• KDI Journal of Economic Policy
• /
• v.34 no.4
• /
• pp.117-156
• /
• 2012

This paper decomposes and estimates trend/cyclical components of some key macro variables-GDP, inflation, and interest rate, using a simple DSGE model along with flexible trend specification. The extracted cyclical components of output and interest rate are similar to HP-filtered counterparts, despite some differences in persistence and volatility, while inflation resembles that from BK filtering. This implies that the usual practice of applying a single filtering method to the data of interest may be problematic. When the baseline model is extended to incorporate consumption habit and price indexation, habit turns out to be important in explaining the persistence of business cycles. Comparison of several alternative models shows that the usual practice of estimation of DSGE model using filtered data leads to biased results. Finally, various sensitivity analyses illustrate that (1) allowing for correlation between structural cyclical shocks and trend shocks and (2) including irregular components (in inflation rate) may deliver interesting/important implication for gap estimates.

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

• The Journal of the Korea Contents Association
• /
• v.17 no.9
• /
• pp.300-309
• /
• 2017

This paper examines the cyclical fluctuation of 'construction orders' and 'construction investment' using HP filter, Bandpass filter and Beveridge-Nelson decomposition methods. The main results are as follows. As a result of the analysis of the cyclical fluctuation of construction orders, it was analyzed that there were about 7 cyclical fluctuations from 1976 to the first quarter of 2017. Construction orders for cyclical fluctuations peaked in the second quarter of 2015 and turned to a downward trend. On the other hand, construction investment has experienced about 6 cycles of fluctuations during the same period, and it has been rising continuously since the bottom of 3Q12. This is consistent with the general theory that construction orders precede construction investments. In addition, the comparison of the construction orders, construction investment, and GDP amplitude shows that the GDP amplitude is the smallest and stable, and the construction orders have the greatest variation in amplitude. Therefore, construction orders should be adjusted by government policy depending on economic fluctuations.

• The Korean Journal of Applied Statistics
• /
• v.27 no.3
• /
• pp.431-444
• /
• 2014

We introduce the empirical model decomposition (EMD) to decompose a time series into a set of components in the time-frequency domain. By using EMD, we also extract cycle and trend components from major Korean macroeconomic indices and forecast the indices with the components combined. In order to evaluate their efficiencies, we investigate volatility, autocorrelation, persistence, Granger causality, nonstationarity, and forecasting performance. They are then compared with those by Hodrick-Prescott filter which is the most commonly used method.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2020.06a
• /
• pp.272-272
• /
• 2020

급격한 도시화와 기후변화로 인해 기존 물순환 체계가 왜곡됨에 따라 저영향개발(Low Impact Development) 기법의 필요성이 증가하고 있는 추세이다. LID 요소 중 하나인 옥상녹화는 국내에 도입이 많이 되었고 적정 설계인자를 설정하기 위해 지속적으로 연구를 수행하고 있다. 따라서 본 연구에서는 옥상녹화 설계인자의 설정과 수문·식생·토양을 고려한 시나리오 구축을 통해 국내 물순환 모형인 K-LIDM을 이용하여 옥상녹화의 정량적 물순환 효율성 분석을 수행하였다. 대상유역은 한국 그린인프라 저영향개발센터 내 옥상녹화 실험장으로 옥상녹화에서 계측하고 있는 측정값을 기반으로 K-LIDM 모형 검보정을 수행하고 옥상녹화의 설계인자에 따른 시나리오를 통해 수문학적 성능평가를 수행하였다. 단기강우 시나리오를 적용하였을 때 옥상녹화는 일반콘트리트 옥상에 비해 5.25 ~ 36.48%의 유출저감률이 산정되었으며, 유출저감 한계효율은 지속시간 60분, 120분, 180분 일 때 각각 8.93%, 6.17%, 5.25%로 산정되었다. 장기강우 시나리오를 적용하였을 때는 연평균 53.54%의 유출저감률이 산정되었다. 또한 토심의 증가에 따른 유출량 감소는 관목>교목>지피 식물 순으로 나타났으며, 토심이 1cm 증가할 때의 저감유출랴은 지피 식물의 경우 0.0002㎥/s, 교목의 경우 0.0005㎥/s, 관목의 경우 0.0008㎥/s로 나타났다. 옥상녹화는 건물 설계에 따른 적재하중과 환경조건, 식물선정, 생육에 많은 영향을 받는 기술요소이므로 다양한 시나리오를 통한 수문학적 성능평가가 필요시 된다. 본 연구결과는 정량적 옥상녹화시스템 효율성 검증을 실시하기 위한 옥상녹화 설계가이드라인에서 고려사항을 제시할 수 있는 기준이 될 것이라고 기대된다.

• Journal of the Korean Recycled Construction Resources Institute
• /
• v.4 no.2
• /
• pp.82-89
• /
• 2009

According to the great city development and the rapid growth of redevelopment project, waste concrete emission has been increased. Waste concrete powder is one of the by-product originated from the recycling of the waste concrete. The more making high quality recycled aggregate to use aggregate for concrete, the more waste concrete powder is producted relatively. Therefore, to realize the total recycling of waste concrete, development of recycling technology for waste concrete powder need very much. This technical note present the discharged process and the various properties of waste concrete powder. As the results, on the average, the maximum particle-size of waste concrete powder is less than $600{\mu}m$, and oven-dry density is less than $2.5g/cm^3$. And waste concrete powder contains more than 50% of $SiO_2$, 30% of CaO and 10% of $Al_2O_3$. Thus qualities of waste concrete powder is lower than those of high quality raw material for concrete. However, if it is processed by grading to the purpose, it will be used as resource of raw materials for construction field.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.13 no.1
• /
• pp.221-232
• /
• 1993

In this study, the period for stabilization in the case of recycling leachate was compared and analyzed with the case of non-cycle type, using lysimeters filled with municipal organic refuse. The lysimeters were operated with various detention time. In addition, the degree of stabilization was estimated by the way of measuring the quantity of gas from landfills. As the results, the recycle of leachate, which was modified as the neutral level of pH, accelerated the biological decomposition of organics with the raped growth of anaerobic bacteria in the system, and reduced the period for the stabilization. In the case of BOD and COD in leachate, COD. which had been originality similar to BOD, had increased more rapidly than BOD as time lapsed. Moreover, the quantity of gas from the recycle reactor was larger than from the non-cycle reactor. The shorter the detention time of leachate gas, the larger the quantity of gas produced in the lysimeters.

• Journal of Korean Society of Transportation
• /
• v.19 no.6
• /
• pp.65-73
• /
• 2001

Until recently, we use only weekly and monthly adjustment factors in order to estimate the AADT. By the way. we can suppose that the traffic is time series data related to flow of time. So we tried to analyse traffic patterns using time series analysis and apply them to estimate the AADT. We could divide traffic patterns into trend, cyclic variation, seasonal variation and irregular variation like as time series data. Also, in order to reduce random error components, we have looked for the weather conditions as an influential factor. There are many weather conditions such as rainfalls, but, temperatures, and sunshine hours among others but we selected rainfalls and lowest temperatures. And then, we have estimated the AADT using time series factors. To compare the results of, we have applied both irregular variation joined to weather factors and that not joined to. RMSE and U-test were opted at methods to appreciate results of AADT estimation.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.14 no.3
• /
• pp.645-654
• /
• 1994

Photocatalytic oxidation conditions of reactant recirculation flow rate 275 mL/min, aeration rate 2 LPM and $UV+TiO_2+H_2O_2$(500 mg/L) proved to be appropriate for water including organic materials treatment. With increasing turbidity and suspended solids concentration, at turbidity 10 NTU-suspended solids concentration 29 mg/L the phenol degradation efficiency increased, which in turn decreased at turbidity 50 NTU-suspended solids concentration 170 mg/L, however no significant differences were observed, demonstrating similar results with those obtained at zero turbidity and suspended solids concentration. The degradation efficiency of phenol decreased with increasing influent phenol concentrations. The $UV+TiO_2+H_2O_2$ photocatalytic advanced oxidation process conducted is considered to be possibly applied to the drinking water treatment, and the post-treatment process of biological wastewater treatment.

• The Korean Journal of Applied Statistics
• /
• v.19 no.2
• /
• pp.319-330
• /
• 2006

A signal in real world usually composes of multiple signals having different scales of frequencies. For example sun-spot data is fluctuated over 11 year and 85 year. Economic data is supposed to be compound of seasonal component, cyclic component and long-term trend. Decomposition of the signal is one of the main topics in time series analysis. However when the signal is subject to nonstationarity, traditional time series analysis such as spectral analysis is not suitable. Huang et. at(1998) proposed data-adaptive method called empirical mode decomposition (EMD) . Due to its robustness to nonstationarity, EMD has been applied to various fields. Huang et. at, however, have not considered denoising when data is contaminated by error. In this paper we propose efficient denoising method utilizing cross-validation.