• The Journal of Asian Finance, Economics and Business
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• 제8권7호
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• pp.393-402
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• 2021

This study aims to examine whether symmetric effects or asymmetric effects of exchange rates exist in determining the money demand in Indonesia. Simple-sum money and Divisia money were included in different models for comparison due to the financial developments in Indonesia. This study uses time-series data from 1996Q1 to 2019Q4 for the estimation. The nonlinear autoregressive distributed lag (NARDL) model is utilized to verify the asymmetric effects of exchange rates on money demand. The Augmented Dickey-Fuller and Phillips-Perron unit root tests were performed to verify the order of integration of the variables. The findings of this study revealed that the exchange rate is one of the most important determinants of money demand in Indonesia and the effect is asymmetric. The findings further indicated that money demand function, which incorporates Divisia monetary aggregate is parsimonious. Monetary targets such as money supply and interest rates are critical for monetary policy conduct to achieve inflation levels set by government. As the adoption of an inflation targeting framework needs to be in keeping with the flexible exchange rate system, the asymmetric effect of exchange rate changes can be used in exchange rate policy conduct to achieve financial system and price stability.

• The Journal of Asian Finance, Economics and Business
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• 제8권2호
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• pp.783-790
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• 2021

The remittances play a major and a very critical role in promoting economic growth and development activities in the developing countries. In this study, the relationship between per capita gross domestic product (GDP) and remittances paid has been investigated based on the case studies in Malaysia from 1987 to 2018. Data was collected from various sources namely statistical yearbook by World Bank and Asian Development Bank. All variables are expressed in natural logarithm form. The technique utilized is the nonlinear autoregressive distributed lags (hereafter NARDL) approach which was introduced by Shin et al.(2014) to examine both short run and long run relationships, as well as the direction of causality, due to the asymmetric relationship between GDP and remittances. The bound test verifies asymmetric cointegration among the variables. The empirical results show that the remittances paid has a momentous short-run and long-run effect towards capital accumulation in Malaysia. Remittances also increase a positive relationship with capital accumulation for Malaysia. We found that remittances form a significant source of external capital and investment for developing countries especially Malaysia which helps in promoting economic development. Furthermore, as a developing country, foreign workers are a source of income to the receiving countries and an indicator to boost sender countries.

• The Journal of Asian Finance, Economics and Business
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• 제8권1호
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• pp.23-36
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• 2021

The study explores the nexus between foreign direct investment (FDI), financial innovation, and exchange rate volatility in selected South Asian countries for 1980 to 2017. The study applies the unit root test, Autoregressive Distributed Lagged, nonlinear ARDL, and causality test following Toda-Yamamoto. Unit root tests ascertain that variables are integrated in a mixed order; few variables are stationary at a level and few after the first difference. Empirical model estimation with ARDL, Long-run cointegration revealed with the tests of FPSS, WPSS, and tBDM by rejecting the null hypothesis of "no cointegration." This finding suggests that, in the long-run financial innovation, FDI inflows, and exchange rate volatility move together. Moreover, study findings established adverse effects running from FDI inflows and financial innovation to exchange rate volatility in the long run. These findings suggest that continual FDI inflows and innovativeness in the financial system assist in lessening the volatility in the foreign exchange market. Furthermore, nonlinear ARDL confirms the presence of asymmetric cointegration in the model. The standard Wald test established asymmetric effects running from FDI inflows and financial innovation to exchange rate volatility, both in the long and short run. Directional causality unveils feedback hypothesis holds for explaining causality between FDI, financial innovation, and exchange rate volatility.

• 디지털융복합연구
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• 제10권9호
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• pp.357-362
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• 2012

본 논문에서는 분산이 각각 다른 이분산성을 갖는 비선형 시계열 자료를 가지고, 비선형 시계열 모형중 1차 일반화 확률계수 자기회귀모형(GRCA(1))과 자료의 형태에 상관없이 적용할 수 있는 신경망 모형을 이용하여 예측을 해서 어느 모형이 최소 평균예측오차제곱의 기준에서 비선형 시계열 자료의 예측에 적합한지를 비교 분석 하는 것이다. 조건부 이분산 모형에 따르는 자료로 확인된 종합주가지수 변동율에 대한 사례 분석 결과를 보면 신경망 모형은 단기 예측에서 좋은 예측 결과를 보였고, 비선형 모형인 GRCA(1) 모형은 장기 예측에서 좋은 예측 결과를 보여 주었다.

• International Journal of Advanced Culture Technology
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• 제2권2호
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• pp.11-14
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• 2014

In the smart grid, it will be possible to communicate with the consumers for the purposes of monitoring and controlling their power consumption without disturbing their business or comfort. This will bring easier administration capabilities for the utilities. On the other hand, consumers will require more advanced home automation tools which can be implemented by using advanced sensor technologies. For instance, consumers may need to adapt their consumption according to the dynamically varying electricity prices which necessitates home automation tools. This paper tries to combine neural network and nonlinear autoregressive with exogenous variable (NARX) class for next week electric load forecasting. The suitability of the proposed approach is illustrated through an application to electric load consumption data. The suggested system provides a useful and suitable tool especially for the load forecasting.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2005년도 춘계학술대회
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• pp.155-163
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• 2005

This paper applies Support Vector Regression (SVR) to estimate and forecast nonlinear autoregressive integrated (ARI) model of the daily exchange rates of four currencies (Swiss Francs, Indian Rupees, South Korean Won and Philippines Pesos) against U.S. dollar. The forecasting abilities of SVR are compared with linear ARI model which is estimated by OLS. Sensitivity of SVR results are also examined to kernel type and other free parameters. Empirical findings are in favor of SVR. SVR method forecasts exchange rate level better than linear ARI model and also has superior ability in forecasting the exchange rates direction in short test phase but has similar performance with OLS when forecasting the turning points in long test phase.

• Journal of Welding and Joining
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• 제17권6호
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• pp.100-107
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• 1999

In this paper, we deal with the speckle noise reduction and parameter estimation of ultrasonic NDT(non-destructive test) signals obtained during weld inspection of piping. The overall approach consists of three major steps, namely, speckle noise analysis, proposition of wavelet domain AR(autoregressive) model and flaw detection by proposed model parameter. The data are first processed whereby signals obtained using vertical and angle beam transducer. Correlation properties of speckle noise are then analyzed using multiresolution analysis in wavelet domain. The parameter estimation curve obtained using the proposed model is classified a flaw in weld region where is contaminated by severe speckle noise and also clear flaw signal is obtained through CA-CFAR threshold estimator that is a nonlinear post-processing method for removing the noise from reconstructed ultrasonic signal.

• 대한의용생체공학회:의공학회지
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• 제24권3호
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• pp.151-158
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• 2003

심장혈관 신호에 있어서 단기간의 beat-to-beat 변이(variability)에 대한 여러 연구에서 선형 분석기법들이 사용되었다. 그러나 단기간 beat-to-beat 변이에 대해 선형기법 사용의 타당성에 대한 연구나 선형과 비선형 특성을 비교한 연구는 수행되지 않았다. 본 논문의 목적은 단기간 beat-to-beat 변이의 비선형성 특성을 조사함으로써 선형기법 사용의 적절함을 증명하고자 한다. 이를 위해 선형 ARMA와 비선형 신경망(NN) 모델을 사용하여 예측을 수행하였는데, 과거의 순시 심박(HR)과 평균 혈압(BP)으로부터 현재의 심박과 혈압 예측을 상호 비교하였다. 이러한 예측모델을 평가하기 위해 MIMIC 데이터베이스로부터 HR와 BP 시계열을 사용하였다. 실험결과에 의하면 신경망에 의한 비선형성은 단기간 beat-to-beat 변이를 생성하는 시스템 동특성을 나타내는데 의미있는 역할을 하지 못하였으며, 이 사실은 ARMA 선형 분석기법이 이러한 시스템 동특성을 나타내는데 적절함을 보여주고 있다

• 대한조선학회논문집
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• 제51권4호
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• pp.274-282
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• 2014

Time series of the dynamic response of a slender marine structure was predicted using quadratic Volterra series. The wave-structure interaction system was identified using the NARX(Nonlinear Autoregressive with Exogenous Input) technique, and the network parameters were determined through the supervised training with the prepared datasets. The dataset used for the network training was obtained by carrying out the nonlinear finite element analysis on the freely standing riser under random ocean waves of white noise. The nonlinearities involved in the analysis were both large deformation of the structure under consideration and the quadratic term of relative velocity between the water particle and structure in Morison formula. The linear and quadratic frequency response functions of the given system were extracted using the multi-tone harmonic probing method and the time series of response of the structure was predicted using the quadratic Volterra series. In order to check the applicability of the method, the response of structure under the realistic ocean wave environment with given significant wave height and modal period was predicted and compared with the nonlinear time domain simulation results. It turned out that the predicted time series of the response of structure with quadratic Volterra series successfully captures the slowly varying response with reasonably good accuracy. It is expected that the method can be used in predicting the response of the slender offshore structure exposed to the Morison type load without relying on the computationally expensive time domain analysis, especially for the screening purpose.

• Industrial Engineering and Management Systems
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• 제12권3호
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• pp.244-253
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• 2013

In industries, shipping is an important issue in improving the forecasting accuracy of sales. This paper introduces a hybrid method and plural methods are compared. Focusing the equation of exponential smoothing method (ESM) that is equivalent to (1, 1) order autoregressive-moving-average (ARMA) model equation, a new method of estimating the smoothing constant in ESM had been proposed previously by us which satisfies minimum variance of forecasting error. Generally, the smoothing constant is selected arbitrarily. However, this paper utilizes the above stated theoretical solution. Firstly, we make estimation of ARMA model parameter and then estimate the smoothing constant. Thus, theoretical solution is derived in a simple way and it may be utilized in various fields. Furthermore, combining the trend removing method with this method, we aim to improve forecasting accuracy. This method is executed in the following method. Trend removing by the combination of linear and 2nd order nonlinear function and 3rd order nonlinear function is executed to the original production data of two kinds of bread. Genetic algorithm is utilized to search the optimal weight for the weighting parameters of linear and nonlinear function. For comparison, the monthly trend is removed after that. Theoretical solution of smoothing constant of ESM is calculated for both of the monthly trend removing data and the non-monthly trend removing data. Then forecasting is executed on these data. The new method shows that it is useful for the time series that has various trend characteristics and has rather strong seasonal trend. The effectiveness of this method should be examined in various cases.