Advanced Time Series Data Analysis

Advanced Time Series Data Analysis

by I. Gusti Ngurah Agung
Released March 2019
Publisher(s): Wiley
ISBN: 9781119504719

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Book description

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.

Table of contents

  1. Cover
  2. About the Author
  3. Preface
  4. 1 Forecasting a Monthly Time Series
    1. 1.1 Introduction
    2. 1.2 Forecasting Using LV(p) Models
    3. 1.3 Forecasting Using the LVARMA(p,q,r) Model
    4. 1.4 Forecasting Using TGARCH(a,b,c) Models
    5. 1.5 Instrumental Variables Models
    6. 1.6 Special Notes and Comments on Residual Analysis
    7. 1.7 Statistical Results Using Alternative Options
  5. 2 Forecasting with Time Predictors
    1. 2.1 Introduction
    2. 2.2 Application of LV(p) Models of HS on MONTH by YEAR
    3. 2.3 Forecast Models of HS on MONTH by YEAR
    4. 2.4 Heterogeneous Classical Growth Models
    5. 2.5 Forecast Models of G in Currency.wf1
    6. 2.6 Forecast Models of G on G(−1) and Polynomial Time Variables
    7. 2.7 Forecast Models of CURR in Currency.wf1
  6. 3 Continuous Forecast Models
    1. 3.1 Introduction
    2. 3.2 Forecasting of FSPCOM
    3. 3.3 Forecasting Based on Subsamples
    4. 3.4 Special LV(12) Models of HS with Upper and Lower Bounds
  7. 4 Forecasting Based on (Xt,Yt)
    1. 4.1 Introduction
    2. 4.2 Forecast Models Based on (Xt,Yt)
    3. 4.3 Data Analysis Based on a Monthly Time Series
    4. 4.4 Forecast Models without a Time Predictor
    5. 4.5 Translog Quadratic Model
    6. 4.6 Forecasting of FSXDP
    7. 4.7 Translog Linear Models
    8. 4.8 Application of VAR Models
    9. 4.9 Forecast Models Based on (Y1t,Y2t)
    10. 4.10 Special Notes and Comments
  8. 5 Forecasting Based On (X1t,X2t,Yt)
    1. 5.1 Introduction
    2. 5.2 Translog Linear Models Based on (X1,X2,Y1)
    3. 5.3 Translog Linear Models Based on (X1,X2,Y2)
    4. 5.4 Forecast Models Using Original (X1,X2,Y)
    5. 5.5 Alternative Forecast Models Using Original (X1,X2,Y)
    6. 5.6 Forecasting Models with Trends Using Original (X1,X2,Y)
    7. 5.7 Application of VAR Models Based on (X1t,X2t,Y1t)
    8. 5.8 Applications of the Object “System”
    9. 5.9 Models Presenting Causal Relationships Y1,Y2, and Y3
    10. 5.10 Extended Models
    11. 5.11 Special Notes and Comments
  9. 6 Forecasting Quarterly Time Series
    1. 6.1 Introduction
    2. 6.2 Alternative LVARMA(p,q,r) Of a Single Time Series
    3. 6.3 Complete Heterogeneous LV(2) Models of GCDAN By @ Quarter
    4. 6.4 LV(2) Models of GCDAN with Exogenous Variables
    5. 6.5 Alternative Forecast Models Based on (Y1,Y2)
    6. 6.6 Triangular Effects Models Based on (X1,X2,Y1)
    7. 6.7 Bivariate Triangular Effects Models Based on (X1,X2,Y1,Y2)
    8. 6.8 Models with Exogenous Variables and Alternative Trends
    9. 6.9 Special LV(4) Models with Exogenous Variables
    10. 6.10 Models with Exogenous Variables by @Quarter
  10. 7 Forecasting Based on Time Series by States
    1. 7.1 Introduction
    2. 7.2 Models Based on a Bivariate (Y1_1,Y1_2)
    3. 7.3 Advanced LP(p) Models of (Y1_1,Y1_2)
    4. 7.4 Advanced LP(p) Models of (Y1_1,Y1_2,Y1_3)
    5. 7.5 Full‐Lag Variables Circular Effects Model
    6. 7.6 Full‐Lag Variables Reciprocal‐Effects Model
    7. 7.7 Successive Up‐and‐Downstream Relationships
    8. 7.8 Forecast Models with the Time Independent Variable
    9. 7.9 Final Notes and Comments
  11. Bibliography
  12. Index
  13. End User License Agreement

Product information

  • Title: Advanced Time Series Data Analysis
  • Author(s): I. Gusti Ngurah Agung
  • Release date: March 2019
  • Publisher(s): Wiley
  • ISBN: 9781119504719