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

6Forecasting Quarterly Time Series

6.1 Introduction

I have found that all forecast models for the monthly time series, presented in the five previous chapters, can be used for the quarterly time series by replacing the time variable @Month = M with @Quarter = Q for all relevant models. For this reason, this chapter only presents selected illustrative examples. Referring to all possible models based on a single time series Y_t, bivariate time series (X_t,Y_t) or (Y1_t,Y2_t), and triple time series (X1_t,X2_t,Y_t) or (Y1_t,Y2_t,Y3_t,), which have been presented in Chapters 1–5 with a summary of alternative trends presented in Table 4.1, then based on a quarterly time series we have the summary of alternative trends as presented in Table 6.1.

Table 6.1 Forecast models with specific trends for all models based on a single quarterly time series, bivariate, and triple time series.

Continuous Regressions with Trend
Additional Time IV to insert	Forecast Model (FM) with
1. @Trend	Linear Trend
2. @Trend ^ 2 @trend	Quadratic Trend
3. @Trend ^ 3 @Trend ^ 2 @Trend	Cubic Trend
4. log(@Trend + 1) = log(t)	Logarithmic Trend
5. Exp(rt)	Exponential Trend, for a fixed selected r
Regressions with Heterogeneous Trends by @Year
6. ^aQ^*@Expand(@Year) @Expand(@Year,@Droplast)
7. ^aQ^*@Expand(@Year) ^aQ ^ 2^*@Expand(@Year) @Expand(@Year,@Droplast)
8. ^aQ^*@Expand(@Year) ^aQ ^ 2^*@Expand(@Year) Q ^ 3 ^a @Expand(@Year) @Expand(@Year,@Droplast)
Regressions with Heterogeneous Trends by ...