Time Series Data Analysis Using EViews

Opis

Do you want to recognize the most suitable models for analysis of statistical data sets?

This book provides a hands-on practical guide to using the most suitable models for analysis of statistical data sets using EViews - an interactive Windows-based computer software program for sophisticated data analysis, regression, and forecasting - to define and test statistical hypotheses. Rich in examples and with an emphasis on how to develop acceptable statistical models, Time Series Data Analysis Using EViews is a perfect complement to theoretical books presenting statistical or econometric models for time series data. The procedures introduced are easily extendible to cross-section data sets.

The author:

Provides step-by-step directions on how to apply EViews software to time series data analysis
Offers guidance on how to develop and evaluate alternative empirical models, permitting the most appropriate to be selected without the need for computational formulae
Examines a variety of times series models, including continuous growth, discontinuous growth, seemingly causal, regression, ARCH, and GARCH as well as a general form of nonlinear time series and nonparametric models
Gives over 250 illustrative examples and notes based on the author's own empirical findings, allowing the advantages and limitations of each model to be understood
Describes the theory behind the models in comprehensive appendices
Provides supplementary information and data sets

An essential tool for advanced undergraduate and graduate students taking finance or econometrics courses. Statistics, life sciences, and social science students, as well as applied researchers, will also find this book an invaluable resource.

Spis treści

TIME SERIES DATA ANALYSIS USING EVIEWS 2
- Contents 12
- Preface 20
- 1 EViews workfile and descriptive data analysis 24
  - 1.1 What is the EViews workfile? 24
  - 1.2 Basic options in EViews 24
  - 1.3 Creating a workfile 26
    - 1.3.2 Creating a workfile using EViews 4 26
    - 1.3.1 Creating a workfile using EViews 5 or 6 26
  - 1.4 Illustrative data analysis 30
    - 1.4.1 Basic descriptive statistical summary 30
    - 1.4.2 Box plots and outliers 34
    - 1.4.3 Descriptive statistics by groups 34
    - 1.4.4 Graphs over times 35
    - 1.4.6 Correlation matrix 38

TIME SERIES DATA ANALYSIS USING EVIEWS 2
- Contents 12
- Preface 20
- 1 EViews workfile and descriptive data analysis 24
  - 1.1 What is the EViews workfile? 24
  - 1.2 Basic options in EViews 24
  - 1.3 Creating a workfile 26
    - 1.3.2 Creating a workfile using EViews 4 26
    - 1.3.1 Creating a workfile using EViews 5 or 6 26
  - 1.4 Illustrative data analysis 30
    - 1.4.1 Basic descriptive statistical summary 30
    - 1.4.2 Box plots and outliers 34
    - 1.4.3 Descriptive statistics by groups 34
    - 1.4.4 Graphs over times 35
    - 1.4.6 Correlation matrix 38
    - 1.4.5 Means seasonal growth curve 38
    - 1.4.7 Autocorrelation and partial autocorrelation 40
    - 1.4.8 Bivariate graphical presentation with regression 41
  - 1.5 Special notes and comments 42
  - 1.6 Statistics as a sample space 45
- 2 Continuous growth models 48
  - 2.1 Introduction 48
  - 2.2 Classical growth models 48
  - 2.3 Autoregressive growth models 52
    - 2.3.1 First-order autoregressive growth models 52
    - 2.3.2 AR(p) growth models 53
  - 2.4. Residual tests 55
    - 2.4.1 Hypothesis of no serial correlation 56
    - 2.4.3 Hypothesis of the normality assumption 57
    - 2.4.2 Hypothesis of the homogeneous residual term 57
    - 2.4.4 Correlogram Q-statistic 58
  - 2.5 Bounded autoregressive growth models 61
  - 2.6 Lagged variables or autoregressive growth models 64
    - 2.6.1 The white estimation method 65
    - 2.6.2 The Newey–West HAC estimation method 66
    - 2.6.3 The Akaike Information and Schwarz Criterions 67
    - 2.6.4 Mixed lagged-variables autoregressive growth models 67
    - 2.6.5 Serial correlation LM test for LV(2,1)_GM 71
  - 2.7 Polynomial growth model 72
    - 2.7.1 Basic polynomial growth models 72
    - 2.7.2 Special polynomial growth models 78
  - 2.8 Growth models with exogenous variables 79
  - 2.9 A Taylor series approximation model 82
  - 2.10 Alternative univariate growth models 83
    - 2.10.2 Translog additive growth models 83
    - 2.10.1 A more general growth model 83
    - 2.10.3 Some comments 86
    - 2.10.4 Growth model having interaction factors 87
    - 2.10.5 Trigonometric growth models 92
  - 2.11 Multivariate growth models 93
    - 2.11.1 The classical multivariate growth model 93
    - 2.11.2 Modified multivariate growth models 97
    - 2.11.3 AR(1) multivariate general growth models 101
    - 2.11.4 The S-shape multivariate AR(1) general growth models 102
  - 2.12 Multivariate AR(p) GLM with trend 102
    - 2.12.1 Kernel density and theoretical distribution 111
  - 2.13 Generalized multivariate models with trend 118
    - 2.13.1 The simplest multivariate autoregressive model 118
    - 2.13.2 Multivariate autoregressive model with two-way interactions 123
    - 2.13.3 Multivariate autoregressive model with three-way interactions 125
  - 2.14 Special notes and comments 127
    - 2.14.1 The true population model 127
    - 2.14.2 Near singular matrix 128
    - 2.14.3 ‘To Test or Not’ the assumptions of the error terms 130
  - 2.15 Alternative multivariate models with trend 136
    - 2.15.1 The lagged endogenous variables: first autoregressive model with trend 136
    - 2.15.2 The lagged endogenous variables: first autoregressive model with exogenous variables and trend 137
    - 2.15.3 The mixed lagged variables: first autoregressive model with trend 138
  - 2.16 Generalized multivariate models with time-related effects 141
- 3 Discontinuous growth models 144
  - 3.2 Piecewise growth models 144
    - 3.2.1 Two-piece classical growth models 145
  - 3.1 Introduction 144
  - 3.3 Piecewise S-shape growth models 152
    - 3.3.1 Two-piece linear growth models 152
  - 3.4 Two-piece polynomial bounded growth models 159
    - 3.4.1 Two-piece quadratic growth models 159
    - 3.4.2 Two-piece third-degree bounded growth model 160
    - 3.4.3 Two-piece generalized exponential growth model 161
  - 3.5 Discontinuous translog linear AR(1) growth models 161
  - 3.6 Alternative discontinuous growth models 161
  - 3.7 Stability test 178
    - 3.7.1 Chow’s breakpoint test 178
    - 3.7.2 Chow’s forecast test 181
  - 3.8 Generalized discontinuous models with trend 182
    - 3.8.1 General two-piece univariate models with trend 183
    - 3.8.2 Special notes and comments 191
    - 3.8.3 General two-piece multivariate models with trend 194
  - 3.9 General two-piece models with time-related effects 197
  - 3.10 Multivariate models by states and time periods 203
    - 3.10.1 Alternative models 205
    - 3.10.2 Not recommended models 206
  - 4 Seemingly causal models 208
    - 4.1 Introduction 208
    - 4.2 Statistical analysis based on a single time series 209
      - 4.2.1 The means model 209
      - 4.2.2 The cell-means models 209
      - 4.2.3 The lagged-variable models 215
      - 4.2.4 Autoregressive models 224
      - 4.2.5 Lagged-variable autoregressive models 224
    - 4.3 Bivariate seemingly causal models 226
      - 4.3.1 The simplest seemingly causal models 227
      - 4.3.2 Simplest models in three-dimensional space 234
      - 4.3.3 General univariate LVAR(p,q) seemingly causal model 235
    - 4.4 Trivariate seemingly causal models 243
      - 4.4.1 Simple models in three-dimensional space 243
      - 4.4.2 General LVAR(p,q) with exogenous variables 246
    - 4.5 System equations based on trivariate time series 249
    - 4.6 General system of equations 251
    - 4.7 Seemingly causal models with dummy variables 255
      - 4.7.1 Homogeneous time series models 255
      - 4.7.2 Heterogeneous time series models 256
    - 4.8 General discontinuous seemingly causal models 261
    - 4.9 Additional selected seemingly causal models 266
      - 4.9.1 A Third-degree polynomial function 267
      - 4.9.2 A Three-dimensional bounded semilog linear model 267
      - 4.9.3 Time series Cobb–Douglas models 268
      - 4.9.4 Time series CES models 272
    - 4.10 Final notes in developing models 279
      - 4.10.1 Expert judgment 279
      - 4.10.2 Other unexpected models 279
      - 4.10.3 The principal component factor analysis 280
    - 5 Special cases of regression models 282
      - 5.1 Introduction 282
      - 5.2 Specific cases of growth curve models 282
        
        5.2.1 Basic polynomial model 283
        
        5.2.3 Heteroskedasticity-consistent covariance (White) 285
        
        5.2.2 An AR(1) regression model 285
      - 5.3 Seemingly causal models 287
        
        5.3.1 Autoregressive models 288
      - 5.4 Lagged variable models 298
        
        5.4.1 The basic lagged-variable model 298
        
        5.4.3 Generalized lagged-variable autoregressive model 305
        
        5.4.2 Some notes 305
      - 5.5 Cases based on the US domestic price of copper 313
        
        5.5.1 Graphical representation 314
        
        5.5.2 Seemingly causal model 316
        
        5.5.3 Generalized translog linear model 319
        
        5.5.4 Constant elasticity of substitution models 323
        
        5.5.5 Models for the first difference of an endogenous variable 327
        
        5.5.6 Unexpected findings 329
        
        5.5.7 Multivariate linear seemingly causal models 333
      - 5.6 Return rate models 334
      - 5.7 Cases based on the BASICS workfile 337
        
        5.7.1 Special notes 340
      - 6 VAR and system estimation methods 342
        
        6.1 Introduction 342
        
        6.2 The VAR models 343
        
        6.2.1 The basic VAR model 344
        
        6.2.2 The VAR models with exogenous variables 346
        
        6.2.3 Cases based on the demo_modified workfile 346
        
        6.2.4 The VAR models with dummy variables 364
        
        6.2.5 Selected VAR models based on the US domestic price of copper data 367
        
        6.3 The vector error correction models 377
        
        6.3.1 The basic VEC model 377
        
        6.3.2 General equation of the basic VEC models 383
        
        6.3.3 The VEC models with exogenous variables 384
        
        6.3.4 Some notes and comments 389
        
        6.4 Special notes and comments 403
      - 7 Instrumental variables models 404
        
        7.1 Introduction 404
        
        7.2 Should we apply instrumental models? 406
        
        7.3 Residual analysis in developing instrumental models 411
        
        7.3.1 Testing an hypothesis corresponding to the instrumental models 412
        
        7.3.2 Graphical representation of the residual series 414
        
        7.4 System equation with instrumental variables 415
        
        7.5 Selected cases based on the US_DPOC data 418
        
        7.6 Instrumental models with time-related effects 423
        
        7.7 Instrumental seemingly causal models 424
        
        7.7.1 Special notes and comments 428
        
        7.8 Multivariate instrumental models based on the US_DPOC 429
        
        7.8.1 Simple multivariate instrumental models 429
        
        7.8.2 Multivariate instrumental models 432
        
        7.9 Further extension of the instrumental models 440
      - 8 ARCH models 442
        
        8.1 Introduction 442
        
        8.2 Options of ARCH models 442
        
        8.3 Simple ARCH models 443
        
        8.3.1 Simple ARCH models 443
        
        8.3.2 Special notes on the ARCH models 447
        
        8.4 ARCH models with exogenous variables 447
        
        8.4.1 ARCH models with one exogenous variable 447
        
        8.4.2 ARCH models with two exogenous variables 448
        
        8.4.3 Advanced ARCH models 452
        
        8.5 Alternative GARCH variance series 459
        
        8.5.1 General GARCH variance series for the GARCH/TARCH model 459
        
        8.5.2 General GARCH variance series for the EGARCH model 460
        
        8.5.3 General GARCH variance series for the PARCH model 461
        
        8.5.4 General GARCH variance series for the component ARCH(1,1) model 462
        
        8.5.5 Special notes on the GARCH variance series 463
        
        9 Additional testing hypotheses 464
        
        9.1 Introduction 464
        
        9.2 The unit root tests 465
        
        9.2.1 Simple unit root test 465
        
        9.2.2 Unit root test for higher-order serial correlation 469
        
        9.2.3 Comments on the unit root tests 470
        
        9.3 The omitted variables tests 471
        
        9.4 Redundant variables test (RV-test) 477
        
        9.5 Nonnested test (NN-test) 479
        
        9.6 The Ramsey RESET test 482
        
        9.7 Illustrative examples based on the Demo.wf1 484
        
        10 Nonlinear least squares models 492
        
        10.1 Introduction 492
        
        10.2 Classical growth models 494
        
        10.3 Generalized Cobb–Douglas models 496
        
        10.3.1 Cases based on the Demo.wf1 497
        
        10.3.2 Cases based on the BASIC.wf1 500
        
        10.3.3 Cases based on the US_DPOC data 502
        
        10.4 Generalized CES models 514
        
        10.5 Special notes and comments 516
        
        10.6 Other NLS models 517
        
        10.6.1 Cases based on Demo.wf1 517
        
        10.6.2 Cases based on the US_DPOC data 520
        
        11 Nonparametric estimation methods 526
        
        11.1 What is the nonparametric data analysis 526
        
        11.2 Basic moving average estimates 527
        
        11.2.1 Simple moving average estimates 527
        
        11.2.2 The weighted moving average estimates 529
        
        11.3 Measuring the best fit model 531
        
        11.4 Advanced moving average models 532
        
        11.4.1 The moving average models 532
        
        11.4.2 The autoregressive moving average models 536
        
        11.4.3 The ARMA models with covariates 537
        
        11.5 Nonparametric regression based on a time series 539
        
        11.5.1 The Hardle moving average models 539
        
        11.5.2 The nearest neighbor fit 540
        
        11.5.3 Mathematical background of the nearest neighbor fit 541
        
        11.6 The local polynomial Kernel fit regression 545
        
        11.7 Nonparametric growth models 547
        
        Appendix A: Models for a single time series 550
        
        A.1 The simplest model 550
        
        A.1.1 OLS estimates 551
        
        A.1.2 Properties of the error terms 551
        
        A.1.3 Maximum likelihood estimates 552
        
        A.2 First-order autoregressive models 553
        
        A.2.1 Properties of the parameters 553
        
        A.2.2 Autocorrelation function of an AR(1) model 554
        
        A.2.3 Estimates of the parameters 555
        
        A.3 Second-order autoregressive model 556
        
        A.3.1 Properties of the parameters 556
        
        A.3.2 Autocorrelation function of an AR(2) model 556
        
        A.3.3 Estimates of the parameters 557
        
        A.4 First-order moving average model 558
        
        A.5 Second-order moving average model 559
        
        A.6 The simplest ARMA model 560
        
        A.7 General ARMA model 561
        
        A.7.1 Derivation of the ACF 561
        
        A.7.2 Estimation method 564
        
        Appendix B: Simple linear models 566
        
        B.1 The simplest linear model 566
        
        B.1.1 Least squares estimators 566
        
        B.2 Linear model with basic assumptions 567
        
        B.2.1 Sampling distributions of the model parameters 568
        
        B.2.2 Student’st-statistic 569
        
        B.2.3 Analysis of variance table 569
        
        B.2.4 Coefficient of determination 570
        
        B.3 Maximum likelihood estimation method 571
        
        B.4 First-order autoregressive linear model 573
        
        B.4.1 Two-stage estimation method 573
        
        B.4.2 Durbin–Watson statistic 574
        
        B.4.3 Properties of the error termt 574
        
        B.4.4 Maximum likelihood estimation method 575
        
        B.5 AR(p) linear model 576
        
        B.5.1 Estimation method 577
        
        B.5.2 Properties oft 577
        
        B.6 Alternative models 578
        
        B.6.1 Alternative 1: The simplest model with trend 578
        
        B.6.2 Alternative 2: The classical growth model 578
        
        B.6.4 Alternative 4: The AR(p) return rate model 579
        
        B.6.5 Alternative 5: The bounded translog linear (Cobb–Douglas) AR(p) model 579
        
        B.6.3 Alternative 3: The AR(p) polynomial model 579
        
        B.7 Lagged-variable model 579
        
        B.8 Lagged-variable autoregressive models 580
        
        B.8.1 The simplest lagged-variable autoregressive model 580
        
        B.8.2 General lagged-variable autoregressive model 582
        
        B.9 Special notes and comments 583
        
        Appendix C: General linear models 584
        
        C.1 General linear model with i.i.d. Gaussian disturbances 584
        
        C.1.1 The OLS estimates 585
        
        C.1.2 Maximum likelihood estimates 586
        
        C.1.3 Student’st-statistic 587
        
        C.1.4 The Wald form of the OLS F-test 587
        
        C.2 AR(1) general linear model 588
        
        C.2.1 Properties oft 589
        
        C.2.2 Estimation method 589
        
        C.3 AR(p) general linear model 590
        
        C.4 General lagged-variable autoregressive model 590
        
        C.5 General models with Gaussian errors 591
        
        C.5.1 Gaussian errors with a known variance covariance matrix 591
        
        C.5.2 Generalized least squares with a known covariance matrix 592
        
        C.5.3 GLS and ML estimations 593
        
        C.5.4 The variance of the error is proportional to the square of one of the explanatory variables 593
        
        C.5.5 Generalized least squares with an unknown covariance matrix 594
        
        Appendix D: Multivariate general linear models 596
        
        D.1 Multivariate general linear models 596
        
        D.2 Moments of an endogenous multivariate 597
        
        D.3 Vector autoregressive model 598
        
        D.5 Vector autoregressive moving average model 599
        
        D.4 Vector moving average model 599
        
        D.6 Simple multivariate models with exogenous variables 600
        
        D.6.1 The simplest multivariate model 600
        
        D.6.2 Simple model with a multidimensional exogenous variable 601
        
        D.6.4 Selected bivariate time series models 602
        
        D.6.3 A more general model 602
        
        D.6.5 Bivariate Granger causality tests 603

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