Do Tourism and Institutional Quality Asymmetrically Effects on FDI Sustainability in BIMSTEC Countries: An Application of ARDL, CS-ARDL, NARDL, and Asymmetric Causality Test

The motivation of the study is to investigate the nature of the relationship between institutional quality, tourism, and FDI in BIMSTEC nations for the period 1996Q1–2018Q4. Exploring their nature of association, the study performed several panel econometric models, namely Panel ARDL, Nonlinear ARDL, and Toda-Yamamoto causality test, with symmetric and asymmetric effects of institutional quality and tourism. The results of the Wald test confirmed the long-run asymmetric relationship between institutional quality, tourism, and FDI, both in the long-run and short-run. Furthermore, directional casualty established a feedback hypothesis explaining the relationship between institutional quality, tourism, and FDI.


Introduction
Foreign direct investment (FDI) is important for economic progress, especially for developing nations. Hence, developing nations have been keen to accept foreign investment since FDI bridges capital, technological expertise, and management gap between domestic and foreign firms. Thus, by allowing FDI in the economy, countries can spur their investment possibilities, in the top prioritized area(s), in the economy that eventually expedite the hustle of economic growth in the long run. Furthermore, in globalization, FDI is considered an important stimulator of productivity enhancement, technological advancement, and job creation. The study by Quazi [1] advocated that FDI accelerates economic growth, playing a vital role in tax revenue, foreign exchange, and development gaps in developing and transition economies.
The motivation of the study is to gauge the role of tourism and institutional development on FDI inflows in BIMSTEC Countries. The study implemented both symmetry and asymmetry frameworks of exploring the insight evidence in empirical assessment. The study detected that FDI inflows positively augmented further development in tourism and institutional quality in BIMSTEC countries. BIMSTEC is a sub-regional organization comprised of seven South and Southeast Asian nations. Its mission is to foster economic growth, accelerate social advancement, and foster cooperation on issues of mutual concern in the Bay of Bengal. The underlying motivation for selecting BIMSTEC as a panel is sharing the common economic dynamics and economic integration.
Acknowledging the potential effects of FDI in the economy, a growing number of studies were performed targeting to discover the key determinants of FDI inflows. Empirical literature signifies several macro fundaments including, level of economic development [2,3], financial markets development [4][5][6], human capital [7][8][9], quality infrastructure [10], size QQ

Institutional Quality and FDI Nexus
In recent research, the institutional quality of a host nation has gained increasing attention as one of the major factors in foreign capital investment decisions. Institutional factors such as legal and political systems are considered critical in reducing the risk of opportunism in foreign direct investment (FDI). Furthermore, less corruption and a fair, reliable, and efficient bureaucracy assist in attracting foreign direct investment. Nexus between institutional quality and FDI has been investigated extensively in the empirical literature, and a growing number of researchers have confirmed positive associations, including Bouchoucha and Benammou [74]; Masron and Abdullah [75]; Masron and Naseem [76]; Shah et al. [77]. Quality institutions, according to Hall and Jones [78], accelerate the growth phenomena by encouraging private investments and improving the overall efficiency of the economic system. The theoretical literature supports the importance of efficient and wellperforming institutions in disciplining economic actors' conduct and enacting rules and regulations that restrict opportunism and foster transactional trust in financial transactions, thus increasing foreign investor confidence and FDI inflows. In a study by Globerman and Shapiro [79], they contended that stronger institutions may benefit FDI inflows by creating favorable conditions for foreign investors. Additionally, they discovered that various metrics of governance quality had a somewhat varied effect on FDI inflows. The study of Masron [80] advocated that although raising IQ is a good thing, it does not always translate into greater FDI. That is, IQ is a required but not sufficient condition for FDI inflows. Ongoing efforts to strengthen ASEAN economies should improve labor markets, natural resource supply stability, and physical infrastructure.
Possessing quality institutions in the economy, countries can have experienced additional benefits for receiving FDI in various ways. First, quality institutions and productivity are interlinked in the long run, and the possibility of achieving higher productivity encourages foreign investors to invest in the economy. Second, an unfavorable institutional environment may raise the cost of conducting business. Corruption, for example, may discourage investment by raising the cost of conducting business. Third, since FDI entails a large sunk cost, it is susceptible to uncertainty, particularly caused by poor government efficiency. Improper contract enforcement, for example, may raise uncertainty about future returns and, as a result, have a detrimental impact on investment.
Regarding IQ and FDI nexus, another group of researchers has observed the adverse association [19,[81][82][83]. In the study of North [84], the study findings postulated that ineffi-Sustainability 2021, 13, 9989 6 of 29 cient institutions are responsible for increasing the production costs through disrupting the supply chain, and excessive formalities in obtaining permits can significantly increase production costs.
However, the empirical literature has also exposed neutral effects running between IQ and FDI, see [85][86][87]. Furthermore, the indirect effects of institutional quality on FDI inwards are also investigated and established in empirical studies such as human capital, healthy labor force, and the quality of public facilities to promote FDI [88]. The study of Michael Michael et al. [89] investigated the moderating effects of institutional quality on inflows of FDI in 40 countries in the Sub-Saharan African region over the period from 1996 to 2011. The study findings revealed that institutional quality augmented the inflows of FDI by reducing the negative effects of macroeconomic uncertainty. The summary of survey literature is displayed display in Table 2.

The Motivation of the Study and Proposed Hypothesis of the Study
Concerning the literature survey, it is apparent that many empirical studies have already been conducted by taking account of several macroeconomic fundamentals with time series and panel data. However, the nexus between institutional quality, tourism, and FDI is yet to be investigated, and their possible asymmetry is still undiscovered in the empirical literature.
Furthermore, it is obvious that directional causality is investigated extensively; however, their asymmetric causality relationship is yet to be unleashed. Therefore, with this study, for the first time, the possible asymmetric relationship between Tourism and FDI will be investigated by applying the nonlinear framework propose by Shin, Yu, and Greenwood-Nimmo [20] in panel form, and asymmetric directional causality will be assessed by following Toda and Yamamoto [21] causality test with the asymmetry of tourism in the equation. It is expected that the current research findings will contribute towards fulfilling the existing research gap and put another view for explaining the nexus between institutional quality, tourism, and FDI that is asymmetry effects. Figure 1 displays the conceptual and hypnotized empirical model for hypothesis testing.

Data and Methodology of the Study
To investigate the dynamic relationships between institutional quality, tourism, and FDI, this study considers annual panel data from 1996Q1 to 2018Q4. Except for the proxy variables of institutional quality, all the relevant data were collected from the World D evelopment Indicator published by World Bank. Furthermore, the proxy variables of tourism were collected from Worldwide Governance Indicators (WGI). All the research variables were transformed into a natural log before estimation.
As a dependent variable of the study, the study employed three different proxies, that is, flows of FDI, (% of GDP) and stock of FDI. The volatility of FDI is measured by the variance of FDI following [19]. The motivation for selecting three proxies is to explore comprehensive and conclusive evidence so that the study findings can contribute substantially to future literature development on the purported topic.

Tourism
Gauging tourism effects on FDI, in the empirical estimation, it is observed that two measures were used extensively. First, international tourism receipts in current USD [46,126,127]. Second, International tourist arrival is measured by the number of tourism visitors/million People, see for instance [29,69,128]. However, a growing number of researchers emphasized using international tourism receipts as a proxy for tourism in the empirical estimation, and this study is on the same trajectory.

Institutional Quality
Measuring institutional quality in the empirical literature, two lines of thought are available. A growing number of empirical studies have utilized a single proxy for IQ in these respective studies see, for instance, Aizenman and Spiegel [129]; Levchenko [130]; Habib and Zurawicki [131]; Wijeweera and Dollery [132]. The second line of empirical findings have been suggesting the use of index measures for institutional quality, which is constructed by taking into account the indicators from World Governance Indicators [133] with the application of Principal component analysis see for an instance Le et al. [134]; Qamruzzaman, Tayachi, Mehta, and Ali [18]; Daude and Stein [104]. In regards to institutional quality measurement, the present study follows the second line of under sting that is the use of the institutional quality index following Qamruzzaman, Tayachi, Mehta, and Ali [18]; Asamoah, Adjasi, and Alhassan [119]; Buchanan, Le, and Rishi [19]. The pair-wise correlation of six indicators of WGI is displayed in Table 3 and the output of PCA is reported in Table 4.  As a result, following existing literature, see, for instance, Asamoah and Alagidede [135], Globerman and Shapiro [88], the study performed principal components of the six indicators of governance employing factor analysis and construct instructional quality index (IQ). The results of PCI are exhibited in Table 4. Apart from the target variables, following existing literature see Carkovic and Levine [136] and Hayat [137], the study considers a list of control variables for robustness in empirical estimation such as trade openness (TO) measured by the sum of export and import as a percentage of GDP. Domestic investment (DI) is measured by gross capital formation as a percentage of GDP, inflation (INF) is measured by consumer price index and money supply (M) which is proxied by Broad money as a percentage of GDP.
Considering all proxies representing FDI in the empirical equation, the generalized empirical model in panel form can be represented in the following Equations (1)-(3), and different methodologies will be applied for assessment purposes.
The subscripts i and t denote the sample countries (i = 1, 2,..., N) and months (t = 1, 2, . . . , T), respectively. FDI, FDI_stock, and FDI_volatility. FDI are inflows of FDI as % of GDP, FDI stock as a % of GDP and FDI volatility is measured by five years standard deviation. Inst indicates a composite index of institutional quality, and Tour represents international tourism receipts. X it for a group of control variables in the equation, which includes trade openness (TO), money supply (M), domestic investment (DI), and inflation (INF), respectively. The results of the descriptive statistics are exhibited in Table 5.

Cross-Sectional Dependency Test
The cross-section dependence test is critical in panel data empirical research, particularly when representative nations have similar economic features, such as emerging countries, growing economies, and transition countries. A similar economy is vulnerable to the impacts of any shock in other countries due to trade internationalization, financial integration, and globalization. As a consequence, cross-sectional dependency analysis is often needed in empirical research using panel data. According to existing literature, a number of CSD tests have emerged and been applied for detecting the presence of common dynamics in research units, such as LM BP test was offered by Breusch and Pagan [138], and the test statistics can be derived with the following equation: where y it , x it stands for dependent and independent variables and the subscript of t, and i represent cross-section and period, respectively. Under the circumstance of larger crosssection units in the model, the LM BP test cannot handle the issue. Overcoming the present limitation Pesaran [139] proposed the following modified Lagrange multiplier (CD lm ) for examining cross-sectional dependency among research units: The empirical model with larger N relative to T, CD lm estimation incapacity to manage this issue and resolve the limitation in CF lm , Pesaran [140] offered the following CD test for the situation with larger N than T.
Finally, Pesaran et al. [141] familiarized the improved version of CD lm test known as the bias-adjusted LM test, and the test statistics can be derived using the following equation: where K refers to the number of regresses, u Tij and υ 2 Tij specifies the mean and variance of (T − K)ρ 2 ij , respectively.

Panel Unit Root Tests
The study performed several unit root tests to discover the properties of the variable, especially with cross-sectional dependency. Second generation panel unit root tests introduced by Pesaran [142], commonly known as CADF and CIPS and have been extensively utilized see [143][144][145]. The Dickey-Fuller Sectional Augmented Statistics (CADF) can be expressed as: where Y it − 1 and y t−1 stands lagged level average and first difference operator for each cross-section, the CIPS unit root test displays in Equation (9).
where the parameter ∂ i (N, T) explain the test statistics of CADF, which can be replaced in the following manner:

Panel Cointegration Test
The present research used several panel cointegration tests following Pedroni Pedroni [146,147], Kao [148] and the bootstrap panel cointegration method developed by Westerlund [149] to find the evidence of a long-run relationship between variables. The Bootstrap panel cointegration technique is more advantageous if each cross section is composed of condensed time series. Because traditional methods do not take CD into account, they accept the null hypothesis of no cointegration even in the presence of CD.

Pooled Grouped Mean Estimation
For detecting the impact of tourism and institutional quality on FDI inflows, the study considered Panel ARDL familiarized by Pesaran et al. [150], which is capable of identifying both long-run and short-run coefficients in empirical assessment. The first fundamental assumption of PGM is that the error correction term is free from correlation dependency and is normally distributed by regressors. Additionally, the dependent and explanatory variables are related throughout time, which means there will be a long-term correlation between them; finally, the long-term parameters will stay consistent across nations. Pesaran proposed the following ARDL (p, q . . . . n) as an empirical structure: where, Following Pesaran, Shin, and Smith [150], the following empirical model is used to detect the association between FDI, tourism, and institutional quality in panel assessment. where Specify the long-run relationship between foreign direct investment and explanatory variables such as institutional quality, tourism, and a list of control variables. The longrun coefficient denoted by ωi; and the speed to the recovery of short-run disequilibrium is explained by the vector of ξi the remaining coefficient (i.e., γ * i,j , β ij ) in Equation (14) represent the short-run dynamics.

Cross-Sectional ARDL
The presence of cross-sectional dependency among research units has raised inconsistency with traditional panel regression estimation. Thus Chudik and Pesaran [151] proposed an advanced, econometrics technique known as the Common Correlated Effects (CCE) approach for gauging the relationship with panel data, which is the extension of Pesaran [140]. Following the proposed framework, the generalized empirical model is as follows: Bootstrap panel cointegration technique is more advantageous if each cross section is composed of condensed time series. Because traditional methods do not take CD into account, they accept the null hypothesis of no cointegration even in the presence of CD.

Pooled Grouped Mean Estimation
For detecting the impact of tourism and institutional quality on FDI inflows, the study considered Panel ARDL familiarized by Pesaran et al. [150], which is capable of identifying both long-run and short-run coefficients in empirical assessment. The first fundamental assumption of PGM is that the error correction term is free from correlation dependency and is normally distributed by regressors. Additionally, the dependent and explanatory variables are related throughout time, which means there will be a long-term correlation between them; finally, the long-term parameters will stay consistent across nations. Pesaran proposed the following ARDL (p, q …. n) as an empirical structure: where, Following Pesaran, Shin, and Smith [150], the following empirical model is used to detect the association between FDI, tourism, and institutional quality in panel assessment.
Specify the long-run relationship between foreign direct investment and explanatory variables such as institutional quality, tourism, and a list of control variables. The longrun coefficient denoted by and the speed to the recovery of short-run disequilibrium is explained by the vector of the remaining coefficient (i.e., , * , ) in Equation (14) represent the short-run dynamics

Cross-Sectional ARDL
The presence of cross-sectional dependency among research units has raised inconsistency with traditional panel regression estimation. Thus Chudik and Pesaran [151] proposed an advanced, econometrics technique known as the Common Correlated Effects (CCE) approach for gauging the relationship with panel data, which is the extension of Pesaran [140]. Following the proposed framework, the generalized empirical model is as follows: Bootstrap panel cointegration technique is more advantageous if each cross section is composed of condensed time series. Because traditional methods do not take CD into account, they accept the null hypothesis of no cointegration even in the presence of CD.

Pooled Grouped Mean Estimation
For detecting the impact of tourism and institutional quality on FDI inflows, the study considered Panel ARDL familiarized by Pesaran et al. [150], which is capable of identifying both long-run and short-run coefficients in empirical assessment. The first fundamental assumption of PGM is that the error correction term is free from correlation dependency and is normally distributed by regressors. Additionally, the dependent and explanatory variables are related throughout time, which means there will be a long-term correlation between them; finally, the long-term parameters will stay consistent across nations. Pesaran proposed the following ARDL (p, q …. n) as an empirical structure: where, Following Pesaran, Shin, and Smith [150], the following empirical model is used to detect the association between FDI, tourism, and institutional quality in panel assessment.
Specify the long-run relationship between foreign direct investment and explanatory variables such as institutional quality, tourism, and a list of control variables. The longrun coefficient denoted by and the speed to the recovery of short-run disequilibrium is explained by the vector of the remaining coefficient (i.e., , * , ) in Equation (14) represent the short-run dynamics

Cross-Sectional ARDL
The presence of cross-sectional dependency among research units has raised inconsistency with traditional panel regression estimation. Thus Chudik and Pesaran [151] proposed an advanced, econometrics technique known as the Common Correlated Effects (CCE) approach for gauging the relationship with panel data, which is the extension of Pesaran [140]. Following the proposed framework, the generalized empirical model is as follows: Thus, the Panel CS-ARDL specification of Equation (15) where, Z = FDI, X and S Z in the number of lagged cross-sectional average, Similarly Equation (11) can be reparametrized to the effects of ECM presentation of Panel CS-ARDL as follows:

The Asymmetric Panel ARDL
The study implements a nonlinear framework following Shin, Yu, and Greenwood-Nimmo [20] in panel form to evaluate the asymmetric effects of tourism and institutional quality on FDI inflows. Taking into account the positive and negative shocks that are (TOR=, TOR−, IQ+, and IQ−), the following empirical asymmetric equation can be derived: where inst + & inst − stand for the positive and negative shock of institutional quality, TOR + and TOR -Represents the positive and negative shock of tourism. The long-run coefficients are computed as

Pooled Grouped Mean Estimation
For detecting the impact of tourism and institutional quality on FDI inflows, the study considered Panel ARDL familiarized by Pesaran et al. [150], which is capable of identifying both long-run and short-run coefficients in empirical assessment. The first fundamental assumption of PGM is that the error correction term is free from correlation dependency and is normally distributed by regressors. Additionally, the dependent and explanatory variables are related throughout time, which means there will be a long-term correlation between them; finally, the long-term parameters will stay consistent across nations. Pesaran proposed the following ARDL (p, q …. n) as an empirical structure: where, Following Pesaran, Shin, and Smith [150], the following empirical model is used to detect the association between FDI, tourism, and institutional quality in panel assessment.
Specify the long-run relationship between foreign direct investment and explanatory variables such as institutional quality, tourism, and a list of control variables. The longrun coefficient denoted by and the speed to the recovery of short-run disequilibrium is explained by the vector of the remaining coefficient (i.e., , * , ) in Equation (14) represent the short-run dynamics

Cross-Sectional ARDL
The presence of cross-sectional dependency among research units has raised inconsistency with traditional panel regression estimation. Thus Chudik and Pesaran [151] proposed an advanced, econometrics technique known as the Common Correlated Effects (CCE) approach for gauging the relationship with panel data, which is the extension of Pesaran [140]. Following the proposed framework, the generalized empirical model is as follows:

Pooled Grouped Mean Estimation
For detecting the impact of tourism and institutional quality on FDI inflows, the study considered Panel ARDL familiarized by Pesaran et al. [150], which is capable of identifying both long-run and short-run coefficients in empirical assessment. The first fundamental assumption of PGM is that the error correction term is free from correlation dependency and is normally distributed by regressors. Additionally, the dependent and explanatory variables are related throughout time, which means there will be a long-term correlation between them; finally, the long-term parameters will stay consistent across nations. Pesaran proposed the following ARDL (p, q …. n) as an empirical structure: where, = ′ + , − = + , − + ′ + µ Following Pesaran, Shin, and Smith [150], the following empirical model is used to detect the association between FDI, tourism, and institutional quality in panel assessment. Specify the long-run relationship between foreign direct investment and explanatory variables such as institutional quality, tourism, and a list of control variables. The longrun coefficient denoted by and the speed to the recovery of short-run disequilibrium is explained by the vector of the remaining coefficient (i.e., , * , ) in Equation (14) represent the short-run dynamics

Cross-Sectional ARDL
The presence of cross-sectional dependency among research units has raised inconsistency with traditional panel regression estimation. Thus Chudik and Pesaran [151] proposed an advanced, econometrics technique known as the Common Correlated Effects (CCE) approach for gauging the relationship with panel data, which is the extension of Pesaran [140]. Following the proposed framework, the generalized empirical model is as follows: where, ̅ = , respectively. These shocks are computed as positive and negative partial sum decomposition of institutional quality and tourism in the following ways: The error correction version of Equation (22) is as follows:

Causality Test with Symmetric and Asymmetric with Toda-Yamamoto
Gauging the possible directional causality between institutional quality, tourism, and FDI, this study applied the non-causality test proposed by Toda and Yamamoto [21]. Zapata and Rambaldi [152] claimed that Toda and Yamamoto's non-causality test outperforms the Granger causality test in certain situations. First, a non-causality test requires no cointegration characteristics in the system equation. Second, the MWALD test may examine existing causality between variables when the integration order is I (0) or I (1). Equation (26) showed symmetrical impacts between institutional quality and tourism.
In the following, integrating the positive and negative shocks of institutional quality [IQ + i , IQ − i ] and tourism (TOR + i , TOR − i ), the symmetric Equation (26) can be rewritten into an asymmetric Equation (27).

Panel Unit Root, Cross-Section Dependence, and Cointegration Tests
Now, we move to assess variables' order of integration that is the test of stationarity. Several first-generation unit-roots were performed in the study, namely, the LLC test [153], the IPS test proposed by Im et al. [154], the Breitung test proposed by Breitung [155], the Fisher-ADF proposed by Maddala and Wu [156] which have the null hypothesis that all the panel contains a unit root. Besides, the Lagrange multiplier (LM) test proposed by Hadri [157] has the null hypothesis that all panels are stationary; the first generation unit root test results are exhibited in Table 6. Furthermore, we believe that data are cross-sectionally correlated since the lists of panel countries are geographically and economically connected. Therefore, we performed a cross-sectional dependency test, and the results are reported in Table 7, given that the variable under investigation has a cross-sectional dependency. So, one can assume that FDI, tourism, institutional quality, and domestic investment seem to exhibit some dynamisms common to all countries. With regards to the results of the cross-sectional dependency test and following empirical literature including, Gengenbach et al. [158] and Dogan and Aslan [159], we perform a two-panel unit root test, which is predominately applied due to the presence of cross-sectional dependency in the panel data that is augmented cross-sectional ADF (CADF) and CIPS unit root test proposed by Pesaran [142]. The results of the panel unit root tests are exhibited in Table 8. Results of panel unit root tests established mixed order of integration, that is, variables are integrated either at a level I (0) or/and after the first difference I (1). In the following, the study performed a residual-based panel cointegration test proposed by Pedroni [146,147] and Kao [148], assessing the possible long-run association between institutional quality, tourism, and FDI. The results of the panel cointegration test are reported in Table 9. Alluding to the outcomes, we can postulate the presence of a long-run equilibrium relationship between FDI, institutional quality, and tourism since the null hypothesis is rejected at a 1% level of significance. This verdict is valid for all empirical model estimations. The existence of a cointegrating equilibrium relationship between the variables paves the way for uncovering both the short-and long-run dynamics. Additionally, the study performed the Westerlund-Durbin-Hausman panel cointegration test proposed by Westerlund [149], and test results are exhibited in Table 10. Model estimation produces two statistics: Group statistics based on panel homogeneity and Panel statistics based on panel heterogeneity report the summary results of the panel cointegration test. Regarding the associate p-value of test statistics, they are statistically significant at a1% level of significance. These findings imply that inflows of FDI will be affected by any changes in institutional quality, tourism, in the economy in the long run. Furthermore, the presence of a long-run relationship can also be assessed by considering the coefficient of ECT in panel PGM estimation. In order to establish a long-run association, the coefficient of ECT should be negative and statistically significant. Referring to the coefficients reported in Column (1) to Column (9), it is observable that all the coefficients are negative in sign and statistically significant at a 1% level. Therefore, we can conclusively postulate that institutional quality, tourism, and FDI move together in the long run. Table 11 displayed the results of PGM estimation, which includes the long-run and the short-run coefficients in panel-A and Panel-B, respectively. Getting insight into the tested nexus between institutional quality, tourism, and FDI, this study has performed nine empirical models based on various proxies for the dependent variable. The Study findings with FDI inflows as a percentage of GDP are reported in columns (1)-(3), in terms of FDI stock displayed in columns (4)-(6), and FDI volatility exhibited in columns (7)-(9). The model estimation outcome is displayed in columns (1)- (3), where FDI inflows are treated as a dependent variable. In the long run, we observed that both institutional quality (a coefficient of 0.440) in column (1) and tourism (a coefficient of 0.240) in column (2) are positively associated with their respective empirical model. Furthermore, the empirical model outcome with the presence of both independent variables (see, column-(3)), it is apparent that tourism (a coefficient of 0.166) and institutional quality (a coefficient of 0.942) induced inflows of FDI with a positive attitude and their coefficients are statistically significant at a 1% level. As such, one can assume that in the long run, inflows of FDI in BMISTEC nations can be accelerated by offering a better institutional perspective and internationalization of tourism services. In the short-run (see, Panel-B, Columns (1)-(3)), the effects of institutional quality and tourism are positively linked to inflows of FDI. Considering the model output reported in Column (3), it is apparent that both institutional quality (a coefficient of 0.092) and tourism (a coefficient of 0.124) are positively connected with inflows of FDI.

Results of Panel-ARDL (PGM) Estimations
The results are reported in columns (4) to (6), where FDI stock is considered a dependent variable. In the long run, institutional quality (a coefficient of 0.536) and tourism (a coefficient of 2.230) are positively associated with FDI inflows in terms of stock in their respective sole empirical assessment. Furthermore, referring to column (6), where both institutional quality and tourism are incorporated in the equation and unveiled positive effects, that is, institutional quality (a coefficient of 0.516) and tourism (a coefficient of 0.487), on FDI stocks. More specifically, if it is possible to implement a 10% acceleration in institutional quality and tourism, such an injection will result in 5.16% of FDI stock flows due to the development of institutional quality and 4.87% due to tourism expansion. In the short run, the long-run equilibrium convergence is established in all tested empirical models, implying that the coefficients of ECT are negative and statistically significant. However, considering the short-run elasticities of institutional quality and tourism on FDI stock. The study findings suggested a negative association between them, but all the coefficients are statistically insignificant. Finally, empirical model estimation with FDI volatility as the dependent variable and the results are reported in Column (7) to (9). In the long run, in their respective equation, that is a sole model, both institutional quality (a coefficient of −0.031) and tourism (a coefficient of −0.413) exhibited a negative association with FDI volatility. Further, referring to results reported in column (9), we observed that both institutional quality (a coefficient of −0.246) and tourism (a coefficient of −0.196) play a negative role. More precisely, these findings suggest that a 10% development in institutional quality and tourism will reduce FDI volatility by 2.46% due to institutional quality and 1.96% due to tourism effects in the economy. Referring to the short-run effects reported in Panel-B, a statistically insignificant positive association between institutional quality, tourism, and FDI volatility is established.
For the control variables, money supply and trade openness play a positive role in increasing FDI inflows and FDI stock in the long run. However, insignificant effects are established in the case of FDI volatility. The coefficient of control variables, especially in the short-run, exhibited statistically insignificant except domestic investment. Domestic investment augments inflows of FDI and FDI stocks, but insignificant effects are observed for FDI volatility.

CS-ARDL Estimation
In the following section, the study investigates the long-run and the short-run association between institutional quality, tourism, and FDI by performing CS-ARDL since the presence of cross-sectional dependency among researched variables. Table 12 exhibits the results of the long-run and short-run effects on FDI. Referring to long-run estimation (see, Panel-A), the noticeable findings are that both institutional quality and tourism are positively associated with FDI; these findings are also valid for all empirical model estimations. More specifically, the following results are reported in Columns (3), (6), and (9) with both institutional quality and tourism present in the equation. However, in the case of FDI volatility as a dependent variable in the equation, the study findings established a negative association, that is, development in institutional quality and tourism will result in the stability in FDI inflows in the long run. In the short run, the coefficients of error correction term, regardless of empirical model investigation, are negative in sign and statistically significant at a 1% level. These findings confirmed the presence of long-run convergence among the variables (see panel-B). Furthermore, analyzing the short-run magnitude running from the institutional quality and tourism, the study findings disclosed positive association (see panel-B, Columns (3), (6), and (9)). Specifically, 10% development in institutional quality will result in further development in FDI inflows by 13.58%, and tourism contributes to the process by 8.16%; furthermore, FDI stock enhancement will be accelerated by 1.5% due to institutional quality and 9.92% assistance from tourism development. However, the short-run effects from the institutional quality and tourism on FDI volatility are statistically insignificant.

Asymmetric Long-Run and Short-Run Effects Estimation
In the following section, the study investigates the potential asymmetric association between institutional quality, tourism, and FDI by following a nonlinear framework introduced by shin. Using the nonlinear equation (see Equation (24)), we performed nine [09} empirical models based on three proxy variables measuring FDI and the combined presence of independent variables in the equation. The results of nonlinear ARDL are presented in Table 13, consisting of long-run effects displayed in Panel-A, short-run coefficient inserted in Panel-B, and the result of the Wald test for assessing symmetry reported in Panel-C, respectively.
Panel-A: Long-run coefficients nique is more advantageous if each cross section is comcause traditional methods do not take CD into account, no cointegration even in the presence of CD.
tourism and institutional quality on FDI inflows, the iliarized by Pesaran et al. [150], which is capable of t-run coefficients in empirical assessment. The first funthat the error correction term is free from correlation ibuted by regressors. Additionally, the dependent and hroughout time, which means there will be a long-term , the long-term parameters will stay consistent across owing ARDL (p, q …. n) as an empirical structure: Smith [150], the following empirical model is used to , tourism, and institutional quality in panel assessment. nique is more advantageous if each cross section is comcause traditional methods do not take CD into account, no cointegration even in the presence of CD.
tourism and institutional quality on FDI inflows, the iliarized by Pesaran et al. [150], which is capable of t-run coefficients in empirical assessment. The first funthat the error correction term is free from correlation ibuted by regressors. Additionally, the dependent and hroughout time, which means there will be a long-term , the long-term parameters will stay consistent across owing ARDL (p, q …. n) as an empirical structure: Smith [150], the following empirical model is used to , tourism, and institutional quality in panel assessment. hip between foreign direct investment and explanatory lity, tourism, and a list of control variables. The longthe speed to the recovery of short-run disequilibrium is emaining coefficient (i.e., , * , ) in Equation (14) repal dependency among research units has raised inconession estimation. Thus Chudik and Pesaran [151] protechnique known as the Common Correlated Effects elationship with panel data, which is the extension of osed framework, the generalized empirical model is as  Source: Authors' estimation. Note: a/b/c indicates levels of significance at 1%, 5%, and 10%, respectively.
Referring to Panel-C, the results of the Wald test with the null hypothesis of both long-run and short-run symmetry. It is observable that the test statistics of the Wald test are statistically significant at a 1% level of significance that means asymmetric effects running from institutional quality and tourism towards FDI. These conclusions are applicable for all nine (09) tested empirical models. Now, we analyze the potential effect and their association (see, Panel-A). The results are reported in columns (1) to (3), where FDI inflows as a percentage of GDP are treated as a dependent variable. Positive shocks in institution quality (a coefficient of 0.066 in column (1) and a coefficient of 0.131 in column 3) and negative shocks in institution quality (a coefficient of 0.046 in column (1) and a coefficient of 0.361 in column (3)) positively linked with inflows of FDI. The study findings suggest that both positive and negative shocks in institutional quality and tourism are critical for inflows of FDI in the long run. However, the possessions of negative shocks are greater than the positive shocks in both variables.
On the other hand, observing the positive and negative shocks in tourism see, Column (2) and (3) we observed, see in column (2), that is, the positive (a coefficient of 0.161) and negative shocks (a coefficient of 0.909) and the results in column (3) positive shock (a coefficient of 0.877) and negative (a coefficient of 0.877), positive association with FDI. The study findings suggest that tourism recipients' increase or decrease will be critical for maintaining stability in FDI inflows in the long run. It is important to maintain the present state and put considerable effort into further development because any possible degradation might produce unwell full consequences.
Referring to the results exhibited in columns (4)-(6), FDI stock was treated as a dependent variable in the equation. In the long run, see column (6), positive shocks in institutional quality is positively linked (a coefficient of 0.253) with FDI stock, but negative shocks exhibit negative association (a coefficient of 0.021). These findings suggested that FDI stock inflows could be accelerated by adopting positive and negative institutional quality changes. However, the elasticity of positive innovation is greater than negative; therefore, policy formulation should understand the fact and do accordingly. In contrast, positive (a coefficient of 0.033) and negative (a coefficient of 0.881) shock in tourism disclosed a positive linkage with FDI stock. However, the negative shocks produce greater intensity than positive shocks in tourism. It refers that any deviation in tourism activities adversely affected the trend of FDI stock inflows in the economy.
Considering the model output displayed in columns (7)- (9), FDI volatility was treated as a dependent variable. In the long run, positive and negative shocks in institutional quality (a coefficient of −0.053, −0.651) and tourism (a coefficient of −0.004, −0.792) are negatively associated with FDI volatility, and coefficients are statistically significant. Considering the elasticity of FDI volatility, negative shocks in institutional quality and tourism have a higher impact than positive shocks in variables. More specifically, a 10% variation in negative shocks in institutional quality and tourism will increase FDI volatility by 6.51% and 7.925, respectively. On the other hand, 10% positive shocks in institutional quality and tourism can reduce FDI volatility by 0.531% and 0.04%, respectively. Furthermore, the results reported in columns (7) and (8) also established a negative linkage with FDI volatility in both cases of positive and negative shocks in institutional quality and tourism.
In the short run, the coefficients of error correction terms exhibit negative signs and are statistically significant at a 1% level of significance. These findings suggest long-run convergence between institutional quality, tourism, and inflows of FDI in selected south Asian countries. Furthermore, referring to short-run elasticities, it is observable that positive shocks in institutional quality are positively linked to FDI, that is, a coefficient of 1.068 in column (3), a coefficient of 0.238 in column (6), and a coefficient of 0.042 in column (9) and all the coefficients are statistically significant. At the same time, the coefficients of negative shocks in institutional qualities are statistically insignificant except for FDI volatility (a coefficient of 1.744).
The positive and negative shocks in tourism established a mixed relationship with FDI. Both coefficients posted in column (3) displayed positive linkage with FDI inflows (a coefficient of 2.003 and a coefficient of 0.329). Results posted in column (6), reveal that positive shocks are positively associated (a coefficient of 0.019), and negative shocks are negatively caused (a coefficient of −0.293), and finally, tourism effects on FDI volatility exhibited mixed effects, but all the coefficients are statistically insignificant.

Causality Analysis with Symmetry
The results of the directional casualty test with symmetry effect from institutional quality and tourism are exhibited in Table 14.
Considering the results reported in Panel-A. The study findings established several causal relationships among research variables. However, we are primarily focusing on investigating casualty between FDI, IQ, and TOR. Regarding the desired causality, study findings established unidirectional causality running from institutional quality to tourism [IQ→TOR]. Furthermore, the feedback hypothesis hold in assessing causality between institutional quality and FDI [IQ←→FDI], and tourism and FDI [TOR←→FDI].
The result is reported in Panel -B, where FDI stock is treated as a proxy for the dependent variable. Similar to Panel-A, study findings established several causal relationships but considering the target relationship, that is, causality between FDI, IQ, and TOR. It has appeared that the Feedback hypothesis hold in explaining the causality between institutional quality and   In the following section, the causality test results considering asymmetry in institutional quality and tourism are exhibited in Table 15. Panel-A reports the results with FDI inflows as a dependent variable, Panel-B displays the results with FDI stock as dependent variables. Finally, Panel C reports the results with FDI volatility as a dependent variable, respectively. Referring to causality results, it appeared that several directional causalities are available, however focusing on the key motivation of the study, the summary results are exhibited in Table 16.

Discussion
Tourism is quickly becoming one of the most important businesses in many nations. It is primarily owing to its significant contribution to foreign exchange inflows, national income, and job possibilities, all of which have a significant economic effect on the individual nations. Refers to tourism-led foreign capital investment, the study documented a positive statistically significant association that is tourism positively assists in increasing the inflows of FDI in the economy. Our study findings align with existing literature see, for instance, Tomohara [31], Samimi, Sadeghi, and Sadeghi [29], and Perić and Radić [32]. Salleh, Othman, and Sarmidi [40] investigated the impact of tourism development on FDI inflows in the south Asian economy by employing ARDL. The study documented the long-run association between tourism development and growth in FDI. Moreover, the causality test established unidirectional causality running from tourism to FDI. The study of Siddiqui and Siddiqui [37] revealed unidirectional causality between tourism and FDI in Pakistan. The study advocated that effective tourism policy implementation can accelerate foreign capital investment in the economy.
Selvanathan, Selvanathan and Viswanathan [52] investigated the dynamic connection between tourism and FDI in India from 1995-2007 using quarterly statistical data under VAR estimation. The results indicated a unidirectional causal relationship between FDI and tourism and advocated that FDI attraction accelerated the development of foreign tourism in India's economy during the past decade. Khoshnevis Yazdi, Homa Salehi, and Soheilzad [46] established that foreign direct investment substantially affects tourist development in developing nations' economies. Inbound tourism generates export income, but it also creates jobs in the service sector via FDI because of tourist-related investment. Thus, to promote inbound tourism, it is necessary first to determine the nature of the connection between inbound tourism and FDI, as well as whether inward FDI flows only to tourism-related sectors, before formulating a more effective strategy based on the degree of correlation.
The growing interest in institutional and political development economics issues has resulted in detailed research on the factors influencing institutional quality [160]. The current study investigated the nexus of institutional quality-led tourism and exposed positive connections in empirical assessment, which is in line with Delgado and McCloud [161], Kim and Choi [162], Qamruzzaman, Tayachi, Mehta, and Ali [18]. Because of good institutional quality, the foreign direct investment (FDI) inflows are strong, and foreign direct investment (FDI) volatility is low. On the other side, there are drivers of FDI outflows that are detrimental, such as corruption and institutional distance between the home and host nations. Quality institutions augmented inflows of FDI in the economy in three different manners. First, strong institutions improve productivity potential, which may attract international investment. Second, a dysfunctional institutional framework may drive up the cost of conducting business. For instance, corruption may discourage investment by increasing the cost of conducting business [163]. Third, FDI is subject to uncertainty, particularly uncertainty caused by inefficient governance, since it entails a large sunk cost.
For instance, imprecise contract enforcement may raise uncertainty about future rewards, thus discouraging investment from foreign soil.

Conclusions
The prominent role of FDI is extensively investigated in empirical studies and the key determinants for accelerating the inflows of FDI, especially for developing countries. The motivation of the study is to unleash the fresh evidence regarding the nexus between institutional quality, tourism, and FDI in BIMSTEC nations during the period 1996Q1-2018Q4. Several econometric methodologies were applied including, panel-ARDL, CS-ARDL, Nonlinear-ARDL, and directional casualty investigated following Toda and Yamamoto [21] with the incorporation of both symmetry and asymmetry effects of institutional quality and tourism. The key findings of this study are reported below: First, the study began with established variables order of integration by applying both first and second-generation panel unit root tests. The study established mixed order integration, that is, few variables are integrated at a level, and few become stationary after the first difference. Furthermore, a cross-sectional dependency test confirmed the presence of common dynamism among the selected variables.
Second, the study findings with Panel-ADRL confirmed the long-run positive association between institutional qualities, tourism, and inflows of FDI. The study findings suggest that further development in institutional quality and tourism activities will result in a positive way in the economy that induces foreign investors and increase possibilities for receiving additional FDI. These studies' findings are in line with Turan Katircioglu et al. [33]; Perić and Radić [32]; Khoshnevis Yazdi, Nateghian and Sheikh Rezaie [67]; Buchanan, Le, and Rishi [19]; Jushi et al. [164]. About CS-ARDL, the study findings also ascertain positive relations between institutional quality, tourism, and inflows of FDI in BIMSTEC nations, especially in the long run. In respective studies, Alfaro et al. [165] and Bénassy-Quéré, Coupet, and Mayer [103] have argued that the investors prefer to locate the environments of cases where property rights are well protected and the actors are the least corrupt as well that they require a high degree of political stability. Considering an empirical model with FDI stocks and FDI volatility as a dependent variable, the study findings revealed positive effects from the institutional quality and tourism towards FDI stock and negative impact towards FDI volatility, especially in the long run. These findings are applicable in both empirical models under panel-ARDL and CS-ARDL.
Third, the study findings with the nonlinear framework of assessing the asymmetric effects, i.e., positive and negative shocks in institutional quality and tourism on FDI.
Referring to the results of the Wald test to establish possible asymmetric effects on both the long run and short run. The study findings revealed a long-run asymmetric relationship between institution quality, tourism, and FID, which applies to all models. These findings suggest that in the long run, the movement of the effects of each variable might not experience by other variables in the linear form, i.e., increasing independent variables may not result in the same progress in the dependent variable.
Fourth, the results of directional causality among research variables with symmetry and asymmetry effects of institutional quality and tourism in the equation. Concerning the traditional casualty test, i.e., symmetric framework, the study findings hold a feedback hypothesis explaining the relationship between institutional quality, tourism, and FDI. The study findings support existing empirical literature including, Chowdhury and Mavrotas [98]; Shah, Ahmad and Ahmed [77]; Arain, Han, Sharif, and Meo [43]. Furthermore, causality tests with the asymmetry of institutional quality and tourism. We observed that the feedback hypothesis explains the casualty between negative shocks in institutional quality and tourism and inflows of FDI and FDI stock. However, unidirectional causality is also revealed i.e., FDI inflows to positive shocks in institutional quality and positive shocks in institutional quality to FDI stock. On the other hand, referring to the asymmetry effect of tourism and FDI, findings divulged unidirectional causality running from FDI to positive shocks in tourism and feedback hypothesis is established between a negative shock in tourism and inflows of FDI.
Understanding the study findings, we also proposed the following policy recommendations for future guidance. First, institutional quality tourism emerged as a strategically critical factor for the economy, especially the decision about FDI. Policy formulation, therefore, and the promotional, strategic decision-making process by the government and private institutions have to put considerable attention on the present state of institutional quality and tourism in respective countries. Second, countries should use financial and tax incentives, as well as attractive rates to attract FDI. Reducing complex procedures (bureaucracy) and defining clear FDI policies in tourism is an important part of the process. Local authorities can also help indirectly to promote FDI by providing basic infrastructures free of cost to the investor.
The present study possesses certain limitations in terms of data aggregation and economical estimation. For institutional quality, the study considered an index derived from WGI information. Nonetheless, taking other measures might produce diverse findings. Inclusion of other variables such as Human capital development, economic policy uncertainty, and financial volatility can robust the estimation and bring another angle in empirical relationships.