epresented below.
The p-value for lntemp and Difflnfdi is greater than the significance level of the study i.e. 0.21856 and 0.50621 > 0.05. Therefore, the null hypothesis of the normal distribution is not rejected and both variables are normally distributed. Hence, the regression test can be performed.
Step 2: Regression
The stationary, cointegrated, and normally distributed variables were used to form the regression model.
|Variables||Nature of variable||Description|
|Dependent||Stationary form of Natural Log-transformation for average water temperature of Indian rivers at the t-time period|
|Independent||Stationary form of Natural log-transformation for net FDI Inflows at the t-time period|
|Coefficients||Intercept, Slope Coefficient|
The results for the regression of the above model is shown below
|LnTemp||Coefficient||t-value||p-value||R2 value||Adjusted R2 value|
The results of the regression model show an R2 value of 0.3272 and an adjusted R2 value of 0.2755. However, it is important to test the heteroscedasticity and autocorrelation presence in the model (Casson & Farmer, 2014) to derive unbiased and efficient results.
Step 3: Diagnostic tests
Autocorrelation means the existence of interdependence between the error terms. The presence of autocorrelation in the model lead to decreasing the validity and precision of the results of the model. No autocorrelation is also the assumption of classical linear regression (Huitema & Laraway, 2006). Durbin Watson test determines whether the residuals are interrelated. The result is presented below.
The Durbin Watson test for the above model has shown the following result.
As the D-statistic is between DU and 4-DU i.e. 1.543<2.147329<2.457, thus there is no autocorrelation present in the model. Okumoko, Akarara, & Opuofoni, (2018) stated that the value of the Durbin Watson statistic should be close to 2 to derive reliable results. As the value of D-statistic is close to 2, thus, the residuals are not related to each other, which makes the results of the regression reliable.
Heteroscedasticity refers to the difference in the variance value for the predicted values of the regression. As classical linear regression stated that the model should be homoscedastic for deriving effective results, thus heteroscedasticity presence is tested (Casson & Farmer, 2014; Salkind, 2007). Bartlett’s periodogram based white noise heteroscedasticity test results are represented in the below table.
|Bartlett’s (B)- Statistic||P-value|
The p-value of the test is greater than the significance level of the study i.e. 0.9340 > 0.05, thus the null hypothesis of no heteroscedasticity is not rejected. Kostakis et al., (2016) in their study stated that the model needs to be homoscedastic. Herein, Table 7 represents a high p-value, thus, the dataset for FDI inflows and water temperature is homoscedastic, making the result acceptable.
The positive impact of FDI inflows on water temperature
The data is free from the problem of heteroscedasticity and autocorrelation. R2 value shows that about 32% of the variation in water temperature is represented by the FDI inflows. Faraway, (2014) stated that the value above 0 of R2 shows that the model correctly links the variables and a value close to 0.5 or 0.6 depicts a good model. The value shown in Table 5 is above 0 but is less than 0.5. The p-value presented for Difflnfdi is 0.026 < 0.05, the significance level. Thus, there is no significant impact of FDI inflows on water pollution is the null hypothesis is rejected.
Coefficient value shows that with a 1% increase in FDI inflows, water temperature decreases by 0.0459771% i.e. increase in FDI inflows in India is beneficial for the environment.
The above graph shows that there is a negative relationship between FDI Inflows and water temperature as the fitted line is downward sloping. Furthermore, the scatter plot of temperature is close to the fitted line thus depicting that there is a significant impact of FDI Inflows on temperature. Hence, water temperature tends to decrease with increasing FDI inflows.
Studies supp