Advanced Time Series Data Analysis E-Book

I. Gusti Ngurah Agung

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 704

Veröffentlichungsjahr: 2018

Beschreibung

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.

Leseprobe

Advanced Time Series Data Analysis

Forecasting Using EViews

I Gusti Ngurah Agung

The Ary Suta Center

Jakarta, Indonesia

This edition first published 2019© 2019 John Wiley & Sons Ltd

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Library of Congress Cataloging‐in‐Publication Data

Names: Agung, I Gusti Ngurah, author.Title: Advanced time series data analysis : forecasting using EViews / I. Gusti Ngurah Agung.Description: Hoboken, NJ : John Wiley & Sons, 2019. | Includes bibliographical references and index. |Identifiers: LCCN 2018040053 (print) | LCCN 2018057198 (ebook) | ISBN 9781119504733 (Adobe PDF) | ISBN 9781119504740 (ePub) | ISBN 9781119504719 (hardcover)Subjects: LCSH: Time-series analysis. | Econometric models.Classification: LCC QA280 (ebook) | LCC QA280 .A38 2019 (print) | DDC 519.5/5–dc23LC record available at https://lccn.loc.gov/2018040053

Cover Design: WileyCover Images: © Butsaya/ Getty Images;© monsitj /Getty Images

Dedicated to my wife, Anak Agung Alit Mas; our children, Martiningsih, Ratnaningsih, and Darma Putra; as well as all our Generation.

About the Author

I have a Ph.D. degree in Biostatistics (1981) and a Master’s degree in Mathematical Statistics (1977) from the North Carolina University at Chapel Hill, NC, USA; a Master’s degree in Mathematics from New Mexico State University, Las Cruces, NM, USA; a degree in Mathematical Education (1962) from Hasanuddin University, Makassar, Indonesia; and a certificate from “Kursus B‐I/B‐II Ilmu Pasti” (B‐I/B‐II Courses in Mathematics), Yogyakarta, which is a five‐year non‐degree program in advanced mathematics. So, I would say that I have a good background knowledge in mathematical statistics as well as applied statistics. In my dissertation on biostatistics, I presented new findings, namely the Generalized Kendall’s tau, Generalized Pair Charts, and Generalized Simon’s Statistics, based on the data censored to the right.

Supported by my knowledge in mathematics, mathematical functions in particular, I can evaluate the limitations, hidden assumptions, or the unrealistic assumption(s) of all regression functions, such as the fixed effects models based on panel data, which are in fact ANCOVA models. As a comparison, Agung (2011a) presents several alternative acceptable ANCOVA models, in the statistical sense, and the worst ANCOVA models, in both theoretical and statistical senses.

Furthermore, based on my exercises and experiments in doing data analyses of various fields of study; such as finance, marketing, education, and population studies since 1981 when I worked at the Population Research Center, Gadjah Mada University, 1985–1987; and while I have been at the University of Indonesia from 1987 up to 2018, I have found unexpected or unpredictable statistical results based on various time series, cross‐section, and panel data models, which have been presented with special notes and comments in Agung (2014, 2009a, 2011a), compared to models that are commonly applied.

Similarly, based on my exercises and experiments in doing forecasting using EViews, I can present various alternative models using the same sets of variables, such as based on a single time series Yt, bivariate time series (Xt,Yt), multivariate time series (X1t,X2t,Y1t,Y2t), and (X1t,X2t,X3t,Y1t,Y2t,Y3t,Z1t), without or with alternative time variables, based on monthly, quarterly, and annual time series. Aside from good forecast models, worse forecast models also are presented as illustrative examples. Many of those models have not been presented in other books, specifically in Business Forecasting by Hankle and Reitch (1992), and by Wilson and Keating (1994), or in the books by Gujarati (2003), Wooldridge (2002), and Tsay (2002).

1Forecasting a Monthly Time Series

1.1 Introduction

It is recognized that all possible models of a single time series, Yt, can easily be applied to a forecast, Yt. 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 Yt, 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 HSt, with 604 time‐observations from 1946M01 to 1999M04. In addition, for comparison, illustrative examples are presented based on other selected data sets.

1.2 Forecasting Using LV(p) Models

1.2.1 Basic or Regular LV(p) Models

It is well known that the LV(p) model of a time series variable, Yt, has the following general form.

(1.1)

therefore, a forecast of any transformed variable G(Yt) can easily be done using the following equation specification, for any integer p, which should be highly dependent on the data used as well as the subjective interest of the researchers. However, this section only presents a few illustrative examples.

(1.2)

where G(Y) can be any functions of Yt without a parameter, such as the original time series Yt, log(Yt), and log((Yt − L)/(U − Y)), with L and U are the fixed lower and upper bounds of Yt.

Example 1.1 A Dynamic Forecast Using the Simplest Model in (1.2)

As a preliminary forecast data analysis, this example presents the graphical presentation of the variable HSt, as presented in Figure 1.1. Based on these figures, the following notes and comments are presented.

Figure 1.1

a presents a line and dot graph of the variable

HS

t

that clearly shows a seasonal pattern. This is similar for the graph of

HS

t