• The Journal of Asian Finance, Economics and Business
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• v.9 no.5
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• pp.295-303
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• 2022

This study investigates the impact of globalization, coal consumption, and economic growth on CO₂ emissions in Malaysia by applying the Kuznets Environmental Curve model. The study employed the Autoregressive Distributed Lag modeling technique on time series data over the period of 1970-2018 to determine the short and long-run relationship between CO₂ emissions and a number of variables, including globalization, coal consumption, and economic growth. The results show that globalization increase CO₂ emissions in both the short and long run in Malaysia. Furthermore, the results reveal that economic growth and coal consumption degrade the environmental quality by accelerating the CO₂ emissions in the short-run and long run. As a result, the findings validate the Kuznets Environmental Curve hypothesis of an inverted U-shaped relationship between economic growth and CO₂ emissions in the long run for Malaysia. The findings of this study suggest that higher globalization levels and usage of coal consumption degrade the environmental quality in Malaysia. The findings also indicate the effect of economic growth on environmental degradation is positive at the initial stage but improves after the economy achieves a threshold level of income per capita in the economic development process with an inverted U-shaped pattern in the long run.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.1
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• pp.177-187
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• 2022

Theoretical literature agrees on the interaction between financial instability and economic activity but explains it's dynamic in two points of view: one is that the transmission mechanism occurs in one unique regime and the other reckons a shift of regime leads to the alteration of the transmission mechanism. This study aims to find evidence of the multi-regime transmission for ASEAN developing countries. The author employs the technique of Threshold vector auto regression using the financial stress index standing for financial instability. Monthly data is collected, covering a period long enough with many episodes of high stress in recent decades. There are two conclusions: (1) A financial shock has a negative and stronger impact on economic activity during a high-stress period than it does during a low-stress period; (2) the response of economic activity to a negative financial shock during high-stress periods is stronger than it is during normal times. The findings point to the importance of the financial stress index as an additional early warning indicator for the real economy sector, as well as the positive effect that a reduction in financial stress may have on economic activity, implying the importance of "unconventional" monetary policy in times of high financial stress.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.406-406
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• 2022

댐은 물을 담아두어 강수량에 따른 유량을 조절하거나, 하천의 물을 끌어와 사용할 수 있게 하는 역할 또는 모래, 자갈 등을 막아 걸러주는 역할 등을 수행한다. 우리나라의 경우 지역별, 계절별 강수량의 차이가 크며, 그로 인해 유량이 지역과 계절에 영향을 크게 받는다. 이런 변동성을 조절하기 위해 치수와 이수, 두 분야 모두에서 댐의 중요성이 크다. 이뿐만 아니라 기후변화로 인한 변동성의 극대화로 인해 그 중요성이 나날이 커지고 있다. 댐을 운영하기 위해서는 강수량에 따른 댐 유입량의 예측을 하여, 적절한 방류 시기 및 방류량을 결정하는 것이 가장 중요한 요소이다. 기후변화로 인한 변동성의 증대로 홍수와 가뭄과 같은 재해의 빈도와 심도가 커지면서 댐 유입량의 예측이 어려워지고 있다. 댐의 설계나 유지관리를 위해 홍수에 대해서는 많은 연구가 이루어졌던 것에 비해, 갈수기의 경우 물 부족으로 인해 유량이 적어져 댐 유입량에 대한 정확한 산정이 어려워 가뭄 시 댐 유입량에 관한 연구가 홍수 시에 비해 적게 연구된 것이 실정이다. 따라서 가뭄 시 댐 연구를 위해 갈수기의 댐 유입량에 대한 정확한 산정 및 예측의 필요성이 대두되고 있다. 이번 연구에서는 댐 주변의 지하수위와 하천수위의 관계성을 보이고 각각 다른 변량 간의 시간적 종속성을 고려하는 동시에 상호연관된 변량의 시간적 종속성을 동시에 고려한VARX(vector autoregressive-exogenous) 모델을 이용하여 정확한 댐의 유입량을 산정 및 예측하고 그에 대한 검증을 시행하여, 댐 분야에서 가뭄에 대비할 수 있는 근간을 마련하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.514-517
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• 2023

본 연구는 인 컨택스트 러닝 (In-Context Learning)을 오디오-언어 작업에 적용하기 위한 멀티모달 (Multi-Modal) 딥러닝 모델을 다룬다. 해당 모델을 통해 학습 단계에서 오디오와 텍스트의 소통 가능한 형태의 표현 (Representation)을 학습하고 여러가지 오디오-텍스트 작업을 수행할 수 있는 멀티모달 딥러닝 모델을 개발하는 것이 본 연구의 목적이다. 모델은 오디오 인코더와 언어 인코더가 연결된 구조를 가지고 있으며, 언어 모델은 6.7B, 30B 의 파라미터 수를 가진 자동회귀 (Autoregressive) 대형 언어 모델 (Large Language Model)을 사용한다 오디오 인코더는 자기지도학습 (Self-Supervised Learning)을 기반으로 사전학습 된 오디오 특징 추출 모델이다. 언어모델이 상대적으로 대용량이기 언어모델의 파라미터를 고정하고 오디오 인코더의 파라미터만 업데이트하는 프로즌 (Frozen) 방법으로 학습한다. 학습을 위한 과제는 음성인식 (Automatic Speech Recognition)과 요약 (Abstractive Summarization) 이다. 학습을 마친 후 질의응답 (Question Answering) 작업으로 테스트를 진행했다. 그 결과, 정답 문장을 생성하기 위해서는 추가적인 학습이 필요한 것으로 보였으나, 음성인식으로 사전학습 한 모델의 경우 정답과 유사한 키워드를 사용하는 문법적으로 올바른 문장을 생성함을 확인했다.

• The Journal of the Acoustical Society of Korea
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• v.26 no.3
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• pp.129-135
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• 2007

In this paper. we propose a prewhitening method for the km reverberation to enhance the target signal. The proposed algorithm uses the dechirping method which inversely compensates the frequency chirp rate of LFM and transforms the LFM reverberation to have stationary frequency property in each data block. Also, using the left and right adjacent beam signals as reference signals. we model frequency response of each data block by AR coefficients. From these coefficients, we implement inverse filter and prewhiten the LFM reverberation of the center beam efficiently.

• Asia-Pacific Journal of Business
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• v.15 no.1
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• pp.225-237
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• 2024

Purpose - This study aims to explore the relationship between housing and land prices, with a specific emphasis on the impact of government policies on these factors such as land supply quantity and the ratio of residential land to total land supplied. The goal is to identify the most effective government intervention strategies for controlling both housing and land prices. Design/methodology/approach - Data from 70 primary and medium-sized cities in China spanning from 2003 to 2017 are utilized in this research. The analysis employs a panel vector autoregressive (PVAR) model, with a primary focus on examining the relationships among housing prices, land prices, and government intervention policies. Findings - Housing and land prices are influenced by various factors. Through impulse response analysis and variance decomposition, it is observed that both housing and land prices are predominantly influenced by their internal dynamics, with comparatively weaker effects attributed to policy interventions. Research implications or Originality - By investigating the impact of government policies on housing and land prices, This study establishes a foundation for effective price control measures. Our study advocates for a comprehensive examination of China's land supply mechanism to enhance understanding of the pathways through which government policies influence the markets.

• The Journal of Economics, Marketing and Management
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• v.12 no.5
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• pp.63-73
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• 2024

Purpose: Changes in energy prices can be considered as one of the factors of macroeconomic uncertainty. This study examines the impact of coal price shocks on Indonesian macroeconomic variables. Research design, data and methodology: The structural vector autoregressive model is used on monthly data from January 2010 to June 2023. Results: The impulse response functions indicate that coal price shocks have a negative impact on output and a positive impact on CPI (Consumer Price Index) and the effective real exchange rate. Following a shock in coal price growth, output growth takes twelve months, CPI growth takes fifteen months, and the effective real exchange rate takes seventeen months to reach equilibrium. Coal price growth shocks generally do not have a significant contribution to the variation in output, CPI growth and effective real exchange rate. On average over a twelve-month simulation, coal price growth shocks contribute 2.06 percent to output growth variation, 0.0042 percent to CPI growth variation, and 0.0046 percent to effective real exchange rate growth variation. Conclusions: This study finds that the impact of rising coal prices, as an energy source in Indonesia, can be offset by coal export revenues. This is possible considering that 70-80% of Indonesia's coal is exported.

• Communications for Statistical Applications and Methods
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• v.31 no.5
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• pp.535-556
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• 2024

Measuring stock market volatility and its determinants is critical for stock market participants, as volatility spillover effects affect corporate performance. This study adopted a novel approach to analysing and implementing GARCH-MIDAS modelling methods. The classical GARCH as a benchmark and the univariate GARCH-MIDAS framework are the GARCH family models whose forecasting outcomes are examined. The outcome of GARCH-MIDAS analyses suggests that inflation, interest rate, exchange rate, and oil price are significant determinants of the volatility of the Johannesburg Stock Market All Share Index. While for Nigeria, the volatility reacts significantly to the exchange rate and oil price. Furthermore, inflation, exchange rate, interest rate, and oil price significantly influence Ghanaian equity volatility, especially for the long-term volatility component. The significant shock of the oil price and exchange rate to volatility is present in all three markets using the generalized autoregressive conditional heteroscedastic-mixed data sampling (GARCH-MIDAS) framework. The GARCH-MIDAS, with a powerful fusion of the GARCH model's volatility-capturing capabilities and the MIDAS approach's ability to handle mixed-frequency data, predicts the volatility for all variables better than the traditional GARCH framework. Incorporating these two techniques provides an innovative and comprehensive approach to modelling stock returns, making it an extremely useful tool for researchers, financial analysts, and investors.

• Journal of the Korean Society of Marine Environment & Safety
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• v.20 no.5
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• pp.526-534
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• 2014

In this study, single-input transfer function model is applied to forecast monthly mean sea surface temperature(SST) in 2010 at Yeosu in Korean coastal waters. As input series, monthly mean air temperature series for ten years(2000-2009) at Yeosu in Korea is used, and Monthly mean SST at Yeosu station in Korean coastal waters is used as output series(the same period of input). To build transfer function model, first, input time series is prewhitened, and then cross-correlation functions between prewhitened input and output series are determined. The cross-correlation functions have just two significant values at time lag at 0 and 1. The lag between input and output series, the order of denominator and the order of numerator of transfer function, (b, r, s) are identified as (0, 1, 0). The selected transfer function model shows that there does not exist the lag between monthly mean air temperature and monthly mean SST, and that transfer function has a first-order autoregressive component for monthly mean SST, and that noise model was identified as $ARIMA(1,0,1)(2,0,0)_{12}$. The forecasted values by the selected transfer function model are generally $0.3-1.3^{\circ}C$ higher than actual SST in 2010 and have 6.4 % mean absolute percentage error(MAPE). The error is 2 % lower than MAPE by ARIMA model. This implies that transfer function model could be more available than ARIMA model in terms of forecasting performance of SST.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.6
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• pp.646-652
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• 2017

The objective of this study was to verify the longitudinal reciprocal causal relationship between depression and the somatic symptoms of depression among adolescents through an autoregressive cross-lagged model using data from the Korean Children & Youth Panel Survey. The subjects of this study included 1,968 adolescents, who participated in the second, fourth, and sixth Korean Children & Youth Panel Surveys. The results showed that both depression and the somatic symptoms of depression at a previous point in time affect depression and the somatic symptoms of depression at a later point in time. It was also found that depression at a previous point in time has a cross-lagged effect on the somatic symptoms of depression at a later point in time, implying that more severe depression at a previous point in time leads to increased severity of somatic symptoms at a later point in time. It was found that the somatic symptoms of depression at a previous point in time have a cross-lagged effect on depression at a later point in time, indicating that more severe somatic symptoms of depression at a previous point in time lead to increased severity of depression at a later point in time. This study is significant in that it provides baseline information about nursing interventions for adolescent mental health.