Us<strong>in</strong>g the SUR Model <strong>of</strong> Tourism Dem<strong>and</strong> for Neighbour<strong>in</strong>g Regions <strong>in</strong> Sweden <strong>and</strong> Norway 105Y X e2 2 2 2Y X em m m m<strong>and</strong> then comb<strong>in</strong>ed <strong>in</strong>to a larger model written as:Y1 X1 0 0 0 1 e1 Y2 0 X2 0 0 2 e2 ... 0 0 ... 0 ... ... Y m 0 0 0 X m m e mThis model can be rewritten compactly as:(8)YX Be(9)where Y <strong>and</strong> e are <strong>of</strong> dimension (TM 1), X is <strong>of</strong> dimension (TM n), n =dimension (K 1).At this stage, I make the follow<strong>in</strong>g assumptions:a. X i is fixed with rank n i .M nii1, <strong>and</strong> B is <strong>of</strong>b. P lim1 ('XX i i )T Q ii is nons<strong>in</strong>gular with f<strong>in</strong>ite <strong>and</strong> fixed elements, i.e. <strong>in</strong>vertible.c. addition, I assume that Plimd.1 ('XX i j ) Q ij is also nons<strong>in</strong>gular with f<strong>in</strong>ite <strong>and</strong> fixedTelements.,Eee ( i i) ijIT, where ij designates the covariance between the i th <strong>and</strong> j th equations foreach observation <strong>in</strong> the sample.<strong>The</strong> above expression can be written as: 11 12 1MEe () 0<strong>and</strong> ,21 22 2MEee ( ) IT, where is an M M positiveM1 M2 MMdef<strong>in</strong>ite symmetric matrix <strong>and</strong> is the Kronecker product. Thus, the errors at eachequation are assumed homoscedastic <strong>and</strong> not autocorrelated, but there is contemporaneouscorrelation between correspond<strong>in</strong>g errors <strong>in</strong> different equations.<strong>The</strong> OLS estimator <strong>of</strong> B <strong>in</strong> (9) is:with the variance 1ˆOLS XX XY Var( ˆ ) X X X X( X X)OLS 1 1 <strong>The</strong> SUR Generalized Least Squares (GLS) estimator <strong>of</strong> B is given by:
106Advances <strong>in</strong> Econometrics - <strong>The</strong>ory <strong>and</strong> Applications<strong>and</strong> the variance is given by: 1ˆ 1 1GLS ( T ) ( T )X I X X I Y ˆ ( 1) 1GLS TV X I XHowever, the system <strong>of</strong> the five equations for Sweden <strong>and</strong> Norway are as follows:Y it= i + S i + XitB i+ Y it-q iq + e ,it i 1, 2, 5 q 1, 2, 12(10)where Y it is a T 1 vector <strong>of</strong> observations on the dependent variable, e it is a T 1 vector <strong>of</strong>r<strong>and</strong>om errors with E(e t ) = 0 , <strong>and</strong> S i are monthly dummy variables that take valuesbetween 1 <strong>and</strong> 11 (the twelfth month is the base). X it is a T nimatrix <strong>of</strong> observations on n <strong>in</strong>onstochastic explanatory variables, <strong>and</strong> B i is an ni 1 dimensional vector <strong>of</strong> unknownlocation parameters. T is the number <strong>of</strong> observations per equation, <strong>and</strong> n i is the number <strong>of</strong>rows <strong>in</strong> the vector B i . iq is a parameter vector associated with the lagged dependentvariable for the respective equation.<strong>The</strong> dependent variables Y i are the natural logarithms <strong>of</strong> the number <strong>of</strong> monthly visitorsfrom Denmark, the UK, Switzerl<strong>and</strong>, Japan, <strong>and</strong> the US to either Sweden or Norway. <strong>The</strong>matrix X i is the natural logarithm <strong>of</strong> three vectors that conta<strong>in</strong>s monthly <strong>in</strong>formation aboutthe CPI <strong>in</strong> Sweden (or Norway), the exchange rate (Ex) <strong>in</strong> Sweden (or Norway), <strong>and</strong> relativeprice (Rp) for Sweden (or Norway) with respect to each <strong>of</strong> the abovementioned countries.Another objective <strong>of</strong> this study is to test for the existence <strong>of</strong> any contemporaneouscorrelation between the equations. If such correlation exists <strong>and</strong> is statistically significant,then least squares applied separately to each equation are not efficient <strong>and</strong> there is need toemploy another estimation method that is more efficient.<strong>The</strong> SUR estimators utilize the <strong>in</strong>formation present <strong>in</strong> the cross regression (or equations)error correlation. In this chapter, we estimated the model <strong>in</strong> Equation (10) by us<strong>in</strong>g the OLSmethod for each equation separately to achieve the best specification <strong>of</strong> each equation. Wethen estimate the whole system us<strong>in</strong>g ISUR, see Tables 2 <strong>and</strong> 3. <strong>The</strong> ISUR techniqueprovides parameter estimates that converge to unique maximum likelihood parameterestimates <strong>and</strong> take <strong>in</strong>to account any possible contemporaneous correlation between theequations.To test whether the estimated correlation between these equations is statistically significant,we apply Breusch <strong>and</strong> Pagan’s (1980) LM statistic. If we denote the covariances between thedifferent equations as 12 , 13 … 45 , the null hypothesis is:H 0 : 12 = 13 . . . = 45 = 0, aga<strong>in</strong>st the alternative hypothesis,H 1 : at least one covariance is nonzero.In our three equations, the test statistic is: = N(r 2 12 + r 2 13 +…r 2 45), where r 2 ij is the squared correlation,r 2 ij = 2 ij / ii jj .Under H 0 , has an asymptotic 2 distribution with five degrees <strong>of</strong> freedom. I may reject H 0for a value <strong>of</strong> greater than the critical value from a 2 (45) distribution (i.e. with 45 degrees <strong>of</strong>freedom) for a specified significance level. In this study, the calculated 2 value for Sweden