Advanced Time Series Data Analysis - Forecasting Using Eviews

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.

List of contents

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

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.

Summary

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.

Product details

• Authors: Agung, I. Gusti Ngurah Agung, I. Gusti Ngurah (University of Indonesia) Agung, Ig Agung, Agung I. Gusti Ngurah
• Publisher: Wiley, John and Sons Ltd
• Languages: English
• Product format: Hardback
• Released: 31.01.2019
• EAN: 9781119504719
• ISBN: 978-1-119-50471-9
• No. of pages: 544
• Subjects: Natural sciences, medicine, IT, technology > Mathematics > Probability theory, stochastic theory, mathematical statistics; Statistik, Volkswirtschaftslehre, Economics, Statistics, Ökonometrie, Zeitreihen, Zeitreihenanalyse, Econometrics, Time Series, Statistics for Finance, Business & Economics, Finanz- u. Wirtschaftsstatistik