5.1. Augmented Dickey–Fuller Unit Root Test
The test was used in the study to check for the stationarity of variables so that further tests could be applied to prove the relationship between the two. P Values for this test is shown in
Table 2. It was found that the variables were nonstationary in their original form and so the results for their Dickey–Fuller test, shown by
Table A3 and
Table A4, were non-negative and non-significant. The p-value for the variable in the natural form was found to be 0.6742, which is much higher than the critical values and the significant value of 0.05 at 5% significance. Therefore, for the variable “GDS” (gross domestic savings), the null hypothesis could not be rejected; the variable was not stationary. Further, the variable “GD” (government debt) was also found to be non-stationary in its natural form and the null hypothesis could not be rejected because the p-value was found to be 0.2723, which is much higher than the critical values and significant value at 5% significance. Apart from this, the household final consumption and gross final consumption expenditure were also non-stationary with the p-value of 0.6306 for this period.
The study of
Luetkepohl and Xu (
2009) pointed out to the importance of log transformations of each variable. Taking the log transformations of variables helps the study in getting optimal results. The variables by the log transformation are made more homogeneous. Therefore, the variables in the current study were also transformed into their log form to apply the augmented Dickey–Fuller unit root test and find stationarity in the log forms of variables, GDS, GD, HFC, and GFCE. The results for the test are depicted in
Table A3 and
Table A4 (
Appendix A). In this context, shown below are the p-values of the variables GDS GD, HFC, and GFCE in their real forms, log forms, and first differences of the log forms. These results were taken from the results in
Table A3 and
Table A4 in
Appendix A to point out to the series the current study lies under. After only the second differentiation, the
p-values of the four variables under consideration became significant with the p-value of 0.000, indicating that after the second differentiation in the logarithmic form, the variables became stationary.
The p-value of unit root test for the log transformation of GDS, depicted by logGDS, was found to be 0.3415, which is again more than the significant value of 0.05 at 5% significance. Hence, the null hypothesis claiming the non-stationary of the log transformation of GDS could not be rejected. Therefore, the variable GDS was found to be non-stationary even in its log transformation form. The second variable in the study, i.e., GD, was also found to be non-stationary in its log transformation form. The p-value for the logGD unit root test was found to be 0.0562, which is higher than the significant level of 0.05 at 5% significance. Hence, the null hypothesis of non-stationarity could not be rejected.
In this context, the study of
Selcuk and Ertugrul (
2001) pointed out that Turkey during the period of 1980–2017 faced a lot of imbalance in the economy, with inflation rates shooting up and the growth in the economy going down; the country fell into a crisis situation. Therefore, the variables such as GD, HFC, GDS, and GFCE seem to show non-stationarity. To fix this issue further in the study, the first difference of the log transformation form was taken and unit root test was applied to see the results.
Since the two variables, i.e., GDS and GD, were not found to be stationary even in their log forms, the augmented Dickey–Fuller unit root test was further run on the log forms of these variables. The results for the unit root test for the first differenced variables are depicted in the
Table A3 and
Table A4 in
Appendix A. The p-values for the first differences of log GDS, log GD, log HFC, and log GFCF were also non-stationary and were greater than 0.05, indicating non-stationarity among the variables. The differentiation to the second-degree level made the p-value significant, indicating that the four variables became stationary.
The p-value for the first difference of logGDS, depicted as dlogGDS, was found to be 0, which is less than the significant value of 0.05 at 5% significance. Therefore, the null hypothesis of non-stationarity in this case can be rejected. Hence, it can be said the variable GDS is stationary in its first difference of log transformation form. The same can also be said for the variable GD, whose p-value for the first difference of log transformation form, depicted as dlogGD, was found to be 0, and hence the null hypothesis was of non-stationarity could be rejected. Those two variables in the study were found to be stationary after their first difference; hence, they belong to the I (1) series which means these variables are integrated at first order.
5.2. Johansen’s Cointegration Test
The study of
Naidu et al. (
2017) explained that the Johansen cointegration test is used with I (1) series of stationarity. The test was used to determine the cointegration between dependent and independent variables in the study. It helps define the number of relationships the variables have with one another. The study of
Drakos (
2001) pointed out that the Ricardian equivalence theorem could be proven by establishing a long-term relationship between variables. For this, Johansen’s cointegration method was used to examine that whether there existed any relationship between the variables in the study. Therefore, the researcher used the test to find out the number of relationships that existed in the study between the variables, which brought the study one step closer to proving the existence of Ricardian equivalence in that period. Johansen’s cointegration test uses the maximum rank limit to showcase the number of cointegrating relationships among variables in the study (
Afzal 2012). The hypothesis is set for each rank, and the maximum rank for the cointegration result must be at least 1. The cointegration rank can be achieved by looking at the test statistic and the maximum statistic which should be greater than the critical value at the level of significance set in the study.
The results in
Appendix A suggest that the null hypothesis stating that there is no cointegration between the variables rank 1 is incorrect. Therefore, the results point out that there is cointegration between variables in the study. This can be verified by looking at the t-statistic value, which was 25.3775, which is much higher than the critical value of 15.41 at 5% significance. The maximum statistic also confirms the same, since the value of the maximum statistic was 22.0371 which is much higher than the critical value of 14.07 at 5% significance.
The alternative hypothesis was accepted in this study since the value of the t-statistic, 3.3404, and that of the maximum statistic, were the same. The value is less than the critical value of 3.76 at 5% significance. There was no value at rank 2 that could be checked to claim the rejection or acceptance of the hypothesis H2. Hence, the results of Johansen’s cointegration test point to the presence of a maximum of 1 cointegrating equation between variables.
The results shown in
Table A7 clearly indicate that at the maximum rank of 0, the trace value (25.3775) exceeds the critical value (15.41). Therefore, the null hypothesis of no cointegration was rejected in this case. Hence, there is cointegration between GDS and GD at maximum rank 0. Similarly, the maximum value (22.0371) exceeds the critical value (14.07). Therefore, the two variables are cointegrated.
Further, for maximum rank 1, the null hypothesis is that there is cointegration of one equation, and the alternative hypothesis is that there is no cointegration of one equation. At maximum rank, the trace statistic (3.3404) does not exceed the critical value (3.76). The same is the case with the maximum statistic. Therefore, the null hypothesis must be accepted in this case. Thus, the variables GDS and GD were cointegrated of one equation.
5.3. Specifying the Static Model
Before applying the ARDL regression analysis, it is very important to specify the static model, which must be the econometric regression equation. The distributed lag model taking into consideration the regressors is defined by the following equation:
The above equation clearly specifies the error taking into consideration the regressors x on y. The above process is the infinite moving average with the specified lag weights and the lag distribution. In such a situation, it becomes very important to specify the lag distribution effectively which becomes zero beyond attaining the q periods. Another way would be considering the average and declining lag weights with more lags while requiring a minimal number of parameters.
In all kinds of equation estimation, it is very important to specify the lag length prior to the estimation. There is no specified method under economic theory which gives any information about the length of the lag. Thus, it becomes important to choose an appropriate method for identifying the length of the lag. Additionally, an alternative would be to test the significance of the all terms when examining the marginal coefficients of the lag terms.
The dependent variables comprise of set of regressors wherein the goodness of fit is measured through the Akaike information criterion and Bayesian information criteria. With the given number of coefficients and the residuals, the information criterion for minimizing the sum of the squared residuals is to choose the model with the smallest values of AIC and SBIC. For the current model, the results of the AIC and HQIC as per the selection order criteria with lags equal to 0, 1, 2, 3, and 4 are given in
Appendix A. The results clearly indicate that among all the values, the lag values of 0, 1, 2, 3, and 4, the minimum value is for the AIC and HQIC with the lag of 0. The table in
Appendix A indicates that the AIC and HQIC values for the lags are −5.12671 and −5.06568, with HFC, GFCE, GD, and GDS having the lowest values.
5.4. Auto-Regressive Distributed Lag Model
The economic analyses which were used to determine the long-run relationships among the variables used the autoregressive distributed lag co-integration technique. The results shown in
Appendix A highlight the results of the auto-regressive distributed lag models. The independent variables included the government final consumption expenditure, gross domestic savings, and government debt. On the other hand, the dependent variable was the household final consumption expenditure. The results shown in
Appendix A clearly indicate that the
p-value was significant for the government final consumption expenditure. With one unit of increase in the government final consumption expenditure, the household final consumption increased by 0.72022 units. The results of the ARDL regression analysis indicate cointegration depicts the long-run relationship between the household and the government expenditure. The results of the autoregressive distributed lag model are shown in
Table A3 and
Table A4 of
Appendix A.
In this context, the study conducted by (
Nyambe and Kanyeumbo 2015) highlighted that government expenditure is the crucial stimulant with the economic activities. If the government offers better quality of public goods through better education, healthcare, and other services, then the households are still willing to spend greater amounts to improve the standard of living. In case of stable inflationary pressure, it would be appropriate to extend the government expenditure.
In order to determine whether there is the causality among variables in the long-run or short-run, the researcher used a vector error correction model in the study. The study of
Asari et al. (
2011) points out that the results of cointegration test define whether VECM is to be performed in the study. If the maximum rank in the cointegration test is 1 or greater, VECM is applied to the model to find out the long-run relationship that exists between variables. If the test of cointegration would have pointed to a maximum rank of 0, then the study could not have proved any long-run relationship between variables. In the study of
Andrei and Andrei (
2015) VECM was explained to take care of deviations and short-run changes in the equilibrium situation. The dependent variable in the VECM model was assumed to be endogenously produced and the independent variables were assumed to be exogenously produced so that long-run and short-run associations between the variables could be determined.
The results of VECM Model are shown by
Table A7 in
Appendix A. The term “ce1” represents the cointegrating equations in the model. The value of ce1 when GDS is the dependent variable and GD is the independent variable is −1.332819, which is negative, and the
p-value is 0, which is below the critical value of 0.05 under 5% significance. Therefore, there exists a long-term causality between GDS and GD.
Furthermore, for short-term causality between GDS and GD, the value of individual lag coefficient of GD (−0.1205058) is a negative number, but the p-value (0.605) is not significant at a 5% critical level of 0.05. Thus, there exists no short-term causal relationship between GDS and GD.
The value of ce1 in a case of GD being the dependent variable and GDS being the independent variable is −0.1220723, which is negative, but the p-value, 0.518, is not significant at the 5% critical level. Hence, there exists no long-run causality between GD and GDS. The causality does not hold in the short-run as well because the value of individual lag coefficient is 0.0737549, which is positive, and the p-value 0.534 is not significant at the 5% critical level.
The term “ce1” represents the cointegrating equations in the model. The value of ce1 when GDS is the dependent variable and GD is the independent variable is −1.332819, which is negative, and the p-value is 0, which is below the critical value of 0.05 under 5% significance. Therefore, there exists long-term causality between GDS and GD.
5.5. Granger Causality Test
The study of
Kumar Narayan and Smyth (
2004), points out that cointegration exists in the study then causality will also exist, through the ECM model. In order to determine the direction of causality, granger causality test is applied in studies involving multivariate analysis. According to the study of (
Foresti 2007), granger causality can be used for two variables with lags, more than two variables and lastly using VAR model so that simultaneity can be maintained in variables. Therefore, the study uses the third way of analyzing Granger causality among the variables. Causality could be obtained between variables in either a unidirectional way where only one variable is proved to be the cause of the other, bidirectional causal relationship wherein both the variables affect each other, or they could be independent of each other. The results of the granger causality test are shown in
Table A6 in the
Appendix A.
In case of granger causality, the null hypothesis is the lagged value of one variable doesn’t granger cause another variable. The findings of the results indicate that the gross domestic saving granger causes government debt with the p-value of less than 0.05. Therefore, the null hypothesis is rejected. Additionally, the government debt granger causes all other variables with less than 5% level of significance. Further the household final consumption also granger causes the government debt. Lastly, the government final consumption expenditure granger causes the government debt with less than 5% level of significance. Therefore, the presence of Granger Causality is bi-directional. This means that the government debt impacts the private savings in the economy. On the other hand, the private savings impact the extent of government debt in the economy. Thus, it can be said that the Ricardian equivalence theory is applicable in this scenario which means that increase in the government borrowing will have considerable impact on the private savings in Turkey. Similarly, low level of savings cannot generate a high level of external debt for the economy.