Argentinean Tourism Demand in Uruguay

ARGENTINEAN TOURISM DEMAND IN

URUGUAY: DETERMINANTS AND

PROJECTIONS

(Preliminary draft, June 2014)1

Gabriela Mordecki

Instituto de Economía, Facultad de Ciencias Económicas y de

Administración, Universidad de la República

[email protected]

1 The autor thanks to Sandra Rodríguez and Adrián Risso, for constructive comments and useful suggestions. All remaining errors are the author’s. ABSTRACT

In Uruguay total yearly tourists represent about 90% of its population of which historically 60% or more have come from Argentina. Tourist activities have a great impact on Uruguayan economy. They represent about

4% of Uruguayan GDP and generate near 6% of total employment and 14% of total exports. For this reason it is important to analyze the determinants behind tourism demand. In this paper we study the relationship between the number of Argentinean tourists in Uruguay, their real tourism expenditure,

Argentinean GDP and the real exchange rate (RER) between Uruguay and

Argentina trying to find long-run relationships between variables, following

Johansen methodology. We found two cointegration relationships, through

Vector error correction models (VECM). In the first one we include real tourism expenditure, Argentinean GDP and the RER between Argentina and

Uruguay, and through this model we project a decline in Argentinean spending during 2014 and recovery in 2015. In the second one, we try to estimate the number of Argentinean tourists, using monthly dada of tourists, a monthly indicator of Argentinean activity and the RER between Uruguay and Argentina. The model’s forecast indicates a slight decrease of

Argentinean tourism expenditure in 2014 and a recovering for 2015.

Key words: tourism demand, cointegration, real exchange rate

JEL: C32, F14, F41.

1. INTRODUCTION

Alexis Papathanassis (2011) asserts that over the last years the tourism

2 sector has grown substantially in a number of ways, and at a global level, tourism appears to be crisis-resistant. The World Tourism Organization

(2013) annual report affirms, “In spite of persisting global economic challenges and geopolitical shifts, tourism continues to grow and even exceed long-term forecasts and expectations”. After reaching in 2012 one billion people traveling around the world annually, tourism maintained the trend with a 5% growth in 2013. However, each region has different characteristics making relevant to study the particular cases. This paper refers to a small country, Uruguay, located in the south of South America, between two big neighbors: Argentina and Brazil, and with a very peculiar geographic and political structure that was defined by its history and afterwards development.

Uruguay has 3.3 million inhabitants, with 700 km of beaches over the Rio de la Plata, with a temperate climate. Argentinean tourists have historically been our main visitors. Our main touristic resort, Punta del Este is about 360 km from Buenos Aires, Argentinean capital city. Then Argentinean tourists came to our country for its annual holidays, long week-ends, winter holidays, and also many of them have their own houses in Uruguay, family relationships, and many times investments and commercial interests.

Total yearly tourists represent about 90% of Uruguayan population.

Argentinean tourists represent nearly 60% of this total, a participation rate that has been maintained over time. Tourist activities have a great impact on

Uruguayan economy. They represent about 4% of Uruguayan GDP and generate about 6% of total employment and 14% of total exports.

Figure 1. Annual tourists arrivals to in Uruguay by nationality

3,500 Rest of the world tourists 3,000 Brazilian tourists

2,500 Argentinean tourists

2,000

1,500

1,000

Thousandspeopleof 500

01 06 11 96

00 05 10

99 09 04

96 11 01 06

98 03 08 13

97 02 07 12

- - - -

- - -

- - - -

Jul Jul Jul Jul

Jan Jan Jan Jan

Sep Sep Sep Sep

Mar Mar Mar

Nov Nov Nov Nov

May May May SOURCE: Ministry of Tourism, Uruguay

Figure 1 shows the evolution of tourism in Uruguay during the period of analysis (Jan-1996 to Dec-2013). Argentinean tourists have increased substantially during the last decade, after the 2001-2002 crises, but they have stagnated and even diminished in recent years, due to internal problems of Argentinean economy and overvaluation of our local currency.

Brazilian tourists, on the other hand, have increased in the last years, but they only represent about 14% of total tourists.

In Figure 2 we have data of annual international tourism receipts, considered by nationality. The evolution is similar to the number of tourists, and although the increase has accelerated in the last decade, the participation rates of the principal nationalities remain similar.

Figure 2. Annual international tourism receipts in Uruguay by nationality

2000 Resto of the world 1500 Brazil Argentina

1000 Million ofMillion dollars 500

97 98 99 01 02 03 05 06 08 09 10 12 13 96 00 04 07 11

------

Dec Dec Dec Dec Dec Dec Dec Dec Dec Dec Dec Dec Dec Dec Dec Dec Dec Dec SOURCE: Ministry of Tourism, Uruguay

To explain this difference it is important to know the determinants of tourists demand. Altmark et al. (2013) estimate the Argentinean and

Brazilian tourism demand, for the period ended in the second quarter of

2011. Since then, Argentina have gotten through Balance of Payment problems consequently devaluating its currency and imposing strict controls to the acquisition of foreign currency for tourists going abroad. Therefore, the main objective of this work is to estimate Argentinean tourist demand

(tourists arrivals and their expenditure), considering data from January 1996 up to December 2013, and using this models to make projections for 2014 and 2015. This is an example of a small economy impacted by devaluation and currency control policies in the main source of tourism.

In this paper we study the relationship between the number of Argentinean tourists in Uruguay, its real spending, Argentinean GDP and the real exchange rate between Uruguay and Argentina trying to find long-run relationships between variables, following the cointegration methodology developed by Johansen (1988).

The present paper is organized as follows. The second section intends to describe the analysis framework and background; in section three we estimate the models; in the fourth section we make some projections and finally, the fifth section draws some conclusions.

2. ANALYSIS FRAMEWORK AND BACKGROUND

Paraskervopoulos (1977), Loeb (1982), Stronge and Redman (1982), Truett and Truett, (1987), Witt and Witt (1995), Mudambi and Baum (1997), are examples of some important works about estimation of the determinants of a tourist demand. Crouch (1994) found 80 empirical studies on the demand function for tourism. Song and Li (2008) review the published studies on tourism demand modeling and forecasting since 2000. Most of these works focused on income of source countries, and the relative price of the exported tourist services as the main determinants of tourism demand.

Works focused on Uruguayan tourism study the relevance of tourism activities on GDP growth, as Brida et al. (2010), but mainly they try to estimate the determinants of the tourism demand, like Robano (2000),

Altmark et al. (2012), or Serviansky (2011). In these works the authors, with different emphasis, try to find a relationship between real tourism spending with real income of the source tourists.

Lim (1997) presents a review of more than 100 published studies of empirical international tourism demand models. Tourist arrivals/departures and expenditures/receipts have been the most frequently used dependent variables. The most popular explanatory variables used have been income, relative tourism prices, and transportation costs. Song and Li (2008) found

6 that the methods used in analyzing and forecasting the demand for tourism have been more diverse than those identified by other review articles, and in addition to the most popular time-series and econometric models, a number of new techniques have emerged in the literature.

3. MODEL

3.1 Data and methodology

In this paper we consider two alternative measures for tourist demand: number of tourists entering the country and real tourists spending. We will try to find a long-run relationship of these variables with the country source income and relative prices, measured by the real exchange rate between both countries (Argentina and Uruguay).

To carry out this investigation, we estimated two vector error correction models (VECM), following Enders (1995), and considered data from

January 1996 to December 2013.

The first model considers quarterly data of Argentinean real tourism expenditure (LGA), Argentinean GDP (GDP_AR) and the real exchange rate between Argentina and Uruguay (LTCRA), all in logarithms (see Figure

3).

Figure 3. Income from Argentinean tourism, GDP and real exchange rate

(quarterly data)

250 LGA 200 LTCRA GDP_AR 150

100

IndexMarch96=100 50

96 99 11 02 05 08

98 01 13 04 07 10

97 00 12 03 06 09

96 99 11 02 05 08

------

dic dic dic dic dic dic

jun jun jun jun jun jun

sep sep sep sep sep sep

mar mar mar mar mar mar SOURCE: MinTur, BCRA, IECON

The second model uses monthly data and the variables considered are: the number of Argentinean tourists entering Uruguay (TUR_ARG), a monthly indicator of Argentinean activity (LEMAE) and the real exchange rate between Argentina and Uruguay (LTA), all considered in logarithms (Figure

4).

Figure 4. Argentinean tourists, activity indicator and real exchange rate

(monthly data)

250 LEMAE

200 TUR_ARG LTA

150

96=100 -

100 IndexJan 50

96 07

99 06 10

02 13

97 00 11 08

98 09

07 96

01 12

- -

- - -

- -

- - - -

- -

Jul Jul

Jan Jan

Jun Jun

Oct Oct

Sep Feb Sep

Apr

Dec Dec

Aug Aug

Mar

Nov Nov May SOURCE: Mintur, IECON, BCRA

The Argentinean GPD with quarterly data, the monthly activity indicator

(EMAE), the number of tourists and the tourism expenditure has high

8 seasonality, and then some significant seasonal dummies were introduced in both models. Before that we performed the Augmented Dickey-Fuller

(ADF) test to test the integration degree, which results are shown in Table 1.

All the cases were non-stationary series with a unit root, i.e., I (1).

According to the theory, this is a result generally expected for economic series, opening the possibility to analyze whether there is a cointegration vector between the series, showing a long-term relationship between variables.

Table 1. Unit root tests

Augmented Dickey-Fuller

HO = there is an unit root

Statistic value Rejection Statistic value of Rejection of the series in H0 up to the series in first H0 up levels 95% differences to 95% LGA 0.398289 No -2.452891 Yes (no constant, (no constant,

8 lags) 10 lags) GDP_AR 1.494793 No -2.611534 Yes (no constant, (no constant,

5 lags) 4 lags) LTCRA -0.785982 No -6.099693 Yes (no constant, (no constant,

4 lags) 3 lags) TUR_ARG -0.002279 No -4.297616 Yes (no constant, 13 (no constant,

lags) 12 lags) LEMAE 0.866585 No -2.508460 Yes (no constant, (no constant,

13 lags) 14 lags) LTA -0.715271 No -6.178068 Yes (no constant, (no constant,

11 lags) 10 lags) Lags are calculated due to Akaike criteria

To test the existence of long term equilibrium relationships among the variables we applied the Johansen (1988) methodology. From this verification, we estimated a vector error correction model VECM (Engle

9 and Granger, 1987 and Johansen, 1992).

3.2 Johansen cointegration method

Following Enders (1995), cointegration analysis is based on an autoregressive vector with Vector Error Correction Model specification for an endogenous variable vector.

t=1, … , T

Where

is a vector of constants and Dt contains a set of dummies (seasonal and interventions).

Information about long-term relationships is included in the matrix, where is the coefficient’s vector for the existing equilibrium relationships, and is the vector for short-term adjustment mechanism coefficients. The identification of the matrix range determines the total cointegration relationships existing among the variables.

Once examined the long-term relationship, we proceed to the short-term analysis, which shows different adjustment mechanisms of the variables to the long-run equilibrium.

The cointegration is analyzed with Johansen test, from the Trace and the

Eigenvalue of matrix Π (Tables 2 and 3). The existence of a cointegrating vector is not rejected, and the signs of the variables were as expected.

Moreover, in the resulting pattern exclusion tests for and weak exogeneity test for all were significant. Furthermore, residuals were well behaved

(see Annex).

Table 2. Cointegration test for first model with quarterly data

Sample (adjusted): 1996Q3 2013Q3 Included observations: 69 after adjustments Trend assumption: No deterministic trend (restricted constant) Series: LGA LTCRA GDP_AR Exogenous series: D(S1) D(S2) D(S4) D(E021) D(I022) D(I121) D(I094) D(I083) D(E092) D(E043) D(E031) Warning: Critical values assume no exogenous series Lags interval (in first differences): 1 to 1

Unrestricted Cointegration Rank Test (Trace)

Hypothesized Trace 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None * 0.366442 39.70656 35.19275 0.0152 At most 1 0.089384 8.214658 20.26184 0.8065 At most 2 0.025099 1.753918 9.164546 0.8260

Trace test indicates 1 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

Hypothesized Max-Eigen 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None * 0.366442 31.49191 22.29962 0.0020 At most 1 0.089384 6.460740 15.89210 0.7347 At most 2 0.025099 1.753918 9.164546 0.8260

Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

Table 3. Cointegration test for second model with monthly data

Sample (adjusted): 1996M08 2013M12 Included observations: 209 after adjustments Trend assumption: Linear deterministic trend Series: TUR_ARG LTA LEMAE Exogenous series: D(I0202) D(E0202) D(I0210) D(S1) D(S2) D(S3) D(S4) D(S5) D(S6) D(S7) D(S8) D(S9) D(S10) D(S11) D(E0201) D(I0205) D(I0301) Warning: Critical values assume no exogenous series Lags interval (in first differences): 1 to 6

Unrestricted Cointegration Rank Test (Trace)

Hypothesized Trace 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None * 0.130030 37.03493 29.79707 0.0062 At most 1 0.037173 7.921986 15.49471 0.4738 At most 2 2.30E-05 0.004803 3.841466 0.9438

Trace test indicates 1 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

Hypothesized Max-Eigen 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None * 0.130030 29.11295 21.13162 0.0031 At most 1 0.037173 7.917182 14.26460 0.3872 At most 2 2.30E-05 0.004803 3.841466 0.9438

Max-eigenvalue test indicates 1 cointegratin g eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

Thus, the vectors found are:

For the first model

LGA  2.697 LTCRA  3.347GDP _ AR  36.036 t t t (7.444) (9.966)

As variables were considered in logarithms, the coefficients can be read as elasticities. Therefore, with the increase of one percentage point (pp) in real exchange rate considered through LTCRA, the tourists spending (LGA) grows 2.697%. On the other hand, with the increase of one pp in activity, the number of tourists increases 3.347%. This confirms that tourism is a luxury

12 activity, because its income-elasticity results greater than 1.

For the second model the coefficients are smaller than the ones that result of the quarterly model, but the implications are the same.

TUR_ ARG 1.925 LTA 1.687LEMAE  5.20 t t t (9.398) (9.275)

In this case, with the increase of one pp in real exchange rate considered through LTA, the number of tourists (TUR_ARG) grows 1.925%. On the other hand, with the increase of one pp in activity, the number of tourists increases 1.687%. This again confirms that tourism is a luxury activity, with the income-elasticity greater than 1.

Expenditure is more elastic to changes in relative prices and income than the number of tourist.

3.3 Impulse-response functions

The impulse response functions show the reaction of the different variables to external changes in the other ones. In this first case, a shock is simulated in real exchange rate and real income. Depending on the case they have an impact on tourists’ arrivals or tourists spending. Figures 5 and 6 show the response of tourists arrivals and spending to a positive shock on

Argentineans income and real exchange rate.

Figure 5. Tourists’ spending impulse-response function

Response of LGA to Cholesky One S.D. Innovations

.08

.07

.06

.05

.04

.03

.02

.01

.00 1 2 3 4 5 6 7 8 9 10

LTCRA GDP_AR

After 4 quarters GDP fits around 7.5% to a positive shock on Argentinean income and the effect of real exchange rate appears positive near 1%.

Figure 6. Number of tourists impulse-response function

Response of TUR_ARG to Cholesky One S.D. Innovations

.05

.04

.03

.02

.01

.00 2 4 6 8 10 12 14 16 18 20

LEMAE LTA

Considering monthly data and number of tourists’ arrivals, the effects seem

14 to be less extreme. After 12 months, the overall impact over the number of tourists to a positive shock on Argentinean income is around 4%, while is

1% to an increase on the bilateral real exchange rate.

4. PROJECTIONS

To complete this analysis of tourism demand, it is important to consider the possible future trajectory of tourists and their spending.

According to the first model, Argentinean tourists’ spending will increase

10% during 2014, and it will grow 3% in 2015.

Figure 7. Argentinean tourists’ spending projections

600 LGA 500 LGA_proj

400

300

Million dollars Million 200

100

11 13 14 15 10 12

10 11 12 14 13 15

10 11 12 13 14 15

------

dic dic dic dic dic dic

jun jun jun jun jun jun

sep sep sep sep sep sep

mar mar mar mar mar mar

In the second model projections indicate that the number of tourists will recover during 2014 (2%), but decrease again during 2015 (1.5%).

Nevertheless, considering the confidence interval these results are not significantly different from zero.

Figure 8. Argentinean tourists’ arrivals projections

400 TUR_ARG 350 TUR_ARG_proj 300

250

200

150

Thousands Thousands people of 100

12 13 14 15

- - - -

Jul Jul Jul Jul

Jan Jan Jan Jan

Oct Oct Oct Oct

Apr Apr Apr Apr

5. CONCLUDING REMARKS

The main objective of this paper was to estimate a demand function for

Argentinean tourism demand in Uruguay. This is an example of a small economy impacted by devaluation and control currency policies applied in the main source of tourism. This objective was instrumented through the estimation of two models, one considering the Argentinean tourists’ spending in Uruguay (using quarterly data) and a second one using the number of tourists entering Uruguay (monthly data). For both models we applied Johansen methodology and we found two long-run relationships, one for each model. The other variables considered were Argentinean income and real exchange rate between Uruguay and Argentina. Both resulted significant in both models.

The two models show income-elasticities larger than one, what shows the characteristic of “luxury” good that applies to tourism. That is, as income grows, this kind of consumption grows more than proportionally.

The impulse-response functions show a greater impact of income changes

16 over relative prices changes, and this difference is more important for spending than for the number of tourists.

Finally we projected the estimated series and the model forecast an increase in expenditure, but tourists’ arrivals seem to increase only slightly or stay equal to the year before. The results suggest that changes in relative prices and Argentinean income have more impact in real expenditure than in the number of tourists.

REFERENCES

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and Brazilian Demands for Tourism in Uruguay. Tourism Analysis,

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Brida, J., Lanzilotta, B., Risso, W. 2010. The tourism-led growth hypothesis

for Uruguay, Tourism Economics, vol. 16 (3), pp. 765-771.

Crouch, G. I. 1995. A meta-analysis of tourism demand. Annals of Tourism

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Enders, W. 1995. Applied Econometric Time Series. Iowa State University,

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Engle, R. F.; Granger, C. W. J. 1987. Co-Integration and Error Correction:

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Johansen, S. 1988. Statistical Analysis of Cointegration Vectors, Journal of

Economic Dynamics and Control, vol. 12, pp. 231-254.

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402.

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849.

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Robano, V. 2000. Determinantes del turismo receptivo en Uruguay,

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Uruguay, Montevideo.

Serviansky, M. 2011, El impacto del costo del transporte en la demanda de

turismo receptivo argentino en Uruguay. Un análisis desagregado de

cointegración y causalidad, thesis presented to obtain the master

degree in economics, Montevideo, Uruguay.

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Pages 203–220

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6. ANNEX

Fist model residual tests

Normality:

VEC Residual Normality Tests Orthogonalization: Cholesky (Lutkepohl) Null Hypothesis: residuals are multivariate normal Sample: 1996Q1 2015Q4 Included observations: 69

Component Skewness Chi-sq df Prob.

1 0.191875 0.423386 1 0.5153 2 0.255207 0.749000 1 0.3868 3 -0.446737 2.295105 1 0.1298

Joint 3.467491 3 0.3250

Component Kurtosis Chi-sq df Prob.

1 2.203154 1.825519 1 0.1767 2 4.029687 3.048232 1 0.0808 3 2.704649 0.250793 1 0.6165

Joint 5.124543 3 0.1629

Component Jarque-Bera df Prob.

1 2.248904 2 0.3248 2 3.797232 2 0.1498 3 2.545898 2 0.2800

Joint 8.592034 6 0.1979

Autocorrelation:

VEC Residual Serial Correlation LM Tests Null Hypothesis: no serial correlation at lag order h Sample: 1996Q1 2015Q4 Included observations: 69

Lags LM-Stat Prob

1 23.93836 0.0044 2 13.56943 0.1385 3 8.442136 0.4903 4 18.90907 0.0260 5 9.164488 0.4222 6 7.517004 0.5835 7 18.66347 0.0282 8 6.834138 0.6544 9 11.45810 0.2456 10 10.78855 0.2905

Probs from chi-square with 9 df.

Second model residual tests

Normality:

VEC Residual Normality Tests Orthogonalization: Cholesky (Lutkepohl) Null Hypothesis: residuals are multivariate normal Sample: 1996M01 2015M12 Included observations: 209

Component Skewness Chi -sq df Prob.

1 -0.075538 0.198 761 1 0.6557 2 0.273338 2.602527 1 0.1067 3 0.223482 1.739725 1 0.1872

Joint 4.541013 3 0.2087

Component Kurtosis Chi -sq df Prob.

1 3.299756 0.782473 1 0.3764 2 3.499022 2.168571 1 0.1409 3 2.918815 0.057397 1 0.8107

Joint 3.008441 3 0.3903

Component Jarque -Bera df Prob.

1 0.981234 2 0.6122 2 4.771097 2 0.0920 3 1.797122 2 0.4072

Joint 7.549453 6 0.2730

Autocorrelation:

VEC Residual Serial Correlation LM Tests Null Hypothesis: no serial correlation at lag order h Sample: 1996M01 2015M12 Included observations: 209

Lags LM- Stat Prob

1 22.21780 0.0082 2 10.51027 0.3108 3 15.93214 0.0683 4 6.661134 0.6724 5 14.98259 0.0914 6 15.12352 0.0876 7 6.460319 0.6931

Probs from chi-square with 9 df.

First Model:

Vector Error Correction Estimates Sample (adjusted): 1996Q3 2013Q3 Included observations: 69 after adjustments Standard errors in ( ) & t-statistics in [ ]

Cointegrating Eq: CointEq1

LGA( -1) 1.000000

LTCRA(-1) -2.697383 (0.36236) [-7.44397]

GDP_AR(-1) -3.347199 (0.34523) [-9.69556]

C 36.03586

Error Correction: D(LGA) D(LTCRA) D(GDP_AR)

CointEq1 -0.107266 0.023199 0.023963 (0.05638) (0.01434) (0.01000) [-1.90271] [ 1.61781] [ 2.39632]

D(LGA(-1)) 0.089760 0.007175 -0.010325 (0.11259) (0.02864) (0.01997) [ 0.79725] [ 0.25056] [-0.51700]

D(LTCRA(-1)) -0.146528 0.169232 0.021503 (0.18708) (0.04759) (0.03318) [-0.78322] [ 3.55628] [ 0.64798]

D(GDP_AR(-1)) 2.347554 -0.316665 0.406232 (0.80550) (0.20489) (0.14288) [ 2.91441] [-1.54555] [ 2.84317]

C -0.015504 0.006536 0.005712 (0.01701) (0.00433) (0.00302) [-0.91141] [ 1.51060] [ 1.89282]

D(S1) 1.603882 -0.009712 -0.042666 (0.08294) (0.02110) (0.01471) [ 19.3381] [-0.46034] [-2.90015]

D(S2) 0.144081 -0.064420 0.073332 (0.22444) (0.05709) (0.03981) [ 0.64195] [-1.12838] [ 1.84197]

D(S4) 0.540757 -0.009582 0.031541 (0.04906) (0.01248) (0.00870) [ 11.0216] [-0.76784] [ 3.62421]

D(E021) -0.284672 -0.487695 -0.034333 (0.13439) (0.03418) (0.02384) [-2.11819] [-14.2665] [-1.44020]

D(I022) 0.110287 -0.357587 0.040685 (0.09463) (0.02407) (0.01679) [ 1.16546] [-14.8560] [ 2.42382]

D(I121) -0.307263 0.009306 0.009311 (0.08697) (0.02212) (0.01543) [-3.53298] [ 0.42067] [ 0.60358]

D(I094) 0.108468 0.081673 0.001074 (0.08465) (0.02153) (0.01501) [ 1.28142] [ 3.79330] [ 0.07156]

D(I083) 0.253743 0.012341 0.013905 (0.09109) (0.02317) (0.01616) [ 2.78563] [ 0.53264] [ 0.86061]

D(E092) 0.439773 -0.029332 -0.009616 (0.12194) (0.03102) (0.02163) [ 3.60658] [-0.94570] [-0.44460]

D(E043) -0.229545 -0.079441 0.022871 (0.11936) (0.03036) (0.02117) [-1.92308] [-2.61650] [ 1.08021]

D(E031) 0.186369 0.140957 0.013923 (0.12135) (0.03087) (0.02153) [ 1.53576] [ 4.56649] [ 0.64681]

R-squared 0.990371 0.921727 0.936440 Adj. R-squared 0.987645 0.899575 0.918451 Sum sq. resids 0.698258 0.045178 0.021970 S.E. equation 0.114781 0.029196 0.020360 F-statistic 363.4008 41.60802 52.05683 Log likelihood 60.56115 155.0218 179.8938 Akaike AIC -1.291627 -4.029617 -4.750546 Schwarz SC -0.773574 -3.511563 -4.232492 Mean dependent 0.009891 -0.003615 0.009262 S.D. dependent 1.032655 0.092130 0.071296

Determinant resid covariance (dof adj.) 3.74E -09 Determinant resid covariance 1.70E-09 Log likelihood 402.9969 Akaike information criterion -10.20281 Schwarz criterion -8.551513

Second Model:

Vector Error Correction Estimates Sample (adjusted): 1996M08 2013M12 Included observations: 209 after adjustments Standard errors in ( ) & t-statistics in [ ]

Cointegrating Eq: CointEq1

TUR_ARG( -1) 1.000000 LTA(-1) -1.924559 (0.20478) [-9.39826] LEMAE(-1) -1.686991 (0.18189) [-9.27455] C 5.203793

Error Correction: D(TUR_ARG) D(LTA) D(LEMAE)

CointEq1 -0.161345 0.026822 0.021196 (0.05687) (0.00849) (0.00647) [-2.83711] [ 3.15759] [ 3.27739]

D(TUR_ARG(-1)) -0.539672 -0.022575 -0.000579 (0.08861) (0.01323) (0.01008) [-6.09066] [-1.70575] [-0.05742]

D(TUR_ARG(-2)) -0.255826 -0.005812 0.006250 (0.09242) (0.01380) (0.01051) [-2.76816] [-0.42106] [ 0.59467]

D(TUR_ARG(-3)) -0.210588 -0.002163 -0.015126 (0.09163) (0.01369) (0.01042) [-2.29835] [-0.15805] [-1.45167]

D(TUR_ARG(-4)) -0.240542 -0.015507 -0.025460 (0.09005) (0.01345) (0.01024) [-2.67109] [-1.15281] [-2.48611]

D(TUR_ARG(-5)) -0.129257 0.008421 -0.003732 (0.09036) (0.01350) (0.01028) [-1.43053] [ 0.62393] [-0.36317]

D(TUR_ARG(-6)) -0.038350 0.019927 0.004118 (0.07453) (0.01113) (0.00848) [-0.51458] [ 1.79009] [ 0.48595] D(LTA(-1)) 0.040268 0.743270 -0.075972 (0.39686) (0.05928) (0.04513) [ 0.10147] [ 12.5388] [-1.68337] D(LTA(-2)) -0.361859 -0.368596 0.085577 (0.38599) (0.05765) (0.04389) [-0.93749] [-6.39324] [ 1.94961] D(LTA(-3)) 0.399072 0.271399 -0.000455 (0.34655) (0.05176) (0.03941) [ 1.15157] [ 5.24316] [-0.01156] D(LTA(-4)) -1.049735 -0.139776 -0.022859 (0.35394) (0.05287) (0.04025) [-2.96590] [-2.64397] [-0.56794]

D(LTA(-5)) 0.614522 -0.109583 0.047561 (0.35851) (0.05355) (0.04077) [ 1.71411] [-2.04640] [ 1.16658] D(LTA(-6)) -0.270644 0.102219 0.043154 (0.33534) (0.05009) (0.03813) [-0.80707] [ 2.04076] [ 1.13161] D(LEMAE(-1)) 1.710190 -0.104338 -0.012513 (0.64083) (0.09572) (0.07288) [ 2.66871] [-1.09005] [-0.17170] D(LEMAE(-2)) 2.215485 0.056751 -0.247619 (0.62297) (0.09305) (0.07084) [ 3.55633] [ 0.60989] [-3.49527] D(LEMAE(-3)) 0.608542 -0.055746 0.418377 (0.65315) (0.09756) (0.07428) [ 0.93170] [-0.57140] [ 5.63270] D(LEMAE(-4)) 0.861218 -0.041965 -0.142943 (0.68107) (0.10173) (0.07745) [ 1.26451] [-0.41252] [-1.84559] D(LEMAE(-5)) -0.408366 -0.123069 0.292217 (0.63257) (0.09449) (0.07194) [-0.64557] [-1.30253] [ 4.06219] D(LEMAE(-6)) -0.593326 -0.028941 0.134745 (0.66854) (0.09986) (0.07603) [-0.88750] [-0.28982] [ 1.77236] C -0.012062 0.002050 0.001687 (0.01084) (0.00162) (0.00123) [-1.11306] [ 1.26663] [ 1.36892]

D(I0202) -0.217777 0.066117 0.006460 (0.17726) (0.02648) (0.02016) [-1.22858] [ 2.49717] [ 0.32046]

D(E0202) 0.128039 -0.286660 -0.005815 (0.27066) (0.04043) (0.03078) [ 0.47306] [-7.09058] [-0.18893]

D(I0210) 0.030877 -0.112163 -0.007298 (0.11521) (0.01721) (0.01310) [ 0.26799] [-6.51763] [-0.55698]

D(S1) 0.939223 -0.007736 -0.143623 (0.07454) (0.01113) (0.00848) [ 12.6002] [-0.69484] [-16.9432]

D(S2) 1.494478 -0.032543 -0.152643 (0.17104) (0.02555) (0.01945) [ 8.73783] [-1.27386] [-7.84791]

D(S3) 1.441136 -0.039988 -0.080813 (0.25336) (0.03784) (0.02881) [ 5.68812] [-1.05666] [-2.80485]

D(S4) 0.915654 -0.046318 0.014586 (0.30245) (0.04518) (0.03439) [ 3.02750] [-1.02529] [ 0.42407] D(S5) 0.301653 -0.058543 0.067486 (0.32125) (0.04798) (0.03653) [ 0.93900] [-1.22005] [ 1.84728] D(S6) -0.502545 -0.078430 0.012501

(0.31303) (0.04676) (0.03560) [-1.60544] [-1.67745] [ 0.35118] D(S7) -0.735700 -0.082586 0.039046 (0.25925) (0.03872) (0.02948) [-2.83780] [-2.13271] [ 1.32440] D(S8) -0.845856 -0.052441 -0.036299 (0.17128) (0.02558) (0.01948) [-4.93831] [-2.04974] [-1.86354] D(S9) -1.032714 -0.025049 -0.045752 (0.10253) (0.01531) (0.01166) [-10.0723] [-1.63564] [-3.92392]

D(S10) -0.778943 -0.021556 -0.053122 (0.08966) (0.01339) (0.01020) [-8.68732] [-1.60951] [-5.20973]

D(S11) -0.581380 0.008889 -0.011795 (0.07067) (0.01056) (0.00804) [-8.22673] [ 0.84213] [-1.46772]

D(E0201) -0.308310 -0.172304 -0.025897 (0.14412) (0.02153) (0.01639) [-2.13921] [-8.00401] [-1.58008]

D(I0205) 0.007025 -0.175820 0.010521 (0.12114) (0.01809) (0.01378) [ 0.05799] [-9.71662] [ 0.76367]

D(I0301) 0.052486 0.086489 -0.007063 (0.10336) (0.01544) (0.01175) [ 0.50779] [ 5.60198] [-0.60086]

D(I0109) -0.073109 -0.003152 -0.031070 (0.09714) (0.01451) (0.01105) [-0.75262] [-0.21722] [-2.81258] D(I0306) -0.064061 -0.073444 0.017493 (0.10146) (0.01516) (0.01154) [-0.63138] [-4.84619] [ 1.51610] D(I1106) -0.025882 0.042119 0.001216 (0.09751) (0.01456) (0.01109) [-0.26543] [ 2.89183] [ 0.10965]

R-squared 0.914770 0.845283 0.950882 Adj. R-squared 0.895101 0.809579 0.939547 Sum sq. resids 2.741737 0.061169 0.035457 S.E. equation 0.127371 0.019025 0.014485 F-statistic 46.50924 23.67474 83.88968 Log likelihood 156.3180 553.7001 610.6873 Akaike AIC -1.113090 -4.915790 -5.461122 Schwarz SC -0.473409 -4.276109 -4.821441 Mean dependent 0.003541 -0.001801 0.002980 S.D. dependent 0.393264 0.043598 0.058911

Determinant resid covariance (dof adj.) 1.21E -09 Determinant resid covariance 6.39E-10 Log likelihood 1322.721 Akaike information criterion -11.48058 Schwarz criterion -9.513563