Table of Contents
 Introduction
 Data and Motivation
 Estimation / Asymmetry
 Bounds Test / Cointegration
 Dynamic Multipliers
 Interpretation and Policy Implications
 Files
 References
Introduction
Tourism is a crucial source of revenue for many economies. In case of Bosnia and Herzegovina (BiH), despite having incredible historical and natural touristic appeal, tourism was an inconsequential contributor to GDP growth in the period preceding Bosnia's horrific period of aggression in the early 1990s. Bosnia's economy at that time was highly reliant on natural resource exploitation (particularly metal ores and forestry), hydroelectric production, and manufacturing. While Bosnia managed to resurrect some of these industries following the end of the aggression in 1996, it also struggled to reinvigorate several highly prospective ones, such as industrial production. In their stead, tourism has been evolving into an increasingly more significant contributor to Bosnia's GDP development.Much of the expansion in foreign tourist interest in Bosnia stems from its active marketing campaigns and encouragement from the European Union (see the EU's 2007/145210 Project). Bosnia's capital, Sarajevo, having hosted the 1984 winter olympics, is also home to the widely popular Sarajevo Film Festival, whereas the UNESCO protected city of Mostar in the south, hosts the Red Bull Cliff Diving World Series and is often on top lists of the most most beautiful cities to visit in Europe. Moreover, as a silver lining to its bloody history at the onset of the 90s, the country today boasts a diaspora of 2.2 million individuals (or 55% of its preaggression population), who live and work in major economies around the globe. These factors, in combination with the country's relatively low cost of living, makes Bosnia and Herzegovina a particularly appealing destination for tourists hailing from relatively larger economies.
On the other hand, Bosnia's domestic tourism sector, while lagging behind its foreign counterpart, witnessed a similar revival. As the country benefits from both summer and winter destinations, beach activities in costal cities along the Adriatic Sea and winter skiing activities in the midwest, both present opportunities for locals to enjoy their homeland year round. Domestic tourism is further stimulated by the nearly 200 local hiking and alpine clubs which are also slowly using these platforms to offer foreign tourists multiday guided tours of the region. Finally, it's worth noting that Bosnia had a significant middleage domicile generation which remained in the country during the aggression period. Economic opportunities in the recovery years for this demographic segment were greatly stunted and today many of them cannot afford international travel; instead confining themselves to domestic excursions.
There are, in fact, numerous articles which demonstrate that tourism has a positive effect on economic growth. The general idea driving this body of research is that tourism is a positive externality which stimulates infrastructural development, foreign direct investments, and the fabric of modern internet and mobile connectivity. Our objective here is to study the magnitude and asymmetries in Bosnia's tourism sector may exert on its overall economic development.
Traditionally, these dynamics can be explored through vector errorcorrection (VEC) models. Nevertheless, this class of models generally assumes that all system variables are integrated of at least order 1, and does not preclude the possibility of multiple equally possible cointegrating relationships. Alternatively, classical ARDL models (see our blog series Part I, Part II, and Part III) allow for varying degrees of integration among the system variables (provided the maximum order of integration is less than 2), and assume a single unique cointegrating relationship among the variables of interest. Nevertheless, this framework assumes that the longrun (cointegrating) relationships is a symmetric linear combination of regressors.
While the classical ARDL framework is perfectly reasonable for many applications, it cannot, however, accommodate behavioral finance and economics research on nonlinearity and asymmetry, which are also often encountered in practice; see the seminal contributions by Kahneman and Tversky (1979) and Shiller (2005). To address this limitation, Shin, Yu, and GreenwoodNimmo (2014) propose a nonlinear ARDL (NARDL) framework in which shortrun and longrun nonlinearities are modeled as positive and negative partial sum decompositions of the distributed lag variables. Recall that any given variable $ z_t $ may be decomposed as $ z_t = z_0 + z_t^{+} + z_t^{} $ where $ z_0 $ is the initial value and $ z_t^{+} $ and $ z_t^{} $ are the partial sum processes of positive and negative changes in $ z_t $, respectively: \begin{align*} z_t^{+} &= \sum_{s = 1}^{t} \max \left( \Delta z_s, 0 \right)\\ z_t^{} &= \sum_{s = 1}^{t} \min \left( \Delta z_s, 0 \right) \end{align*} Note that when $ z_t $ is a distributed lag variable and $ z_t^{+} \neq z_t^{} $, the distributed lag variable exhibits asymmetric effects where positive changes have a different impact on the dependent variable than their negative counterparts. On the other hand, when $ z_t^{+} = z_t^{} $, the distributed lag variable exhibits symmetric effects on the dependent variable and reduces to the classical ARDL effect.
The NARDL framework also provides asymmetric dynamic multipliers. These constructs, which are similar to impulseresponse curves in the VAR literature, trace asymmetric paths of adjustment of each nonlinear distributed lag regressor to its longrun (cointegrating) state.
Below, we will apply the NARDL framework to identify the longrun (cointegrating) and shortrun (adjusting) dynamics which relate Bosnia's tourism sector to its state of the economy.
Data and Motivation
To conduct the analysis, we will collect data directly from the Agency for Statistics of Bosnia and Herzegovina (ASBH). In particular, we are interested in 5 different time series: GDP: gross domestic product
 FTA: foreign tourist arrivals.
 DTA: domestic tourist arrivals
 FTS: foreign tourist length of stay
 DTS: domestic tourist length of stay
It's also important to mention a few caveats regarding our data. First, Bosnia's GDP, measured using the expenditure approach using previous year prices, is collected quarterly. Furthermore, while GDP data does exists from Q1 2000 to Q4 2021, ASBH has labeled data after 2021 as forecasted and not actual. As a precaution, we've decided to ignore this data altogether and shorten the GDP series to cover the last actual measurements made in Q4 of 2020.
In contrast, tourism sector variables are collected monthly and date back to January 2008. This clearly presents a challenge in terms of using all variables in the same framework simultaneously. While methods such as MIDAS do exist to address these issues, we've opted to handle the problem using manual frequency conversion using the official EViews frequency conversion tutorial. In particular, to benefit from the longer time series available among tourism variables, we've decided to convert GDP from its low quarterly frequency, into the higher monthly frequency, using the Denton method. As a convenience, we have made available a preprocessed version of this data as an EViews workfile, which may be downloaded from here.
Before engaging in any advanced analysis, it's also encouraged to understand the data we're dealing with. This will not only give us some idea about the state of Bosnia's tourism sector, but also help us identify meaningful patterns we may try to exploit later.
While Bosnia's foreign and domestic tourist arrivals have both seen considerable activity, over the reporting period between 2008 and 2022, foreign tourism seems to have undergone a nearly exponential transformation. The stark contrast is illustrated in Figure 1a below. In fact (see Figure 1b), in the period preceding the COVID19 pandemic years, the average yearonyear percent change of annual tourist arrivals was 13% and 4.12% for foreign and domestic sectors, respectively. In 2015, the yearonyear growth in tourist arrivals hovered around 26.53% among foreigners, and 13.1% among locals.


























Estimation / Asymmetry
Our objective in this section is to estimate a NARDL model linking the relationship of Bosnia's tourist variables DTA, DTS, FTA, FTS to its sate of the economy, measured as the variable GDP. Formally, the NARDL model  expressed below in its conditional error correction (CEC) form (see the EViews manual for details)  we are interested in studying is: \begin{align*} \class{bold} { \Delta \ln(GDP) } & \class{bold} { = } \class{bold col_red}{ \phi_{\scriptsize \text{GDP}} \ln(\text{GDP})_{t  1} }\\ &\class{bold col_red}{ + \phi_{\scriptsize{\text{DTA}}}^{+} \ln(\text{DTA})_{t  1}^{+} + \phi_{\scriptsize{\text{DTA}}}^{} \ln(\text{DTA})_{t  1}^{} + \phi_{\scriptsize{\text{FTA}}}^{+} \ln(\text{FTA})_{t  1}^{+} + \phi_{\scriptsize{\text{FTA}}}^{} \ln(\text{FTA})_{t  1}^{} }\\ &\class{bold col_red}{ + \phi_{\scriptsize{\text{DTS}}}^{+} \text{DTS}_{t  1}^{+} + \phi_{\scriptsize{\text{DTS}}}^{} \text{DTS}_{t  1}^{} + \phi_{\scriptsize{\text{FTS}}}^{+} \text{FTS}_{t  1}^{+} + \phi_{\scriptsize{\text{FTS}}}^{} \text{FTS}_{t  1}^{} }\\ &\class{bold col_blue}{ + \sum_{j = 1}^{p  1} \gamma_{\scriptsize{\text{GDP}} \normalsize{, \, j}} \Delta \ln(\text{GDP})_{t  j} }\\ &\class{bold col_blue}{ + \sum_{k_1 = 1}^{q_1  1} \left( \gamma_{\scriptsize{\text{DTA}} \normalsize{, \, k_1}}^{+} \Delta \ln(\text{DTA})_{t  k_1}^{+} + \gamma_{\scriptsize{\text{DTA}} \normalsize{, \, k_1}}^{} \Delta \ln(\text{DTA})_{t  k_1}^{} \right) + \sum_{k_2 = 1}^{q_2  1} \left( \gamma_{\scriptsize{\text{FTA}} \normalsize{, \, k_2}}^{+} \Delta \ln(\text{FTA})_{t  k_2}^{+} + \gamma_{\scriptsize{\text{FTA}} \normalsize{, \, k_2}}^{} \Delta \ln(\text{FTA})_{t  k_2}^{} \right) }\\ &\class{bold col_blue}{ + \sum_{k_3 = 1}^{q_3  1} \left( \gamma_{\scriptsize{\text{DTS}} \normalsize{, \, k_3}}^{+} \Delta \ln(\text{DTS})_{t  k_3}^{+} + \gamma_{\scriptsize{\text{DTS}} \normalsize{, \, k_3}}^{} \Delta \ln(\text{DTS})_{t  k_3}^{} \right) + \sum_{k_4 = 1}^{q_4  1} \left( \gamma_{\scriptsize{\text{FTS}} \normalsize{, \, k_4}}^{+} \Delta \ln(\text{FTS})_{t  k_4}^{+} + \gamma_{\scriptsize{\text{FTS}} \normalsize{, \, k_4}}^{} \Delta \ln(\text{FTS})_{t  k_4}^{} \right) }\\ &\class{bold col_green}{ + \alpha_0 + \alpha_1 t + \sum_{i = 1}^{11} \delta_{i} m_i + \epsilon_t } \end{align*} This describes a NARDL$ (p, q_1, q_2, q_3, q_4) $ model where GDP enters as an autoregressive process of order $p$, and DTA, FTA, DTS, FTS enter as asymmetric distributed lag variables with orders $ q_1, q_2, q_3, q_4 $, respectively. For easier identification, we have also coloured some portions of the CEC relation as red, blue, and green. The latter characterize the cointegrating (levels or longrun), the adjusting (differences or shortrun), and the deterministic (seasonality) dynamics, respectively. Furthermore, variables with + and  superscripts denote, respectively, the positive and negative partial sum decompositions of the underlying distributed lag variable. The positive and negative partial sums here explicitly model how asymmetries in Bosnia's tourism sector, both in the long and short run, reflect on its economic development.The deterministic dynamics deserve a brief comment as well. In particular, $ \alpha_0 $ and $ \alpha_1 $ respectively capture the effect of the constant and linear trend. To also capture monthly seasonality, the coefficients $ \delta_{i} $ are associated with seasonal dummy variables $ m_i $ for month $ i $.
We will start the analysis by estimating the model above, treating all tourism variables as asymmetric in both the adjusting and cointegrating dynamics. To identify the autoregressive and distributed lag orders $ p, q_1, q_2, q_3, q_4 $, we will perform automatic lag selection, allowing at most 3 lags for the dependent variable and each of the regressors (the default options). This effectively states that all variables depend on values at most 3 periods (months) in the past; in other words, a single quarter.
The variables we need are located in the workfile page tourism_monthly. To avoid the complications of the pandemic years, we will only use data in the years prior to 2020. We can do so in the Command window by typing
smpl if @year<2020Next, bring up the NARDL dialog (see Figure 8a) and enter the specifications as follows:
 From the main EViews menu, click on Quick/Estimate Equation...
 Change the Method dropdown to ARDL  Autoregressive Distributed Lag Models (including NARDL)
 Under Linear dynamic specification specify @log(gdp)
 Under Long and shortrun asymmetry specify @log(dta) @log(fta) dts fts
 Under Fixed regressors specify @expand(@month, @droplast)
 Set the Trend specification to Constant
 Set both of the Max. lags dropdowns to 3
 Click on OK






Following estimation, the aim of the first inferential exercise is to formally validate assumptions on asymmetry. Although we have written the model above to reflect that all distributed lag variables are asymmetric both among adjusting and cointegrating dynamics, the NARDL model is flexible enough to accommodate partially asymmetry. This manifests when variables enter asymmetrically either the adjusting or cointegrating dynamics, but not both. For instance, consider an arbitrary variable $ z_t $ with asymmetric decompositions $ z_t^{} $ and $ z_t^{+} $ and associated asymmetric level coefficients $ \phi^{} $ and $ \phi^{+} $, and associated asymmetric difference coefficients $ \gamma_k^{} $ and $ \gamma_k^{+} $, for $ k = 1, \ldots, q $. Partial asymmetry in this framework manifests by imposing the restrictions below: \begin{align*} \text{Partial Shortrun asymmetry (Longrun Symmetry):}& \quad \phi = \phi^{} = \phi^{+} \\ \text{Partial Longrun asymmetry (Shortrun Symmetry):}& \quad \gamma_k = \gamma_k^{} = \gamma_k^{+} \\ \end{align*} As NARDL models are typically estimated using leastsquares, (partial) asymmetry can be formally tested. These tests reduce to the usual Waldlike hypotheses on the equivalence of positive and negative asymmetry coefficients. Formally, \begin{align*} \text{Longrun symmetry only } H_0 &:\quad \phi^{} = \phi^{+} \\ \text{Shortrun symmetry only } H_0 &:\quad \begin{cases} \gamma_k^{} = \gamma_k^{+} \text{ for each } k \\ \\ \text{or}\\ \\ \sum_{k = 1}^q \gamma_k^{} = \sum_{k = 1}^q \gamma_k^{+} \end{cases} \\ \text{Joint Short and Long run symmetry } H_0 &:\quad \begin{cases} \gamma_k^{} = \gamma_k^{+} \text{ for each } k \text{ and } \phi^{} = \phi^{+} \\ \\ \text{or}\\ \\ \sum_{k = 1}^q \gamma_k^{} = \sum_{k = 1}^q \gamma_k^{+} \text{ and } \phi^{} = \phi^{+} \end{cases} \end{align*} In EViews, these tests are performed after estimating a NARDL model by clicking on View/ARDL Diagnostics/Symmetry Test. Figure 10 below summarizes the output for the regression above.


Turning to specific insights, we reject the null hypothesis of longrun symmetry for @LOG(FTA) at all reasonable significance levels and for FTS at the 5% significance level. While we also reject the joint symmetry test for @LOG(FTA) at all reasonable significance levels, we cannot evaluate the joint test for FTS as it enters the current model with zero lags. We also fail to reject the null hypothesis for the remaining coefficients. This suggests that the model we ought to consider next assumes the form:
\begin{align*} \class{bold} { \Delta \ln(GDP) } & \class{bold} { = } \class{bold col_red}{ \phi_{\scriptsize \text{GDP}} \ln(\text{GDP})_{t  1} + \phi_{\scriptsize \text{DTA}} \ln(\text{DTA})_{t  1} + \phi_{\scriptsize \text{DTS}} \ln(\text{DTS})_{t  1} }\\ &\class{bold col_red}{ + \phi_{\scriptsize{\text{FTA}}}^{+} \ln(\text{FTA})_{t  1}^{+} + \phi_{\scriptsize{\text{FTA}}}^{} \ln(\text{FTA})_{t  1}^{} }\\ &\class{bold col_red}{ + \phi_{\scriptsize{\text{FTS}}}^{+} \text{FTS}_{t  1}^{+} + \phi_{\scriptsize{\text{FTS}}}^{} \text{FTS}_{t  1}^{} }\\ &\class{bold col_blue}{ + \sum_{j = 1}^{p  1} \gamma_{\scriptsize{\text{GDP}} \normalsize{, \, j}} \Delta \ln(\text{GDP})_{t  j} }\\ &\class{bold col_blue}{ + \sum_{k_1 = 1}^{q_1  1} \gamma_{\scriptsize{\text{DTA}} \normalsize{, \, k_1}} \Delta \ln(\text{DTA})_{t  k_1} + \sum_{k_2 = 1}^{q_2  1} \gamma_{\scriptsize{\text{FTA}} \normalsize{, \, k_2}} \Delta \ln(\text{FTA})_{t  k_2} }\\ &\class{bold col_blue}{ + \sum_{k_3 = 1}^{q_3  1} \gamma_{\scriptsize{\text{DTS}} \normalsize{, \, k_3}} \Delta \ln(\text{DTS})_{t  k_3} + \sum_{k_4 = 1}^{q_4  1} \gamma_{\scriptsize{\text{FTS}} \normalsize{, \, k_4}} \Delta \ln(\text{FTS})_{t  k_4} }\\ &\class{bold col_green}{ + \alpha_0 + \alpha_1 t + \sum_{i = 1}^{11} \delta_{i} m_i + \epsilon_t } \end{align*} We estimate this model next; see Figures 11a and 11b.






Bounds Test / Cointegration
Now that we have settled on a model, we'll test for cointegration among the system variables using the famous bounds test. We proceed by clicking on View/ARDL Diagnostics/Bounds Test; the results are summarized in Figure 13.


C(2)=0, C(3)=0, C(4)=0, C(5)=0, C(6)=0, C(7)=0




Given the existence of a nondegenerate cointegrating relationship, we can identify the normalized, longrun coefficients in the cointegrating space which are associated with each of the distributed lag variables. Recall that if $ \phi_{\scriptsize \text{DEP}} $ and $ \phi_{\scriptsize \text{k}} $ are the coefficients associated with the dependent variable $ y_t $ and the k$^{\text{th}}$ distributedlag variable $ x_{k, t} $ in levels in the (N)ARDL CEC form, respectively, the normalized, longrun distributed lag coefficient in the cointegration space is defined as \begin{align*} \beta_{k} \equiv  \frac{\phi_k}{\phi_{DEP}} \end{align*} In other words, for a NARDL model with $ K $ distributed lag variables, the cointegrating relationship is formalized as: \begin{align*} \class{bold}{ \text{CE} = \ln(\text{GDP})_{t  1}  \sum_{r = 1}^{K} \beta_{r} \, x_{r, t  1} } \end{align*} We can estimate these values (see Figure 15) for our concrete model above by clicking on View/ARDL Diagnostics/Cointegrating Relation.




Dynamic Multipliers
An important exercise in classical regression analysis is estimating the causal effect or multiplier of a regressor on the dependent variable, ceteris paribus; recall also that this is just the partial derivative of the dependent variable with respect to (wrt) a regressor. While (N)ARDL models can be cast into a classical regression framework, they are dynamic in that lagged values of both the dependent and distributedlag regressors affect the current state of the dependent variable. Accordingly, (N)ARDL models lend themselves to the derivation of dynamic causal effects or dynamic multipliers  causal effects which can be traced over time.In practice, dynamic causal effects can be thought of as analogous to impulse response curves in classical VAR / VEC models. They can be derived as response curves to unitary positive shocks in distributedlag variables. In particular, for any distributedlag regressor $ x_{t} $, holding all other regressors unchanged, a single positive unitary shock is introduced at $ T −h $, where $ T $ is the length of the estimation sample, and the evolution of the dependent variable, $ y_t $, is measured through the period $ [T − h, T] $ where $ h \geq 0 $ is some horizon length. This can also be derived as the difference $ \tilde{y}_t  \hat{y}_t $, where $ \tilde{y}_t $ is the insample dynamic forecasts of $ y_t $ when $ x_t $ is perturbed at $ t = T  h $ to equal $ x_{T  h} + 1 $, and $ \hat{y}_t $ is the insample dynamic forecasts of $ y_t $ when $ x_t $ is left unchanged.
A natural extension of the dynamic multiplier is the cumulative dynamic multiplier (CDM) a cumulative sum of dynamic multipliers at each point in time on the interval $ [T  h, T] $. In fact, as $ h \rightarrow \infty $, the cumulative dynamic multiplier converges to the longrun (cointegrating) coefficients discussed in the previous section. In other words, we can trace out the adjustment patterns (the shortrun dynamics) as they evolve to converge to their cointegrating (longrun) equilibrium state. See Shin, Yu, and GreenwoodNimmo (2014) for details.
Cumulative dynamic multipliers are particularly interesting for asymmetric distributed lagvariables, such as those characterizing NARDL models. They allow researchers to study the evolution of adjustment patterns following negative and positive shocks to asymmetric regressors and quantify the path of asymmetry as CDMs evolve towards their respective (cointegrating) equilibrium states. Furthermore, confidence intervals for the evolution of asymmetry can also be derived via nonparametric bootstrapping.
To derive CDMs for the model we estimated earlier with a 95% confidence interval derived over 999 bootstrap replications, we can proceed as follows:
 From the estimated equation object, click on View/ARDL Diagnostics/Dynamic Multiplier Graph...
 Change the Horizon to 50
 Set the evolution type to Shock or Dynamic multiplier
 Leave the rest at their default values and click on OK
Next, note that there are two options for the evolution type: 1) Shock and 2) Dynamic multiplier. As noted in the EViews manual, this distinction is only relevant for NARDL models with asymmetric regressors, and only affects asymmetric negative response curves. In particular, both shock evolution and dynamic multiplier evolution plot the response to a one unit positive change in the symmetric and positive asymmetric cumulated differences. However, unlike the shock evolution framework which plots the response to a one unit negative change in cumulated differences in the negative asymmetric case, the dynamic multiplier evolution framework plots an “improvement” producing a one unit positive increase (reduction of one unit of negative change) in the negative cumulative differences. In fact, the shock evolution plot can be derived from the dynamic multiplier evolution plot by reflecting the negative response curve in the dynamic multiplier evolution plot along the xaxis.
While the dynamic multiplier evolution framework is reasonable from a technical perspective, it is not ideal if we wish to study the properties of the models under parallel unit increases in the absolute amount of positive and negative asymmetry, as in when determining whether an increase in positive asymmetry has the same effect as an increase in negative asymmetry. Furthermore, as the dynamic multiplier framework is better aligned to a technical analysis, in contrast to the shock evolution framework, it will also plot the longrun coefficient values to which the CDMs converge as the horizon lengths approaches infinity.
We will start with the Dynamic multiplier evolution and display the output in Figures 17a through 17d.








Let's also plot the same information in terms of a shock evolution framework.








Interpretation and Policy Implications
We devote this section to a brief interpretation of the results above and suggestions for policy implementations. First, recall that the results of the bounds test summarized in Figure 13 and 14b confirm the existence of a cointegrating relationship between Bosnia's tourist variables and its gross domestic product. In fact, this cointegrating relationship is itself significant as the COINTEQ coefficient in the errorcorrection regression in Figure 16 is highly significant. On the other hand, only certain components of the cointegrating relationship are significant themselves. Note that the second table in Figure 15 indicates that while domestic tourist arrivals and foreign tourist stays are insignificant in the longrun, domestic tourist length of stay and foreign tourist arrivals are indeed significant in the equilibrium.We can gain further insight into how each tourist variables contributes the evolution of Bosnia's GDP by looking at their response curves plotted in Figures 17 and 18. In particular, Figures 17a and 18a illustrate that a 1% positive shock to domestic tourist arrivals increases GDP by 0.04% in roughly two years (25 months), with a considerable shortrun boost in the first 6 months.
Similarly, as shown in Figures 17b and 18b, a single unit positive shock to domestic tourist length of stay (in other words, a single day prolonged stay) lifts GDP by 0.10% in roughly 2 years, with another significant shortrun boost in the first 5 months.
On the other hand, Figures 17c and 18c indicate that a 1% increase to foreign tourist arrivals produces a 0.17% increase in GDP in roughly 25 months. This is in contrast to a 1% decrease (see Figure 18c instead of 17c) in foreign tourist arrivals which will decrease GDP by 0.12% in again, 25 months. As shown by the asymmetry curve and its associated 95% confidence interval, this asymmetry is significant.
Lastly, Figures 17d and 18d show that a 1 unit positive shock to foreign tourist length of stay produces a 0.02% increase in GDP in approximately two years. Interestingly, as shown in Figure 18d, a 1 unit negative shock to foreign tourist length of stay will also increase GDP by 0.01% in roughly 25 months. This asymmetry is also significant.
The analysis above paints a general picture of Bosnia's tourism sector and how it impacts its GDP. This picture suggests that Bosnia's domestic tourism, while lagging behind its foreign counterpart, has a symmetric effect on Bosnia's economy. This symmetry suggests that policies which equally bolster or hinder domestic tourism will have similar, but opposite effects on the GDP. Furthermore, considering that domestic tourist length of stay seems to have a larger impact on GDP than domestic tourist arrivals, policies should focus on encouraging longer domestic tourists stays, perhaps by encouraging infrastructural changes that will reduce travel times (think better intranational highways or fast train services) or by offering services / deals (think hotel discounts for longer stays or hotel loyalty programs) which incentivize longer visits.
In contrast, Bosnia's foreign tourist industry has a statistically significant asymmetric impact on Bosnia's GDP. In particular, foreign tourist arrivals can benefit from policies which focus more on increasing tourist arrivals. Possible strategies here include international advertisement campaigns, easier tourist visa issuances and ideally a digitized process which can be completed upon arrival, and improved international airline connections. On the other hand, negative shocks to foreign tourist arrivals, while decreasing GDP, have a smaller impact on Bosnia's economy than positive shocks. This suggests that Bosnia can insulate from downtrends in foreign arrivals by attempting to recoup losses with policies which bolster arrivals.
In the end, it's Bosnia's foreign tourist length of stay which is the most interesting variable. This is a variable which also has a statistically significant asymmetric effect on Bosnia's longrun economy, but as the longrun coefficient on the negative partial sums of changes in FTS is negative, GDP actually benefits from a decrease in foreign tourist length of stay. This suggests that Bosnia benefits from higher foreign tourist turnover, perhaps due to revenues gained on tourist visas. Another possible explanation is overcrowding which can possibly lead to inefficiencies in providing services, as well as crowding out of domestic tourists which refuse to participate in the tourist market at times when it is overwhelmed by foreigners. This is certainly suggested in Figure 4b which shows that domestic and foreign tourist length of stays do not peak at the same time during the year.
Files
References
 Kahneman, D. and Tversky, A., (1979). Prospect theory: an analysis of decision under risk. Econometrica, 47: 263–291.
 Pesaran, M. H., Shin, Y., and Smith, R. J, (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3): 289–326.
 Shiller, R. J., (2005). Irrational exuberance. Princeton University Press, Princeton, 2nd edition.
 Shin Y., Yu B., GreenwoodNimmo M., (2014). Modelling Asymmetric Cointegration and Dynamic Multipliers in a Nonlinear ARDL Framework Festschrift in honor of Peter Schmidt, 281–314.
Great to see all of these features now in EViews! This post is absolutely superb  thanks!
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