• Journal of Korean Institute of Industrial Engineers
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• v.27 no.3
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• pp.289-296
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• 2001

This paper proposes multivariate control chart for autocorrelated data which are common in chemical and process industries and lead to increase in the number of false alarms when conventional control charts are applied. The effect of autocorrelated data is modeled as a vector autoregressive process, and canonical analysis is used to reduce the dimensionality of the data set and find the canonical variables that explain as much of the data variation as possible. Charting statistics are constructed based on the residual vectors from the canonical variables which are uncorrelated over time, and therefore the control charts for these statistics can attenuate the autocorrelation in the process data. The charting procedures are illustrated with a numerical example and Monte Carlo simulation is conducted to investigate the performances of the proposed control charts.

• Communications for Statistical Applications and Methods
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• v.4 no.3
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• pp.903-909
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• 1997

The ordinary least squares estimator and the corresponding pivotal statistics have been widely used for the unit test. Recently several test criteria based on maximum likelihood estimators and weighted symmetric estimator have been proposed for testing the unit root hypothesis in the autoregressive processes. Pantula at el. (1994) showed that the weighted symmetric estimator has good power properties. In this article we use an adjusted estimator for mean in the model when we use weighted symmetric estimator. A simulation study shows that for the small samples, this new test criterion has better power properties than the weighted symmetric estimator.

• KIEE International Transactions on Power Engineering
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• v.4A no.3
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• pp.159-166
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• 2004

Forecasting prices in electricity markets is critical for consumers and producers in planning their operations and managing their price risk. We utilize the generalized autoregressive conditionally heteroskedastic (GARCH) method to forecast the electricity prices in two regions of New York： New York City and Central New York State. We contrast the one-day forecasts of the GARCH against techniques such as dynamic regression, transfer function models, and exponential smoothing. We also examine the effect on our forecasting of omitting some of the extreme values in the electricity prices. We show that accounting for the extreme values and the heteroskedactic variance in the electricity price time-series can significantly improve the accuracy of the forecasting. Additionally, we document the higher volatility in New York City electricity prices. Differences in volatility between regions are important in the pricing of electricity options and for analyzing market performance.

• The Korean Journal of Applied Statistics
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• v.26 no.6
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• pp.1043-1051
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• 2013

We use a structural vector error correction model of the labor market to investigate the effect of shocks to Korean unemployment. We associate technology, labor demand, labor supply, and wage-setting shocks with equations for productivity, employment, unemployment, and real wages, respectively. Subsequently, labor demand and supply shocks have significant long-run and contemporaneous effects on unemployment, respectively.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.5
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• pp.324-326
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• 2005

An adaptive Chunk algorithm is newly devised and a collaborative streaming is designed for high quality multimedia streaming service under time varying traffic conditions. An LMS based prediction filter is used to compensate the effect of time varying background traffic of the WAN. The underflow is generated for the $20\~28\%$ of the data stored in the central server by applying the FARIMA(Fractional Autoregressive Integrated Moving Average) traffic modeling method. The proposed algorithm is tested with the MPEG-2 video files and compensates $71\~85\%$ of central stream underflow.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.24 no.67
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• pp.27-36
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• 2001

A standard assumption when using a control chart to monitor a process is that the observations from the process output are statistically independent. However, for many processes the observations are autocorrelated and this autocorrelation can have a significant effect on the performance of the control chart. In this paper, we consider combined control chart of monitoring the mean of a process in which the observations can be modeled as a first-order autoregressive process. The Shewhart control chart of residuals-EWMA control chart of the observations is considered and the method of combination is recommended. The performance of the proposed control chart is compared with the performance of other control charts using a simulation.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.11
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• pp.189-198
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• 1998

Recently, monitoring of tool life is a matter of common interesting because tool life affects precision, productivity and cost in machining process. Especially flank wear has a direct effect on cutting mechanism, so the various pattern of cutting force is obtained experimentally according to variation of wear condition. By investigating cutting force signal, AR(Autoregressive) modeling and correlation dimension analysis is conducted in turning operation. In this modeling and analysis, we extract features through 6th AR model, correlation integral and normalized correlation integral. After the back-propagation model of the neural network is utilized to monitor tool life according to flank wear. As a result. a very reliable classification of tool life was obtained.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.2
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• pp.85-94
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• 1994

The cutting mechanism of ultrasonic vibration machining is characterized as two phases, that is, an impact at the cutting edge and a reduction of cutting force due to non-contact interval between tool and workpiece. In this paper, in order to identify cutting dynamics of a system with ultrasonically vibrated cutting tool, an ARMA modeling is performed on experimental cutting force signals which have a dominant effect on cutting dynamics. The aim of this study is, through Dynamic Date System methodology, to find the inherent characteristics of an ultrasonic vibration cutting process by considering natural frequency and damping coefficient. Surface roughness and stability of cutting process under ultrasonic vibration are also considered

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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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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• v.7 no.6
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• pp.109-116
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• 2020

The study examines the nexus between financial development and economic growth in India during Q1: 1996 to Q3: 2018. This study employs time-series data of real GDP and ratio of broad money to GDP as a proxy for economic and financial development, respectively. The data are obtained from RBI database on the Indian economy. All variables are seasonally adjusted using X12-arima technique and expressed in natural logarithm form. Non-linear Autoregressive Distributed Lag (NARDL) bound test has been used to check for cointegrating relationship of these two variables. Empirical findings suggest that, unlike in the short run, in the long run financial development does impact economic growth positively. Further, a symmetric effect of positive and negative components of financial development is found for the Indian economy, whereas the effect of control variable like exchange rate and trade openness is in consonance with common economic intuition. Exchange rate is in consonance with intuitive economic logic that a fall in exchange rate makes exports cheaper and increases the quantity of export, which improves the balance of payment and leads to a rise in aggregate demand, hence improves economic growth. This paper contributes to the existing literature on India by breaking down financial indicator into positive and negative components to examine the finance-growth relationship.