• International Area Studies Review
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• v.21 no.4
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• pp.159-172
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• 2017

In a typical trend-cycle decomposition of GDP, the trend component is usually assumed to follow a random walk process. This paper considers an ARIMA trend and assesses the validity of the ARIMA trend model. I construct univariate and bivariate unobserved-components(UC) models, allowing the ARIMA trend. Estimation results using U.S. data are favorable to the ARIMA trend models. I, also, compare the forecasting performance of the UC models. Dynamic pseudo-out-of-sample forecasting exercises are implemented with recursive estimations. I find that the bivariate model outperforms the univariate model, the smoothed estimates of trend and cycle components deliver smaller forecasting errors compared to the filtered estimates, and, most importantly, allowing for the ARIMA trend can lead to statistically significant gains in forecast accuracy, providing support for the ARIMA trend model. It is worthy of notice that trend shocks play the main source of the output fluctuation if the ARIMA trend is allowed in the UC model.

• Health Policy and Management
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• v.13 no.3
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• pp.91-103
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• 2003

The purpose of this study was to provide basic reference data for stabilization scheme of health expenditure through forecasting of health expenditure. The authors analyzed the health expenditure from 1985 to 2000 that had been calculated by Korean institute for health and social affair using transfer function model as ARIMA model with input series. They used GDP as the input series for more precise forecasting. The model of error term was identified ARIMA(2,2,0) and Portmanteau statics of residuals was not significant. Forecasting health expenditure as percent of GDP at 2010 was 6.8%, under assumption of 5% GDP increase rate. Moreover that was 7.4%, under assumption of 3% GDP increase rate and that was 6.4%, under assumption of 7% GDP increase rate.

• Journal of the East Asian Society of Dietary Life
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• v.17 no.4
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• pp.571-579
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• 2007

The aim of this study was to predict the development process of the Korean food service industry by forecasting the per capita GDP. Forecasting the GDP, involved two primary approaches. One was related to looking at the Korean food service industry's situation by per capita GDP and comparing it to that of the US and Japan. The other was to predict food service industry projections in Korea by quantitative forecasting models. Holt's simple exponential smoothing method and new types of the series models(Damped trend exponential smoothing method), were employed to predict the per capita GDP. The accuracy of the models was measured by MAPE. The empirical results of the forecasting models indicate that the three time series models performed fairly well. Of these Damped trend Damped trend exponential smoothing performed best with the lowest MAPE(9.9%). The results show that the time for reaching a per capita GDP level of $20,000 was 2008 with the Damped trend model and 2009 with the Holt model. Moreover, we found that a per capita GDP level of $30,000 will be achieved in 2012 from the Damped trend model and in 2013 from the Holt model. Within this study, the implications for the Korean food service industry are further discussed. It was predicted there will be a stabilization period in 2008 or 2009 in Korea with achievement of a per capita GDP of $20,000. At this time, major food service industry companies will need to invest in equipment toy external growth and there will be industry trends toward ethnic food and theme restaurants. Also, if a per capita GDP of $30,000 is achieved by 2012 or 2013, the Korean food industry will need to be highly responsive. Therefore, food industry companies should forecast and study customer values and prepare for changes.

• The Korean Journal of Applied Statistics
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• v.37 no.2
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• pp.255-263
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• 2024

GDP represents the total market value of goods and services produced by all economic entities, including households, businesses, and governments in a country, during a specific time period. It is a representative economic indicator that helps identify the size of a country's economy and influences government policies, so various studies are being conducted on it. This paper presents a GDP growth rate forecasting model based on a dynamic factor model using key macroeconomic indicators of G20 countries. The extracted factors are combined with various regression analysis methodologies to compare results. Additionally, traditional time series forecasting methods such as the ARIMA model and forecasting using common components are also evaluated. Considering the significant volatility of indicators following the COVID-19 pandemic, the forecast period is divided into pre-COVID and post-COVID periods. The findings reveal that the dynamic factor model, incorporating ridge regression and lasso regression, demonstrates the best performance both before and after COVID.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.109-114
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• 2020

The aim of this study is to develop basic artificial neural network models in forecasting the in-sample gross domestic product (GDP) of Malaysia. GDP is one of the main indicators in presenting the macro economic condition of a country as set by the world authority bodies such as the World Bank. Hence, this study uses an artificial neural network-based approach to make predictions concerning the economic growth of Malaysia. This method has been proposed due to its ability to overcome multicollinearity among variables, as well as the ability to cope with non-linear problems in Malaysia's growth data. The selected inputs and outputs are based on the previous literatures as well as the economic growth theory. Therefore, the selected inputs are exports, imports, private consumption, government expenditure, consumer price index (CPI), inflation rate, foreign direct investment (FDI) and money supply, which includes M1 and M2. Whilst, the output is real gross domestic product growth rate. The results of this study showed that the neural network method gives the smallest value of mean error which is 0.81 percent with a total difference of 0.70 percent. This implies that the neural network model is appropriate and is a relevant method in forecasting the economic growth of Malaysia.

• The Korean Journal of Applied Statistics
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• v.12 no.2
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• pp.397-404
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• 1999

1997년말 발생한 외환위기 이후 불확실성의 증대로 시계열모형을 이용한 경제예측에 한계가 노정되고 있다. 이를 극복하기 위하여 경제주체의 기대(expectation)를 파악할수 있는 기업경기실사지수를 경제예측에 도입할 필요가 있다. 본고에서는 기업경기실사지수를 이용한 모형과 시계열모형을 추정하고 이들을 예측력 측면에서 비교, 분석해보았다. 분석결과 불확실성이 높았던 외환위기이후 기간에는 기업경기실사지수를 이용한 모형이 시계열모형보다 예측력면에서 우수한 것으로 나타났다.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.808-809
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• 2007

전력수요는 경기변동과 밀접한 관련성을 가지고 동행적으로 움직이며, 전력자료는 경제자료에 비해 조기 관측되는 선행성이 있다. 본 연구에서는 GDP 전망을 위해 발전량이 유용하게 사용될 수 있는가를 살펴 보았다. 발전량과 GDP의 관련성은 그랜저 인과관계 검정을 통해서 검증해 보았으며, 발전량 자료 취득의 선행성은 선행차수를 변화시켜 보면서 관련성이 어떻게 변하는가를 살펴보았다. 실제 자료를 이용하여 분석하고, 2004년부터 2006년 기간의 전망치를 평가한 결과, 본 논문에서 살펴 보고자 했던 발전량과 GDP 사이에는 아주 높은 관련성이 있음을 확인할 수 있었고 또한 발전량 자료를 이용함으로써 실제로 GDP 전망의 예측력을 상당히 개선시킬 수 있음을 볼 수 있었다. 발전량과 GDP 사이의 관계는 시간변동계수를 가지는 공적분 및 오차수 정모형을 이용하여 모형화하였다.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.27 no.3
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• pp.98-105
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• 2019

In the past 30 years, our aviation demand has been growing continuously. As such, the importance of the demand forecasting field is increasing. In this study, the factors influencing Korea's international air demand were selected, and the international air demand was analyzed, forecasted and reviewed through OLS multiple regression analysis. As a result, passenger demand was affected by GDP per capita, oil price and exchange rate, while cargo demand was affected by GDP per capita and private consumption growth rate. In particular, passenger demand was analyzed to be sensitive to temporary external shocks, and cargo demand was more affected by economic variables than temporary external shocks. Demand forecasting, OLS multiple regression analysis, passenger demand, cargo demand, transient external shocks, economic variables.

• Asian Journal of Innovation and Policy
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• v.9 no.2
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• pp.187-206
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• 2020

Under the on-going evolution of the COVID-19 pandemic, estimating the economic impact of the pandemic is highly uncertain and challenging. This situation makes it difficult for policymakers, governors, and economic entities to formulate appropriate responses and decision makings. To provide useful information about the effect of the COVID-19 pandemic on the Korean economy, this study examined macroeconomic impact analysis stemming from the pandemic shocks with different scenarios for the Korean economy. Based on three scenarios using the growth rate of 2020 GDP and consumer expenditure patterns, the 2021 GDP by industry sector was forecast with two new approaches. First, the recovering process of the Korean economy from the shock was analyzed by applying a Flex-IO method. Second, a new forecasting approach combined with an IO coefficient matrix was applied to forecast the future GDP changes. The findings of this study are summarized as follows: First, the total GDP growth rate under the Pessimistic Scenario demonstrates less rebound from the shock than that of the Base Scenario. Second, agriculture, culture, and tourism-related sectors that are suffering from the severe losses of COVID-19 showed lower resilience than other different industries. Third, information and communications technology (ICT) industry maintains a stable growth trend and is expected to take the leading role for the Korean economy in the post-COVID-19 and the Industry 4.0 eras. The findings deliver that it needs to analyze how government expenditure responding the shock into the forecasting model, which can be more useful and reliable to simulate the resilience from the pandemic.

• Environmental and Resource Economics Review
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• v.28 no.3
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• pp.437-465
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• 2019

In this study, we propose a new method to forecast long-term energy demand in Korea. Based on Chang et al. (2016), which models the time varying long-run relationship between electricity demand and GDP with a function coefficient panel model, we design several schemes to retain objectivity of the forecasting model. First, we select the bandwidth parameters for the income coefficient based on the out-of-sample forecasting performance. Second, we extend the income coefficient using the functional principal component analysis method. Third, we proposed a method to reflect the elasticity change patterns inherent in Korea. In the empirical analysis part, we forecasts the long-term energy demand in Korea using the proposed method to show that the proposed method generates more stable long term forecasts than the existing methods.