602 Avoiding the Problems Caused by Stochastic Trends 607 20 Contents 14.7 Nonstationarity II: Breaks 607 What Is a Break? 608 Testing for Breaks 608 Pseudo Out-of-Sample Forecasting 613 Avoiding the Problems Caused by Breaks 619 14.8 Conclusion 619 APPENDIX 14.1 Time Series Data Used in Chapter 14 629 APPENDIX 14.2 Stationary in the AR(1) Model 630 APPENDIX 14.3 Lag Operator Notation 631 APPENDIX 14.4 ARMA Models 632 APPENDIX 14.5 Consistency of the BIC Lag Length Estimator 633 CHAPTER 15 Estimation of Dynamic Causal Effects 635 15.1 An Initial Taste of the Orange Juice Data 636 15.2 Dynamic Causal Effects 639 Causal Effects and Time Series Data 639 Two Types of Exogeneity 642 15.3 Estimation of Dynamic Causal Effects with Exogenous Regressors 643 The Distributed Lag Model Assumptions 644 Autocorrelated up Standard Errors, and Inference 645 Dynamic Multipliers and Cumulative Dynamic Multipliers 646 15.4 Heteroskedasticity- and Autocorrelation-Consistent Standard Errors 647 Distribution of the OLS Estimator with Autocorrelated Errors 602 НАС Standard Errors 650 15.5 Estimation of Dynamic Causal Effects with Strictly Exogenous Regressors 652 The Distributed Lag Model with AR(1 ) Errors 653 OLS Estimation of the ADL Model 656 GLS Estimation 657 The Distributed Lag Model with Additional Lags and AR(p) Errors 659 15.6 Orange Juice Prices and Cold Weather 662 Contents 21 15.7 Is Exogeneity Plausible? Some Examples 670 U.S. Income and Australian Exports 670 Oil Prices and Inflation 671 Monetary Policy and Inflation 672 The Growth Rate of GDP and the Term Spread 672 15.8 Conclusion 673 APPENDIX 15.1 The Orange Juice Data Set 680 APPENDIX 15.2 The ADL Model and Generalized Least Squares in Lag Operator Notation 680 CHAPTER і б Additional Topics in Time Series Regression 684 16.1 Vector Autoregressions 684 The VAR Model 685 A VAR Model of the Growth Rate of GDP and the Term Spread 688 16.2 Multiperiod Forecasts 689 Iterated Multiperiod Forecasts 689 Direct Multiperiod Forecasts 691 Which Method Should You Use? 694 16.3 Orders of Integration and the DF-GLS Unit Root Test 695 Other Models of Trends and Orders of Integration 695 The DF-GLS Test for a Unit Root 697 Why Do Unit Root Tests Have Nonnormal Distributions? 700 16.4 Cointegration 702 Cointegration and Error Correction 702 How Can You Tell Whether Two Variables Are Cointegrated? 704 Estimation of Cointegrating Coefficients 705 Extension to Multiple Cointegrated Variables 707 Application to Interest Rates 708 16.5 Volatility Clustering and Autoregressive Conditional Heteroskedasticity 710 Volatility Clustering 410 Autoregressive Conditional Heteroskedasticity 712 Application to Stock Price Volatility 713 16.6 Conclusion 716 22 Contents PART FIVE The Econometric Theory of Regression Analysis _________ CHAPTER 17 The Theory of Linear Regression with One Regressor 722 17.1 The Extended Least Squares Assumptions and the OLS Estimator 723 The Extended Least Squares Assumptions 723 The OLS Estimator 725 17.2 Fundamentals of Asymptotic Distribution Theory 725 Convergence in Probability and the Law of Large Numbers 726 The Central Limit Theorem and Convergence in Distribution 728 Slutsky s Theorem and the Continuous Mapping Theorem 729 Application to the f-Statistic Based on the Sample Mean 730 17.3 Asymptotic Distribution of the OLS Estimator and r-Statistic 731 Consistency and Asymptotic Normality of the OLS Estimators 731 Consistency of Heteroskedasticity-Robust Standard Errors 731 Asymptotic Normality of the Heteroskedasticity-Robust f-Statistic 733 17.4 Exact Sampling Distributions When the Errors Are Normally Distributed 733 Distribution of jß1 with Normal Errors 733 Distribution of the Homoskedasticity-Only f-Statistic 735 17.5 Weighted Least Squares 736 WLS with Known Heteroskedasticity 736 WLS with Heteroskedasticity of Known Functional Form 737 Heteroskedasticity-Robust Standard Errors or WLS? 740 APPENDIX 17.1 The Normal and Related Distributions and Moments of Continuous Random Variables 746 APPENDIX 17.2 Two Inequalities 749 CHAPTER 18 The Theory of Multiple Regression 751 18.1 The Linear Multiple Regression Model and OLS Estimator in Matrix Form 752 The Multiple Regression Model in Matrix Notation 752 The Extended Least Squares Assumptions 754 The OLS Estimator 755 Contents 23 18.2 Asymptotic Distribution of the OLS Estimator and ŕ-Statistic 756 The M u Iti variate Central Limit Theorem 756 Asymptotic Normality of β 757 Heteroskedasticity-Robust Standard Errors 758 Confidence Intervals for Predicted Effects 759 Asymptotic Distribution of the f-Statistic 759 18.3 Tests of Joint Hypotheses 759 Joint Hypotheses in Matrix Notation 760 Asymptotic Distribution of the F-Statistic 760 Confidence Sets for Multiple Coefficients 761 18.4 Distribution of Regression Statistics with Normal Errors 762 Matrix Representations of OLS Regression Statistics 762 Distribution of jš with Normal Errors 763 Distribution of sj 764 Homoskedasticity-Only Standard Errors 764 Distribution of the f-Statistic 765 Distribution of the F-Statistic 765 18.5 Efficiency of the OLS Estimator with Homoskedastic Errors 766 The Gauss-Markov Conditions for Multiple Regression 766 Linear Conditionally Unbiased Estimators 766 The Gauss-Markov Theorem for Multiple Regression 767 18.6 Generalized Least Squares 768 The GLS Assumptions 769 GLS When Ω Is Known 771 GLS When Ω Contains Unknown Parameters 772 The Zero Conditional Mean Assumption and GLS 772 18.7 Instrumental Variables and Generalized Method of Moments Estimation 774 The ÍV Estimator in Matrix Form 775 Asymptotic Distribution of the TSLS Estimator 776 Properties of TSLS When the Errors Are Homoskedastic 777 Generalized Method of Moments Estimation in Linear Models 780 APPENDIX 18.1 Summary of Matrix Algebra 792 APPENDIX 18.2 Multivariate Distributions 795 APPENDIX 18.3 Derivation of the Asymptotic Distribution of β 797 24 Contents APPENDIX 18.4 Derivations of Exact Distributions of OLS Test Statistics with Normal Errors 798 APPENDIX 18.5 Proof of the Gauss-Markov Theorem for Multiple Regression 799 APPENDIX 18.6 Proof of Selected Results for IV and GMM Estimation 800 Appendix 803 References 811 Glossary 817 Index 825
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genre_facet	Lehrbuch
id	DE-604.BV042017068
illustrated	Illustrated
indexdate	2024-12-20T17:00:10Z
institution	BVB
isbn	9781292071312 1292071311 9780133486872
language	English
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-027458833
oclc_num	896354881
open_access_boolean
owner	DE-20 DE-739 DE-573 DE-634 DE-19 DE-BY-UBM DE-188 DE-1050 DE-355 DE-BY-UBR DE-M382 DE-1043 DE-11 DE-945 DE-1049 DE-92 DE-384 DE-521 DE-1047 DE-706 DE-29
owner_facet	DE-20 DE-739 DE-573 DE-634 DE-19 DE-BY-UBM DE-188 DE-1050 DE-355 DE-BY-UBR DE-M382 DE-1043 DE-11 DE-945 DE-1049 DE-92 DE-384 DE-521 DE-1047 DE-706 DE-29
physical	836 S. graph. Darst.
publishDate	2015
publishDateSearch	2015
publishDateSort	2015
publisher	Pearson
record_format	marc
series2	The Pearson series in economics Always learning
spellingShingle	Stock, James H. Watson, Mark W. 1952- Introduction to econometrics Ökonometrie (DE-588)4132280-0 gnd
subject_GND	(DE-588)4132280-0 (DE-588)4123623-3
title	Introduction to econometrics
title_alt	Econometrics
title_auth	Introduction to econometrics
title_exact_search	Introduction to econometrics
title_full	Introduction to econometrics James H. Stock ; Mark W. Watson
title_fullStr	Introduction to econometrics James H. Stock ; Mark W. Watson
title_full_unstemmed	Introduction to econometrics James H. Stock ; Mark W. Watson
title_short	Introduction to econometrics
title_sort	introduction to econometrics
topic	Ökonometrie (DE-588)4132280-0 gnd
topic_facet	Ökonometrie Lehrbuch
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027458833&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
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