# Causality between Oil Prices and Tourist Arrivals

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## Abstract

**:**

## 1. Introduction

## 2. Literature Review

## 3. Methodology of Causality Tests

#### 3.1. Time Domain Granger Causality

**Simple Granger Causality**If the forecast error of X based on all the information I is smaller than the forecast error of X based on the past information apart from series Y, which is denoted as ${\sigma}^{2}(X|{I}_{t})<{\sigma}^{2}(X|\overline{{I}_{t}-{Y}_{t}})$, then Y is causing X. Ref. [58] stated, “if we are better able to predict X using all available information than if the information apart from Y had been used, we say that Y is causing X”.**Feedback Model**If the Simple Granger Causality from Y to X is donated as $Y\Rightarrow X$, then the feedback indicates that when X is causing Y and also Y is causing X, which can be represented as $X\iff Y$, can also be denoted as the following:if ${\sigma}^{2}(X|\overline{I})={\sigma}^{2}(X|\overline{I-Y})$ and ${\sigma}^{2}(Y|\overline{I})={\sigma}^{2}(Y|\overline{I-X})$, then we say $X\iff Y$.**Instantaneous Granger Causality**Instantaneous causality is indicated if a better forecast of current value of X can be conducted when the present value of Y is also considered, rather than only considering the set of past information. This can be donated as the following: if ${\sigma}^{2}(X|\overline{I},\overline{\overline{Y}})$, the instantaneous causality of ${Y}_{t}\Rightarrow {X}_{t}$ is occurring.

#### 3.2. Frequency Domain Causality

#### 3.3. Convergent Cross Mapping (CCM)

## 4. Data

#### 4.1. Descriptive Statistics

#### 4.2. Stationarity of Data

## 5. Causality Results

#### 5.1. Time Domain Granger Causality

#### 5.2. Frequency Domain Causality

#### 5.3. Convergent Cross Mapping (CCM)

## 6. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## Appendix A

## Appendix B

## References

- Becken, S.; Lennox, J. Implications of a long-term increase in oil prices for tourism. Tour. Manag.
**2012**, 33, 133–142. [Google Scholar] [CrossRef] - Becken, S. Developing indicators for managing tourism in the face of peak oil. Tour. Manag.
**2008**, 29, 695–705. [Google Scholar] [CrossRef] - Chatziantoniou, I.; Filis, G.; Eeckels, B.; Apostolakis, A. Oil prices, tourism income and economic growth: A structural VAR approach for European Mediterranean countries. Tour. Manag.
**2013**, 36, 331–341. [Google Scholar] [CrossRef][Green Version] - Hamilton, J.D. This is what happened to the oil price-macroeconomy relationship. J. Monetary Econ.
**1996**, 38, 215–220. [Google Scholar] [CrossRef] - Ferderer, J.P. Oil price volatility and the macroeconomy. J. Macroecon.
**1997**, 18, 1–26. [Google Scholar] [CrossRef] - Sadorsky, P. Oil price shocks and stock market activity. Energy Econ.
**1999**, 21, 449–469. [Google Scholar] [CrossRef] - Leduc, S.; Sill, K. A quantitative analysis of oil-price shocks, systematic monetary policy, and economic downturns. J. Monet. Econ.
**2004**, 51, 781–808. [Google Scholar][Green Version] - Huang, B.N.; Hwang, M.J.; Peng, H.P. The asymmetry of the impact of oil price shocks on economic activities: An application of the multivariate threshold model. Energy Econ.
**2005**, 27, 455–476. [Google Scholar] [CrossRef] - Park, J.; Ratti, R.A. Oil price shocks and stock markets in the US and 13 European countries. Energy Econ.
**2008**, 30, 2587–2608. [Google Scholar] [CrossRef] - Cong, R.G.; Wei, Y.M.; Jiao, J.L.; Fan, Y. Relationships between oil price shocks and stock market: An empirical analysis from China. Energy Policy
**2008**, 36, 3544–3553. [Google Scholar] [CrossRef] - Hassani, H.; Zhigljavsky, A. Singular spectrum analysis: Methodology and application to economics data. J. Syst. Sci. Complex.
**2009**, 22, 372–394. [Google Scholar] [CrossRef] - Ji, Q.; Fan, Y. How does oil price volatility affect non-energy commodity markets? Appl. Energy
**2012**, 89, 273–280. [Google Scholar] [CrossRef] - Kilian, L.; Hicks, B. Did unexpectedly strong economic growth cause the oil price shock of 2003–2008? J. Forecast.
**2013**, 32, 385–394. [Google Scholar] [CrossRef] - Acemoglu, D.; Finkelstein, A.; Notowidigdo, M.J. Income and health spending: Evidence from oil price shocks. Rev. Econ. Stat.
**2013**, 95, 1079–1095. [Google Scholar] [CrossRef] - Wang, Y.; Wu, C.; Yang, L. Oil price shocks and stock market activities: Evidence from oil-importing and oil-exporting countries. J. Comp. Econ.
**2013**, 41, 1220–1239. [Google Scholar] [CrossRef] - Yeoman, I.; Lennon, J.J.; Blake, A.; Galt, M.; Greenwood, C.; McMahon-Beattie, U. Oil depletion: What does this mean for Scottish tourism? Tour. Manag.
**2007**, 28, 1354–1365. [Google Scholar] - Pentelow, L.; Scott, D. The implications of climate change mitigation policy and oil price volatility for tourism arrivals to the Caribbean. Tour. Hosp. Plan. Dev.
**2010**, 7, 301–315. [Google Scholar] [CrossRef] - Tang, C.F. Temporal Granger Causality and the Dynamics Relationship Between RealTourism Receipts, Real Income and RealExchange Rates in Malaysia. Int. J. Tour. Res.
**2013**, 15, 272–284. [Google Scholar] [CrossRef] - Goh, C. Exploring impact of climate on tourism demand. Ann. Tour. Res.
**2012**, 39, 1859–1883. [Google Scholar] [CrossRef] - Sheldon, P.J.; Var, T. Tourism forecasting: A review of empirical research. J. Forecast.
**1985**, 4, 183–195. [Google Scholar] - Chou, C.-M.; Hsieh, S.F.; Tseng, H.P. The crowding-out effects of Chinese tourists on inbound tourism in Taiwan. Tour. Econ.
**2014**, 20, 1235–1251. [Google Scholar] [CrossRef] - Sugihara, G.; May, R.; Ye, H.; Hsieh, C.H.; Deyle, E.; Fogarty, M.; Munch, S. Detecting causality in complex ecosystems. Science
**2012**, 338, 496–500. [Google Scholar] [CrossRef] [PubMed] - Song, H.; Li, G. Tourism demand modelling and forecasting: A review of recent research. Tour. Manag.
**2008**, 29, 203–220. [Google Scholar] [CrossRef][Green Version] - Gunter, U.; Onder, I. Forecasting international city tourism demand for Paris: Accuracy of uni- and multivariate models employing monthly data. Tour. Manag.
**2015**, 46, 123–135. [Google Scholar] [CrossRef] - Becken, S. A critical review of tourism and oil. Ann. Tour. Res.
**2011**, 38, 359–379. [Google Scholar] [CrossRef] - Selvanathan, S.; Selvanathan, E.A.; Viswanathan, B. Causality Between Foreign Direct Investment and Tourism: Empirical Evidence from India. Tour. Anal.
**2012**, 17, 91–98. [Google Scholar] [CrossRef] - Massidda, C.; Mattana, P. A SVECM Analysis of the Relationship between International Tourism Arrivals, GDP and Trade in Italy. J. Travel Res.
**2012**, 52, 93–105. [Google Scholar] [CrossRef] - Tang, C.F.; Tan, E.C. How stable is the tourism-led growth hypothesis in Malaysia? Evidence from disaggregated tourism markets. Tour. Manag.
**2013**, 37, 52–57. [Google Scholar] [CrossRef] - Ghartey, E.E. Effects of tourism, economic growth, real exchange rate, structural changes and hurricanes in Jamaica. Tour. Econ.
**2013**, 19, 919–942. [Google Scholar] [CrossRef] - Cellini, R.; Cuccia, T. Museum and monument attendance and tourism flow: A time series analysis approach. Appl. Econ.
**2013**, 45, 473–3482. [Google Scholar] [CrossRef][Green Version] - Albaladejo, I.P.; González-Martínez, M.I.; Martínez-García, M.P. Quality and endogenous tourism: An empirical approach. Tour. Manag.
**2014**, 41, 141–147. [Google Scholar] [CrossRef] - Katircioğlu, S.T. Testing the tourism-induced EKC hypothesis: The case of Singapore. Econ. Model.
**2014**, 41, 383–391. [Google Scholar] [CrossRef] - Katircioğlu, S.T.; Feridun, M.; Kilinc, C. Estimating tourism-induced energy consumption and CO
_{2}emissions: The case of Cyprus. Renew. Sustain. Energy Rev.**2014**, 29, 634–640. [Google Scholar] [CrossRef] - Tang, C.F.; Abosedra, S. Small sample evidence on the tourism-led growth hypothesis in Lebanon. Curr. Issues Tour.
**2014**, 17, 234–246. [Google Scholar] [CrossRef] - Fereidouni, H.G.; Al-mulali, U. The interaction between tourism and FDI in real estate in OECD countries. Curr. Issues Tour.
**2014**, 17, 105–113. [Google Scholar] [CrossRef] - Solarin, S.A. Tourist arrivals and macroeconomic determinants of CO
_{2}emissions in Malaysia. Anatolia**2014**, 25, 228–241. [Google Scholar] [CrossRef] - Antonakakis, N.; Dragouni, M.; Filis, G. Tourism and growth: The times they are a-changing. Ann. Tour. Res.
**2015**, 50, 165–169. [Google Scholar] [CrossRef][Green Version] - Chen, M.-H.; Lin, C.-P.; Chen, B.T. Drivers of Taiwan’s Tourism Market Cycle. J. Travel Tour. Mark.
**2015**, 32, 260–275. [Google Scholar] [CrossRef] - Tang, C.F.; Tan, E.C. Does tourism effectively stimulate Malaysia’s economic growth? Tour. Manag.
**2015**, 46, 158–163. [Google Scholar] [CrossRef] - Paerez-Rodríguez, J.V.; Ledesma-Rodríguez, F.; Santana-Gallego, M. Testing dependence between GDP and tourism’s growth rates. Tour. Manag.
**2015**, 48, 268–282. [Google Scholar] [CrossRef] - Antonakakis, N.; Dragouni, M.; Filis, G. How strong is the linkage between tourism and economic growth in Europe? Econ. Model.
**2015**, 44, 142–145. [Google Scholar] [CrossRef][Green Version] - Dogan, E.; Seker, F.; Bulbul, S. Investigating the impacts of energy consumption, real GDP, tourism and trade on CO
_{2}emissions by accounting for cross-sectional dependence: A panel study of OECD countries. Curr. Issues Tour.**2015**, 20, 1701–1719. [Google Scholar] [CrossRef] - Shahbaz, M.; Kumar, R.R.; Ivanov, S.; Loganathan, N. The nexus between tourism demand and output per capita, with the relative importance of trade openness and financial development: A study of Malaysia. Tour. Econ.
**2015**, 23, 168–186. [Google Scholar] [CrossRef] - Al-Mulali, U.; Fereidouni, H.G.; Mohammed, A.H. The effect of tourism arrival on CO
_{2}emissions from transportation sector. Anatolia**2015**, 26, 230–243. [Google Scholar] [CrossRef] - Durbarry, R.; Seetanah, B. The Impact of Long Haul Destinations on Carbon Emissions: The Case of Mauritius. J. Hosp. Mark. Manag.
**2015**, 24, 401–410. [Google Scholar] [CrossRef] - Ertugrul, H.M.; Mangir, F. The tourism-led growth hypothesis: Empirical evidence from Turkey. Curr. Issues Tour.
**2015**, 18, 633–646. [Google Scholar] [CrossRef] - Tang, C.F.; Tan, E.C. Tourism-Led Growth Hypothesis in Malaysia: Evidence Based Upon Regime Shift Cointegration and Time-Varying Granger Causality Techniques. Asia-Pac. J. Tour. Res.
**2015**, 20, 1430–1450. [Google Scholar] [CrossRef] - Tsui, W.H.K.; Fung, M.K.Y. Causality between business travel and trade volumes: Empirical evidence from Hong Kong. Tour. Manag.
**2016**, 52, 395–404. [Google Scholar] [CrossRef] - Zaman, K.; Shahbaz, M.; Loganathan, N.; Raza, S.Y. Tourism development, energy consumption and Environmental Kuznets Curve: Trivariate analysis in the panel of developed and developing countries. Tour. Manag.
**2016**, 54, 275–283. [Google Scholar] [CrossRef] - Rakotondramaro, H.; Andriamasy, L. Multivariate Granger Causality among tourism, poverty and growth in Madagascar. Tour. Manag. Perspect.
**2016**, 20, 109–111. [Google Scholar] [CrossRef] - Tang, C.F.; Abosedra, S. Tourism and growth in Lebanon: New evidence from bootstrap simulation and rolling causality approaches. Empir. Econ.
**2016**, 50, 679–696. [Google Scholar] [CrossRef] - Toda, H.Y.; Yamamoto, T. Statistical inference in vector autoregressions with possibly integrated processes. J. Econom.
**1995**, 66, 225–250. [Google Scholar] [CrossRef] - Dolado, J.J.; Lütkepohl, H. Making Wald tests work for cointegrated VAR system. Econom. Rev.
**1996**, 15, 369–386. [Google Scholar] [CrossRef] - Zhang, H.Q.; Kulendran, N. The Impact of Climate Variables on Seasonal Variation in Hong Kong Inbound Tourism Demand. J. Travel Res.
**2016**, 26, 94–107. [Google Scholar] [CrossRef] - Hatemi-J, A. On the tourism-led growth hypothesis in the UAE: A bootstrap approach with leveraged adjustments. Appl. Econ. Lett.
**2016**, 23, 424–427. [Google Scholar] [CrossRef] - Li, X.; Pan, B.; Law, R.; Huang, X. Forecasting tourism demand with composite search index. Tour. Manag.
**2017**, 59, 57–66. [Google Scholar] [CrossRef] - Valadkhani, A.; Smyth, R.; O’Mahony, B. Asymmetric causality between Australian inbound and outbound tourism flows. Appl. Econ.
**2017**, 49, 33–50. [Google Scholar] [CrossRef] - Granger, C.W. Investigating causal relations by econometric models and cross-spectral methods. Econ. J. Econ. Soc.
**1969**, 37, 424–438. [Google Scholar] [CrossRef] - Sims, C.A. Money, income, and causality. Am. Econ. Rev.
**1972**, 62, 540–552. [Google Scholar] - Hsiao, C. Autoregressive modelling and money-income causality detection. J. Monetary Econ.
**1981**, 7, 85–106. [Google Scholar] [CrossRef] - Sims, C.A.; Stock, J.H.; Watson, M.W. Inference in linear time series models with some unit roots. Econ. J. Econ. Soc.
**1990**, 58, 113–144. [Google Scholar] [CrossRef] - Toda, H.Y.; Phillips, P.C. Vector autoregressions and causality. Econ. J. Econ. Soc.
**1993**, 61, 1367–1393. [Google Scholar] [CrossRef] - Pesaran, H.H.; Shin, Y. Generalized impulse response analysis in linear multivariate models. Econ. Lett.
**1998**, 58, 17–29. [Google Scholar] [CrossRef][Green Version] - Chen, Y.; Bressler, S.L.; Ding, M. Frequency decomposition of conditional Granger causality and application to multivariate neural field potential data. J. Neurosci. Methods
**2006**, 150, 228–237. [Google Scholar] [CrossRef] [PubMed][Green Version] - Gow, D.W.; Segawa, J.A.; Ahlfors, S.P.; Lin, F.H. Lexical influences on speech perception: A Granger causality analysis of MEG and EEG source estimates. Neuroimage
**2008**, 43, 614–623. [Google Scholar] [CrossRef] [PubMed][Green Version] - Deshpande, G.; Sathian, K.; Hu, X. Effect of hemodynamic variability on Granger causality analysis of fMRI. Neuroimage
**2010**, 52, 884–896. [Google Scholar] [CrossRef] [PubMed][Green Version] - Geweke, J. Measurement of linear dependence and feedback between multiple time series. J. Am. Stat. Assoc.
**1982**, 77, 304–324. [Google Scholar] [CrossRef] - Ciner, C. Eurocurrency interest rate linkages: A frequency domain analysis. Rev. Econ. Financ.
**2011**, 20, 498–505. [Google Scholar] [CrossRef] - Breitung, J.; Candelon, B. Testing for short- and long-run causality: A frequency-domain approach. J. Econom.
**2006**, 132, 363–378. [Google Scholar] [CrossRef] - Deyle, E.; Fogarty, M.; Hsieh, C.; Kaufman, L.; MacCall, A.; Munch, S.; Perretti, C.; Ye, H.; Sugihara, G. Predicting climate effects on Pacific sardine. Proc. Natl. Acad. Sci. USA
**2013**, 110, 6430–6435. [Google Scholar] [CrossRef] [PubMed] - Ye, H.; Deyle, E.; Gilarranz, L.; Sugihara, G. Distinguishing time-delayed causal interactions using convergent cross mapping. Sci. Rep.
**2015**, 5, 14750. [Google Scholar] [CrossRef] [PubMed][Green Version] - Clark, A.T.; Ye, H.; Isbell, F.; Deyle, E.; Cowles, J.; Tilman, G.; Sugihara, G. Spatial convergent cross mapping to detect causal relationships from short time series. Ecology
**2015**, 96, 1174–1181. [Google Scholar] [CrossRef] [PubMed] - Sugihara, G.; May, R. Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series. Nature
**1990**, 344, 734–741. [Google Scholar] [CrossRef] [PubMed] - Takens, F. Detecting strange attractors in turbulence Dynamical Systems and Turbulence. Dyn. Syst. Turbul.
**1981**, 898, 366–381. [Google Scholar] - EIA. U.S. Energy Information Administration. 2016. Available online: http://www.eia.gov/outlooks/steo/outlook.cfm (accessed on 15 December 2016).
- Hassani, H.; Mahmoudvand, R.; Omer, H.N.; Silva, E.S. A Preliminary Investigation into the Effect of Outlier(s) on Singular Spectrum Analysis. Fluct. Noise Lett.
**2014**, 13, 1450029. [Google Scholar] [CrossRef] - Akaike, H. Maximum likelihood identification of Gaussian autoregressive moving average models. Biometrika
**1973**, 60, 255–265. [Google Scholar] [CrossRef] - Schwarz, G. Estimating the dimension of a model. Ann. Stat.
**1978**, 6, 461–464. [Google Scholar] [CrossRef] - Hannan, E.J.; Quinn, B.G. The determination of the order of an autoregression. J. R. Stat. Soc. Ser. B-Stat. Methodol.
**1979**, 42, 190–195. [Google Scholar] - Akaike, H. Fitting autoregressive models for prediction. Ann. Inst. Stat. Math.
**1969**, 21, 243–247. [Google Scholar] [CrossRef]

Oil Prices | ||||||||

Obs | Mean | Median | Max | Min | SD | Skewness | Kurtosis | |

BRT | 240 | 56.41 | 49.22 | 132.72 | 9.82 | 35.24 | 0.47 | 1.85 |

WTI | 240 | 54.78 | 49.06 | 133.88 | 11.35 | 31.19 | 0.40 | 1.89 |

Tourist Arrivals | ||||||||

Obs | Mean | Median | Max | Min | Std. Dev. | Skewness | Kurtosis | |

Austria | 240 | 1,481,894 | 1,434,455 | 3,205,966 | 446,240 | 504,448 | 0.39 | 3.21 |

Germany | 240 | 1,918,394 | 1,788,583 | 4,401,682 | 747,141 | 724,552 | 0.75 | 3.29 |

Greece | 240 | 765,847 | 564,523 | 3,107,955 | 29,856 | 710,611 | 1.11 | 3.66 |

Italy | 240 | 3,343,953 | 3,277,084 | 8,084,209 | 907,367 | 1,709,118 | 0.50 | 2.45 |

Netherland | 240 | 870,900 | 864,200 | 1,745,779 | 275,000 | 284,180 | 0.34 | 2.79 |

Portugal | 240 | 539,796 | 522,395 | 1,359,284 | 155,438 | 256,280 | 0.70 | 3.03 |

Spain | 240 | 3,229,314 | 2,934,373 | 7,443,749 | 671,109 | 1,533,209 | 0.51 | 2.42 |

Sweden | 240 | 357,927 | 239,902 | 1,428,207 | 98,357 | 289,081 | 1.93 | 5.97 |

UK | 240 | 1,668,020 | 1,541,000 | 3,390,515 | 692,120 | 582,239 | 0.59 | 2.64 |

US | 240 | 4,325,374 | 4,222,034 | 8,364,940 | 2,094,287 | 1,292,787 | 0.59 | 2.88 |

Variables | Series | Methods | None | Intercept | Intercept and Trend | |||
---|---|---|---|---|---|---|---|---|

Level | Decision | Level | Decision | Level | Decision | |||

Oil Prices (240 Obs) January 1996–December 2015 | BRT | KPSS | --------- | --------- | 1.675 ***(11) | I(1) | 0.139 *(11) | I(0) |

ADF | −10.284 ***(0) | I(1) | −10.264 ***(0) | I(1) | −10.294 ***(0) | I(1) | ||

PP | −10.279 ***(4) | I(1) | −10.258 ***(4) | I(1) | −10.283 ***(4) | I(1) | ||

WTI | KPSS | --------- | --------- | 1.663 ***(11) | I(1) | 0.166 **(11) | I(1) | |

ADF | −10.104 ***(0) | I(1) | −10.083 ***(0) | I(1) | −10.109 ***(0) | I(1) | ||

PP | −10.104 ***(0) | I(1) | −10.083 ***(0) | I(1) | −10.109 ***(0) | I(1) | ||

Tourist Arrivals (240 Obs) January 1996–December 2015 | Austria | KPSS | --------- | --------- | 1.458 ***(15) | I(1) | 0.144 *(27) | I(0) |

ADF | −3.938 ***(14) | I(1) | −16.637 ***(11) | I(1) | −17.093 ***(11) | I(0) | ||

PP | −49.801 ***(23) | I(1) | −9.945 ***(31) | I(0) | −10.345 ***(24) | I(0) | ||

Germany | KPSS | --------- | --------- | 2.305 ***(9) | I(1) | 0.115(1) | I(0) | |

ADF | −2.524 ***(13) | I(1) | −3.581 ***(13) | I(1) | −3.825 ***(13) | I(1) | ||

PP | −12.185 ***(16) | I(1) | −4.832 ***(5) | I(0) | −5.169 ***(0) | I(0) | ||

Greece | KPSS | --------- | --------- | 0.755 ***(3) | I(1) | 0.058(2) | I(0) | |

ADF | −4.411 ***(11) | I(1) | −4.791 ***(11) | I(1) | −4.985 ***(11) | I(1) | ||

PP | −4.056 ***(5) | I(0) | −5.414 ***(6) | I(0) | −5.529 ***(6) | I(0) | ||

Italy | KPSS | --------- | --------- | 1.079 ***(5) | I(1) | 0.014(2) | I(0) | |

ADF | −3.527 ***(13) | I(1) | −4.403 ***(13) | I(1) | −4.527 ***(13) | I(1) | ||

PP | −2.828 ***(3) | I(0) | −6.291 ***(4) | I(0) | −6.604 ***(4) | I(0) | ||

Netherland | KPSS | --------- | --------- | 1.744 ***(8) | I(1) | 0.084(4) | I(0) | |

ADF | −2.976 ***(13) | I(1) | −3.496 ***(13) | I(1) | −3.503 ***(13) | I(1) | ||

PP | −14.361 ***(3) | I(1) | −5.952 ***(2) | I(0) | −6.548 ***(1) | I(0) | ||

Portugal | KPSS | --------- | --------- | 1.653 ***(7) | I(1) | 0.111(1) | I(0) | |

ADF | −4.077 ***(12) | I(1) | −4.658 ***(12) | I(1) | −4.848 ***(12) | I(1) | ||

PP | −2.101 **(6) | I(0) | −5.731 ***(5) | I(0) | −5.672 ***(6) | I(0) | ||

Spain | KPSS | --------- | --------- | 1.991 ***(8) | I(1) | 0.071(1) | I(0) | |

ADF | −2.353 **(12) | I(1) | −2.857 *(12) | I(0) | −3.469 **(13) | I(0) | ||

PP | −2.306 **(4) | I(0) | −5.646 ***(4) | I(0) | −6.118 ***(5) | I(0) | ||

Sweden | KPSS | --------- | --------- | 1.052 ***(2) | I(1) | 0.161 **(9) | I(1) | |

ADF | −5.708 ***(13) | I(1) | −6.117 ***(13) | I(1) | −6.104 ***(13) | I(1) | ||

PP | −3.940 ***(14) | I(0) | −5.961 ***(19) | I(0) | −5.794 ***(24) | I(0) | ||

UK | KPSS | --------- | --------- | 0.818 ***(5) | I(1) | 0.090(3) | I(0) | |

ADF | −4.889 ***(12) | I(1) | −4.981 ***(12) | I(1) | −5.196 ***(12) | I(1) | ||

PP | −10.446 ***(4) | I(1) | −5.821 ***(1) | I(0) | −6.387 ***(2) | I(0) | ||

US | KPSS | --------- | --------- | 1.825 ***(11) | I(1) | 0.392 ***(9) | I(1) | |

ADF | −3.591 ***(12) | I(1) | −3.928 ***(12) | I(1) | −4.074 ***(12) | I(1) | ||

PP | −19.331 ***(6) | I(1) | −3.796 ***(8) | I(0) | −7.063 ***(8) | I(0) |

Country | Oil Prices | |||||||
---|---|---|---|---|---|---|---|---|

BRT | WTI | |||||||

→ | ← | → | ← | |||||

p-Value | Yes/No | p-Value | Yes/No | p-Value | Yes/No | p-Value | Yes/No | |

Austria | 0.68 | No | 0.56 | No | 0.81 | No | 0.34 | No |

Germany | 0.52 | No | 0.27 | No | 0.29 | No | 0.17 | No |

Greece | 0.54 | No | 0.36 | No | 0.46 | No | 0.44 | No |

Italy | 0.60 | No | 0.98 | No | 0.67 | No | 0.74 | No |

Netherlands | 0.30 | No | 0.83 | No | 0.29 | No | 0.65 | No |

Portugal | 0.38 | No | 0.41 | No | 0.72 | No | 0.31 | No |

Spain | 0.62 | No | 0.24 | No | 0.54 | No | 0.12 | No |

Sweden | 0.21 | No | 0.55 | No | 0.14 | No | 0.93 | No |

UK | 0.63 | No | 0.95 | No | 0.53 | No | 0.82 | No |

US | 0.48 | No | 0.85 | No | 0.53 | No | 0.48 | No |

Country | Oil Prices | |||
---|---|---|---|---|

BRT | WTI | |||

→ | ← | → | ← | |

Austria | No | No | No | No |

Germany | No | No | No | No |

Greece | No | No | No | No |

Italy | No | No | No | No |

Netherland | No | No | No | No |

Portugal | No | No | No | No |

Spain | No | No | No | No |

Sweden | No | No | No | No |

UK | No | No | No | No |

US | No | No | No | No |

Country | Oil Prices | |||
---|---|---|---|---|

BRT | WTI | |||

→ | ← | → | ← | |

Austria | No | Yes | No | Yes |

Germany | No | Yes | No | Yes |

Greece | No | Yes | No | Yes |

Italy | No | Yes | No | Yes |

Netherland | No | Yes | No | Yes |

Portugal | No | Yes | No | Yes |

Spain | No | Yes | No | Yes |

Sweden | No | Yes | No | Yes |

UK | No | Yes | No | Yes |

US | No | Yes | No | Yes |

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## Share and Cite

**MDPI and ACS Style**

Huang, X.; Silva, E.; Hassani, H.
Causality between Oil Prices and Tourist Arrivals. *Stats* **2018**, *1*, 134-154.
https://doi.org/10.3390/stats1010010

**AMA Style**

Huang X, Silva E, Hassani H.
Causality between Oil Prices and Tourist Arrivals. *Stats*. 2018; 1(1):134-154.
https://doi.org/10.3390/stats1010010

**Chicago/Turabian Style**

Huang, Xu, Emmanuel Silva, and Hossein Hassani.
2018. "Causality between Oil Prices and Tourist Arrivals" *Stats* 1, no. 1: 134-154.
https://doi.org/10.3390/stats1010010