• 한국의류학회지
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• 제46권5호
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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.

• 무역학회지
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• 제46권6호
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• pp.191-209
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• 2021

In this paper, various time series analysis models and machine learning models are presented for long-term prediction of export growth rate, and the prediction performance is compared and reviewed by RMSE and MAE. Export growth rate is one of the major economic indicators to evaluate the economic status. And It is also used to predict economic forecast. The export growth rate may have a negative (-) value as well as a positive (+) value. Therefore, Instead of using the ReLU function, which is often used for time series prediction of deep learning models, the PReLU function, which can have a negative (-) value as an output value, was used as the activation function of deep learning models. The time series prediction performance of each model for three types of data was compared and reviewed. The forecast data of long-term prediction of export growth rate was deduced by three forecast methods such as a fixed forecast method, a recursive forecast method and a rolling forecast method. As a result of the forecast, the traditional time series analysis model, ARDL, showed excellent performance, but as the time period of learning data increases, the performance of machine learning models including LSTM was relatively improved.

• 자원ㆍ환경경제연구
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• 제23권2호
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• pp.187-224
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• 2014

본 연구에서는 담합의 경제분석에서 고려해야 할 쟁점들을 논의한 후, 이러한 쟁점들을 반영한 방법론을 특정 수송용 연료시장의 분석에 적용하였다. 가상 경쟁가격과 과잉징수를 산정하는 5가지 방법 중 표준시장비교방법에 기반한 회귀분석방법이 최선이다. 수송용 연료시장에서와 같이 국제가격과 환율이 국내가격에 영향을 미치는 제품의 실거래가격을 분석하는 경우 논리에 부합하면서 유연한 함수형태는 로그-로그 함수형태이다. 경제분석의 대상이 되는 자료가 시계열자료인 경우에 ARDL 모형을 시장별 회귀분석모형의 근간으로 채택하는 것이 필요하며, 표준시장비교방법에 기반한 회귀분석방법에서는 구성 회귀식 간에 모수제약이 포함된 ARDL 회귀식 체계를 구축하고 system FGLS로 추정하여야 한다. Friedman 동질성 검정을 통하여 표준시장 여부를 판별할 수 있다. 통계적 유의성은 불확실성 하에서 입증하고자 하는 명제를 확립하는데 요구되는 최소의 요건이다. 담합관련 소송의 경제분석에서는 민감도 분석은 그다지 유용성이 없으며, 최적모형 선별과정이 더 중요한 절차이다. 위 방법론을 특정 수송용 연료시장의 분석에 적용한 결과, 해당 시장에서는 담합에 기인하는 손해액이 없다는 귀무가설을 기각할 수 없었다.

• 웰빙융합연구
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• 제2권2호
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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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• 제8권5호
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• pp.325-334
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• 2021

This study aims to investigate whether economic growth is elevated by banking industry development in Jordan. The study adopts time-series econometric methodologies, which comprise the bounds testing approach within the autoregressive distributed lag (ARDL) and the conditional causality analysis. Consistent with the assumptions of the adopted methodology, the study utilized annual time-series data for a relatively long period of thirty-nine years, between 1980 and 2018. The empirical results show that Jordan's economic growth is strongly responsive in respect to any changes in banking industry development. Also, the results reveal the harmful impact of rising lending interest rate; as this rate increases, economic growth will decrease. The findings are in line with the conceptual arguments of the supply-leading hypothesis, which confirmed that banking development is considered as one of the main pillars that have stimulating effects on economic growth. The evidence of the current study may provide important implications for policymakers and bankers. Those professionals should work to maintain a stable regulatory system that enhances the banking system function in activating economic growth. Also, a considerable focus should be placed on designing a steady interest rate policy to avoid the inherently undesirable impacts of high-interest rates on the Jordanian economy.

• The Journal of Asian Finance, Economics and Business
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• 제7권12호
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• pp.693-702
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• 2020

The present study aims to investigate the impact of tourism growth on human development in Indian economy. For this purpose, the study uses annual data from 1980 to 2018 and utilizes two proxies for tourism growth - tourism receipt and tourist arrivals - and uses human development index calculated by UNDP. The study uses control variables such as government expenditure and trade openness. The study employs auto regressive distributed lag (ARDL) approach to investigate the cointegrating relationship among the variables in the model. Further, the study also explores the causal nexus between tourism sector and human development by using the Toda-Yamamoto Granger non-causality test. The result of ARDL bounds test reveals the existence of cointegrating relationship between human development indicators, government expenditure, trade openness, and tourism sector growth. The cointegating coefficient confirms a positive and significant relationship between tourism sector growth and human development in India. The causality result suggests that economic growth and tourism have a positive impact while trade openness has a negative impact on human development in India. The major findings of this study suggest that tourism plays an important role in the socio-economic development of Indian economy in recent years and the country must develop this sector to achieve sustainable development.

• 문화경제연구
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• 제21권3호
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• pp.59-88
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• 2018

최근 들어 침체되어 있던 영화 부가시장 매출이 증가세를 보이고 있으며 특히 IPTV 및 디지털 케이블 TV 영화 VOD 매출 증가가 두드러진다. TV VOD 시장의 중요성이 높아짐에 따라 본 연구에서는 TV VOD 관련 시계열 총계(aggregate) 자료를 사용하여 TV VOD 수요의 특징과 수요 결정요인에 대한 실증적 결과를 제시하였다. 구체적으로 2013 년 1월부터 2018년 6월 기간 동안 우리나라 TV VOD 월별 총 이용실적 자료와 시계열모형(ARDL)을 통해 실증분석을 하였다. 분석 결과, TV VOD 수요의 특성과 관련하여 VOD 수요는 극장 수요에 비해 계절성이 약하며, 월별 VOD 이용실적 1위 영화의 성과가 해당월의 전체 수요에서 차지하는 비중이 극장에 비해 낮음을 알 수 있었다. 또한 홀드백와 극장 개봉성과 간에 일관된 관계가 존재한다고 보기는 어려웠다. 수요 결정요인에 대한 분석 결과, 단기적 관계에서 1인당 실질 GDP, IPTV 가입자 수, 극장 관객 수 및 대체재의 가격이 TV VOD 수요에 유의한 영향을 미치는 것으로 나타났으나, 장기탄력성에서는 1인당 실질 GDP를 제외한 나머지 변수들의 영향은 통계적으로 유의하지 않았다. 극장 관객 수의 경우는 장단기 모두에서 유의하게는 나타났으나 10% 유의수준에서 유의성이 확인되었으므로 본 변수의 영향이 강건하다고 보기는 어렵다.

• 유통과학연구
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• 제20권8호
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• pp.1-8
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• 2022

Purpose: Research on attracting foreign direct investment (FDI) plays an important role in helping provinces attract more FDI projects. However, with local competition, FDI enterprises also have to consider their investment. This study evaluates the provincial competitiveness to attract FDI in Thai Nguyen province, a province of Vietnam. In which provincial distribution of competitiveness is measured through nine indicators. Research design, data, and methodology: The study collects data (FDI and the provincial competitiveness index) from 2006 to 2020. The study uses Autoregressive Distributed Lag (ARDL) to text the impact of distribution of competitivenes on foreign direct investment. With time-series, the ARDL is suitable for data analysis. Results: The regression results indicate that the competition index of market entry and informal costs negatively impact attracting FDI into the province; The human resource training quality index has a positive effect on FDI. The results show that FDI enterprises pay much attention to business establishment procedures, hidden costs, and quality of human resources in the province. Conclusions: At the same time, in terms of practice, the results of this study, the authors also offer solutions to help improve the ability to attract FDI into Thai Nguyen province. The significant provincial competitiveness indicators should be taken into account for improvement first.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.163-163
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• 2023

Flooding has become an increasing event which is one of the major natural disasters responsible for direct economic damage in South Korea. Driven by climate change, precipitation extremes play significant role on the flood damage and its further increase is expected to exacerbate the socioeconomic impact in the country. However, the empirical evidence associating changes in precipitation extremes to the historical flood damage is limited. Thus, there is a need to assess the causal relationship between changes in precipitation extremes and flood damage, especially in agricultural region like Chungcheong region in South Korea. The spatial and temporal changes of precipitation extremes from 10 synoptic stations based on daily precipitation data were analyzed using the ClimPACT2 tool and Mann-Kendall test. The four precipitation extreme indices consisting of consecutive wet days (CWD), number of very heavy precipitation wet days (R30 mm), maximum 1-day precipitation amount (Rx1day), and simple daily precipitation intensity (SDII), which represent changes in intensity, frequency, and duration, respectively, and the time series data on flooded area and flood damage from 1985 to 2020 were used to investigate the causal relationship in the ARDL-ECM framework and pairwise Granger causality analysis. The trend results showed that majority of the precipitation indices indicated positive trends, however, CWD showed no significant changes. ARDL-ECM framework showed that there was a long-run relationship among the variables. Further analysis on the empirical results showed that flooded area and Rx1day have significant positive impacts on the flood damage in both short and long-runs while R30 mm only indicated significant positive impact in the short-run, both in the current period, which implies that an increase in flooded area, Rx1day, and R30 mm will cause an increase in the flood damage. The pairwise Granger analysis showed unidirectional causality from the flooded area, R30 mm, Rx1day, and SDII to flood damage. Thus, these precipitation indices could be useful as indicators of pluvial flood damage in Chungcheong region of South Korea.

• The Journal of Asian Finance, Economics and Business
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• 제8권7호
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• pp.1-9
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• 2021

This paper investigates the impact of the domestic and global outbreak of the coronavirus (COVID-19) pandemic on the trading size of the Malaysian stock (MS) market. The theoretical model posits that stock markets are affected by their response to disasters and events that arise in the international or local environments, as well as to several financial factors such as stock volatility and spread bid-ask prices. Using daily time-series data from 27 January to 12 May 2020, this paper utilizes the traditional Augmented Dickey and Fuller (ADF) technique and Zivot and Andrews with structural break' procedures for a stationarity test analysis, while the autoregressive distributed lag (ARDL) method is applied according to the trading size of the MS market model. The analysis considered almost all 789 listed companies investing in the main stock market of Malaysia. The results confirmed our hypotheses that both the daily growth in the active domestic and global cases of coronavirus (COVID-19) has significant negative effects on the daily trading size of the stock market in Malaysia. Although the COVID-19 has a negative effect on the Malaysian stock market, the findings of this study suggest that the COVID-19 pandemic may have an asymmetric effect on the market.