Advanced Time Series Data Analysis: Forecasting Using Eviews - I. Gusti Ngurah Agung

Résumé :
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.

Biographie:
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.

Sommaire:
About the Author Preface 1. Forecasting A Monthly Time Series 1.1. Introduction 1.2. Forecasting Using LV(p) Models 1.3. Forecasting Using 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 The Residual Analysis 1.7. Statistical Results Using Alternative Options 2. Forecasting With Time Predictors 2.1. Intruduction 2.2. Application 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 Of HS 2.5. Forecast Models of G in Currency.wf1 2.6. Forecast Models of G on G(-1) and Polynomial Time Variable 2.7. Forecast Models of CURR in Currency.wf1 3. Cotinuous Forecast Models 3.1. Introduction 3.2. Forecasting of FSPCOM 3.3. Forecasting Based on Subsamples 3.4. Special LV(12) Models 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 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. Transslog 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 Base On (X1t,X2t,Y1t) 5.8. Applications Of The Object System 5.9. Models Presenting Causal Relationships Between 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) Models Based 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-Down Stream Relationships 7.8. Forecast Models With The Time Independent Variable 7.9. Final Notes And Comments