• Title/Summary/Keyword: ARDL 시계열 분석

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Fashion Brand Sales Forecasting Analysis Using ARDL Time Series Model -Focusing on Brand and Advertising Endorser's Web Search Volume, Information Amount, and Brand Promotion- (ARDL 시계열 모형을 활용한 패션 브랜드의 매출 예측 분석 -패션 브랜드와 광고모델의 웹 검색량, 정보량, 가격할인 프로모션을 중심으로-)

  • Seo, Jooyeon;Kim, Hyojung;Park, Minjung
    • 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.

An Analysis on TV VOD Demand: Focusing on Time Series Analysis (TV VOD 수요 분석: 시계열분석을 중심으로)

  • Kim, Ki Jin;Choi, Sung-Hee
    • Review of Culture and Economy
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    • v.21 no.3
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    • pp.59-88
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    • 2018
  • This study examines demand of the Korean TV VOD using monthly aggregate data and time series analysis models. In particular, the impact of box office attendance, number of IPTV subscribers, income and price of substitutes on TV VOD market is analyzed. Data on TV VOD download during the period 2013 January to 2018 June are used for the empirical analysis. TV VOD demand shows lower level of seasonality than box office attendance and the share of monthly top1 movie in TV VOD platform is also lower than that of box office attendance. The relationship between a movie's holdback and box office performance does not seem consistent. The empirical result of ARDL model reveals that in the short-run box office attendance, number of IPTV subscribers and price of substitutes have significant impact on TV VOD demand. The result on the long-term relation shows that income is the only determinant of TV VOD demand. The impact of box office attendance on TV VOD is not shown to be robust both for the short-term and long-term.

Comparison of long-term forecasting performance of export growth rate using time series analysis models and machine learning analysis (시계열 분석 모형 및 머신 러닝 분석을 이용한 수출 증가율 장기예측 성능 비교)

  • Seong-Hwi Nam
    • Korea Trade Review
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    • v.46 no.6
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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.

The Impact of Nuclear Power Generation on Wholesale Electricity Market Price (원자력발전이 전력가격에 미치는 영향 분석)

  • Jung, Sukwan;Lim, Nara;Won, DooHwan
    • Environmental and Resource Economics Review
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    • v.24 no.4
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    • pp.629-655
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    • 2015
  • Nuclear power generation is a major power source which accounts for more than 30% of domestic electricity generation. Electricity market needs to secure stability of base load. This study aimed at analyzing relationships between nuclear power generation and wholesale electricity price (SMP: System Marginal Price) in Korea. For this we conducted ARDL(Autoregressive Distributed Lag) approach and Granger causality test. We found that in terms of total effects nuclear power supply had a positive relationship with SMP while nuclear capacity had a negative relationship with SMP. There is a unidirectional Granger causality from nuclear power supply to SMP while the reverse was not. Nuclear power is closely related to SMP and provides useful information for decision making.

Research on Relationship between Urbanization and Energy Consumption (중국의 도시화와 에너지 소비 관계에 대한 연구)

  • Won, Doohwan;Jung, Sukwan
    • Journal of International Area Studies (JIAS)
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    • v.22 no.1
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    • pp.91-112
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    • 2018
  • This study examined the dynamic relationship between urbanization and energy consumption in China. As an alternative to the conventional method of having the same integration of time series and large samples, ARDL method and Toda-Yamamoto causality analysis were applied. As a result, urbanization income, income, and energy consumption have a long-term stable equilibrium. Urbanization and income have a positive effect on energy consumption in the long run, but short-term changes of urbanization and income have no significant effect on energy consumption changes. The adjusted coefficient was -0.2395, which was statistically significant. In the causality test, income and energy consumption are useful to predict each other, but urbanization is exogenous because there are no causality with other variables. Since the process of urbanization in China has been proceeding slowly and deliberately by the government, it can be seen that the long-term effects of urbanization are clear and exogenous.

The Dynamic Analysis between Environmental Quality, Energy Consumption, and Income (소득 및 에너지소비와 환경오염의 관계에 대한 분석)

  • Jung, Sukwan;Kang, Sangmok
    • Journal of Environmental Policy
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    • v.12 no.3
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    • pp.97-122
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    • 2013
  • The ARDL(Autoregressive Distributed Lag) method is employed analyzes the long-run equilibrium relationships among environmental pollution($CO_2$ emissions) per capita, income levels per capita, and energy consumption per capita. The error correction model is employed to analyze the short-term effects of income and energy consumption on $CO_2$ emissions. The Toda-Yammamoto method is employed for causal analysis among the three variables. The results show that income levels, energy consumption, and $CO_2$ emissions are cointegrated. We found the N type relationship between income and $CO_2$ emissions. Long-term elasticities of income and energy consumption with respect to $CO_2$ emission were greater than their short-term elasticities. There were a bilateral causality between energy consumption and $CO_2$ emissions. There was a unilateral causality from $CO_2$ emissions to income and from energy consumption to income not vice versa. Energy consumption can be an important variable to contribute to forecasting $CO_2$ emissions.

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Theoretical and Empirical Issues in Conducting an Economic Analysis of Damage in Price-Fixing Litigation: Application to a Transportation Fuel Market (담합관련 손해배상 소송의 경제분석에서 고려해야 할 이론 및 실증적 쟁점: 수송용 연료시장에의 적용)

  • Moon, Choon-Geol
    • 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.

An Empirical Study on Main Factors Affecting Technology Balance of Payments (기술무역수지에 영향을 미치는 주요 요인들에 대한 실증연구)

  • Pak, Cheolmin;Ku, Bonchul
    • Journal of Technology Innovation
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    • v.25 no.1
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    • pp.61-89
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    • 2017
  • This study aims to estimate empirically the respective impacts of R&D expenditure, R&D labor, overseas direct investment, commodity trade balance, and technology trade openness on technology balance of payments. To examine the presence of co-integration between them, this paper employed the ARDL-bounds test using time series data from 1981 to 2014, and the result shows that there is a stable long-run equilibrium relationship among them. Furthermore, we estimated long- and short-run coefficients of the technology balance of payments with respect to each variables based on long-run equilibrium equation and error correction model. As a result, the technology balance of payments respond negatively to R&D labor and technology trade openness, and R&D expenditure does produce positive effects in the long-run, while coefficients of overseas direct investment and commodity trade balance in the long-run are not statistically significant. Besides, according to results of error correction model, overseas direct investment only has clearly a positive effects in the short-run, in contrast, the short-term relationships between the other variables and the technology balance of payments could not definitively derived. This implies that it is necessary to procure and cultivate talented personnel, as well as to enlarge gradually technology trade size in order to improve technology balance of payments from a long-term point of view.

Estimation of Potential Supply of Offset from Household Electric Appliances (가정용 전자기기의 잠재 상쇄 공급량 추정)

  • Jin, Hyun Joung;Kim, Jeong In;You, Eun Young;Park, Seo Hwa
    • Environmental and Resource Economics Review
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    • v.24 no.3
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    • pp.463-488
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    • 2015
  • A more detailed design of offset system is needed according to the emission trading system started in 2015. This study aims to estimate the supply of potential offset that can be secured by expanding high-efficiency household electric appliances. The target commodities for analysis are three different householding electric appliances: TV, washing machine, electric fan, refrigerator and air conditioner. By using the ARDL model, we estimated the coefficients of diffusion of these high-efficiency appliances from 2016 to 2022. Then, the potential supply of offset was drawn by calculating the amount of electricity saving by efficiency improvement and by applying the rates of carbon exchange. Supposing that the electricity savings rates of high-efficiency appliances are each 10% and 20%, the accumulated carbon decrement in 2022 was respectively $361,899CO_2t$ and $723,797CO_2t$. The appliance that showed the biggest carbon decrement was air conditioner, and the second biggest was refrigerator and the next was TV, followed by washing machine, electric fan.