• Management & Information Systems Review
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• v.36 no.4
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• pp.81-102
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• 2017

This study examined the income and price elasticities of tourism demand model by using the ARDL models. This paper used the ARDL & ARDL-RECM model based on the annual number of tourists arrivals, GDP and CPI including tourists from the US, Japan and China entering Korea. First, the income elasticity of the US was inelastic and insensitive necessities for long-run US tourists in the ARDL model. China's income elasticity was elastically sensitive luxuries. Second, the US and China's own price elasticities were very elastic to tourism demand in both models. Third, the US's cross price elasticity showed the relationship between inelastic positive substitutes and inelastic negative complements in China in ARDL model. The cross price elasticities of the US and China showed inelastic positive substitutes in the ARDL-RECM model. Fourth, the coefficients of the error correction term were such that the actual sign and the expected sign of the US and China coincided with the negative sign in the ARDL-RECM model. Therefore, first, it can be established in a tourist policy or tourism strategy through income elasticity. Second, we can improve the quality and differentiation of products, recognizing that Korea's tourism price is more elastic than other markets through price elasticity.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.12
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• pp.295-303
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• 2021

The aim of this study is to analyze the effects of oil prices on economic growth and exchange rate in Saudi Arabia during the period 1980-2020. For this purpose, the linear and nonlinear ARDL models are estimated. The linear ARDL model shows that the oil price and economic growth are cointegrated. Moreover, the two variables have a significant positive association in the long run. However, the oil price has no significant impact on the exchange rate. When estimating the nonlinear ARDL model, it has been shown that oil price is only cointegrated with economic growth but not with the exchange rate. The estimation of nonlinear effects using the nonlinear ARDL model shows that economic growth is affected by both positive and negative oil shocks in the long run. However, the impact of positive shocks is higher than those of negative shocks. Moreover, results show that the short-run effects of positive and negative oil shocks are not statistically significant. Regarding the exchange rate, our results show that the effects of positive and negative oil shocks are not statistically significant. Consequently, this study concludes that the oil price has an asymmetric effect on economic growth in Saudi Arabia, but not on the exchange rate.

• Journal of the Korean Society of Clothing and Textiles
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• v.46 no.5
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• pp.868-889
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• 2022

Fashion companies are using a big data approach as a key strategic analysis to predict and forecast sales. This study investigated the effectiveness of the past sales, web search volume, information amount, brand promotion, and the advertising endorser on the sales forecasting model. The study conducted the autoregressive distributed lag (ARDL) time series model using the internal and external social big data of a national fashion brand. Results indicated that the brand's past sales, search volume, promotion, and amount of advertising endorser information amount significantly affected the sales forecast, whereas the brand's advertising endorser search volume and information amount did not significantly influence the sales forecast. Moreover, the brand's promotion had the highest correlation with sales forecasting. This study adds to information-searching behavior theory by measuring consumers' brand involvement. Last, this study provides digital marketers with implications for developing profitable marketing strategies on the basis of consumers' interest in the brand and advertising endorser.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.191-209
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• 2022

This paper is a review of studies that focus on the prediction of a won/dollar exchange rate before and after the covid 19 pandemic. The Korea economy has an unprecedent situation starting from 2021 up till 2022 where the won/dollar exchange rate has exceeded 1,400 KRW, a first time since the global financial crisis in 2008. The US Federal Reserve has raised the interest rate up to 2.5% (2022.7) called a 'Big Step' and the Korea central bank has also raised the interested rate up to 2.5% (2022.8) accordingly. In the unpredictable economic situation, the prediction of the won/dollar exchange rate has become more important than ever. The authors separated the period from 2015.Jan to 2022.Aug into three periods and built a best fitted ARIMA/ARDL prediction model using the period 1. Finally using the best the fitted prediction model, we predicted the won/dollar exchange rate for each period. The conclusions of the study were that during Period 3, when the usual relationship between exchange rates and economic factors appears, the ARDL model reflecting the variable relationship is a better predictive model, and in Period 2 of the transitional period, which deviates from the typical pattern of exchange rate and economic factors, the SARIMA model, which reflects only historical exchange rate trends, was validated as a model with a better predictive performance.

• International Area Studies Review
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• v.16 no.1
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• pp.3-27
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• 2012

This paper adopts recently developed panel unit root test that is cross-sectionally robust. Cointegration test is also used to find whether regional house prices are in line with gross regional domestic production (GRDP) in the long run in Korea during 1989-2009. Based on the panel VECM and the panel ARDL models, we examine causal relationships among the variables and estimate the long-run elasticity. We find evidence of cointegration and bidirectional causal relationships between regional house prices and GRDP. The results of long-run estimates, using both fixed effect and ARDL models, show that house prices positively and significantly influence on the GRDP and vice versa. Together with these results, the findings of ARDL-ECM imply that there exists a long-run equilibrium relationship between house prices and regional economic variables even if there is a possibility of short-run deviation from its long-run path.

• Journal of Wellbeing Management and Applied Psychology
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• v.2 no.2
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• pp.19-37
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• 2019

Over the last few decades, the atmospheric carbon dioxide emission has been amplified to a great extent in Turkey. This amplification may cause global warming, climate change and environmental degradation in Turkey. Consequently, ecological condition and human life may suffer in the near future from these indicated threats. Therefore, an attempt was made to test the relationship among a number of expected factors and carbon dioxide emissions in the case of Turkey. The study covers the time series data over the period of 1970-2017. We employed the modern econometric techniques such as Johansen co-integration, ARDL bound testing approach and the block exogeneity. The results of the Johansen co-integration test show that there is a significant long-run relationship between carbon dioxide emissions and expected factors. The long-run elasticities of the ARDL model show that a 1% increase in the GDP per capita, electric consumption, fiscal development and trade openness will increase carbon dioxide emissions by 0.14, 0.52, 0.09 and 0.20% respectively. Further, our findings reveal that the environmental Kuznets curve (EKC) hypothesis and inverted U-shaped relationship between carbon dioxide emission and economic growth prevails. Therefore, the EKC hypothesis is valid and prevailing in the Turkish economy. The diagnostic test results show that the parameters of the ARDL model are credible, sTable and reliable in the current form. Finally, Block exogeneity analysis displays that all the expected factors are contributing significantly to carbon dioxide emissions in the Turkish economy.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.23-36
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• 2021

The study explores the nexus between foreign direct investment (FDI), financial innovation, and exchange rate volatility in selected South Asian countries for 1980 to 2017. The study applies the unit root test, Autoregressive Distributed Lagged, nonlinear ARDL, and causality test following Toda-Yamamoto. Unit root tests ascertain that variables are integrated in a mixed order; few variables are stationary at a level and few after the first difference. Empirical model estimation with ARDL, Long-run cointegration revealed with the tests of FPSS, WPSS, and tBDM by rejecting the null hypothesis of "no cointegration." This finding suggests that, in the long-run financial innovation, FDI inflows, and exchange rate volatility move together. Moreover, study findings established adverse effects running from FDI inflows and financial innovation to exchange rate volatility in the long run. These findings suggest that continual FDI inflows and innovativeness in the financial system assist in lessening the volatility in the foreign exchange market. Furthermore, nonlinear ARDL confirms the presence of asymmetric cointegration in the model. The standard Wald test established asymmetric effects running from FDI inflows and financial innovation to exchange rate volatility, both in the long and short run. Directional causality unveils feedback hypothesis holds for explaining causality between FDI, financial innovation, and exchange rate volatility.

• Management & Information Systems Review
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• v.36 no.2
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• pp.81-92
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• 2017

This study was to investigate the elasticity in tourism demand of Chinese tourists visiting Malaysia through ARDL models by using Chinese tourists arrivals, GDP, CPI, transportation costs and others. When China was implementing an open-door policy with foreign countries in the early 15th century, the movement of Chinese was very limited, and then communication between China and other countries was very weak. However, the Chinese government persistently and entirely implemented an open-door policy by participating in the World Trade Organization(WTO) in 2001. The Chinese government has opened the economy through foreign direct investment by providing various incentives for foreign investment. As a result, inbound and outbound Chinese movements increased in the early 21st century. China was one of the top five most visited tourist destinations in the world by 2016, and also Chinese tourists traveling abroad increased, so they made Malaysia a popular tourists destination because of increase sharply to around 1.41 million. This study examined the significance of major economic factors affecting the increase in Chinese tourists arriving in Malaysia. Other factors that induced their arrival included income, tourism prices, transportation costs and promotional activities. Short-run shocks from the Asian economic crisis and the outbreak of SARS were included to understand how tourism demand in Malaysia was affected. Finally this study found that the combination of the ARDL and the Error Correction Model were useful to statistically estimate the elasticities of tourism demand.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.6
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• pp.676-686
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• 2016

This study analyzed the long term relationship between the K-REITs' lending rate and interest rate variables based on ARDL (autoregressive distributed lag) and also examined the short term relationship based on the ARDL-ECM model. In the results of the empirical test, there is a co-integration relationship among the K-REITs' lending rate, 3 year government bond (rate), 3 year government bond (rate), corporation bond (rate) (AA-, 3year) and general fund loan rate. This means that the K-REITs' lending rate is related to the long term interest rate. The corporate general fund loan rate has a significant correlation with the K-REITs' lending rate in the long term relation and short term adjustment process. The establishment of a management plan by the REITs considering the trends in the corporate general fund loan rate in the decision making process for finance sector borrowings can be practically helpful for the K-REITs.

• Environmental and Resource Economics Review
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• v.23 no.2
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• pp.187-224
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• 2014

We present key issues to consider in estimating damages from price-fixing cases and then apply the procedure addressing those issues to a transportation fuel market. Among the five methods of overcharge calculation, the regression analysis incorporating the yardstick method is the best. If the price equation relates the domestic price to the foreign price and the exchange rate as in the transportation fuel market, the functional form satisfying both logical consistency and modeling flexibility is the log-log functional form. If the data under analysis is of time series in nature, then the ARDL model should be the base model for each market and the regression analysis incorporating the yardstick method combines these ARDL equations to account for inter-market correlation and arrange constant terms and collusion-period dummies across component equations appropriately so as to identify the overcharge parameter. We propose a two-step test for the benchmarked market: (a) conduct market-by-market Spearman or Kendall test for randomness of the individual market price series first and (b) then conduct across-market Friedman test for homogeneity of the market price series. Statistical significance is the minimal requirement to establish the alleged proposition in the world of uncertainty. Between the sensitivity analysis and the model selection process for the best fitting model, the latter is far more important in the economic analysis of damage in price-fixing litigation. We applied our framework to a transportation fuel market and could not reject the null hypothesis of no overcharge.