Includes bibliographical references and index.

About the Author xiiiPreface xv1 Forecasting a Monthly Time Series 11.1 Introduction 11.2 Forecasting Using LV(p) Models 11.2.1 Basic or Regular LV(p) Models 11.2.2 Special LV(p) Models 61.3 Forecasting Using the LVARMA(p,q,r) Model 81.3.1 Special Notes on the ARMA Model 91.3.2 Application of Special LVAR Models 101.4 Forecasting Using TGARCH(a,b,c) Models 121.4.1 Application of ARCH(a), GARCH(b), and TARCH(c) Models 141.4.2 Application of TGARCH(a,b,0) Models 141.4.3 Application of TGARCH(a,b,c) Models 201.4.4 Other Alternative Models 201.5 Instrumental Variables Models 201.5.1 Application of the GMM Estimation Method 211.5.2 Application of the TSLS Estimation Method 361.6 Special Notes and Comments on Residual Analysis 421.6.1 Specific Residual Analysis 431.6.2 Additional Special Notes and Comments 611.6.3 Serial Correlation Tests 651.7 Statistical Results Using Alternative Options 671.7.1 Application of an Alternative Coefficient Covariance Matrix 671.7.2 Application of Selected Combinations of Options 701.7.3 Final Notes and Conclusions 712 Forecasting with Time Predictors 732.1 Introduction 732.2 Application of LV(p) Models of HS on MONTH by YEAR 732.2.1 Special LV(12) Models of HS on MONTH by YEAR 732.2.2 Application of the Omitted Variables Test - Likelihood Ratio 752.2.3 Heterogeneous Model of HS on HS( 12) and Month by YEAR 792.3 Forecast Models of HS on MONTH by YEAR 792.3.1 Application of LV(1) Models of HS on MONTH by YEAR 792.3.2 Application of Basic LV(p) Models of HS on MONTH by YEAR 822.3.3 Application of AR(q) Models of HS on MONTH by YEAR 862.3.4 Application of ARMA(q,r) Models of HS on MONTH by YEAR 892.3.5 Application of LVAR(p,q) Models of HS on MONTH by YEAR 892.3.6 Application of LVAR(p,q) Models of HS on YEAR by MONTH 922.4 Heterogeneous Classical Growth Models 952.4.1 Forecasting Based on LV(p) Het_CGMs of HS 952.4.2 Forecasting Based on AR(q) Het_CGMs 992.4.3 Forecasting Based on LVAR(p,q) Het_CGMs 1012.5 Forecast Models of G in Currency.wf1 1032.5.1 LVAR(p,q) Additive Models of G by @Month with @Trend 1042.5.2 LV(1) Heterogeneous Models of G by @Month 1112.6 Forecast Models of G on G( 1) and Polynomial Time Variables 1162.6.1 Heterogeneous Model of G on G( 1) and Polynomial T by @Month 1162.6.2 Forecast Model of G on G( 1) with Heterogeneous Polynomial Trend 1382.7 Forecast Models of CURR in Currency.wf1 1402.7.1 Developing Scatter Graphs with Regressions 1412.7.2 Additive Forecast Models of CURR with a Time Predictor 1432.7.3 Interaction Forecast Models of CURR 1592.7.4 Forecast Models Based on Subsamples 1693 Continuous Forecast Models 1853.1 Introduction 1853.2 Forecasting of FSPCOM 1853.2.1 Simple Continuous Models of FSPCOM 1853.2.2 LVAR(P,Q) Models of Y = FSPCOM with Polynomial Trend 1903.2.3 Translog Models with Time Predictor 1953.3 Forecasting Based on Subsamples 2073.3.1 Lag Variable Models With Lower and Upper Bounds 2093.4 Special LV(12) Models of HS with Upper and Lower Bounds 2223.4.1 Special LVARMA(12,q,r) Model of LNYul Without Time Predictor 2233.4.2 Special LVARMA(12,q,r) of LNYul With Time Predictor 2234 Forecasting Based on (Xt,Yt) 2294.1 Introduction 2294.2 Forecast Models Based on (Xt,Yt) 2294.3 Data Analysis Based on a Monthly Time Series 2304.4 Forecast Models without a Time Predictor 2304.4.1 Two-Way Interaction Models 2304.4.2 Cobb-Douglass Model and Alternatives 2354.5 Translog Quadratic Model 2364.5.1 Forecasting Using a Subsample 2404.5.2 Forecast Model with Trend 2434.6 Forecasting of FSXDP 2474.6.1 Forecasting of Y2 Based on a Subsample 2474.6.2 Extension of the Model (4.25) with Time Variables 2524.7 Translog Linear Models 2564.7.1 Basic Translog Linear Model 2564.7.2 Tanslog Linear Model with Trend 2564.7.3 Heterogeneous Tanslog Linear Model 2604.8 Application of VAR Models 2624.8.1 Unstructured VAR Models Based on (X1t,Y1t) 2624.8.2 The Simplest VAR Models with Alternative Trends 2644.8.3 Complete Heterogeneous VAR Models by @Month 2704.8.4 Bayesian VAR Models 2714.8.5 VEC Models 2714.9 Forecast Models Based on (Y1t,Y2t) 2754.9.1 Forecast Models Based on Figures 4.42a and b 2754.9.2 Reciprocal Causal Effects Models 2794.9.3 Models with the Time Independent Variables 2804.10 Special Notes and Comments 2875 Forecasting Based On (X1t,X2t,Yt) 2895.1 Introduction 2895.2 Translog Linear Models Based on (X1,X2,Y1) 2895.2.1 Basic Translog Linear Model 2895.2.2 Tanslog Linear Model with Trend 2925.2.3 Tanslog Linear Model with Heterogeneous Trends 2925.3 Translog Linear Models Based on (X1,X2,Y2) 2935.3.1 Translog Linear Models Using the Subsample {@Year>1990} 2965.3.2 Translog Linear Models Using the Subsample {@Year>1975} 2985.3.3 Translog Linear Models Using the Whole Sample 2985.4 Forecast Models Using Original (X1,X2,Y) 3005.4.1 Model Based on Figure 5.6a 3005.4.2 Model Based on Figure 5.6b 3015.4.3 Model Based on Figure 5.6c 3075.5 Alternative Forecast Models Using Original (X1,X2,Y) 3105.5.1 Three-Way Interaction Based on Figure 5.14a 3115.5.2 Three-Way Interaction Based on Figure 5.14b and c 3115.6 Forecasting Models with Trends Using Original (X1,X2,Y) 3115.7 Application of VAR Models Based on (X1t,X2t,Y1t) 3165.7.1 Unrestricted VAR Models 3165.7.2 The Simplest Two-Way Interaction VAR Model 3175.7.3 The Simplest Three-Way Interaction VAR Model 3185.8 Applications of the Object "System" 3205.8.1 The MLV(1,1,1) Models of (Y1,Y2,Y3) on (Y1( 1),Y2( 1),Y3( 1)) 3205.8.2 Circular Effects MLV(1,1,1) Models 3285.9 Models Presenting Causal Relationships Y1,Y2, and Y3 3355.9.1 Triangular Effects Models 3355.9.2 Circular Effects Models 3405.9.3 Reciprocal Effects Models 3415.10 Extended Models 3445.10.1 Extension to the Models with Additional Exogenous Variables 3445.10.2 Extension to the Models with Alternative Trends 3475.10.3 Extension to LVARMA(p,q,r) 3525.10.4 Extension to Heterogeneous Regressions by Months 3565.11 Special Notes and Comments 3696 Forecasting Quarterly Time Series 3716.1 Introduction 3716.2 Alternative LVARMA(p,q,r) Of a Single Time Series 3716.2.1 LV(P) Forecast Model of GCDANt 3716.2.2 LVARMA(p,q.r) Forecast Models of GCDN 3726.2.3 Forecast Models of GCDAN with Time Variables 3746.2.4 Special Notes on Uncommon Models 3816.3 Complete Heterogeneous LV(2) Models of GCDAN By @Quarter 3836.3.1 Using the Simplest Equation Specification 3836.3.2 Using a Complete Equation Specification 3876.4 LV(2) Models of GCDAN with Exogenous Variables 3876.4.1 LV(2) Models with an Exogenous Variable 3876.4.2 LV(2) Models with Two Exogenous Variables 3906.5 Alternative Forecast Models Based on (Y1,Y2) 3936.5.1 LV(2) Basic Interaction Models 3936.5.2 LV(2) Models of (Y1,Y2) with an Exogenous Variable and @Trend 3946.5.3 LV(2) Models of (Y1,Y2) with two Exogenous Variables and Trend 4006.5.4 LV(2) Models of (Y1,Y2) with Three Exogenous Variables and Trend 4096.6 Triangular Effects Models Based on (X1,X2,Y1) 4136.6.1 Partial Two-Way Interaction LV(p) TE_Models 4136.6.2 A Complete Two-Way Interaction LV(p) TE_Models 4146.6.3 Three-Way Interaction LV(p) TE_ Models 4156.7 Bivariate Triangular Effects Models Based on (X1,X2,Y1,Y2) 4176.7.1 Partial Two-Way Interaction Models 4176.7.2 Three-Way Interaction TE_Models 4186.8 Models with Exogenous Variables and Alternative Trends 4226.8.1 Models Based on (X1,X2,Y1) 4226.8.2 Models Based on (X1,X2,Y1,Y2) with Trend 4246.9 Special LV(4) Models with Exogenous Variables 4276.10 Models with Exogenous Variables by @Quarter 4336.10.1 Alternative Models Based on the Whole Sample 4336.10.2 Forecasting Based on each Quarter's Level 4357 Forecasting Based on Time Series by States 4477.1 Introduction 4477.2 Models Based on a Bivariate (Y1_1,Y1_2) 4477.2.1 Alternative LV(p) Models Based on Figure 7.1a 4487.2.2 Alternative LV(p) Models Based on Figure 7.1b 4517.2.3 Alternative LV(p) Models Based on Figure 7.1c 4547.3 Advanced LP(p) Models of (Y1_1,Y1_2) 4557.3.1 Two-Way Interaction LV(p) Models 4557.3.2 Three-Way Interaction LV(p) Models 4567.3.3 Alternative Additive Models 4567.4 Advanced LP(p) Models of (Y1_1,Y1_2,Y1_3) 4577.4.1 Triangular Effects Model of (Y1_1,Y1_2,Y1_3) 4577.4.2 Full-Lag Variables Triangular Effects Model 4627.4.3 Translog-Linear Triangular Effects Model 4667.5 Full-Lag Variables Circular Effects Model 4667.5.1 Two-Way Interaction Circular Effects Models 4667.5.2 Three-Way Interaction Circular Effects Models 4677.6 Full-Lag Variables Reciprocal-Effects Model 4677.6.1 Two-Way Interaction Reciprocal-Effects Models 4677.6.2 Three-Way Interaction Reciprocal-Effects Models 4687.7 Successive Up-and-Downstream Relationships 4687.7.1 A Set of the Simplest Two-Way Interaction Models 4687.7.2 Successive Two-Way Interaction Triangular Effects Models 4697.7.3 Successive Three-Way Interaction Triangular Effects Models 4717.8 Forecast Models with the Time Independent Variable 4747.8.1 Forecast Models with Alternative Trends 4747.8.2 Two-Way Interaction with Time-Related Effects Models 4807.8.3 Three-Way Interaction Time-Related Effects Models 4837.9 Final Notes and Comments 4917.9.1 The Manual Multistage Selection Method 4917.9.2 Notes on the Best Possible Forecast Models 491Bibliography 493Index 503

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.

College of Engineering and Computer Studies Bachelor of Science in Computer Science

College of Engineering and Computer Studies Bachelor of Science in Information Technology

College of Engineering and Computer Studies Bachelor of Science in Computer Engineering

College of Engineering and Computer Studies Bachelor of Science in Civil Engineering

College of Engineering and Computer Studies Bachelor of Science in Electronics Engineering

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