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Time Series Data Analysis Using EViews (Statistics in Practice) [Hardcover]
I. Gusti Ngurah Agung (Author)
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Preface xvii
1 EViews workfile and descriptive data analysis 1
1.1 What is the EViews workfile? 1
1.2 Basic options in EViews 1
1.3 Creating a workfile 3
1.3.1 Creating a workfile using EViews 5 or 6 3
1.3.2 Creating a workfile using EViews 4 3
1.4 Illustrative data analysis 7
1.4.1 Basic descriptive statistical summary 7
1.4.2 Box plots and outliers 11
1.4.3 Descriptive statistics by groups 11
1.4.4 Graphs over times 12
1.4.5 Means seasonal growth curve 15
1.4.6 Correlation matrix 15
1.4.7 Autocorrelation and partial autocorrelation 17
1.4.8 Bivariate graphical presentation with regression 18
1.5 Special notes and comments 19
1.6 Statistics as a sample space 22
2 Continuous growth models 25
2.1 Introduction 25
2.2 Classical growth models 25
2.3 Autoregressive growth models 29
2.3.1 First-order autoregressive growth models 29
2.3.2 AR(p) growth models 30
2.4. Residual tests 32
2.4.1 Hypothesis of no serial correlation 33
2.4.2 Hypothesis of the homogeneous residual term 34
2.4.3 Hypothesis of the normality assumption 34
2.4.4 Correlogram Q-statistic 35
2.5 Bounded autoregressive growth models 38
2.6 Lagged variables or autoregressive growth models 41
2.6.1 The white estimation method 42
2.6.2 The Newey–West HAC estimation method 43
2.6.3 The Akaike Information and Schwarz Criterions 44
2.6.4 Mixed lagged-variables autoregressive growth models 44
2.6.5 Serial correlation LM test for LV(2,1)_GM 48
2.7 Polynomial growth model 49
2.7.1 Basic polynomial growth models 49
2.7.2 Special polynomial growth models 55
2.8 Growth models with exogenous variables 56
2.9 A Taylor series approximation model 59
2.10 Alternative univariate growth models 60
2.10.1 A more general growth model 60
2.10.2 Translog additive growth models 60
2.10.3 Some comments 63
2.10.4 Growth model having interaction factors 64
2.10.5 Trigonometric growth models 69
2.11 Multivariate growth models 70
2.11.1 The classical multivariate growth model 70
2.11.2 Modified multivariate growth models 74
2.11.3 AR(1) multivariate general growth models 78
2.11.4 The S-shape multivariate AR(1) general growth models 79
2.12 Multivariate AR(p) GLM with trend 79
2.12.1 Kernel density and theoretical distribution 88
2.13 Generalized multivariate models with trend 95
2.13.1 The simplest multivariate autoregressive model 95
2.13.2 Multivariate autoregressive model with two-way
interactions 100
2.13.3 Multivariate autoregressive model with three-way
interactions 102
2.14 Special notes and comments 104
2.14.1 The true population model 104
2.14.2 Near singular matrix 105
2.14.3 ‘To Test or Not’ the assumptions of the error terms 107
2.15 Alternative multivariate models with trend 113
2.15.1 The lagged endogenous variables: first autoregressive
model with trend 113
2.15.2 The lagged endogenous variables: first autoregressive
model with exogenous variables and trend 114
2.15.3 The mixed lagged variables: first autoregressive
model with trend 115
2.16 Generalized multivariate models with time-related effects 118
3 Discontinuous growth models 121
3.1 Introduction 121
3.2 Piecewise growth models 121
x Contents
3.2.1 Two-piece classical growth models 122
3.3 Piecewise S-shape growth models 129
3.3.1 Two-piece linear growth models 129
3.4 Two-piece polynomial bounded growth models 136
3.4.1 Two-piece quadratic growth models 136
3.4.2 Two-piece third-degree bounded growth model 137
3.4.3 Two-piece generalized exponential growth model 138
3.5 Discontinuous translog linear AR(1) growth models 138
3.6 Alternative discontinuous growth models 138
3.7 Stability test 155
3.7.1 Chow’s breakpoint test 155
3.7.2 Chow’s forecast test 158
3.8 Generalized discontinuous models with trend 159
3.8.1 General two-piece univariate models with trend 160
3.8.2 Special notes and comments 168
3.8.3 General two-piece multivariate models with trend 171
3.9 General two-piece models with time-related effects 174
3.10 Multivariate models by states and time periods 180
3.10.1 Alternative models 182
3.10.2 Not recommended models 183
4 Seemingly causal models 185
4.1 Introduction 185
4.2 Statistical analysis based on a single time series 186
4.2.1 The means model 186
4.2.2 The cell-means models 186
4.2.3 The lagged-variable models 192
4.2.4 Autoregressive models 201
4.2.5 Lagged-variable autoregressive models 201
4.3 Bivariate seemingly causal models 203
4.3.1 The simplest seemingly causal models 204
4.3.2 Simplest models in three-dimensional space 211
4.3.3 General univariate LVAR(p,q) seemingly causal model 212
4.4 Trivariate seemingly causal models 220
4.4.1 Simple models in three-dimensional space 220
4.4.2 General LVAR(p,q) with exogenous variables 223
4.5 System equations based on trivariate time series 226
4.6 General system of equations 228
4.7 Seemingly causal models with dummy variables 232
4.7.1 Homogeneous time series models 232
4.7.2 Heterogeneous time series models 233
4.8 General discontinuous seemingly causal models 238
4.9 Additional selected seemingly causal models 243
4.9.1 A Third-degree polynomial function 244
Contents xi
4.9.2 A Three-dimensional bounded semilog linear model 244
4.9.3 Time series Cobb–Douglas models 245
4.9.4 Time series CES models 249
4.10 Final notes in developing models 256
4.10.1 Expert judgment 256
4.10.2 Other unexpected models 256
4.10.3 The principal component factor analysis 257
5 Special cases of regression models 259
5.1 Introduction 259
5.2 Specific cases of growth curve models 259
5.2.1 Basic polynomial model 260
5.2.2 An AR(1) regression model 262
5.2.3 Heteroskedasticity-consistent covariance (White) 262
5.3 Seemingly causal models 264
5.3.1 Autoregressive models 265
5.4 Lagged variable models 275
5.4.1 The basic lagged-variable model 275
5.4.2 Some notes 282
5.4.3 Generalized lagged-variable autoregressive model 282
5.5 Cases based on the US domestic price of copper 290
5.5.1 Graphical representation 291
5.5.2 Seemingly causal model 293
5.5.3 Generalized translog linear model 296
5.5.4 Constant elasticity of substitution models 300
5.5.5 Models for the first difference of an endogenous variable 304
5.5.6 Unexpected findings 306
5.5.7 Multivariate linear seemingly causal models 310
5.6 Return rate models 311
5.7 Cases based on the BASICS workfile 314
5.7.1 Special notes 317
6 VAR and system estimation methods 319
6.1 Introduction 319
6.2 The VAR models 320
6.2.1 The basic VAR model 321
6.2.2 The VAR models with exogenous variables 323
6.2.3 Cases based on the demo_modified workfile 323
6.2.4 The VAR models with dummy variables 341
6.2.5 Selected VAR models based on the US domestic
price of copper data 344
6.3 The vector error correction models 354
6.3.1 The basic VEC model 354
6.3.2 General equation of the basic VEC models 360
xii Contents
6.3.3 The VEC models with exogenous variables 361
6.3.4 Some notes and comments 366
6.4 Special notes and comments 380
7 Instrumental variables models 381
7.1 Introduction 381
7.2 Should we apply instrumental models? 383
7.3 Residual analysis in developing instrumental models 388
7.3.1 Testing an hypothesis corresponding to the instrumental
models 389
7.3.2 Graphical representation of the residual series 391
7.4 System equation with instrumental variables 392
7.5 Selected cases based on the US_DPOC data 395
7.6 Instrumental models with time-related effects 400
7.7 Instrumental seemingly causal models 401
7.7.1 Special notes and comments 405
7.8 Multivariate instrumental models based on the US_DPOC 406
7.8.1 Simple multivariate instrumental models 406
7.8.2 Multivariate instrumental models 409
7.9 Further extension of the instrumental models 417
8 ARCH models 419
8.1 Introduction 419
8.2 Options of ARCH models 419
8.3 Simple ARCH models 420
8.3.1 Simple ARCH models 420
8.3.2 Special notes on the ARCH models 424
8.4 ARCH models with exogenous variables 424
8.4.1 ARCH models with one exogenous variable 424
8.4.2 ARCH models with two exogenous variables 425
8.4.3 Advanced ARCH models 429
8.5 Alternative GARCH variance series 436
8.5.1 General GARCH variance series for the
GARCH/TARCH model 436
8.5.2 General GARCH variance series for the EGARCH model 437
8.5.3 General GARCH variance series for the PARCH model 438
8.5.4 General GARCH variance series for the component
ARCH(1,1) model 439
8.5.5 Special notes on the GARCH variance series 440
9 Additional testing hypotheses 441
9.1 Introduction 441
9.2 The unit root tests 442
9.2.1 Simple unit root test 442
9.2.2 Unit root test for higher-order serial correlation 446
Contents xiii
9.2.3 Comments on the unit root tests 447
9.3 The omitted variables tests 448
9.4 Redundant variables test (RV-test) 454
9.5 Nonnested test (NN-test) 456
9.6 The Ramsey RESET test 459
9.7 Illustrative examples based on the Demo.wf1 461
10 Nonlinear least squares models 469
11 Nonparametric estimation methods 503
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