Workshop on Introduction to eviews

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Published on March 1, 2014

Author: vignesgopal

Source: slideshare.net

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Presentation slides-Workshop on Introduction to Eviews-26th of February 2014

Introduction to Eviews Vignes Gopal Krishna Fast track PhD student & SLAI fellow Faculty of Economics and Administration University of Malaya Email Address: vignesgopal@yahoo.com/vignesgopal@um.edu.my

Steps for Quantitative Analysis Missing values Data Screening/Cleaning Outliers Standard errors/Standard deviation Data reliability & validity Logical sequence of numerical presentations Conditions Data Analysis Sources & Measurement of Data Linear regression, Granger causality, Cointegration

Data Cleaning/Screening Deals with the management of missing values and outliers. Crucial element and it will be very helpful in avoiding the dubiousness of results Useful in monitoring the trends of numerical presentations

Missing values • Common occurrence in research • Significant impact on the results except for some tests such as survival analysis, impact analysis and etc • Types of missing values a) Missing Completely at Random(MCAR) b) Missing at Random(MAR) c) Missing not at Random(MNAR)

Types of Missing Values Missing Completely at Random(MCAR) *Missing values of Y – do not depend on X & Y * Ex: Selection of survey questions Missing at Random(MAR) *Missing values of Y –depend on X, but, not on Y *Ex: Income reporting is quite weak among respondents in service industry.

Missing not at Random(MNAR) • Pr(Y,…)=f(Y,…) • Example: Respondents with high income are less likely to deal with income reporting Methods:a) Heckman selection Model b) Patterns of missing values

Outliers • Inconsistent with existing range of data points • Deals with lower and higher levels of outliers • Positively related to error terms/residuals • Inclusion and exclusion of outliers • Main methods that can be used to identify outliers a) Chauvenet’s criterion b) Grubbs test for outliers c) Peirce’s criterion d) Box-Plot e) Extreme Values Ways to remove outliers (a) By reducing the effects of autocorrelation (b) Winsorizing (c) Robustness of the standard errors –Minimization of standard errors (d) Normalization (e) Trimming

Normality *requirement for parametric analysis *Most of the tests require all the variables to be normally distributed in order to ensure the normal distribution of error terms • + (skewed to right), -(Skewed to left) – skewness = 0 Kurtosis should be approximately 3(JB test) • Available normality tests a) Jarque Bera test – Skewness & Kurtosis b) Shapiro Wilk test c) Shapiro Francia test d) Zero skewness Log transform e) Box Cox Transform Data transformations Histogram with normal curve, Quantile-Quantile (QQ-plot)/Qnorm, and etc

Linear regression • Associations between DV and IV • DV-continuous/interval/scale/ratio variable • IV-continuous/interval/scale/ratio/categorical variables • Assumptions:a)Linear parameters b)No endogeneity problem c) No multicollinearity d) Homoscedasticity (No heteroscedasticity) e) Number of variables>number of observations f) Error terms must be normally distributed.

Diagnostic Testing Multicollinearity(Deals with multivariate Analysis) • Correlations between independent variables • High R square, large covariances and correlations, more insignificant t-ratios • Variance Inflation Factor(VIF), Tolerance Value(TL), Auxillary regressions – Graphical Method • Ways to reduce the effects – a)Drop variables that have high correlations b)Data transformation and etc

Heteroscedasticity • No homoscedasticity (Unequal spread of variances) a) Error learning models b) Outliers c) Techniques of data collections Common way – Remove outliers(Winsorizing/Trimming), GLS, Park Test, Glejser Test, Goldfeld –Quandt Test, White Test, graphical method and etc

Autocorrelation (Correlation between residuals) • Predicted error terms will underestimate the population error terms • R square will be overestimated • Misleading results – F and t tests are not valid • Methods:a) Graphical Method b) Runs test c) Durbin Watson d Test d) Breusch Godfrey Test e) Corrected version of GLS f) Newey West Method

Unit root test • Stationary & Non-stationary • Intercept, Trend, Intercept + Trend General Hypothesis: Null Hypothesis: A variable has an unit root Alternative Hypothesis: A variable has no unit root (Applicable for Augmented Dickey Fuller(ADF), Dickey FullerGLS(DF-GLS), Phillips-Perron(PP), ERS point Optimal and Ng-Perron) The reversed hypotheses can be observed in the case of KPSS. It is a requirement for cointegration tests (e.g. Johansen Juselius cointegration test) I(1) at the level form, I(0) at the first different form

Logit/Probit regression • Type of probabilistic model • Used when DV=Binary/Categorical variable • Error terms should not be normally distributed (Note: The error terms should be normally distributed for Probit regression) Pr(0,1) = f(X1,X2,X3…) Methods to identify the goodness of fitness (a) Likelihood ratio tests (b) Pseudo R2 (c) Hosmer-Lemeshow test (d) Binary classification

Cointegration • Two or more variables are said to be cointegrated if the two shares same portion of stochastic trends/drifts. • In general, the variables have to be I(1) at the level form before getting to cointegration Types of cointegration test (a) Engle-Granger 2 step method (b) Johansen Juselius test (c) Phillips–Ouliaris cointegration test (d) Autoregressive Distributed Lag(ARDL)

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