alysis.
Impact of FDI inflows on water temperature in the rivers during 2007-2017
The first step in a time series analysis is to check the data for variability. According to Lütkepohl & Xu, (2009), any dataset should be stable with minimum variation. The variability in this dataset was found to be high. Therefore using natural log transformation in MS Excel, the dataset was first stabilized. This stabilized dataset was then used for further analysis.
In order to test the impact of FDI inflows on water temperature in Indian rivers, the following hypothesis was framed.
H0: There is no significant impact of FDI inflows on water temperature in Indian rivers.
HA: There is a significant impact of FDI Inflows on water temperature in Indian rivers.
Step 1: Pre-condition tests
The dataset should first be tested for stationarity, normality, and cointegration of the variables. Augmented Dickey-Fuller (ADF) test, Johansen cointegration test, and Shapiro-Wilk tests were applied respectively in STATA software.
Stationarity is the property of the time series that ensures the mean and variance value of the variable is constant. This assumption helps in determining the relationship reliably and forecasting (Adhikari, 2013; Gujarati & Porter, 2009; Nason, 2018). The below table represents the results of the ADF test for stationarity.
|Variable||Test-Statistic||5% Critical Value||p-value|
|LnFDI with drift||-1.511||-1.771||0.0774|
|LnFDI with trend||-1.259||-3.600||0.8976|
For Lntemp, the absolute test-statistic is greater than the critical value i.e. 4.905 > 3.000. The p-value is also less than the significance level i.e. 0.0000 < 0.05. Thus, the null hypothesis of data being non-stationary is rejected and the natural-log transformation of water temperature is stationary. Furthermore, for LnFDI initially the absolute test-statistic is less than the critical value and p-value is also greater than the significance level. Thus, the further model is considered in case of drift and trend to test the stationarity in the presence of intercept and deterministic trend (Gujarati & Porter, 2009). However, still the null hypothesis is not rejected and the FDI inflows are non-stationary. Thus, the first-order differentiation for the variable was done. DifflnFDI series is generated which represents the stationary form of FDI inflows.
In the case of DifflnFDI, stationarity is derived as the absolute value of test statistic is greater than critical value and the p-value is also small i.e. 3.584 > 3.000 and 0.0061 < 0.05. Thus, DifflnFDI is a stationary variable.
Cointegration test studies the nature of the relationship between the variables. It determines whether there is any relationship or linkage between the variables or not. The existence of cointegration between the variables ensures that there is a linkage between the variables and no spurious or non-sense relationship is studied (Gujarati & Porter, 2009). Johansen’s cointegration results for testing the long-run relationship between FDI inflows and water temperature of Indian rivers are given below.
|Max. Ranks||Trace Statistic||5% Critical Value||Max Statistic||5% Critical Value|
*represent significant at 5%level
The null hypothesis of no cointegration is rejected for 0 ranks as the values are greater than critical value i.e. 20.3730 > 15.41, and 17.6082 > 14.07. Furthermore, for 1 rank the null hypothesis is not rejected i.e. 2.7649 < 3.76.
Hence, at most 1 cointegrating vector exists between FDI inflows and water temperature Kumar & Chander, (2016). Thus, the results of trace statistic and max statistic shown in Table 2 depicts that long-run and short-run relationship exists between FDI inflows and water temperature.
A dataset is said to be normally distributed if the values are symmetrically distributed. The existence of normality in the value of the variables is the assumption of the classical linear regression (Casson & Farmer, 2014). Thus, to depict the impact of FDI inflows on water temperature, the normality of the data is tested. Shapiro-Wilk test was used and the result of the test is r