Advanced time series data analysis : forecasting using EViews / I. Gusti Ngurah Agung.
By: Agung, I Gusti Ngurah [author.]
Language: English Publisher: Hoboken, NJ : John Wiley & Sons, 2019Description: 1 online resource (544 pages)Content type: text Media type: computer Carrier type: online resourceISBN: 9781119504740 (ePub); 9781119504733 (Adobe PDF)Subject(s): Time-series analysis | Econometric modelsGenre/Form: Electronic books.DDC classification: 519.55 LOC classification: QA280Online resources: Full text available at Wiley Online Library Click here to view| Item type | Current location | Home library | Call number | Status | Date due | Barcode | Item holds |
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| 519.542 N98 2019 The Bayesian way : introductory statistics for economists and engineers / | 519.542 Sh449 2017 Introduction to Bayesian estimation and copula models of dependence / | 519.544 Og9 2019 Understanding least squares estimation and geomatics data analysis / | 519.55 Ag95 2019 Advanced time series data analysis : forecasting using EViews / | 519.55 H278 2018 Time series analysis with long memory in view / | 519.55 T7829 2019 Nonlinear time series analysis / | 519.55 W4242 2019 Multivariate time series analysis and applications / |
ABOUT THE AUTHOR
I. Gusti Ngurah Agung, PhD, has been an advisor at the Ary Suta Center, Jakarta since 2008. He recently retired from his position as a lecturer at the Graduate School of Management, University of Indonesia. In addition to teaching and being an academic advisor, he has served as independent consultant for various institutions, such as the World Bank, UNFPA, ADB, USAID, and the Rand Corporation. He is the author of several statistical textbooks and research papers in the area of statistics and management. His area of interest is in statistical analysis based on censored data, multiple regression analysis, multivariate statistical analysis, and sociodemographic development.
Includes bibliographical references and index.
About the Author xiii
Preface xv
1 Forecasting a Monthly Time Series 1
1.1 Introduction 1
1.2 Forecasting Using LV(p) Models 1
1.2.1 Basic or Regular LV(p) Models 1
1.2.2 Special LV(p) Models 6
1.3 Forecasting Using the LVARMA(p,q,r) Model 8
1.3.1 Special Notes on the ARMA Model 9
1.3.2 Application of Special LVAR Models 10
1.4 Forecasting Using TGARCH(a,b,c) Models 12
1.4.1 Application of ARCH(a), GARCH(b), and TARCH(c) Models 14
1.4.2 Application of TGARCH(a,b,0) Models 14
1.4.3 Application of TGARCH(a,b,c) Models 20
1.4.4 Other Alternative Models 20
1.5 Instrumental Variables Models 20
1.5.1 Application of the GMM Estimation Method 21
1.5.2 Application of the TSLS Estimation Method 36
1.6 Special Notes and Comments on Residual Analysis 42
1.6.1 Specific Residual Analysis 43
1.6.2 Additional Special Notes and Comments 61
1.6.3 Serial Correlation Tests 65
1.7 Statistical Results Using Alternative Options 67
1.7.1 Application of an Alternative Coefficient Covariance Matrix 67
1.7.2 Application of Selected Combinations of Options 70
1.7.3 Final Notes and Conclusions 71
2 Forecasting with Time Predictors 73
2.1 Introduction 73
2.2 Application of LV(p) Models of HS on MONTH by YEAR 73
2.2.1 Special LV(12) Models of HS on MONTH by YEAR 73
2.2.2 Application of the Omitted Variables Test – Likelihood Ratio 75
2.2.3 Heterogeneous Model of HS on HS(−12) and Month by YEAR 79
2.3 Forecast Models of HS on MONTH by YEAR 79
2.3.1 Application of LV(1) Models of HS on MONTH by YEAR 79
2.3.2 Application of Basic LV(p) Models of HS on MONTH by YEAR 82
2.3.3 Application of AR(q) Models of HS on MONTH by YEAR 86
2.3.4 Application of ARMA(q,r) Models of HS on MONTH by YEAR 89
2.3.5 Application of LVAR(p,q) Models of HS on MONTH by YEAR 89
2.3.6 Application of LVAR(p,q) Models of HS on YEAR by MONTH 92
2.4 Heterogeneous Classical Growth Models 95
2.4.1 Forecasting Based on LV(p) Het_CGMs of HS 95
2.4.2 Forecasting Based on AR(q) Het_CGMs 99
2.4.3 Forecasting Based on LVAR(p,q) Het_CGMs 101
2.5 Forecast Models of G in Currency.wf1 103
2.5.1 LVAR(p,q) Additive Models of G by @Month with @Trend 104
2.5.2 LV(1) Heterogeneous Models of G by @Month 111
2.6 Forecast Models of G on G(−1) and Polynomial Time Variables 116
2.6.1 Heterogeneous Model of G on G(−1) and Polynomial T by @Month 116
2.6.2 Forecast Model of G on G(−1) with Heterogeneous Polynomial Trend 138
2.7 Forecast Models of CURR in Currency.wf1 140
2.7.1 Developing Scatter Graphs with Regressions 141
2.7.2 Additive Forecast Models of CURR with a Time Predictor 143
2.7.3 Interaction Forecast Models of CURR 159
2.7.4 Forecast Models Based on Subsamples 169
3 Continuous Forecast Models 185
3.1 Introduction 185
3.2 Forecasting of FSPCOM 185
3.2.1 Simple Continuous Models of FSPCOM 185
3.2.2 LVAR(P,Q) Models of Y = FSPCOM with Polynomial Trend 190
3.2.3 Translog Models with Time Predictor 195
3.3 Forecasting Based on Subsamples 207
3.3.1 Lag Variable Models With Lower and Upper Bounds 209
3.4 Special LV(12) Models of HS with Upper and Lower Bounds 222
3.4.1 Special LVARMA(12,q,r) Model of LNYul Without Time Predictor 223
3.4.2 Special LVARMA(12,q,r) of LNYul With Time Predictor 223
4 Forecasting Based on (Xt,Yt) 229
4.1 Introduction 229
4.2 Forecast Models Based on (Xt,Yt) 229
4.3 Data Analysis Based on a Monthly Time Series 230
4.4 Forecast Models without a Time Predictor 230
4.4.1 Two-Way Interaction Models 230
4.4.2 Cobb–Douglass Model and Alternatives 235
4.5 Translog Quadratic Model 236
4.5.1 Forecasting Using a Subsample 240
4.5.2 Forecast Model with Trend 243
4.6 Forecasting of FSXDP 247
4.6.1 Forecasting of Y2 Based on a Subsample 247
4.6.2 Extension of the Model (4.25) with Time Variables 252
4.7 Translog Linear Models 256
4.7.1 Basic Translog Linear Model 256
4.7.2 Tanslog Linear Model with Trend 256
4.7.3 Heterogeneous Tanslog Linear Model 260
4.8 Application of VAR Models 262
4.8.1 Unstructured VAR Models Based on (X1t,Y1t) 262
4.8.2 The Simplest VAR Models with Alternative Trends 264
4.8.3 Complete Heterogeneous VAR Models by @Month 270
4.8.4 Bayesian VAR Models 271
4.8.5 VEC Models 271
4.9 Forecast Models Based on (Y1t,Y2t) 275
4.9.1 Forecast Models Based on Figures 4.42a and b 275
4.9.2 Reciprocal Causal Effects Models 279
4.9.3 Models with the Time Independent Variables 280
4.10 Special Notes and Comments 287
5 Forecasting Based On (X1t,X2t,Yt) 289
5.1 Introduction 289
5.2 Translog Linear Models Based on (X1,X2,Y1) 289
5.2.1 Basic Translog Linear Model 289
5.2.2 Tanslog Linear Model with Trend 292
5.2.3 Tanslog Linear Model with Heterogeneous Trends 292
5.3 Translog Linear Models Based on (X1,X2,Y2) 293
5.3.1 Translog Linear Models Using the Subsample {@Year>1990} 296
5.3.2 Translog Linear Models Using the Subsample {@Year>1975} 298
5.3.3 Translog Linear Models Using the Whole Sample 298
5.4 Forecast Models Using Original (X1,X2,Y) 300
5.4.1 Model Based on Figure 5.6a 300
5.4.2 Model Based on Figure 5.6b 301
5.4.3 Model Based on Figure 5.6c 307
5.5 Alternative Forecast Models Using Original (X1,X2,Y) 310
5.5.1 Three-Way Interaction Based on Figure 5.14a 311
5.5.2 Three-Way Interaction Based on Figure 5.14b and c 311
5.6 Forecasting Models with Trends Using Original (X1,X2,Y) 311
5.7 Application of VAR Models Based on (X1t,X2t,Y1t) 316
5.7.1 Unrestricted VAR Models 316
5.7.2 The Simplest Two-Way Interaction VAR Model 317
5.7.3 The Simplest Three-Way Interaction VAR Model 318
5.8 Applications of the Object “System” 320
5.8.1 The MLV(1,1,1) Models of (Y1,Y2,Y3) on (Y1(−1),Y2(−1),Y3(−1)) 320
5.8.2 Circular Effects MLV(1,1,1) Models 328
5.9 Models Presenting Causal Relationships Y1,Y2, and Y3 335
5.9.1 Triangular Effects Models 335
5.9.2 Circular Effects Models 340
5.9.3 Reciprocal Effects Models 341
5.10 Extended Models 344
5.10.1 Extension to the Models with Additional Exogenous Variables 344
5.10.2 Extension to the Models with Alternative Trends 347
5.10.3 Extension to LVARMA(p,q,r) 352
5.10.4 Extension to Heterogeneous Regressions by Months 356
5.11 Special Notes and Comments 369
6 Forecasting Quarterly Time Series 371
6.1 Introduction 371
6.2 Alternative LVARMA(p,q,r) Of a Single Time Series 371
6.2.1 LV(P) Forecast Model of GCDANt 371
6.2.2 LVARMA(p,q.r) Forecast Models of GCDN 372
6.2.3 Forecast Models of GCDAN with Time Variables 374
6.2.4 Special Notes on Uncommon Models 381
6.3 Complete Heterogeneous LV(2) Models of GCDAN By @Quarter 383
6.3.1 Using the Simplest Equation Specification 383
6.3.2 Using a Complete Equation Specification 387
6.4 LV(2) Models of GCDAN with Exogenous Variables 387
6.4.1 LV(2) Models with an Exogenous Variable 387
6.4.2 LV(2) Models with Two Exogenous Variables 390
6.5 Alternative Forecast Models Based on (Y1,Y2) 393
6.5.1 LV(2) Basic Interaction Models 393
6.5.2 LV(2) Models of (Y1,Y2) with an Exogenous Variable and @Trend 394
6.5.3 LV(2) Models of (Y1,Y2) with two Exogenous Variables and Trend 400
6.5.4 LV(2) Models of (Y1,Y2) with Three Exogenous Variables and Trend 409
6.6 Triangular Effects Models Based on (X1,X2,Y1) 413
6.6.1 Partial Two-Way Interaction LV(p) TE_Models 413
6.6.2 A Complete Two-Way Interaction LV(p) TE_Models 414
6.6.3 Three-Way Interaction LV(p) TE_ Models 415
6.7 Bivariate Triangular Effects Models Based on (X1,X2,Y1,Y2) 417
6.7.1 Partial Two-Way Interaction Models 417
6.7.2 Three-Way Interaction TE_Models 418
6.8 Models with Exogenous Variables and Alternative Trends 422
6.8.1 Models Based on (X1,X2,Y1) 422
6.8.2 Models Based on (X1,X2,Y1,Y2) with Trend 424
6.9 Special LV(4) Models with Exogenous Variables 427
6.10 Models with Exogenous Variables by @Quarter 433
6.10.1 Alternative Models Based on the Whole Sample 433
6.10.2 Forecasting Based on each Quarter’s Level 435
7 Forecasting Based on Time Series by States 447
7.1 Introduction 447
7.2 Models Based on a Bivariate (Y1_1,Y1_2) 447
7.2.1 Alternative LV(p) Models Based on Figure 7.1a 448
7.2.2 Alternative LV(p) Models Based on Figure 7.1b 451
7.2.3 Alternative LV(p) Models Based on Figure 7.1c 454
7.3 Advanced LP(p) Models of (Y1_1,Y1_2) 455
7.3.1 Two-Way Interaction LV(p) Models 455
7.3.2 Three-Way Interaction LV(p) Models 456
7.3.3 Alternative Additive Models 456
7.4 Advanced LP(p) Models of (Y1_1,Y1_2,Y1_3) 457
7.4.1 Triangular Effects Model of (Y1_1,Y1_2,Y1_3) 457
7.4.2 Full-Lag Variables Triangular Effects Model 462
7.4.3 Translog-Linear Triangular Effects Model 466
7.5 Full-Lag Variables Circular Effects Model 466
7.5.1 Two-Way Interaction Circular Effects Models 466
7.5.2 Three-Way Interaction Circular Effects Models 467
7.6 Full-Lag Variables Reciprocal-Effects Model 467
7.6.1 Two-Way Interaction Reciprocal-Effects Models 467
7.6.2 Three-Way Interaction Reciprocal-Effects Models 468
7.7 Successive Up-and-Downstream Relationships 468
7.7.1 A Set of the Simplest Two-Way Interaction Models 468
7.7.2 Successive Two-Way Interaction Triangular Effects Models 469
7.7.3 Successive Three-Way Interaction Triangular Effects Models 471
7.8 Forecast Models with the Time Independent Variable 474
7.8.1 Forecast Models with Alternative Trends 474
7.8.2 Two-Way Interaction with Time-Related Effects Models 480
7.8.3 Three-Way Interaction Time-Related Effects Models 483
7.9 Final Notes and Comments 491
7.9.1 The Manual Multistage Selection Method 491
7.9.2 Notes on the Best Possible Forecast Models 491
Bibliography 493
Index 503
Introduces the latest developments in forecasting in advanced quantitative data analysis
This book presents advanced univariate multiple regressions, which can directly be used to forecast their dependent variables, evaluate their in-sample forecast values, and compute forecast values beyond the sample period. Various alternative multiple regressions models are presented based on a single time series, bivariate, and triple time-series, which are developed by taking into account specific growth patterns of each dependent variables, starting with the simplest model up to the most advanced model. Graphs of the observed scores and the forecast evaluation of each of the models are offered to show the worst and the best forecast models among each set of the models of a specific independent variable.
Advanced Time Series Data Analysis: Forecasting Using EViews provides readers with a number of modern, advanced forecast models not featured in any other book. They include various interaction models, models with alternative trends (including the models with heterogeneous trends), and complete heterogeneous models for monthly time series, quarterly time series, and annually time series. Each of the models can be applied by all quantitative researchers.
Presents models that are all classroom tested
Contains real-life data samples
Contains over 350 equation specifications of various time series models
Contains over 200 illustrative examples with special notes and comments
Applicable for time series data of all quantitative studies
Advanced Time Series Data Analysis: Forecasting Using EViews will appeal to researchers and practitioners in forecasting models, as well as those studying quantitative data analysis. It is suitable for those wishing to obtain a better knowledge and understanding on forecasting, specifically the uncertainty of forecast values.
500-599 519
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