Introduction to Eviews

Vignes Gopal KrishnaFast 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 AnalysisData Screening/Cleaning

Data reliability & validity

Data Analysis

Missing values

Outliers

Standard errors/Standard deviation

Logical sequence of numerical presentations

Sources & Measurement of Data

Linear regression, Granger causality, Cointegration

Conditions

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 questionsMissing 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 reportingMethods:-a) Heckman selection Modelb) 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 outliersa) Chauvenet’s criterionb) Grubbs test for outliersc) Peirce’s criteriond) 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 testsa) Jarque Bera test – Skewness & Kurtosis b) Shapiro Wilk test c) Shapiro Francia test d) Zero skewness Log transforme) Box Cox Transform - Data transformationsHistogram 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 parametersb)No endogeneity problemc) No multicollinearityd) Homoscedasticity (No heteroscedasticity)e) Number of variables>number of observationsf) 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 correlationsb)Data transformationand etc

Heteroscedasticity• No homoscedasticity (Unequal spread of variances)a) Error learning modelsb) Outliersc) Techniques of data collectionsCommon 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 Methodb) Runs testc) Durbin Watson d Testd) Breusch Godfrey Teste) Corrected version of GLSf) Newey West Method

Unit root test • Stationary & Non-stationary • Intercept, Trend, Intercept + TrendGeneral Hypothesis: Null Hypothesis: A variable has an unit rootAlternative Hypothesis: A variable has no unit root (Applicable for Augmented Dickey Fuller(ADF), Dickey Fuller-GLS(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)