Determinants of international tourism demand: Evidence from Australian states and territories

2018 ◽  
Vol 25 (2) ◽  
pp. 274-296 ◽  
Author(s):  
Muhammad Shafiullah ◽  
Luke Emeka Okafor ◽  
Usman Khalid
Keyword(s):  
Time Series ◽  
State Level ◽  
International Tourism ◽  
Tourism Demand ◽  
Causality Analysis ◽  
Foreign Born ◽  
Demand Model ◽  
Explanatory Variables ◽  
Cointegration Tests ◽  
World Income

This article explores whether the determinants of international tourism demand differ by states and territories in Australia. This is the first attempt at econometric modelling of international tourism demand in the states and territories of Australia. A demand model is specified where international visits to states and territories is a function of world income, state-level transportation costs, stock of foreign-born residents, the Australian real exchange rate and the price levels of international and domestic substitutes. Panel and time series econometric techniques are employed to test the model variables for stationarity, cointegration and direction of causality. Panel and time series cointegration tests show that the model is cointegrated. The causality analysis indicates that all explanatory variables Granger cause international visits to the Australian states and territories. Further, we show that the impacts of the determinants of international tourism vary by states and territories. The results underscore the importance of targeted policymaking that takes into account the economic and social structure of each state and territory instead of designing tourism policies on the basis of one-size-fits-all approach.

A high order seasonal fuzzy time series model and application to international tourism demand of Turkey

2014 ◽  
Vol 26 (1) ◽  
pp. 295-302 ◽  
Author(s):  
Cagdas Hakan Aladag ◽  
Erol Egrioglu ◽  
Ufufk Yolcu ◽  
Vedide R. Uslu
Keyword(s):  
Time Series ◽  
High Order ◽  
Time Series Model ◽  
Fuzzy Time Series ◽  
International Tourism ◽  
Tourism Demand

The effect of freedom on international tourism demand: Empirical evidence from the top eight most visited countries

2019 ◽  
Vol 26 (8) ◽  
pp. 1358-1373
Author(s):  
Umit Bulut ◽  
Emrah Kocak ◽  
Courtney Suess
Keyword(s):  
Panel Data ◽  
Civil Liberties ◽  
The United States ◽  
Policy Implications ◽  
International Tourism ◽  
Political Rights ◽  
Tourism Demand ◽  
Data Set ◽  
Cointegration Tests ◽  
The Impact

The present study investigates the impact of freedom (i.e. the effects of political rights and civil liberties) on tourist arrivals for the eight countries with the highest tourist arrivals in 2016 (France, the United States, Spain, China, Italy, the United Kingdom, Germany, and Mexico), using annual data from 1998 to 2016, through advanced panel data methods. Notably, the key strengths of this study are as follows: (i) it examines the impact of institutional quality on international tourism demand for the most visited countries and (ii) it employs advanced panel data techniques, which have been suggested in recent years. We first constituted a freedom index using political rights and civil liberties data. Second, we performed cross-sectional dependence (CD) tests to examine whether there existed CD in the panel data set. After detecting the presence of CD, we used panel unit root and cointegration tests, which are robust to CD to avoid problems from spurious regression. Finally, we estimated long-run parameters of the empirical model through a panel data estimator that is capable of presenting efficient and unbiased output in the presence of CD. Our empirical findings show that the level of freedom may play a role in explaining the volume of international tourist arrivals. Theoretical and policy implications are discussed in the study, particularly with respect to the importance of rights and freedom in the context of international inbound tourist arrivals.

Inbound Australian tourism demand from Asia: a panel gravity model

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sudeshna Ghosh
Keyword(s):  
Gravity Model ◽  
Recent Literature ◽  
Gravity Models ◽  
Tourism Demand ◽  
Demand Model ◽  
Cross Sectional ◽  
Content Type ◽  
Explanatory Variables ◽  
Common Correlated Effects ◽  
Per Capita

PurposeThis study attempts to explore the determinants of tourism demand that impact tourist arrivals in Australia from Asia using an augmented panel gravity model.Design/methodology/approachThe augmented panel gravity model was utilised to analyse the demand for Australian tourism from 15 major countries of Asia over the period 1991 to 2018. Tourist arrivals were the dependent variable while per capita gross domestic product (GDP) and weighted distance were important explanatory variables. Further other indicators like population, money supply, globalisation, price index, exchange rate, uncertainty and two dummy variables were added as control variables.FindingsThe results demonstrate based on the novel methodology of Pesaran (2006), namely CCE (common correlated effects) that tourist arrivals are impacted positively and significantly by per capita GDP of both the country of origin and destination country, globalisation also impacts tourist flows positively. However, tourist arrivals are adversely affected by distance and prices confirming the economic theory.Originality/valueGravity models have been intensively used in the recent literature on tourism; however, this study has attempted to explore tourism demand from Asia into Australia which is indeed an unexplored area further the study has used the CCE methodology which takes care of the problems of cross-sectional dependence unlike the earlier methods widely used in the literature like the DOLS and the FMOLS. Last by utilising a wide-ranging set of macro factors the study contributes a novel assessment to the recent literature on tourism demand model.

The nexus between agricultural production and agricultural loans for banking sector groups in Turkey

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Emine Kaya ◽  
Esra Kadanalı
Keyword(s):  
Time Series ◽  
Agricultural Production ◽  
Banking Sector ◽  
Dynamic Structure ◽  
Ordinary Least Squares ◽  
Variance Decomposition ◽  
Causality Analysis ◽  
Content Type ◽  
Cointegration Tests ◽  
Time Series Analyses

PurposeThis study aims to determine the nexus between agricultural production and agricultural loans for the period Q1 2003–Q4 2018 in Turkey.Design/methodology/approachThe authors employ the time-series analyses within the scope of the study. Firstly, they run the Engle–Granger two-step cointegration test and the Toda–Yamamoto causality analysis. They also use the dynamic ordinary least squares (DOLS) model estimator and estimate the vector autoregression model for predicting the dynamic structure of time series.FindingsThe results of time series analyses reveal that the variables are cointegrated and there are causal relationships between agricultural loans and agricultural production. Also, the variance decomposition findings indicate that the effect of agricultural loans provided by development-investment banks and participation banks on agricultural production has increased over the years, and the deposit banks have a high impact on agricultural production. The results of the DOLS model indicate that agricultural loans have a positive effect on agricultural production.Originality/valueThis research is one of the few studies that comprehensively determines the direction of nexus between agricultural production and agricultural loans in Turkey economy. This is the first contribution of the study in the literature. Another contribution of this study is to investigate the nexus between agricultural production and agricultural loans for banking sector groups. Unlike other studies in the literature, this study calculates the variance decomposition by going beyond unit root and cointegration tests. Thus, this study has deep findings.

An Econometric Classification and Review of International Tourism Demand Models

1997 ◽  
Vol 3 (1) ◽  
pp. 69-81 ◽  
Author(s):  
Christine Lim
Keyword(s):  
Diagnostic Tests ◽  
Empirical Studies ◽  
International Tourism ◽  
Tourism Demand ◽  
Explanatory Variables ◽  
Proxy Variables ◽  
Demand Models

The purpose of the paper is to provide an econometric classification and evaluation of 100 published empirical studies on modelling international tourism demand, according to the recognition and type of omitted explanatory variables, number and type of proxy variables used, method of estimation, and use of various diagnostic tests of the auxiliary assumptions of the various models. An analysis of the adequacy of model specifications and the statistical deficiencies of existing empirical tourism demand models will permit a greater appreciation of the factors which determine changes in international tourism demand and will aid in forecasting future tourism demand.

scholarly journals A Time Series Sustainability Assessment of a Partial Energy Portfolio Transition

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 141
Author(s):  
Jacob Hale ◽  
Suzanna Long
Keyword(s):  
Time Series ◽  
Electricity Generation ◽  
Sustainability Assessment ◽  
Moving Average ◽  
Time Series Prediction ◽  
State Level ◽  
Autoregressive Integrated Moving Average ◽  
Energy Transitions ◽  
Univariate Time Series ◽  
Partial Energy

Energy portfolios are overwhelmingly dependent on fossil fuel resources that perpetuate the consequences associated with climate change. Therefore, it is imperative to transition to more renewable alternatives to limit further harm to the environment. This study presents a univariate time series prediction model that evaluates sustainability outcomes of partial energy transitions. Future electricity generation at the state-level is predicted using exponential smoothing and autoregressive integrated moving average (ARIMA). The best prediction results are then used as an input for a sustainability assessment of a proposed transition by calculating carbon, water, land, and cost footprints. Missouri, USA was selected as a model testbed due to its dependence on coal. Of the time series methods, ARIMA exhibited the best performance and was used to predict annual electricity generation over a 10-year period. The proposed transition consisted of a one-percent annual decrease of coal’s portfolio share to be replaced with an equal share of solar and wind supply. The sustainability outcomes of the transition demonstrate decreases in carbon and water footprints but increases in land and cost footprints. Decision makers can use the results presented here to better inform strategic provisioning of critical resources in the context of proposed energy transitions.

Sign in / Sign up

Export Citation Format

Share Document