# Time Series Data Analysis Using EViews

## I. Gusti Ngurah Agung

Language: English

Pages: 632

ISBN: 0470823674

Format: PDF / Kindle (mobi) / ePub

**Do you want to recognize the most suitable models for analysis of statistical data sets?**

This book provides a hands-on practical guide to using the most suitable models for analysis of statistical data sets using EViews - an interactive Windows-based computer software program for sophisticated data analysis, regression, and forecasting - to define and test statistical hypotheses. Rich in examples and with an emphasis on how to develop acceptable statistical models, Time Series Data Analysis Using EViews is a perfect complement to theoretical books presenting statistical or econometric models for time series data. The procedures introduced are easily extendible to cross-section data sets.

The author:

- Provides step-by-step directions on how to apply EViews software to time series data analysis
- Offers guidance on how to develop and evaluate alternative empirical models, permitting the most appropriate to be selected without the need for computational formulae
- Examines a variety of times series models, including continuous growth, discontinuous growth, seemingly causal, regression, ARCH, and GARCH as well as a general form of nonlinear time series and nonparametric models
- Gives over 250 illustrative examples and notes based on the author's own empirical findings, allowing the advantages and limitations of each model to be understood
- Describes the theory behind the models in comprehensive appendices
- Provides supplementary information and data sets

An essential tool for advanced undergraduate and graduate students taking finance or econometrics courses. Statistics, life sciences, and social science students, as well as applied researchers, will also find this book an invaluable resource.

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relationship between a pair of variables should be defined based on a theoretical and substantive basis. (3) The variance, covariance and the moment product correlation based on the time series Xt and Yt are defined as follows: VarðXÞ ¼ T 1 X 2 ðXt ÀXÞ TÀ1 t¼1 ð1:2Þ VarðYÞ ¼ T 1 X 2 ðYt ÀYÞ TÀ1 t¼1 ð1:3Þ CovðX; YÞ ¼ T 1 X t ÀYÞ ðXt ÀXÞðY TÀ1 t¼1 CovðX; YÞ CorrðX; YÞ ¼ pﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ VarðXÞ:VarðYÞ ð1:4Þ ð1:5Þ 1.4.7 Autocorrelation and partial autocorrelation For

model in (2.54) shows that it depends on the function having an interaction factor, namely {c(5) þ c(6) X1 þ c(7)X2 þ c(8)X1X2}. The model in (2.53) is a two-way interaction model and the model in (2.54) is a three-way interaction model, since it has a three-way interaction factor, X1Ã X2Ã t, as an independent variable. These types of model could be considered as time series models with linear trend and time-related effects (Bansal, 2005). Example 2.20. (Growth model having interaction factors)

variables Y2, X1 and X2 may have either pairwise or complete associations. Similarly for the three variables, X1, X2 and X3. If they have a complete association, then using a model with three-way interaction(s) could be considered. Hence, corresponding to the path diagram in Figure 2.89, a multivariate autoregressive model with three-way interactions may also be obtained, as follows: y1 ¼ cð11Þ þ cð12Þ*t þ cð13Þ*y2 þ cð14Þ*x1 þ cð15Þ*y2*x1 þ cð16Þ*y2*x2 þ cð17Þ*x1*x2 þ cð18Þ*x1*x3 þ

unpredictable. & 2.15 Alternative multivariate models with trend Based on the set of variables, many more models with trend could be defined. Thus an infinite number of lagged-variable autoregressive models might be produced by using lagged variables, either endogenous or exogenous variables, or both. For illustrative purposes, the following alternative models are presented, which could be extended to more complex models. However, the empirical examples for all models will be presented with only

their help in reading and making corrections to my drafts. Puri AGUNG Jimbaran, Bali 1 EViews workfile and descriptive data analysis 1.1 What is the EViews workfile? The EViews workfile is defined as a file in EViews, which provides many convenient visual ways, such as (i) to enter and save data sets, (ii) to create new series or variables from existing ones, (iii) to display and print series and (iv) to carry out and save results of statistical analysis, as well as each equation of the models