Advanced Time Series Data Analysis Forecasting Using EViews 1st Edition by Gusti Ngurah Agung 1119504716 9781119504719

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ISBN-10 :  1119504716 

ISBN-13 :  9781119504719

Author:  Gusti Ngurah Agung 

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 1st table of contents: 

1 Forecasting a Monthly Time Series
1.1 Introduction
1.2 Forecasting Using LV(p) Models
1.3 Forecasting Using the LVARMA(p,q,r) Model
1.4 Forecasting Using TGARCH(a,b,c) Models
1.5 Instrumental Variables Models
1.6 Special Notes and Comments on Residual Analysis
1.7 Statistical Results Using Alternative Options
2 Forecasting with Time Predictors
2.1 Introduction
2.2 Application of LV(p) Models of HS on MONTH by YEAR
2.3 Forecast Models of HS on MONTH by YEAR
2.4 Heterogeneous Classical Growth Models
2.5 Forecast Models of G in Currency.wf1
2.6 Forecast Models of G on G(−1) and Polynomial Time Variables
2.7 Forecast Models of CURR in Currency.wf1
3 Continuous Forecast Models
3.1 Introduction
3.2 Forecasting of FSPCOM
3.3 Forecasting Based on Subsamples
3.4 Special LV(12) Models of HS with Upper and Lower Bounds
4 Forecasting Based on (Xt,Yt)
4.1 Introduction
4.2 Forecast Models Based on (Xt,Yt)
4.3 Data Analysis Based on a Monthly Time Series
4.4 Forecast Models without a Time Predictor
4.5 Translog Quadratic Model
4.6 Forecasting of FSXDP
4.7 Translog Linear Models
4.8 Application of VAR Models
4.9 Forecast Models Based on (Y1t,Y2t)
4.10 Special Notes and Comments
5 Forecasting Based On (X1t,X2t,Yt)
5.1 Introduction
5.2 Translog Linear Models Based on (X1,X2,Y1)
5.3 Translog Linear Models Based on (X1,X2,Y2)
5.4 Forecast Models Using Original (X1,X2,Y)
5.5 Alternative Forecast Models Using Original (X1,X2,Y)
5.6 Forecasting Models with Trends Using Original (X1,X2,Y)
5.7 Application of VAR Models Based on (X1t,X2t,Y1t)
5.8 Applications of the Object “System”
5.9 Models Presenting Causal Relationships Y1,Y2, and Y3
5.10 Extended Models
5.11 Special Notes and Comments
6 Forecasting Quarterly Time Series
6.1 Introduction
6.2 Alternative LVARMA(p,q,r) Of a Single Time Series
6.3 Complete Heterogeneous LV(2) Models of GCDAN By @ Quarter
6.4 LV(2) Models of GCDAN with Exogenous Variables
6.5 Alternative Forecast Models Based on (Y1,Y2)
6.6 Triangular Effects Models Based on (X1,X2,Y1)
6.7 Bivariate Triangular Effects Models Based on (X1,X2,Y1,Y2)
6.8 Models with Exogenous Variables and Alternative Trends
6.9 Special LV(4) Models with Exogenous Variables
6.10 Models with Exogenous Variables by @Quarter
7 Forecasting Based on Time Series by States
7.1 Introduction
7.2 Models Based on a Bivariate (Y1_1,Y1_2)
7.3 Advanced LP(p) Models of (Y1_1,Y1_2)
7.4 Advanced LP(p) Models of (Y1_1,Y1_2,Y1_3)
7.5 Full‐Lag Variables Circular Effects Model
7.6 Full‐Lag Variables Reciprocal‐Effects Model
7.7 Successive Up‐and‐Downstream Relationships
7.8 Forecast Models with the Time Independent Variable
7.9 Final Notes and Comments

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Tags: Advanced Time, Data Analysis, Using EViews, Gusti Ngurah Agung