LITERATURE REVIEW
3.7 Data Analysis
To investigate the relationship between independent variables and dependent variable, certain tests have been identified such as Unit Root test, Granger Causality, Ordinary least square, Johansen Cointegration, Impulse Response Function and Variance Decomposition.
3.7.1 Ordinary least square (OLS)
To model a single response variable that recorded on at least an interval scales, Ordinary least-squares (OLS) regression, which is a generalized linear modeling technique will be used in this study.This technique may be applied to categorical explanatory variables that have been appropriately coded and also single or multiple explanatory variables.
According to the study by Hoyt (2003), Ordinary Least Square (OLS) is a statistical technique that uses sample data for the estimation of true population relationship between two variables. Before proceeding to any analyses, Ordinary Least Square (OLS) will be the first model to test the economic equation that suggested in this paper. Through the application of Ordinary Least Square (OLS), economic problems can be detected and identified. To identify the economic problems such as autocorrelation, model specification error and heteroscedasticity, some of the techniques will be used in this study.
When the error terms do not have constant variables, the problem of Heteroscedasticity will occur. Through Probability of F- statistic, Heteroscedasticity can be detected (Stock and Watson, 2006).
According to Stock and Watson (2006), the condition of residuals are related to each other will be defined as problem of autocorrelation and it can be confirmed from Probability of Chi-Square.
Lastly, there are several types of model specification error such as inclusion of unnecessary variables, incorrect functional forms, omission of relevant
variables and others(Gujarati and Porter, 2009).
3.7.2 Unit Root Test
In order to determine whether trending data should be first differenced or regressed on deterministic functions of time, Unit root tests will be used to render the data stationary. The existence of long-run equilibrium relationships among nonstationary time series variables is proposed by Economic and finance theory. If these variables are I(1), then cointegration techniques can be used to model these long-run relations. Hence, pre-testing for unit roots is often a first step in the cointegration modeling.
Unit roots tests serve the purpose of establishing the order of integration of each variable. Through analyzing the stationary properties of those variables by applying the unit root, will then be able to analyze the effects of the selected macroeconomic variables on the selected stock markets of emerging nations. A statistical property is a stationary time series including autocorrelation, variance, and others are constant over time. There will be lesser spurious regression, when stronger stationarity occurred.To test the presence of unit root and stationarity of each variable in this paper, Augmented Dickey-Fuller (ADF) test and Phillips-Perron (PP) test are the test to be used in this study (Gan et al., 2006).
According to some previous researches, such as Nopphon (2012) and Sari and Soytas (2006), the non-stationary data can create spurious result due to invalid
analysis. To ensure the validity of analysis, the augmented Dickey-Fuller test of unit root (Dickey and Fuller, 1979) is conducted. Besides, it serves the purpose of examine on the coefficient of the regression. ADF consists a running regression of the first difference of the series against the lagged difference terms,series lagged once and optionally, a constant and a time trend (Al-Zoubi and Al-Sharkas, 2011). Alternatively, in order to avoid spurious regressions that arise due to carrying out regressions on time series data without subjecting them for test whether they contain unit root by using E- views, ADF test will be used (Asaolu and Ogunmuyiwa, 2011). However, ADF test has poor power properties is the weakness (Paramaia and Akway, 2008).
Phillips-Perron (PP) test is conducted in a similar manner by using regression, without the lagged first differenced terms. There is similarity with ADF test but it has a difference of automatic correlation was incorporated to DF procedure and controls the higher-order serial correlation. A non-parametric statistical method used for PP Test and the use of adding lagged difference terms can be avoided in ADF test (Asmy et al., 2009).
3.7.3 Johansen Cointegration
The cointegration properties of the data series continue to be assessed, as long as order of the integration is established for each variable.To determine whether the linear combination of the series contains long run equilibrium relationship, Johansens co-integration test will be conducted. Besides,
whether the linear combination of the series contains long run equilibrium relationship. Furthermore, the relationship between dependent variable and independent variable in short run or long run period can be explained by Johansens co-integration test (Ali et al., 2010). Generally, if a set of variables is individually non-stationary and integrated of the same order, yet their linear combination is stationary, it will be said as cointegrated (Ibrahim, 2000). The dependent and independent variables move closely together in the long run is the basic idea of cointegration(Azizan and Sulong, 2011).
The data from a linear combination of two variables can be stationary will be defined as Cointegration. If there is at least one is cointegrating relationship among the variables, by estimating the vector error-correction models (VECM), then the causal relationship among these variables can be determined. For this purpose, a Johansen method of multivariate cointegration will be used (Asmy et al., 2009). To examine the number of cointegrating vectors in the model,the Johansen maximum likelihood method from Johansen and Juselius (1990) is utilized (Chin and Jayaraman, 2007). To test the whole system in one step, Johansen vector error-correction models (VECM) is a full information maximum likelihood estimation model that suitable (Maysami et al, 2004).
3.7.4 Granger Causality
In the absence of any cointegration relationship between the above variables, Granger causality tests would be applied. In the year of 1969, in order to
determine causality between two time series and whether one time series is useful in forecasting another, Granger Causality has been proposed by Clive Granger (Harasheh and Abu-Libdeh, 2011). For the purpose of testing on short run relationship between dependent and independent variables, Granger Causality test will be used. To test the existence of short run relationship, stationary data is more important than non stationary data. In this technique, the methodology is sensitive to lag length used for the investigation of stationary property of data.
There is an examination relationship between the dependent and independent variables proposed by Granger (Ali et al., 2010). To analyze the relationship between stock market returns and macroeconomic variables in different countries around the world, this method is popular as most of the previous researches used this technique (Granger, Huang and Yang, 1998; Ali et al., 2010). For instance, Gan et el. (2006) used this method to examine whether there are lead-lag relationship between NZSE returns and the selected macroeconomic variables. The examination on Mexico’s stock prices lead to exchange rates in the short run and there is no long run relationship between them was conducted by Kutty (2010) by using Granger causality test.
However, Granger causality tests are inappropriate when the variables were being analyzed as a non-stationary and cointegrated (Ibrahim, 2000). To capture the long run and short run causal dynamics in terms of interactive feedbacks (lead-lag relationships) among the variables, relevant vector error-
correction models (VECM) are estimated (Agrawalla and Tuteja, 2008). Last but not least, error-correction term is included in an augmented form of Granger causality test (Shahbaz, Ahmed and Ali, 2008).
3.7.5 Variance Decomposition
A substantial part of the variation in stock market returns over the short and medium-run, namely, one, four and eight years can be explained by macroeconomic variables, which is Variance decomposition. Vector auto regression (VAR) with orthogonal residuals will constructthe Variance Decompisition. The contribution of macroeconomic variables in forecasting the variance of stock market returns can be expressed (Kazi, 2008).
According to Okuda and Shiiba (2010), the examination of the information content of several earnings components can be done by variance decomposition methodology, which is a complementary approach. For instance, the accruals and show that news associated with accruals, cash flows, and expected future returns are included when there is extension of variance decomposition framework (Callen and Segal, 2004).These are the important aspects in driving stock market returns. In addition, Variance decomposition has been used by other researchers in examining the relative importance of the various forecasting variables in causing unexpected stock returns (Sari and Soytas, 2006)
3.7.6 Impulse Response Function
In order to investigate the short run dynamic linkages between NZSE40 and macroeconomic variable throughout the testing period, Impulse Response Function can be used (Gan et al., 2006). By regressing the series of interest on estimated innovations, which are the residuals obtained from a prior-stage
‘long auto regression’,the impulse responses can be (Chang and Sakata, 2007). Typical orthogonalization and ordering problems can be avoided through this methodology, which would be hardly feasible in the case of highly interrelated financial time series observed at high frequencies (Panopoulou and Pantelidis, 2009).
Furthermore, with a stationary time series, the impulse response functions are only reliable. After the second difference, data will turn into stationary after.
To examine the short-run impact caused by the vector auto regression model (VECM) when it received certain impulses, this act as an econometric technique. With the conditions of time varying second moments, these approaches also provide a system consistent solution for multivariate linear autoregressive models (Elder, 2003).
For instance, in order to check the existence of short run relationship between stock market returns and macroeconomic variables, Impulse response function has been chosen (Philinkus and Boguslauskas, 2009).