Using EViews: for Undergraduate econometrics, second edition, R. Carter Hill, William E. Griffiths, George G. Judge
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York [u.a.]
Wiley
2001
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| Ausgabe: | 2. ed. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | CD-ROM u.d.T.: EViews 3.1 |
| Beschreibung: | VIII, 181 S. Ill. 1 CD-ROM (12 cm) |
| ISBN: | 0471412392 |
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| 245 | 1 | 0 | |a Using EViews |b for Undergraduate econometrics, second edition, R. Carter Hill, William E. Griffiths, George G. Judge |c Mark A. Reiman ; R. Carter Hill |
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| 250 | |a 2. ed. | ||
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Datensatz im Suchindex
| _version_ | 1804145053612900352 |
|---|---|
| adam_text | Titel: Undergraduate econometrics
Autor: Hill, Rufus Carter
Jahr: 2001
Contents
Preface v
Chapter 1 An Introduction to Econometrics 1
1.1 Why Study Econometrics? 1
1.2 What Is Econometrics? 2
1.2.1 Some Examples 3
1.3 The Econometric Model 4
1.4 How Do We Obtain Data? 5
1.4.1 Experimental Data 5
1.4.2 Nonexperimental Data 8
1.5 Statistical Inference 9
1.6 A Research Format 9
Chapter 2 Some Basic Probability Concepts 11
2.1 Experiments, Outcomes and Random Variables 11
2.1.1 Controlled Experiments—Experimental Data 11
2.1.2 Uncontrolled Experiments—Nonexperimental Data 12
2.1.3 Discrete and Continuous Random Variables 13
2.2 The Probability Distribution of a Random Variable 14
2.2.1 Probability Distributions of Discrete Random Variables 14
2.2.2 The Probability Density Function of a Continuous
Random Variable 15
2.3 Expected Values Involving a Single Random Variable 16
2.3.1 The Rules of Summation 17
2.3.2 The Mean of a Random Variable 18
2.3.3 Expectation of a Function of a Random Variable 20
2.3.4 The Variance of a Random Variable 21
2.4 Using Joint Probability Density Functions 21
2.4.1 Marginal Probabillity Density Functions 23
2.4.2 Conditional Probability Density Functions 24
2.4.3 Independent Random Variables 25
2.5 The Expected Value of a Function of Several Random Variables:
Covariance and Correlation 26
2.5.1 The Mean of a Weighted Sum of Random Variables 30
2.5.2 The Variance of a Weighted Sum of Random Variables 31
2.6 The Normal Distribution 31
2.7 Learning Objectives 35
2.8 Exercises 36
? CONTENTS
Chapter 3 The Simple Linear Regression Model:
Specification and Estimation 42
3.1 An Economic Model 42
3.2 An Econometric Model 45
3.2.1 Introducing the Error Term 47
3.3 Estimating the Parameters for the Expenditure Relationship 50
3.3.1 The Least Squares Principle 51
3.3.2 Estimates for the Food Expenditure Function 55
3.3.3 Interpreting the Estimates 56
3.3.3a Elasticities 56
3.3.3b Prediction 57
3.3.3c Examining Computer Output 57
3.3.4 Other Economic Models 58
3.4 Learning Objectives 60
3.5 Exercises 61
Chapter 4 Properties of the Least Squares Estimators 68
4.1 The Least Squares Estimators as Random Variables 68
4.2 The Sampling Properties of the Least Squares Estimators 69
4.2.1 The Expected Values of b and ¿2 70
4.2.1a The Repeated Sampling Context 71
4.2.1b Derivation of Equation 4.2.1 72
4.2.2 The Variances and Covariance of b and fø 73
4.2.3 Linear Estimators 77
4.3 The Gauss-Markov Theorem 77
4.4 The Probability Distributions of the Least Squares Estimators 79
4.5 Estimating the Variance of the Error Term 80
4.5.1 Estimating the Variances and Co variances of the Least
Squares Estimators 81
4.5.2 The Estimated Variances and Covariances for the Food
Expenditure Example 81
4.5.3 Sample Computer Output 82
4.6 Learning Objectives 84
4.7 Exercises 85
Chapter 5 Inference in the Simple Regression Model:
Interval Estimation, Hypothesis Testing, and Prediction 90
5.1 Interval Estimation 91
5.1.1 The Theory 91
5.1.1a The Chi-Square Distribution 92
5.1.1b The Probability Distribution of s2 93
5.1.1c The r-Distribution 93
5.1.Id A Key Result 94
5.1.2 Obtaining Interval Estimates 95
5.1.3 The Repeated Sampling Context 96
5.1.4 An Illustration 97
5.2 Hypothesis Testing 98
5.2.1 The Null Hypothesis 99
5.2.2 The Alternative Hypothesis 99
CONTENTS xi
5.2.3 The Test Statistic 100
5.2.4 The Rejection Region 101
5.2.5 The Food Expenditure Example 102
5.2.6 Type I and Type II Errors 103
5.2.7 The p-Value of a Hypothesis Test 104
5.2.8 Tests of Significance 105
5.2.8a A Significance Test in the Food Expenditure
Model 105
5.2.8b Reading Computer Output 107
5.2.9 A Relationship Between Two-Tailed Hypothesis Tests
and Interval Estimation 107
5.2.10 One-Tailed Tests 108
5.2.11 A Comment on Stating Null and Alternative
Hypotheses 109
5.3 The Least Squares Predictor 110
5.3.1 Prediction in the Food Expenditure Model 113
5.4 Learning Objectives 113
5.5 Exercises 114
Chapter 6 The Simple Linear Regression Model:
Reporting the Results and Choosing the Functional Form 121
6.1 The Coefficient of Determination 121
6.1.1 Analysis of Variance Table and R2 for Food Expenditure
Example 124
6.1.2 Correlation Analysis 125
6.1.3 Correlation Analysis and R2 126
6.2 Reporting Regression Results 126
6.2.1 The Effects of Scaling the Data 127
6.3 Choosing a Functional Form 128
6.3.1 Some Commonly Used Functional Forms 129
6.3.2 Examples Using Alternative Functional Forms 132
6.3.2a The Food Expenditure Model 132
6.3.2b Some Other Economic Models and Functional
Forms 133
6.3.3 Choosing a Functional Form: Empirical Issues 135
6.4 Are the Residuals Normally Distributed? 138
6.5 Learning Objectives 139
6.6 Exercises 140
Chapter 7 The Multiple Regression Model 145
7.1 Model Specification and the Data 145
7.1.1 The Economic Model 145
7.1.2 The Econometric Model 147
7.1.2a The General Model 148
7.1.2b The Assumptions of the Model 149
7.2 Estimating the Parameters of the Multiple Regression Model 150
7.2.1 Least Squares Estimation Procedure 151
7.2.2 Least Squares Estimates Using Hamburger Chain Data 151
7.2.3 Estimation of the Error Variance ó2 153
xii CONTENTS
7.3 Sampling Properties of the Least Squares Estimator 154
7.3.1 The Variances and Covariances of the Least Squares
Estimators 154
7.3.2 The Properties of the Least Squares Estimators Assuming
Normally Distributed Errors 156
7.4 Interval Estimation 157
7.5 Hypothesis Testing for a Single Coefficient 159
7.5.1 Testing the Significance of a Single Coefficient 159
7.5.2 One-Tailed Hypothesis Testing for a Single Coefficient 160
7.5.2a Testing for Elastic Demand 161
7.5.2b Testing Advertizing Effectiveness 161
7.6 Measuring Goodness of Fit 162
7.7 Learning Objectives 164
7.8 Exercises 164
Chapter 8 Further Inference in the Multiple Regression
Model 170
8.1 The F-Test 170
8.1.1 The F-Distribution: Theory 173
8.2 Testing the Significance of a Model 174
8.2.1 The Relationship Between Joint and Individual Tests 176
8.3 An Extended Model 177
8.4 Testing Some Economic Hypotheses 178
8.4.1 The Significance of Advertising 178
8.4.2 The Optimal Level of Advertising 179
8.4.3 The Optimal Level of Advertising and Price 181
8.5 The Use of Nonsample Information 181
8.6 Model Specification 184
8.6.1 Omitted and Irrelevant Variables 185
8.6.1a Omitted Variable Bias: A Proof 186
8.6.2 Testing for Model Misspecification: The RESET Test 187
8.7 Collinear Economic Variables 189
8.7.1 The Consequences of Collinearity 189
8.7.2 Identifying and Mitigating Collinearity 190
8.8 Prediction 191
8.9 Learning Objectives 192
8.10 Exercises 193
Chapter 9 Dummy (Binary) Variables 199
9.1 Introduction 199
9.2 The Use of Intercept Dummy Variables 200
9.3 Slope Dummy Variables 202
9.4 An Example: The University Effect on House Prices 203
9.5 Common Applications of Dummy Variables 205
9.5.1 Interactions Between Qualitative Factors 205
9.5.2 Qualitative Variables with Several Categories 206
9.5.3 Controlling for Time 207
9.5.3a Seasonal Dummies 207
9.5.3b Annual Dummies 207
9.5.3c Regime Effects 208
CONTENTS xiii
9.6 Testing the Existence of Qualitative Effects 208
9.6.1 Testing for a Single Qualitative Effect 208
9.6.2 Testing Jointly for the Presence of Several Qualitative
Effects 209
9.7 Testing the Equivalence of Two Regressions Using Dummy
Variables 209
9.7.1 The Chow Test 210
9.7.2 An Empirical Example of the Chow Test 211
9.8 Learning Objectives 213
9.9 Exercises 213
Chapter 10 Nonlinear Models 218
10.1 Polynomial and Interaction Variables 218
10.1.1 Polynomial Terms in a Regression Model 219
10.1.2 Interactions Between Two Continuous Variables 220
10.2 A Simple Nonlinear-in-the-Parameters Model 222
10.3 A Logistic Growth Curve 224
10.4 Poisson Regression 227
10.5 Learning Objectives 229
10.6 Exercises 229
Chapter 11 Heteroskedasticity 235
11.1 The Nature of Heteroskedasticity 235
11.2 The Consequences of Heteroskedasticity for the Least Squares
Estimator 238
11.2.1 White s Approximate Estimator for the Variance of the
Least Squares Estimator 240
11.3 Proportional Heteroskadesticity 241
11.4 Detecting Heteroskedasticity 244
11.4.1 Residual Plots 244
11.4.2 The Goldfeld-Quandt Test 245
11.5 A Sample with a Heteroskedastic Partition 246
11.5.1 Economic Model 246
11.5.2 Generalized Least Squares Through Model Transformation 248
11.5.3 Implementing Generalized Least Squares 249
11.5.4 Testing the Variance Assumption 250
11.6 Learning Objectives 251
11.7 Exercises 251
Chapter 12 Autocorrelation 258
12.1 The Nature of the Problem 258
12.1.1 Area Response Model for Sugar Cane 259
12.1.1a Least Squares Estimation 260
12.2 First-Order Autoregressive Errors 261
12.2.1 Properties of an AR(1) Error 262
12.3 Consequences for the Least Squares Estimator 263
12.4 Generalized Least Square 265
12.4.1 A Transformation 265
12.4.1a Transforming the First Observation 267
xiv CONTENTS
12.5 Implementing Generalized Least Squares 268
12.5.1 The Sugar Cane Example Revisited 269
12.6 Testing for Autocorrelation 271
12.6.1 The Durbin-Watson Test 271
12.6.1a The Bounds Test 273
12.6.2 A Lagrange Multiplier Test 274
12.7 Prediction with AR(1) Errors 275
12.8 Learning Objectives 277
12.9 Exercises 277
Chapter 13 Random Regressors and Moment Based
Estimation 283
13.1 Introduction 283
13.2 Linear Regression with Random x s 284
13.2.1 The Finite (Small) Sample Properties of the Least Squares
Estimator 285
13.2.2 The Asymptotic (Large) Sample Properties of the Least
Squares Estimator When ÷ Is Not Random 285
13.2.3 The Asymptotic (Large) Sample Properties of the Least
Squares Estimator When ÷ Is Random 286
13.2.4 The Inconsistency of the Least Squares Estimator When
cov(x, e) ¥= O 287
13.2.4a A Geometric Explanation of Why the Least Squares
Estimator is Inconsistent When cov(;c, e) Ö 0 287
13.2.4b Algebraic Proof That the Least Squares Estimator
Is Inconsistent When covOt, e) Ö Ï 288
13.2.5 Measurement Errors in Regression Equations 290
13.2.6 An Example of the Consequences of Measurement Errors 291
13.3 Estimators Based on the Method of Moments 292
13.3.1 Method of Moments Estimation of the Mean and Variance
of a Random Variable 292
13.3.2 Method of Moments Estimation in the Simple Linear
Regression Model 293
13.3.3 Instrumental Variables Estimation in
the Simple Linear Regression Model 294
13.3.3a The Consistency of the Instrumental Variables
Estimator 295
13.3.4 An Empirical Example of Instrumental Variables
Estimation 296
13.3.5 Instrumental Variables Estimation When Surplus
Instruments Are Available 297
13.4 Testing for Correlation Between Explanatory Variables and the
Error Term 299
13.4.1 An Empirical Example of the Hausman Test 300
13.5 Learning Objectives 300
13.6 Exercises 301
Chapter 14 Simultaneous Equations Models 304
14.1 Introduction 304
14.2 A Supply and Demand Model 304
CONTENTS
14.3 The Reduced Form Equations 306
14.4 The Failure of Least Squares Estimation in Simultaneous
Equations Models 307
14.4.1 An Intuitive Explanation of the Failure of Least Squares 307
14.4.2 An Algebraic Explanation of the Failure of Least Squares 308
14.5 The Identification Problem 309
14.6 Two-Stage Least Squares Estimation 311
14.6.1 The General Two-Stage Least Squares Estimation
Procedure 312
14.6.2 The Properties of the Two-Stage Least Squares Estimator 313
14.7 An Example of Two-Stage Least Squares Estimation 313
14.7.1 Identification 314
14.7.2 The Reduced Form Equations 314
14.8 Learning Objectives 316
14.9 Exercises 317
Chapter 15 Distributed Lag Models 319
15.1 Introduction 319
15.2 Finite Distributed Lag Models 320
15.2.1 An Economic Model 320
15.2.2 The Econometric Model 321
15.2.3 An Empirical Illustration 321
15.2.4 Polynomial Distributed Lags 323
15.2.5 Selection of the Length of the Finite Lag 325
15.3 The Geometric Lag 327
15.4 The Koyck Transformation 328
15.4.1 Instrumental Variables Estimation of the Koyck Model 328
15.4.2 Testing for Autocorrelation in Models with Lagged
Dependent Variables 329
15.5 Autoregressive Distributed Lags 330
15.5.1 The Autoregressive Distributed Lag Model 330
15.5.2 An Illustration of the ARDL Model 331
15.6 Learning Objectives 332
15.7 Exercises 332
Chapter 16 Regression with Time Series Data 335
16.1 Stationary Time Series 335
16.2 Spurious Regressions 338
16.3 Checking Stationarity Using the Autocorrelation Function 341
16.4 Unit Root Tests for Stationarity 343
16.4.1 The Dickey-Fuller Tests 344
16.4.2 The Dickey-Fuller Tests: An Example 345
16.5 Cointegration 346
16.5.1 An Example of a Cointegration Test 347
16.6 Summarizing Estimation Strategies When Using Time Series Data 347
16.7 Learning Objectives 348
16.8 Exercises 349
xvi CONTENTS
Chapter 17 Pooling Time-Series and Cross-Sectional Data 351
17.1 An Economic Model * 351
17.2 Seemingly Unrelated Regressions 352
17.2.1 Estimating Separate Equations 353
17.2.2 Joint Estimation of the Equation 354
17.2.3 Separate or Joint Estimation 355
17.2.4 Testing Cross-Equation Restrictions 357
17.3 A Dummy Variable Specification 357
17.3.1 The Model 358
17.4 An Error Components Model 359
17.5 Learning Objectives 360
17.6 Exercises 361
Chapter 18 Qualitative and Limited Dependent Variable
Models 368
18.1 Introduction 368
18.2 Models with Binary Dependent Variables 368
18.2.1 The Linear Probability Model 369
18.2.2 The Probit Model 370
18.2.3 Maximum Likelihood Estimation of the Probit Model 372
18.2.4 Interpretation of the Probit Model 373
18.2.5 An Example 374
18.3 The Logit Model for Binary Choice 376
18.4 Other Models with Qualitative Dependent Variables 376
18.4.1 Multinomial Choice Models 377
18.4.2 Ordered Choice Models 377
18.4.3 Count Data Models and Poisson Regression 378
18.5 Limited Dependent Variable Models 378
18.5.1 The Tobit Model 378
18.5.2 Sample Selection 379
18.6 Learning Objectives 379
18.7 Exercises 380
Chapter 19 Writing an Empirical Research Report,
and Sources of Economic Data 383
19.1 Selecting a Topic for an Economics Project 383
19.1.1 Choosing a Topic 383
19.1.2 Writing an Abstract 384
19.2 A Format for Writing a Research Report 384
19.3 Sources of Economic Data 386
19.3.1 Links to Economic Data on the Internet 386
19.3.2 Economic Data on the Internet 387
19.3.3 Traditional Sources of Economic Data 387
19.3.4 Interpreting Economic Data 388
19.4 Exercises 388
Statistical Tables 389
Index 397
|
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| language | English |
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| spelling | Reiman, Mark A. Verfasser aut Using EViews for Undergraduate econometrics, second edition, R. Carter Hill, William E. Griffiths, George G. Judge Mark A. Reiman ; R. Carter Hill EViews 3.1 2. ed. New York [u.a.] Wiley 2001 VIII, 181 S. Ill. 1 CD-ROM (12 cm) txt rdacontent n rdamedia nc rdacarrier CD-ROM u.d.T.: EViews 3.1 Anwendungssoftware (DE-588)4120906-0 gnd rswk-swf Ökonometrie (DE-588)4132280-0 gnd rswk-swf Ökonometrie (DE-588)4132280-0 s Anwendungssoftware (DE-588)4120906-0 s DE-188 Hill, Rufus Carter Verfasser aut Hill, R. Carter aut Undergraduate econometrics HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=022046671&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Reiman, Mark A. Hill, Rufus Carter Hill, R. Carter Using EViews for Undergraduate econometrics, second edition, R. Carter Hill, William E. Griffiths, George G. Judge Anwendungssoftware (DE-588)4120906-0 gnd Ökonometrie (DE-588)4132280-0 gnd |
| subject_GND | (DE-588)4120906-0 (DE-588)4132280-0 |
| title | Using EViews for Undergraduate econometrics, second edition, R. Carter Hill, William E. Griffiths, George G. Judge |
| title_alt | EViews 3.1 Undergraduate econometrics |
| title_auth | Using EViews for Undergraduate econometrics, second edition, R. Carter Hill, William E. Griffiths, George G. Judge |
| title_exact_search | Using EViews for Undergraduate econometrics, second edition, R. Carter Hill, William E. Griffiths, George G. Judge |
| title_full | Using EViews for Undergraduate econometrics, second edition, R. Carter Hill, William E. Griffiths, George G. Judge Mark A. Reiman ; R. Carter Hill |
| title_fullStr | Using EViews for Undergraduate econometrics, second edition, R. Carter Hill, William E. Griffiths, George G. Judge Mark A. Reiman ; R. Carter Hill |
| title_full_unstemmed | Using EViews for Undergraduate econometrics, second edition, R. Carter Hill, William E. Griffiths, George G. Judge Mark A. Reiman ; R. Carter Hill |
| title_short | Using EViews |
| title_sort | using eviews for undergraduate econometrics second edition r carter hill william e griffiths george g judge |
| title_sub | for Undergraduate econometrics, second edition, R. Carter Hill, William E. Griffiths, George G. Judge |
| topic | Anwendungssoftware (DE-588)4120906-0 gnd Ökonometrie (DE-588)4132280-0 gnd |
| topic_facet | Anwendungssoftware Ökonometrie |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=022046671&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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