book

Advanced Time Series Data Analysis

by I. Gusti Ngurah Agung

March 2019

Beginner to intermediate

544 pages

16h

English

Wiley

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1.1 Introduction1.2 Forecasting Using LV(p) Models1.3 Forecasting Using the LVARMA(p,q,r) Model1.4 Forecasting Using TGARCH(a,b,c) Models1.5 Instrumental Variables Models1.6 Special Notes and Comments on Residual Analysis1.7 Statistical Results Using Alternative Options
2.1 Introduction2.2 Application of LV(p) Models of HS on MONTH by YEAR2.3 Forecast Models of HS on MONTH by YEAR2.4 Heterogeneous Classical Growth Models2.5 Forecast Models of G in Currency.wf12.6 Forecast Models of G on G(−1) and Polynomial Time Variables2.7 Forecast Models of CURR in Currency.wf1
3.1 Introduction3.2 Forecasting of FSPCOM3.3 Forecasting Based on Subsamples3.4 Special LV(12) Models of HS with Upper and Lower Bounds
4.1 Introduction4.2 Forecast Models Based on (Xt,Yt)4.3 Data Analysis Based on a Monthly Time Series4.4 Forecast Models without a Time Predictor4.5 Translog Quadratic Model4.6 Forecasting of FSXDP4.7 Translog Linear Models4.8 Application of VAR Models4.9 Forecast Models Based on (Y1t,Y2t)4.10 Special Notes and Comments
5.1 Introduction5.2 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 Y35.10 Extended Models5.11 Special Notes and Comments
6.1 Introduction6.2 Alternative LVARMA(p,q,r) Of a Single Time Series6.3 Complete Heterogeneous LV(2) Models of GCDAN By @ Quarter6.4 LV(2) Models of GCDAN with Exogenous Variables6.5 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 Trends6.9 Special LV(4) Models with Exogenous Variables6.10 Models with Exogenous Variables by @Quarter
7.1 Introduction7.2 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 Model7.6 Full‐Lag Variables Reciprocal‐Effects Model7.7 Successive Up‐and‐Downstream Relationships7.8 Forecast Models with the Time Independent Variable7.9 Final Notes and Comments

Content preview from Advanced Time Series Data Analysis

1Forecasting a Monthly Time Series

1.1 Introduction

It is recognized that all possible models of a single time series, Y_t, can easily be applied to a forecast, Y_t. Refer to Agung (2009a), which presents a number of time series models that could easily be extended to many more possible models, as well as models based on panel data presented in Agung (2014), which can be used in forecasting. So, a researcher should never have to present the best possible forecasting, which should be highly dependent on his/her subjective expert judgment.

This chapter specifically presents forecasting based on a single monthly time series, namely Y_t, without taking into account the effects of exogenous variables, except for any lags or the time variable. More alternative and advanced models will be presented in the following chapters. For illustration, this chapter only presents selected illustrative forecasting based on the data in House.wf1, which contains only one single time series variable, namely HS_t, with 604 time‐observations from 1946M01 to 1999M04. In addition, for comparison, illustrative examples are presented based on other selected data sets.