• The Journal of Asian Finance, Economics and Business
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• 제8권5호
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• pp.61-68
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• 2021

The exchange rate is considered a tool improving the volume of exports and reducing imports. This paper aims to determine the impact of the exchange rate on exports and imports between Vietnam and the United States in the context of the trade war. The research uses Autoregressive Distributed Lag (ARDL) and Nonlinear Autoregressive Distributed Lag (NARDL) Model in the time-series data from 2010:1 to 2020:9. The ARDL's results support that real exchange rate impact on export and import volumes, but less than the trade war. The trade war helps trade balance increase 0.35%, while the exchange rate increases trade balance 0.191% when the Vietnamese currency devalues 1% in the long run. In the short term, the real exchange rate makes the trade balance decrease. Therefore, the J curve exists between Vietnam and the U.S. The NARDL expresses that the exchange rate is asymmetric both in the short term and the long term. The findings of this study point to two important elements. Firstly, the exchange rate plays a minor role in exports and imports. Secondly, trade war plays a vital role in increasing exports and imports volume between two countries, and the J curve exists between the two countries.

• 응용통계연구
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• 제19권1호
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• pp.33-41
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• 2006

최근 들어 시계열 자료 분석에서 관측된 각 시점에서의 관측치의 분산을 서로 다른 분산(조건부 이분산성)을 따른다고 가정하고, 이를 분석하는 모형(ARCH, GARCH, EGARCH, IGARCH 등)들이 옵션 가격 분석이나 환율 변화 등 경제적 시계열 자료의 예측 모형을 위하여 활발히 연구되고 있다. 본 논문에서는 한국의 KOSPI 데이터 (1999년 1월 4일 $\sim$ 2003년 12월 30일, 총 1227일)를 바탕으로 조건부 우도함수 모수 추정 방법을 이용한 GARCH(1,1), IGARCH(1,1), EGARCH(1,1) 모형에 KOSPI 자료를 적합 시켜 각 모형들의 성능을 비교하여 보았다.

• International Journal of Aeronautical and Space Sciences
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• 제11권4호
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• pp.326-337
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• 2010

This work addresses problems regarding trajectory planning for unmanned aerial vehicle sensors. Such sensors are used for taking measurements of large nonlinear systems. The sensor investigations presented here entails methods for improving estimations and predictions of large nonlinear systems. Thoroughly understanding the global system state typically requires probabilistic state estimation. Thus, in order to meet this requirement, the goal is to find trajectories such that the measurements along each trajectory minimize the expected error of the predicted state of the system. The considerable nonlinearity of the dynamics governing these systems necessitates the use of computationally costly Monte-Carlo estimation techniques, which are needed to update the state distribution over time. This computational burden renders planning to be infeasible since the search process must calculate the covariance of the posterior state estimate for each candidate path. To resolve this challenge, this work proposes to replace the computationally intensive numerical prediction process with an approximate covariance dynamics model learned using a nonlinear time-series regression. The use of autoregressive time-series featuring a regularized least squares algorithm facilitates the learning of accurate and efficient parametric models. The learned covariance dynamics are demonstrated to outperform other approximation strategies, such as linearization and partial ensemble propagation, when used for trajectory optimization, in terms of accuracy and speed, with examples of simplified weather forecasting.

• 전자공학회논문지S
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• 제35S권10호
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• pp.166-174
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• 1998

신경망은 분산된 비선형 처리구조와 학습능력 때문에 높은 차수의 비선형 동특성 구현능력을 갖고 있으므로 비정적 신호에 대한 적응예측을 수행할 수 있다. 본 논문에서는 두 가지 방법 (비선형 모듈구조와 비선형과 선형모듈이 직렬로 연결된 예측구조)으로 비정적 신호의 비선형 예측을 다루고 있다. 완전 궤환된 리커런트 신경망과 기존의 TDL(tapped-delay-line) 필터가 비선형과 선형모듈로 각각 사용되었다. 제안된 예측기의 동특성은 카오스 시계열과 음성신호에 대해 시험하였으며, 예측성능의 상대적인 비교를 위해 기존의 ARMA(autoregressive moving average) 구조의 선형 예측모델과 비교하였다. 실험결과에 의하면 신경망을 이용한 적응 예측기는 선형 예측기보다 예측성능이 훨씬 우수하였으며, 특히 직렬구조의 예측기는 신호가 크게 변화하는 시계열의 예측에 효과적으로 사용할 수 있음을 확인하였다.

• 대한토목학회논문집
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• 제40권3호
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• pp.303-314
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• 2020

수자원분야에서 이용되는 강우에 따른 유역의 수문학적 시스템, 도시지역 및 하천에 대한 수리학적 시스템은 비선형성이 강하고 많은 변수들을 포함하고 있다. 이러한 특성을 가진 시계열 자료에서 기계학습을 통한 예측은 예측시점 이전의 자료 특성을 반영하지 못하는 등 기본적인 신경망으로는 부족한 상황이 발생하기도 한다. 본 연구에서 적용할 강우-유출량과 같이 비선형성이 강하고 시간종속성이 높은 복잡한 시계열 자료를 예측하기 위해 신경망의 학습능력을 극대화한 순환형 동적 신경망(Recurrent Dynamic Neural Network)의 한 종류인 동시에, 시간 지연 신경망(Time-Delay Neural Network)의 특성을 가진 비선형 자기회귀(NARX, Nonlinear Autoregressive Exogenous Model) 인공신경망을 사용하였다. 이를 태화강 지방하천 구간에 적용하여 NARX 인공신경망의 시간 지연 매개변수를 10분에서 120분까지 조정하며 모의한 결과에 대해 여러 통계지표를 이용해 정량적으로 평가하였다. 그 결과 지연시간이 증가할수록 효율계수(NSE)가 0.530에서 0.988으로 증가하고, 평균제곱근편차(RMSE)가 379.9 ㎥/s에서 16.1 ㎥/s로 감소하는 등 정교한 예측이 가능함을 확인하였다.

• 한국수자원학회논문집
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• 제51권11호
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• pp.1021-1029
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• 2018

본 연구에서는 미급수지역의 주요 수원인 지하수의 수위 변동 상황을 기반으로 한 미급수지역 가뭄 예보 기법 개발을 목적으로 하였다. 이를 위해 지역화된 표준지하수지수(SGI)와 표준강수지수들(SPIs)의 상관관계를 분석하였다. 관측 지하수위로부터 산정된 SGI의 자기회귀 특성 및 지속기간별 SPI와 SGI의 상관관계를 동시에 고려할 수 있는 NARX (nonlinear autoregressive exogenous model) 인공신경망 모형을 이용하여 지역별 예측모형을 구축하였다. 학습기간 동안 관측 SGI와 모델 출력 SGI의 상관계수는 0.7 이상인 곳이 전체 167개 지역별 모형 중 146개(87%)로 상관성이 높은 것으로 분석되었다. 적용기간에 대해서는 평균제곱근오차와 상관계수로 모형을 평가하였다. 본 연구를 통해 기상청에서 제공하는 59개 관측소별 강수량 전망 값으로부터 산정된 지속기간별 SPI와 관측된 지하수위를 이용한 지역별 SGI 전망이 가능하도록 하였으며, 미급수지역의 가뭄 예 경보를 위한 기초자료로 활용이 가능토록 하였다.

• 대한약침학회지
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• 제19권3호
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• pp.207-212
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• 2016

Objectives: Acupuncture treatments are safe and effective for a wide variety of diseases involving autonomic dysregulation. Heart rate variability (HRV) is a noninvasive method for assessing sympathovagal balance. The low frequency/high frequency (LF/HF) spectral power ratio is an index of sympathovagal influence on heart rate and of cardiovascular health. This study tests the hypothesis that from rest to 30% to 50% of peak oxygen consumption, the nonlinear Conte-Zbilut-Federici (CZF) method of computing the LF/HF ratio is a more reliable index of changes in the HRV than linear methods are. Methods: The subjects of this study were 10 healthy young adults. Electrocardiogram RR intervals were measured during 6-minute periods of rest and aerobic exercise on a cycle ergometer at 30% and 50% of peak oxygen consumption ($VO_{2peak}$). Results: The frequency domain CZF computations of the LF/HF ratio and the time domain computations of the standard deviation of normal-to-normal intervals (SDNN) decreased sequentially from rest to 30% $VO_{2peak}$ (P < 0.001) to 50% $VO_{2peak}$ (P < 0.05). The SDNN and the CZF computations of the LF/HF ratio were positively correlated (Pearson's r = 0.75, P < 0.001). fast Fourier transform (FFT), autoregressive (AR) and Lomb periodogram computations of the LF/HF ratio increased only from rest to 50% $VO_{2peak}$. Conclusion: Computations of the LF/HF ratio by using the nonlinear CZF method appear to be more sensitive to changes in physical activity than computations of the LF/HF ratio by using linear methods. Future studies should determine whether the CZF computation of the LF/HF ratio improves evaluations of pharmacopuncture and other treatment modalities.

• The Journal of Asian Finance, Economics and Business
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• 제7권12호
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• pp.95-107
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• 2020

The study investigates the asymmetric effect of exchange rate changes on stock prices in Vietnam. We use the nonlinear autoregressive-distributed lag (ARDL) analysis for monthly data from 2001:01 to 2018:05, based on VN-Index stock price collected from Ho Chi Minh Stock Exchange (HOSE); the nominal exchange rate is separated into currency depreciation and appreciation through a partial sum decomposition process. Asymmetry is estimated both in the long-run relationship and the short-run error correction mechanism. The research results show that the effect of exchange rate changes on stock prices is asymmetrical, both in the short run and in long run. Accordingly, the stock prices react to different levels to depreciation and appreciation. However, the currency appreciation affects a stronger transmission of stock prices when compared to the long-run currency depreciation. In the absence of asymmetry, the exchange rate only has a short-run impact on stock prices. This implies a symmetrical assumption that underestimates the impact of exchange rate changes on stock prices in Vietnam. This study points to an important implication for regulators in Vietnam. They should consider the relationship between exchange rate changes and stock prices in both the long run and the short run to manage the stock and foreign exchange market.

• The Journal of Asian Finance, Economics and Business
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• 제6권4호
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• pp.115-128
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• 2019

The study aims to investigate the pattern of relationships such as symmetric or asymmetric, between exchange rate and foreign direct investment in Bangladesh by applying Autoregressive Distributed Lagged (ARDL) and nonlinear ARDL. In this study, we employed quarterly data for the period of 1974Q1 to 2016Q4. Data were collected and aggregated from various sources namely, Bangladesh Economic Review published by Ministry of Finance and statistical yearbook published by Bangladesh Bureau of Statistics and an annual report published by Bangladesh Bank. The relationship between exchange rate and FDI inflows attract immense interest in the recent periods, especially for developing countries' perspective. The results of the study ascertain the long run relationship between FDI, exchange rate, monetary policy, and fiscal policy. Considering the asymmetric assumption, the findings from NARDL confirm the existence of a long-run asymmetric relationship in the empirical equation. In the long run, it is observed that positive change that is the appreciation of exchange rate against USD decrease FDI inflows and negative shocks results in grater inflows of FDI, however, the positive shocks produce higher intensity that negative shocks in Exchange rate. For directional causality, the coefficients of error correction term confirm long-run causality, in particular, bidirectional causality unveiled between FDI and exchange rate.

• The Journal of Asian Finance, Economics and Business
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• 제7권2호
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• pp.143-149
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• 2020

Low inflation and sustainable growth have been the major macroeconomic goals being pursued by every developing country, Vietnam inclusive. The effect of inflation on economic growth has been intensively analyzed by a variety of studies, but the empirical evidence more often than not remains controversial and ambiguous. One common hypothesis of previous studies is that they have assumed that the effect of inflation on growth is symmetric. The main purpose of this study is to investigate the asymmetric effect of inflation and money supply on economic growth using the Nonlinear Autoregressive Distributed Lag approach introduced by Shin, Byungchul, and Greenwood-nimmo (2013) for Vietnam over the period 1990-2017. Empirical results provide evidence that the effects of inflation on economic growth are negative and asymmetric in the long run. The impact of money supply on growth is positive in both the short-run and long-run. Accordingly, the impact of the increase in the inflation rate is bigger than the decreasing in the long-run. This different impact is significant and high inflation will destruct economic activities. As a result, the study provides empirical evidence for the authorities to plan monetary policies and control the rate of inflation to achieve sustainable economic development in the long-run.