Econometrics in Theory and Practice (eBook)
XXVII, 565 Seiten
Springer Singapore (Verlag)
978-981-329-019-8 (ISBN)
This book introduces econometric analysis of cross section, time series and panel data with the application of statistical software. It serves as a basic text for those who wish to learn and apply econometric analysis in empirical research. The level of presentation is as simple as possible to make it useful for undergraduates as well as graduate students. It contains several examples with real data and Stata programmes and interpretation of the results. While discussing the statistical tools needed to understand empirical economic research, the book attempts to provide a balance between theory and applied research. Various concepts and techniques of econometric analysis are supported by carefully developed examples with the use of statistical software package, Stata 15.1, and assumes that the reader is somewhat familiar with the Strata software.
The topics covered in this book are divided into four parts. Part I discusses introductory econometric methods for data analysis that economists and other social scientists use to estimate the economic and social relationships, and to test hypotheses about them, using real-world data. There are five chapters in this part covering the data management issues, details of linear regression models, the related problems due to violation of the classical assumptions. Part II discusses some advanced topics used frequently in empirical research with cross section data. In its three chapters, this part includes some specific problems of regression analysis. Part III deals with time series econometric analysis. It covers intensively both the univariate and multivariate time series econometric models and their applications with software programming in six chapters. Part IV takes care of panel data analysis in four chapters. Different aspects of fixed effects and random effects are discussed here. Panel data analysis has been extended by taking dynamic panel data models which are most suitable for macroeconomic research. The book is invaluable for students and researchers of social sciences, business, management, operations research, engineering, and applied mathematics.Panchanan Das is a Professor of Economics, currently teaching Time Series and Panel Data Econometrics at the Department of Economics, University of Calcutta. His main research areas are Development Economics, Indian Economics, and Applied Macroeconomics. He has published several articles and book chapters on growth, inequality and poverty, and is a principal author of Economics I and Economics II, graduate-level textbooks published by Oxford University Press, New Delhi. He was also a major contributor to the West Bengal Development Report - 2008, published by the Academic Foundation, New Delhi, in collaboration with the Planning Commission, Government of India.
This book introduces econometric analysis of cross section, time series and panel data with the application of statistical software. It serves as a basic text for those who wish to learn and apply econometric analysis in empirical research. The level of presentation is as simple as possible to make it useful for undergraduates as well as graduate students. It contains several examples with real data and Stata programmes and interpretation of the results. While discussing the statistical tools needed to understand empirical economic research, the book attempts to provide a balance between theory and applied research. Various concepts and techniques of econometric analysis are supported by carefully developed examples with the use of statistical software package, Stata 15.1, and assumes that the reader is somewhat familiar with the Strata software.The topics covered in this book are divided into four parts. Part I discusses introductory econometric methods for data analysis thateconomists and other social scientists use to estimate the economic and social relationships, and to test hypotheses about them, using real-world data. There are five chapters in this part covering the data management issues, details of linear regression models, the related problems due to violation of the classical assumptions. Part II discusses some advanced topics used frequently in empirical research with cross section data. In its three chapters, this part includes some specific problems of regression analysis. Part III deals with time series econometric analysis. It covers intensively both the univariate and multivariate time series econometric models and their applications with software programming in six chapters. Part IV takes care of panel data analysis in four chapters. Different aspects of fixed effects and random effects are discussed here. Panel data analysis has been extended by taking dynamic panel data models which are most suitable for macroeconomic research. The book is invaluable for students and researchers of social sciences, business, management, operations research, engineering, and applied mathematics.
Preface 6
Acknowledgements 9
Contents 11
About the Author 20
List of Figures 21
List of Tables 23
Introductory Econometrics 24
1 Introduction to Econometrics and Statistical Software 25
1.1 Introduction 26
1.2 Economic Model and Econometric Model 28
1.3 Population Regression Function and Sample Regression Function 30
1.4 Parametric and Nonparametric or Semiparametric Model 32
1.5 Steps in Formulating an Econometric Model 33
1.5.1 Specification 33
1.5.2 Estimation 35
1.5.3 Testing of Hypothesis 36
1.5.4 Forecasting 36
1.6 Data 37
1.6.1 Cross Section Data 37
1.6.2 Time Series Data 38
1.6.3 Pooled Cross Section 38
1.6.4 Panel Data 39
1.7 Use of Econometric Software: Stata 15.1 39
1.7.1 Data Management 40
1.7.2 Generating Variables 43
1.7.3 Describing Data 44
1.7.4 Graphs 44
1.7.5 Logical Operators in Stata 45
1.7.6 Functions Used in Stata 46
1.8 Matrix Algebra 46
1.8.1 Matrix and Vector: Basic Operations 46
1.8.2 Partitioned Matrices 50
1.8.3 Rank of a Matrix 50
1.8.4 Inverse Matrix 52
1.8.5 Positive Definite Matrix 53
1.8.6 Trace of a Matrix 53
1.8.7 Orthogonal Vectors and Matrices 54
1.8.8 Eigenvalues and Eigenvectors 54
References 57
2 Linear Regression Model: Properties and Estimation 58
2.1 Introduction 58
2.2 The Simple Linear Regression Model 59
2.3 Multiple Linear Regression Model 63
2.4 Assumptions of Linear Regression Model 67
2.4.1 Non-stochastic Regressors 67
2.4.2 Linearity 67
2.4.3 Zero Unconditional Mean 68
2.4.4 Exogeneity 68
2.4.5 Homoscedasticity 69
2.4.6 Non-autocorrelation 69
2.4.7 Full Rank 70
2.4.8 Normal Distribution 71
2.5 Methods of Estimation 71
2.5.1 The Method of Moments (MM) 72
2.5.2 The Method of Ordinary Least Squares (OLS) 72
2.5.3 Maximum Likelihood Method 80
2.6 Properties of the OLS Estimation 84
2.6.1 Algebraic Properties 84
2.6.2 Statistical Properties 87
References 94
3 Linear Regression Model: Goodness of Fit and Testing of Hypothesis 95
3.1 Introduction 95
3.2 Goodness of Fit 96
3.2.1 The R2 as a Measure of Goodness of Fit 96
3.2.2 The Adjusted R2 as a Measure of Goodness of Fit 99
3.3 Testing of Hypothesis 100
3.3.1 Sampling Distributions of the OLS Estimators 102
3.3.2 Testing of Hypothesis for a Single Parameter 103
3.3.3 Use of P-Value 109
3.3.4 Interval Estimates 109
3.3.5 Testing of Hypotheses for More Than One Parameter: t Test 110
3.3.6 Testing Significance of the Regression: F Test 111
3.3.7 Testing for Linearity 113
3.3.8 Tests for Stability 115
3.3.9 Analysis of Variance 116
3.3.10 The Likelihood-Ratio, Wald and Lagrange Multiplier Test 117
3.4 Linear Regression Model by Using Stata 15.1 121
3.4.1 OLS Estimation in Stata 121
3.4.2 Maximum Likelihood Estimation (MLE) in Stata 124
References 128
4 Linear Regression Model: Relaxing the Classical Assumptions 129
4.1 Introduction 129
4.2 Heteroscedasticity 130
4.2.1 Problems with Heteroscedastic Data 130
4.2.2 Heteroscedasticity Robust Variance 132
4.2.3 Testing for Heteroscedasticity 135
4.2.4 Problem of Estimation 136
4.2.5 Illustration of Heteroscedastic Linear Regression by Using Stata 138
4.3 Autocorrelation 146
4.3.1 Linear Regression Model with Autocorrelated Error 147
4.3.2 Testing for Autocorrelation: Durbin–Watson Test 148
4.3.3 Consequences of Autocorrelation 150
4.3.4 Correcting for Autocorrelation 151
4.3.5 Illustration by Using Stata 152
References 155
5 Analysis of Collinear Data: Multicollinearity 156
5.1 Introduction 156
5.2 Multiple Correlation and Partial Correlation 157
5.3 Problems in the Presence of Multicollinearity 159
5.4 Detecting Multicollinearity 161
5.4.1 Determinant of (X?X) 162
5.4.2 Determinant of Correlation Matrix 162
5.4.3 Inspection of Correlation Matrix 162
5.4.4 Measure Based on Partial Regression 162
5.4.5 Theil’s Measure 163
5.4.6 Variance Inflation Factor (VIF) 163
5.4.7 Eigenvalues and Condition Numbers 165
5.5 Dealing with Multicollinearity 166
5.6 Illustration by Using Stata 168
References 170
Advanced Analysis of Cross Section Data 171
6 Linear Regression Model: Qualitative Variables as Predictors 172
6.1 Introduction 172
6.2 Regression Model with Intercept Dummy 174
6.2.1 Dichotomous Factor 174
6.2.2 Polytomous Factors 175
6.3 Regression Model with Interaction Dummy 177
6.4 Illustration by Using Stata 179
7 Limited Dependent Variable Model 184
7.1 Introduction 184
7.2 Linear Probability Model 185
7.3 Binary Response Models: Logit and Probit 187
7.3.1 The Logit Model 190
7.3.2 The Probit Model 191
7.3.3 Difference Between Logit and Probit Models 191
7.4 Maximum Likelihood Estimation of Logit and Probit Models 192
7.4.1 Interpretation of the Estimated Coefficients 193
7.4.2 Goodness of Fit 195
7.4.3 Testing of Hypotheses 196
7.4.4 Illustration of Binary Response Model by Using Stata 197
7.5 Regression Model with Truncated Distribution 202
7.5.1 Illustration of Truncated Regression by Using Stata 206
7.6 Problem of Censoring: Tobit Model 208
7.6.1 Illustration of Tobit Model by Using Stata 210
7.7 Models with Sample Selection Bias 212
7.7.1 Illustration of Sample Selection Model by Using Stata 216
7.8 Multinomial Logit Regression 218
7.8.1 Illustration by Using Stata 220
References 223
8 Multivariate Analysis 224
8.1 Introduction 224
8.2 Displaying Multivariate Data 225
8.2.1 Multivariate Observations 225
8.2.2 Sample Mean Vector 228
8.2.3 Population Mean Vector 228
8.2.4 Covariance Matrix 229
8.2.5 Correlation Matrix 230
8.2.6 Linear Combination of Variables 232
8.3 Multivariate Normal Distribution 235
8.4 Principal Component Analysis 236
8.4.1 Calculation of Principal Components 237
8.4.2 Properties of Principal Components 240
8.4.3 Illustration by Using Stata 240
8.5 Factor Analysis 242
8.5.1 Orthogonal Factor Model 243
8.5.2 Estimation of Loadings and Communalities 245
8.5.3 Factor Loadings Are not Unique 249
8.5.4 Factor Rotation 249
8.5.5 Illustration by Using Stata 250
8.6 Multivariate Regression 253
8.6.1 Structure of the Regression Model 253
8.6.2 Properties of Least Squares Estimators of B 255
8.6.3 Model Corrected for Means 256
8.6.4 Canonical Correlations 256
References 259
Analysis of Time Series Data 261
9 Time Series: Data Generating Process 262
9.1 Introduction 262
9.2 Data Generating Process (DGP) 263
9.2.1 Stationary Process 265
9.2.2 Nonstationary Process 267
9.3 Methods of Time Series Analysis 268
9.4 Seasonality and Seasonal Adjustment 269
9.5 Creating a Time Variable by Using Stata 270
References 273
10 Stationary Time Series 275
10.1 Introduction 276
10.2 Univariate Time Series Model 276
10.3 Autoregressive Process (AR) 278
10.3.1 The First-Order Autoregressive Process 279
10.3.2 The Second-Order Autoregressive Process 283
10.3.3 The Autoregressive Process of Order p 289
10.3.4 General Linear Processes 290
10.4 The Moving Average (MA) Process 292
10.4.1 The First-Order Moving Average Process 292
10.4.2 The Second-Order Moving Average Process 293
10.4.3 The Moving Average Process of Order q 294
10.4.4 Invertibility in Moving Average Process 295
10.5 Autoregressive Moving Average (ARMA) Process 295
10.6 Autocorrelation Function 298
10.6.1 Autocorrelation Function for AR(1) 299
10.6.2 Autocorrelation Function for AR(2) 301
10.6.3 Autocorrelation Function for AR(p) 304
10.6.4 Autocorrelation Function for MA(1) 305
10.6.5 Autocorrelation Function for MA(2) 306
10.6.6 Autocorrelation Function for MA(q) 307
10.6.7 Autocorrelation Function for ARMA Process 307
10.7 Partial Autocorrelation Function (PACF) 308
10.7.1 Partial Autocorrelation for AR Series 310
10.7.2 Partial Autocorrelation for MA Series 312
10.8 Sample Autocorrelation Function 313
10.8.1 Illustration by Using Stata 314
References 317
11 Nonstationarity, Unit Root and Structural Break 319
11.1 Introduction 320
11.2 Analysis of Trend 321
11.2.1 Deterministic Function of Time 321
11.2.2 Stochastic Function of Time 322
11.2.3 Stochastic and Deterministic Function of Time 324
11.3 Concept of Unit Root 326
11.4 Unit Root Test 327
11.4.1 Dickey–Fuller Unit Root Test 329
11.4.2 Augmented Dickey–Fuller (ADF) Unit Root Test 332
11.4.3 Phillips–Perron Unit Root Test 340
11.4.4 Dickey–Fuller GLS Test 343
11.4.5 Stationarity Tests 345
11.4.6 Multiple Unit Roots 348
11.4.7 Some Problems with Unit Root Tests 350
11.4.8 Macroeconomic Implications of Unit Root 350
11.5 Testing for Structural Break 351
11.5.1 Tests with Known Break Points 351
11.5.2 Tests with Unknown Break Points 355
11.6 Unit Root Test with Break 363
11.6.1 When Break Point is Exogenous 363
11.6.2 When Break Point is Endogenous 368
11.7 Seasonal Adjustment 369
11.7.1 Unit Roots at Various Frequencies: Seasonal Unit Root 370
11.7.2 Generating Time Variable and Seasonal Dummies in Stata 373
11.8 Decomposition of a Time Series into Trend and Cycle 374
References 378
12 Cointegration, Error Correction and Vector Autoregression 381
12.1 Introduction 381
12.2 Regression with Trending Variables 382
12.3 Concept of Cointegration 384
12.4 Granger’s Representation Theorem 387
12.5 Testing for Cointegration: Engle–Granger’s Two-Step Method 388
12.5.1 Illustrations by Using Stata 390
12.6 Vector Autoregression (VAR) 391
12.6.1 Stationarity Restriction of a VAR Process 395
12.6.2 Autocovariance Matrix of a VAR Process 398
12.6.3 Estimation of a VAR Process 400
12.6.4 Selection of Lag Length of a VAR Model 404
12.6.5 Illustration by Using Stata 405
12.7 Vector Moving Average Processes 406
12.8 Impulse Response Function 407
12.8.1 Illustration by Using Stata 412
12.9 Variance Decomposition 413
12.10 Granger Causality 414
12.10.1 Illustration by Using Stata 415
12.11 Vector Error Correction Model 417
12.11.1 Illustration by Using Stata 420
12.12 Estimation and Testing of Hypotheses of Cointegrated Systems 422
12.12.1 Illustration by Using Stata 427
References 429
13 Modelling Volatility Clustering 431
13.1 Introduction 431
13.2 Modelling Non-constant Conditional Variance 433
13.3 The ARCH Model 435
13.4 The GARCH Model 439
13.5 Asymmetric ARCH Models 443
13.6 ARCH-in-Mean Model 444
13.7 Testing and Estimation of a GARCH Model 446
13.7.1 Testing for ARCH Effect 446
13.7.2 Maximum Likelihood Estimation for GARCH (1, 1) 446
13.8 The ARCH Regression Model in Stata 447
13.8.1 Illustration with Market Capitalisation Data 448
References 451
14 Time Series Forecasting 452
14.1 Introduction 452
14.2 Simple Exponential Smoothing 453
14.3 Forecasting—Univariate Model 454
14.4 Forecasting with General Linear Processes 458
14.5 Multivariate Forecasting 460
14.6 Forecasting of a VAR Model 460
14.7 Forecasting GARCH Processes 462
14.8 Time Series Forecasting by Using Stata 463
References 466
Analysis of Panel Data 467
15 Panel Data Analysis: Static Models 468
15.1 Introduction 469
15.2 Structure and Types of Panel Data 470
15.2.1 Data Description by Using Stata 15.1 471
15.3 Benefits of Panel Data 476
15.4 Sources of Variation in Panel Data 476
15.5 Unrestricted Model with Panel Data 478
15.6 Fully Restricted Model: Pooled Regression 479
15.6.1 Illustration by Using Stata 480
15.7 Error Component Model 482
15.8 First-Differenced (FD) Estimator 484
15.8.1 Illustration by Using Stata 484
15.9 One-Way Error Component Fixed Effects Model 485
15.9.1 The “Within” Estimation 485
15.9.2 Least Squares Dummy Variable (LSDV) Regression 494
15.10 One-Way Error Component Random Effects Model 497
15.10.1 The GLS Estimation 501
15.10.2 Maximum Likelihood Estimation 503
15.10.3 Illustration by Using Stata 505
Reference 508
16 Panel Data Static Model: Testing of Hypotheses 509
16.1 Introduction 509
16.2 Measures of Goodness of Fit 510
16.3 Testing for Pooled Regression 511
16.4 Testing for Fixed Effects 513
16.4.1 Illustration by Using Stata 513
16.5 Testing for Random Effects 515
16.5.1 Illustration by Using Stata 516
16.6 Fixed or Random Effect: Hausman Test 517
16.6.1 Illustration by Using Stata 519
References 520
17 Panel Unit Root Test 522
17.1 Introduction 522
17.2 First-Generation Panel Unit Root Tests 523
17.2.1 Wu (1996) Unit Root Test 524
17.2.2 Levin, Lin and Chu Unit Root Test 525
17.2.3 Im, Pesaran and Shin (IPS) Unit Root Test 530
17.2.4 Fisher-Type Unit Root Tests 533
17.3 Stationarity Tests 535
17.3.1 Illustration by Using Stata 537
17.4 Second-Generation Panel Unit Root Tests 537
17.4.1 The Covariance Restrictions Approach 538
17.4.2 The Factor Structure Approach 540
References 548
18 Dynamic Panel Model 550
18.1 Introduction 551
18.2 Linear Dynamic Model 551
18.3 Fixed and Random Effects Estimation 553
18.3.1 Illustration by Using Stata 556
18.4 Instrumental Variable Estimation 557
18.4.1 Illustration by Using Stata 558
18.5 Arellano–Bond GMM Estimator 561
18.5.1 Illustration by Using Stata 565
18.6 System GMM Estimator 569
18.6.1 Illustration by Using Stata 571
Appendix: Generalised Method of Moments 573
References 574
| Erscheint lt. Verlag | 5.9.2019 |
|---|---|
| Zusatzinfo | XXVII, 565 p. 661 illus., 528 illus. in color. |
| Sprache | englisch |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Angewandte Mathematik |
| Mathematik / Informatik ► Mathematik ► Computerprogramme / Computeralgebra | |
| Mathematik / Informatik ► Mathematik ► Finanz- / Wirtschaftsmathematik | |
| Wirtschaft ► Allgemeines / Lexika | |
| Wirtschaft ► Volkswirtschaftslehre ► Ökonometrie | |
| Schlagworte | Applied Econometrics • Cross-Sectional Models • Econometrics • Models with Panel Data • Time-series Models |
| ISBN-10 | 981-329-019-6 / 9813290196 |
| ISBN-13 | 978-981-329-019-8 / 9789813290198 |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
| Haben Sie eine Frage zum Produkt? |
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