new zealand tourist arrivals - christchurch, new zealand · covec: new zealand tourist arrivals 2...

Research Report

New Zealand Tourist Arrivals

Prepared for

Foundation for Research, Science & Technology

May 2009

Covec is an applied economics practice that provides rigorous and independent analysis and advice. We have a reputation for producing high quality work that includes quantitative analysis and strategic insight. Our consultants solve problems arising from policy, legal, strategic, regulatory, market and environmental issues, and we provide advice to a broad range of companies and government agencies. Covec develops strategies, designs policy, and produces forecasts, reports, expert testimony and training courses. Our commitment to high-quality, objective advice has provided confidence to some of the largest industrial and governmental organisations in Australasia. Authorship This document was written by John Small and Chris Sweetman. For further information email [email protected] or phone (09) 916-1966 Disclaimer Although every effort has been made to ensure the accuracy of the material and the integrity of the analysis presented herein, Covec Ltd accepts no liability for any actions taken on the basis of its contents.

© Copyright 2009 Covec Ltd. All rights reserved.

Covec Limited Level 15 Qantas House 191 Queen Street PO Box 3224 Shortland Street Auckland New Zealand t: (09) 916-1970 f: (09) 916-1971 w: www.covec.co.nz

Contents Executive Summary 1

1. Introduction 2

2. Data Issues 4

2.1. Explanatory Variables 5

3. Model Selection and Estimation 7

4. Results 9

5. Conclusion 12

References 13

Appendix – Detailed Statistics and Results 14

Executive Summary The objective of this research was to investigate the statistical correlations between visitor arrivals to New Zealand and characteristics of the visitors. We used quarterly data over the period 1997 to 2008, drawn from a variety of sources. In earlier work, visitor arrivals were segmented into thirteen origin markets. For some origin countries (eg Australia, UK) more than one segment was identified based on the stated purpose of travel. We matched these arrival markets with price and income data that was potentially relevant to visitation decisions. On the income side, we used macro‐economic aggregates: GDP and consumption spending in the origin country. A richer set of price variables was available, including origin country CPI, exchange rates with New Zealand, a tourism price index reflecting the cost of bundles of goods and services typically purchased in New Zealand by origin markets, airfares to New Zealand and the world price of oil. A general‐to‐specific econometric model specification search was used, beginning in each case with a set of current and lagged values for all explanatory variables. We tested these models sequentially, eliminating insignificant variables. During this process we monitored autocorrelation in the residuals and switched to an alternative covariance matrix estimator where required for consistent standard error estimation. Our strongest conclusion is that consumption spending in origin markets is strongly correlated with visitation to New Zealand. This is not surprising given that international travel is a consumption activity for the majority of travellers (who are non‐business travellers). We also found that the exchange rate and the price of goods and services in New Zealand were relatively important price variables. Airfares and the oil price appear to be materially less influential.

Covec: New Zealand Tourist Arrivals 1


1. Introduction Tourism is one of New Zealand’s most important export industries. In 2007 it earned export revenues of $8.8billion1 which is over 18% of all foreign earnings and more than the $7.5billion earned from selling milk powder, butter and cheese. Relative to our other large export industries, tourism is not well co‐ordinated. The supply side of the industry within New Zealand is highly fragmented and poorly understood. Inbound travel capacity and pricing is determined by an imperfectly competitive airline industry. And while there is some co‐ordinated marketing aimed at attracting visitors, there is scope for better alignment of these efforts with airline initiatives. For these reasons, and others beyond our control, the economic performance of the New Zealand tourism industry is vulnerable to many types of shock. On the demand side, these include changes in the disposable income of potential visitors, and to their travel preferences. Supply side shocks are also a hazard, whether they affect the cost of getting to New Zealand or of enjoying a holiday once here. To help build an information base for the tourism industry, this paper studies the demand for travel to New Zealand by international visitors. We use a regression‐based approach to examine the way the total number of visitors arriving from particular origin markets is correlated with changes in factors affecting:

• The price of travel to New Zealand; • The “on the ground cost” of travel in New Zealand; and • Macro‐economic conditions in the origin country

While each of these sets of factors is largely beyond the control of the New Zealand tourism industry, there is value in understanding how each affects the pattern of arrivals. For example, if incomes were to fall dramatically in some origin markets, we could predict the likely impact on visitation from those countries. Similarly, the likely impact of a rise in the price of oil is potentially predictable through models of the type estimated here. This work contrasts with much of the published empirical literature on tourism (eg Brons et al 2002 and the papers cited therein) in that we make no attempt to estimate price and income elasticities of demand for travel to New Zealand. To do so, or to study choices at all (eg Nicolau and Mas, 2006), we would require detailed information on the prices of other, unchosen, options available to New Zealand’s visitors. Neither do we focus on individual variables such as length of stay (eg Alegre and Pou, 2006). Rather, this work is undertaken from the perspective of a destination (New Zealand) seeking to understand the main influences on visitor arrival numbers. Recent work with similar focus is the study from the perspective of the Balearic Islands by Garin‐Munoza and Montero‐Martin (2007).

1 http://www.stats.govt.nz/store/2008/06/tourism‐satellite‐account‐07‐mr.htm

In the remainder of this report, we discuss data issues (section 2), model selection and estimation (section 3), and then report the results (section 4). A brief conclusion follows.



2. Data Issues The overall objective is to seek to explain the observed variation in visitor arrival numbers from key markets. We have records of the number of visitor arrivals on a quarterly basis over the period 1997‐2008 inclusive. These records are drawn from the arrivals cards completed by all visitors, which include disclosure of the purpose of travel, so the data can be viewed as an accurate measure of total arrival numbers. We defined the following markets and collated arrivals data for each.

• Australia o Holiday o Visiting friends and relatives (‘VFR’) o Other

• China o Holiday o VFR and Other

• Japan o Holiday o VFR and Other

• UK o Holiday o VFR and Other

• USA o Holiday o VFR and Other

• Germany • Korea

The resulting thirteen variables were used as the dependent variables in the regression models. Note that the purpose of travel is classified as “other” unless it was stated as Holiday or VFR, so “other” includes business travel. This choice of markets for analysis reflects a trade‐off between developing detailed information, which would point towards a very fine graduation of markets by purpose of travel, and ensuring that the data are sufficiently reliable. Reliability criteria were dictated by one of the most important explanatory variables, which is the airfare. As discussed below, we used reported airfares from the International Visitor Survey (‘IVS’), and this survey captured quite small numbers of visitors in some categories. 2 For this reason, we were obliged to aggregate data into the larger categories shown above.

2 http://www.stats.govt.nz/analytical‐reports/tourism‐migration‐2000/tourism‐and‐migration‐appendix5.htm

2.1. Explanatory Variables At a theoretical level, we should expect that decisions to travel to New Zealand will be affected by prices, incomes and the relative attractions of other alternatives. Information on this set of factors is what would lie under a complete specification of a system of demand equations (see, for example, Bewley 1986). We have access to five different types of price variables:

• Consumer price indices in the origin country (denoted CPI) • Exchange rates between the origin country and the NZ dollar expressed as

$NZ/foreign currency (so higher values imply a depreciation of the $NZ and lower effective prices for visitors earning foreign currencies).

• Airfares paid from origin country to New Zealand in foreign currency • Tourism price indices for travel in New Zealand in foreign currency; and • A world price for oil in $US

The first two of these are reasonably self‐explanatory. As noted above, airfares were drawn from the IVS responses collated in each of the 15 markets. We used the average of all responses in a market for each quarter. The tourism price indices were drawn from related work as documented by Becken et al (2008). There are some interactions between these variables. Oil prices affect airfares and to a lesser extent the tourism price indices in New Zealand. We have reasonable data on incomes in origin markets in the form of GDP per capita. This is not an ideal measure, for example because foreign ownership of productive resources makes it different from the income actually available to the population of a country. However it has the great benefit of being widely available and well understood. It is the need to understand, and have information on, the other choices open to New Zealand’s visitors that prevents us from estimating a complete demand system. Lacking such data, and preferring in any case to investigate many important origin markets rather than any particular one in detail, we instead use a very rough proxy for the attraction of other competing choices for the spending power of New Zealand’s visitors. This is in the form of data on general consumption in the origin markets. The effect of the explanatory variables may not have an immediate impact on the number of visitor arrivals. It is likely that it may take some time for the effects to filter through. For this reason, in our modelling it is assumed that the effects may take up to several months to work through, so we allow for up to two lags of each explanatory variable to be included in the models. Visitor patterns fluctuate throughout the year. Higher visitor numbers occur during the summer period. This seasonal effect can be modelled with quarterly dummy variables. Equivalently, by including significant seasonal dummy variables, we can interpret our results as net of constant seasonal factors.


The tables in the appendix give summary statistics and the correlation coefficients of the explanatory variables used to model the variation in visitor arrival numbers to New Zealand.


3. Model Selection and Estimation Our estimation and testing was undertaken using Stata 10.0. We began by considering how to specify the regression models. While there a number of alternative approaches when it comes to model selection, we have adopted the so‐called general‐to‐specific methodology, as discussed for example by Krolzig and Hendrey (2000). This approach has the potential benefit of allowing the analyst to keep track of overall significance levels within a multi‐test process, and therefore to understand pre‐test bias and related issues (Danilov and Magnus, 2004). The process starts with a general dynamic statistical model with all explanatory variables included. Standard testing procedures are then used to reduce the model’s complexity by eliminating the statistically insignificant variables. This is the approach we have used for the model selection and estimation in this report. For each of the thirteen markets, initially a general model is set up with all the explanatory variables mentioned in the previous section included. Lags of one and two periods are included for the explanatory variables. The model is estimated using ordinary linear least squares (OLS). The general to specific modelling technique involves eliminating the statistically insignificant variables. Explanatory variables with p‐values greater than 0.05 are regarded as statistically insignificant. A p‐value is the probability of obtaining a result at least as extreme as the one that was actually observed, assuming the null hypothesis is true. Therefore the lower the p‐value, the less likely the probability of obtaining such a result and the more statistically significant the result is. P‐values greater than 0.05 offer insufficient evidence to reject the hypothesis that the coefficient of the explanatory variable is statistically different from zero (the null hypothesis). The explanatory variable with the highest p‐value is eliminated and the model is re‐estimated. This continues until all the explanatory variables are statistically significant (have p‐values less than 0.05). A key assumption of OLS is that the residuals are uncorrelated. The classical test for first‐order autocorrelation is the Durbin‐Watson statistic. As the dataset for this model is quarterly data, testing for higher orders of autocorrelation will be necessary. The Breusch‐Godfrey Lagrange multiplier test (Godfrey, 1988) is used to check each model for evidence of these higher orders of autocorrelation. The test is carried out up to the fourth order as this corresponds to a period of one year. A p‐value less than 0.05 for any of the orders, provides evidence that the residuals are correlated which violates the assumptions for an unbiased estimator for OLS. When autocorrelation is present, a common response is to use Newey‐West Heteroscedasticity and Autocorrelation Consistent (HAC) estimator (Newey and West, 1987). This regression technique produces Newey‐West standard errors for the coefficients that are assumed to sufficient reliable for model selection purposes.


The Newey‐West HAC estimator is used to model the markets where there is evidence of autocorrelation of the residuals from the OLS regression. For our regressions, the maximum lag that is considered in the autocorrelation structure is four. The general to specific modelling technique is used again to eliminate the statistically insignificant variables. In considering the modelling, it needs to be remembered that our focus is not on forecasting. If it was, we would be more concerned with residual autocorrelation because that indicates the presence of information yet to be explained by the model, and therefore a potential loss of forecast accuracy. Instead, we wish to understand the types of economic factors that influence visitation, so we are particularly interested in seeing which potential explanatory variables survive the model selection process.


4. Results This section provides the estimation results obtained after following the model selection process described above for each of the thirteen markets. As a result of the general to specific modelling method, each market or model has a slightly different set of significant explanatory variables. Not all of the explanatory variables are statistically significant in each market. The regression test statistics are shown in Table 29 in the appendix. These include the results from the OLS regressions and the Newey‐West estimators. The coefficients of the explanatory variables provide an estimate of the response in the number of visitor arrivals to an increase of one unit in the explanatory variable. The units of measure for the dataset differ across explanatory variables as well as the markets so a direct comparison of the coefficients is not appropriate. Tables 1 and 2 below gives the impact on visitor numbers of a one percent increase in the explanatory variable, assuming there is no change in any of the other variables. The impact of each explanatory variable in its market is determined by summing the coefficients of the statistically significant lags of the variable and multiplying it by one percent of the average value of the explanatory variable from 1996 to 2008. The first column of the explanatory variables in the tables refers to the timing of the impact on visitor arrivals (the second column). It represents the number of quarters it takes for the change in the explanatory variable to take full effect.

Table 1: Impact of Price Explanatory Variables on Visitor Arrival Numbers

Market CPI Exchange Rate Airfares TPI Oil Price

#qtrs Impact #qtrs Impact #qtrs Impact #qtrs Impact #qtrs Impact

Aus Holi 2 -2,164 2 -1,616 1 -273 2 2,004 0 -63

Aus VFR 0 -3,980 1 342 0 -150 2 243

Aus Other 0 -909 2 -3,019 1 -112 2 2,925

China Holi 0 745 1 -111 0 -733

China VO 2 -976 2 -732 2 873 0 -51

Germany 1 191 1 -160 1 19

Japan Holi 0 -2,600 2 885 2 -79 2 -965 0 52

Japan VO 0 -500 1 7

Korea 2 4,424 2 3,414 0 -112 2 -3,770

UK Holi 1 -1,346 1 30 1 62 0 -106

UK VO 2 -6 2 -65 1 -1 0 -31

USA Holi 1 742 2 73 1 -835

USA VO 2 -479 2 253 1 -249

The following illustrates an example of how to interpret the tables. If Australia’s CPI increased by one percent with the other explanatory variables remaining constant, then the model estimates that the number of Australians visiting New Zealand on holiday will be 2,164 lower than the seasonal adjusted number in the quarter six months from the time of the change.


All five price explanatory variables are significant to varying extents across the markets. The exchange rate and tourism price index (TPI) are the most commonly statistically significant explanatory variables which are significant in twelve and eleven of the thirteen markets respectively. Airfares and oil price appear to have a smaller impact on visitor arrivals than the other price variables. It is important to realise that the estimated coefficients are based on the assumption that all other factors in the regression model are not changing. These are the marginal impacts of a single variable, holding constant the other variables in the model. However, in reality the explanatory variables are correlated with each other to varying degrees (as the tables in the Appendix show). For example, the price of oil affects the CPI in the foreign countries as well as the TPI in New Zealand and both airfares and TPI are based in the foreign currency so will be affected by the exchange rate. Most of the CPI coefficients are negative, indicating that general inflation in origin markets is detrimental to the number of visitors we attract. A possible reason is that the negative wealth effect of inflation deters foreign travel. Exchange rates are mostly positive which accords with prior expectation: a lower New Zealand dollar makes travelling here cheaper and therefore more attractive. There are a few apparently anomalous signs (for two Australian markets, China VO and UK VO) which would benefit from closer investigation. Airfares and local prices (TPI) both behave largely as expected.

Table 2: Impact of Income Explanatory Variables on Visitor Arrival Numbers

Market GDP per Capita Consumption per Capita

#qtrs Impact #qtrs Impact

Aus Holi 2 -3,112 1 5,220

Aus VFR 1 -3,504 0 6,594

Aus Other 2 -2,104 2 1,973

China Holi 2 -303 1 826

China VO 2 -330 2 544

Germany 1 206

Japan Holi 1 862

Japan VO 1 -230 2 -358

Korea 2 -683 2 408

UK Holi 1 -509 2 1,658

UK VO 1 433

USA Holi 1 697

USA VO 2 -379 1 930

Turning now to income‐related variables, we note that both GDP and consumption per capita are statistically significant in eleven of the thirteen markets. In the majority of cases GDP per capita has a negative impact on visitor arrival numbers, which is counter to standard intuition. However it is relevant that positive impacts are estimated for the only two markets in which consumption did not appear (Japan Holiday and UK VO). For the other markets, the GDP impact is the estimated effect of GDP increases holding constant consumption spending, so it is the impact from GDP sourced from the non‐


consumption components (investment, government spending, international trade). Consumption rather than these elements is the major source of visitor funding, so the counter‐intuitive estimated signs on the GDP variables are not of concern. Consumption impacts tend to be positive, which is consistent with our prior expectations. They are also quite large in magnitude, at least relative to GDP, especially bearing in mind that 1% of consumption is considerably less than 1% of GDP. An overall finding is that consumption spending in origin markets is strongly correlated with visitation to New Zealand. In respect of the price indicators, our results suggest that airfares and oil prices are somewhat less important than exchange rates and prices on the ground in New Zealand.



5. Conclusion The objective of this report is to explain the observed variation in visitor arrival numbers to New Zealand from key markets in Australia, China, Japan, Korea, Germany, United Kingdom and the United States of America. At a theoretical level, we should expect that decisions to travel to New Zealand will be affected by prices, incomes and the relative attractions of other alternatives. Seven explanatory variables were included in the analysis; CPI, exchange rates, airfares, oil price, tourism price index (TPI), GDP per capita and consumption per capita. Lags for the explanatory variables and seasonal dummy variables were included. Data on visitor arrivals to New Zealand was available on a quarterly basis from 1997 to 2008. The analysis was done in the form of general to specific regression modelling using ordinary least squares. All seven of the explanatory variables were statistically significant to varying degrees across the markets. While there was some variation from theoretically expected coefficient signs, it was nevertheless possible to draw some general conclusions as to the relative importance of the factors we studied. Consumption spending in the origin market appears to be strongly correlated with visitation to New Zealand. This can perhaps be viewed as a necessary condition for foreign travel, which is a consumption activity for all non‐business travel. Unless potential visitors have sufficient consumption spending power, foreign travel is unlikely. Prices also matter, though it seems that in quantitative terms the price of travel to New Zealand and the world price of oil are relatively minor considerations. Our models predict that there will be relative stronger responses in visitor numbers from changes in exchange rates and in the price of tourism products and services in New Zealand.

References Alegre, J., and L. Pou, (2006) The length of stay in the demand for tourism, Tourism Management, 27, pp. 1343‐1355. Becken, S., A. Carboni, S. Vuletich and A. Schiff (2008) Analysis of tourist consumption, expenditure and prices for key market international tourism segments, Technical Report for the Foundation for Research Science and Technology. Brons, M., E. Pels, P. Nijkamp and P. Rietveld (2002) Price elasticities of demand for passenger air travel: a meta‐analysis, Journal of Air Transport Management, 8, pp. 165‐175. Bewley, R. (1986) Allocation Models: Specification, Estimation and Applications, Ballinger, Cambridge Ma. Danilov, D. and J.R.Magnus (2004) On the harm that ignoring pretesting can cause, Journal of Econometrics, 122, pp. 27‐46. Garin‐Munoza, T., and L.F. Montero‐Martin (2007) Tourism in the Balearic Islands: A dynamic model for international demand using panel data, Tourism Management, 28, pp. 1224‐1235. Godfrey, L.G. (1988), Specification Tests in Econometrics, Cambridge, UK: Cambridge Krolzig, H.‐M., and D.F. Hendry (2000) Computer Automation of General‐to‐Specific Model Selection Procedures, Department of Economics and Nuffield College, Oxford. Newey, W.K., and K.D.West (1987) A simple positive definite heteroscedasticity and autocorrelation consistent covariance matrix, Econometrica, 55, pp.703‐8. Nicolau, J.L and F.J. Mas (2006) The influence of distance and prices on the choice of tourist destinations: The moderating role of motivations, Tourism Management, 27, pp. 981‐996.


Appendix – Detailed Statistics and Results

Table 3: Australia ‐ Holiday ‐ Summary Statistics

Variable Mean Std Dev Min Max

cpia 108.9 11.4 93.2 129.7

xra 0.859 0.044 0.769 0.933

fah 636 83 442 811

tpiah 900.3 107.6 748.1 1077.7

gdpa 10186 2026 7076 14595

cona 5872 1016 4193 7968

oil 40.3 26.8 11.2 121.4

Table 4: Australia ‐ Holiday ‐ Correlation Coefficients

Variable cpia xra fah tpiah gdpa cona oil

cpia 1.000

xra 0.264 1.000

fah 0.224 0.115 1.000

tpiah 0.912 0.601 0.216 1.000

gdpa 0.980 0.230 0.213 0.900 1.000

cona 0.978 0.250 0.210 0.894 0.994 1.000

oil 0.883 0.104 0.237 0.798 0.887 0.852 1.000

Table 5: Australia ‐ Visiting Friends & Relatives ‐ Summary Statistics


cpia 108.9 11.4 93.2 129.7

xra 0.859 0.044 0.769 0.933

fav 662 76 533 897

tpiav 907.3 104.0 755.9 1071.5

gdpa 10186 2026 7076 14595

cona 5872 1016 4193 7968

oil 40.3 26.8 11.2 121.4

Table 6: Australia ‐ Visiting Friends & Relatives ‐ Correlation Coefficients

Variable cpia xra fav tpiav gdpa cona oil

cpia 1.000

xra 0.264 1.000

fav -0.080 -0.138 1.000

tpiav 0.906 0.626 -0.119 1.000

gdpa 0.980 0.230 -0.091 0.890 1.000

cona 0.978 0.250 -0.099 0.889 0.994 1.000

oil 0.883 0.104 0.040 0.777 0.887 0.852 1.000


Table 7: Australia ‐ Other ‐ Summary Statistics


cpia 108.9 11.4 93.2 129.7

xra 0.859 0.044 0.769 0.933

fao 809 130 593 1258

tpiao 908.6 104.2 754.8 1068.7

gdpa 10186 2026 7076 14595

cona 5872 1016 4193 7968

oil 40.3 26.8 11.2 121.4

Table 8: Australia ‐ Other ‐ Correlation Coefficients

Variable cpia xra fao tpiao gdpa cona oil

cpia 1.000

xra 0.264 1.000

fao -0.141 -0.393 1.000

tpiao 0.898 0.641 -0.294 1.000

gdpa 0.980 0.230 -0.143 0.883 1.000

cona 0.978 0.250 -0.145 0.882 0.994 1.000

oil 0.883 0.104 -0.105 0.769 0.887 0.852 1.000

Table 9: China ‐ Holiday ‐ Summary Statistics


cpic 103.5 4.4 99.6 113.7

xrc 4.758 0.813 3.390 5.930

fch 8856 1080 5470 11424

tpich 830.9 185.3 558.9 1131.8

gdpc 2741 1070 1651 4982

conc 1113 315 747 1766

oil 35.2 19.9 11.2 88.5

Table 10: China ‐ Holiday ‐ Correlation Coefficients

Variable cpic xrc fch tpich gdpc conc oil

cpic 1.000

xrc 0.689 1.000

fch 0.020 0.034 1.000

tpich 0.862 0.943 0.055 1.000

gdpc 0.951 0.576 0.080 0.810 1.000

conc 0.930 0.532 0.094 0.780 0.997 1.000

oil 0.913 0.552 0.099 0.778 0.952 0.948 1.000

Table 11: China ‐ Visiting Friends & Relatives and Other ‐ Summary Statistics


cpic 103.5 4.4 99.6 113.7

xrc 4.758 0.813 3.390 5.930

fcv 9244 1406 6941 14670

tpicv 833.0 184.4 560.6 1132.4

gdpc 2741 1070 1651 4982

conc 1113 315 747 1766

oil 35.2 19.9 11.2 88.5


Table 12: China ‐ Visiting Friends & Relatives and Other ‐ Correlation Coefficients

Variable cpic xrc fcv tpicv gdpc conc oil

cpic 1.000

xrc 0.689 1.000

fcv 0.194 0.048 1.000

tpicv 0.861 0.944 0.118 1.000

gdpc 0.951 0.576 0.216 0.808 1.000

conc 0.930 0.532 0.220 0.778 0.997 1.000

oil 0.913 0.552 0.300 0.776 0.952 0.948 1.000

Table 13: Germany ‐ Summary Statistics


cpig 104.5 5.6 96.6 115.8

xrg 0.515 0.039 0.439 0.605

fg 1364 240 792 2077

tpig 902.0 110.7 712.7 1088.0

gdpg 6598 547 5518 7699

cong 3849 295 3257 4420

oil 40.3 26.8 11.2 121.4

Table 14: Germany ‐ Correlation Coefficients

Variable cpig xrg fg tpig gdpg cong oil

cpig 1.000

xrg 0.021 1.000

fg -0.119 0.021 1.000

tpig 0.775 0.639 -0.076 1.000

gdpg 0.962 -0.075 -0.094 0.694 1.000

cong 0.916 -0.099 -0.074 0.635 0.967 1.000

oil 0.908 0.107 -0.149 0.773 0.880 0.788 1.000

Table 15: Japan ‐ Holiday ‐ Summary Statistics


cpij 99.1 1.2 97.6 101.6

xrj 68.511 12.036 44.960 89.480

fjh 153713 39460 107628 278820

tpijh 830.8 196.7 505.7 1211.4

gdpj 987525 38368 923752 1094362

conj 561064 14093 539392 586129

oil 40.3 26.8 11.2 121.4

Table 16: Japan ‐ Holiday ‐ Correlation Coefficients

Variable cpij xrj fjh tpijh gdpj conj oil

cpij 1.000

xrj -0.284 1.000

fjh 0.112 -0.149 1.000

tpijh -0.446 0.929 -0.217 1.000

gdpj 0.266 0.155 0.014 0.143 1.000

conj 0.087 0.127 -0.122 0.237 0.756 1.000

oil -0.401 0.531 -0.212 0.779 0.082 0.343 1.000


Table 17: Japan ‐ Visiting Friends & Relatives and Other ‐ Summary Statistics


cpij 99.1 1.2 97.6 101.6

xrj 68.511 12.036 44.960 89.480

fjv 189790 102041 103952 594892

tpijv 832.1 196.5 506.5 1210.8

gdpj 987525 38368 923752 1094362

conj 561064 14093 539392 586129

oil 40.3 26.8 11.2 121.4

Table 18: Japan ‐ Visiting Friends & Relatives and Other ‐ Correlation Coefficients

Variable cpij xrj fjv tpijv gdpj conj oil

cpij 1.000

xrj -0.284 1.000

fjv -0.062 0.232 1.000

tpijv -0.448 0.930 0.225 1.000

gdpj 0.266 0.155 0.103 0.142 1.000

conj 0.087 0.127 0.117 0.235 0.756 1.000

oil -0.401 0.531 0.088 0.777 0.082 0.343 1.000

Table 19: Korea ‐ Summary Statistics


cpik 114.2 9.2 99.1 130.9

xrk 654.288 91.950 478.030 791.390

fk 1242947 190259 890371 1646465

tpik 1002.4 196.5 659.1 1357.3

gdpk 4246966 703697 2989933 5424903

conk 2317904 353340 1696958 2913493

oil 48.2 26.5 19.4 121.4

Table 20: Korea ‐ Correlation Coefficients

Variable cpik xrk fk tpik gdpk conk oil

cpik 1.000

xrk 0.758 1.000

fk 0.744 0.489 1.000

tpik 0.914 0.954 0.634 1.000

gdpk 0.972 0.732 0.717 0.885 1.000

conk 0.982 0.704 0.773 0.873 0.969 1.000

oil 0.869 0.537 0.669 0.734 0.845 0.859 1.000

Table 21: United Kingdom ‐ Holiday ‐ Summary Statistics


cpiuk 104.4 6.0 95.4 118.0

xruk 0.347 0.037 0.283 0.427

fukh 935 140 556 1220

tpiukh 895.7 152.9 679.3 1202.6

gdpuk 4696 762 3424 5966

conuk 2927 467 2051 3752

oil 40.3 26.8 11.2 121.4


Table 22: United Kingdom ‐ Holiday ‐ Correlation Coefficients

Variable cpiuk xruk fukh tpiukh gdpuk conuk oil

cpiuk 1.000

xruk 0.371 1.000

fukh -0.196 -0.065 1.000

tpiukh 0.810 0.842 -0.125 1.000

gdpuk 0.976 0.348 -0.150 0.785 1.000

conuk 0.967 0.295 -0.182 0.742 0.990 1.000

oil 0.912 0.464 -0.071 0.829 0.872 0.849 1.000

Table 23: United Kingdom ‐ Visiting Friends & Relatives and Other ‐ Summary Statistics


cpiuk 104.4 6.0 95.4 118.0

xruk 0.347 0.037 0.283 0.427

fukv 977 144 630 1379

tpiukv 896.0 151.9 680.0 1198.9

gdpuk 4696 762 3424 5966

conuk 2927 467 2051 3752

oil 40.3 26.8 11.2 121.4

Table 24: United Kingdom ‐ Visiting Friends & Relatives and Other ‐ Correlation Coefficients

Variable cpiuk xruk fukv tpiukv gdpuk conuk oil

cpiuk 1.000

xruk 0.371 1.000

fukv -0.405 -0.061 1.000

tpiukv 0.811 0.840 -0.260 1.000

gdpuk 0.976 0.348 -0.414 0.789 1.000

conuk 0.967 0.295 -0.436 0.746 0.990 1.000

oil 0.912 0.464 -0.331 0.828 0.872 0.849 1.000

Table 25: United States of America ‐ Holiday ‐ Summary Statistics


cpius 107.0 10.0 92.7 127.3

xrus 0.594 0.112 0.410 0.790

fush 1285 249 775 1925

tpiush 834.9 220.3 532.3 1290.1

gdpus 9535 1358 7225 11878

conus 6620 1040 4848 8335

oil 40.3 26.8 11.2 121.4

Table 26: United States of America ‐ Holiday ‐ Correlation Coefficients

Variable cpius xrus fush tpiush gdpus conus oil

cpius 1.000

xrus 0.604 1.000

fush 0.380 0.483 1.000

tpiush 0.818 0.949 0.507 1.000

gdpus 0.988 0.598 0.371 0.810 1.000

conus 0.988 0.578 0.387 0.793 0.997 1.000

oil 0.917 0.689 0.395 0.860 0.893 0.881 1.000



Table 27: United States of America ‐ Visiting Friends & Relatives and Other ‐ Summary Statistics


cpius 107.0 10.0 92.7 127.3

xrus 0.594 0.112 0.410 0.790

fusv 1692 303 1073 2323

tpiusv 838.0 218.9 534.4 1285.8

gdpus 9535 1358 7225 11878

conus 6620 1040 4848 8335

oil 40.3 26.8 11.2 121.4

Table 28: United States of America ‐ Visiting Friends & Relatives and Other ‐ Correlation Coefficients

Variable cpius xrus fusv tpiusv gdpus conus oil

cpius 1.000

xrus 0.604 1.000

fusv -0.182 -0.135 1.000

tpiusv 0.815 0.952 -0.154 1.000

gdpus 0.988 0.598 -0.148 0.807 1.000

conus 0.988 0.578 -0.145 0.791 0.997 1.000

oil 0.917 0.689 -0.149 0.855 0.893 0.881 1.000

Table 29: Regression Test Statistics

Market Model df(m) df(res) F-

Statistic R-

Squared Dickey-

Fuller Breusch-Godfrey P-Values

lag 1 lag 2 lag 3 lag 4

Aus Holi OLS 13 32 195.1 0.9875 -8.03 0.174 0.389 0.590 0.631

Aus VFR OLS 10 37 258.1 0.9859 -9.35 0.052 0.149 0.283 0.424

Aus Other OLS 13 32 107.6 0.9776 -8.13 0.114 0.121 0.132 0.228

China Holi OLS 4 40 42.8 0.8107 -4.52 0.020 0.054 0.114 0.088

China Holi Newey 10 32 380.8 0.9589 -7.12

China VO OLS 11 30 46.3 0.9444 -3.61 0.001 0.002 0.002 0.003

China VO Newey 11 30 195.3 0.9444 -3.61

Germany OLS 9 37 432.9 0.9906 -7.31 0.691 0.765 0.156 0.250

Japan Holi OLS 9 36 41.8 0.9126 -6.97 0.655 0.722 0.550 0.499

Japan VO OLS 6 41 48.6 0.8768 -5.66 0.228 0.377 0.370 0.343

Korea OLS 4 41 13.8 0.5743 -4.57 0.004 0.013 0.004 0.004

Korea Newey 10 23 142.6 0.9248 -6.04

UK Holi OLS 13 33 137.7 0.9819 -6.20 0.595 0.125 0.132 0.021

UK Holi Newey 14 32 731.5 0.9847 -6.13

UK VO OLS 10 36 184.4 0.9809 -5.70 0.294 0.030 0.063 0.115

UK VO Newey 12 33 440.6 0.9831 -6.48

USA Holi OLS 9 36 178.7 0.9781 -8.38 0.162 0.134 0.243 0.316

USA VO OLS 11 35 81.9 0.9626 -6.89 0.879 0.181 0.270 0.311

new zealand tourist arrivals - christchurch, new zealand · covec: new zealand tourist arrivals 2...

Documents