• Journal of Distribution Science
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• v.18 no.6
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• pp.77-82
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• 2020

Purpose: In the period of international integration, how is the implementation of intellectual property regulation in developing countries? Do intellectual property rights help attract more Foreign direct investment (FDI)? This study aims to show the effect of intellectual property rights, reflect in the number of patent registered (Patent distribution into two components: Patent_residents and Patent_non-residents) on FDI attraction in Vietnam. Research design, data and methodology: Using Autoregressive distributed lag (ARDL) model for the data collected from 1990 to 2018 with EViews version 9 software. Conclusions: The results indicate that the number of patent protection has a positive effect on FDI in both short term and long term. In particular, only patent registration of foreign individuals and organizations has a significant positive effect on attracting FDI, while that of Vietnamese patents is not statistically significant. From the results of this study, we provide some recommendations to help attract FDI based on raising awareness of intellectual property rights: Increase international cooperation for innovation to learn and encourage patent; Improve the capac ity of inventing as well as the ability to register patents of Vietnamese people; Government agencies are tasked to support a nd review registration procedures; Encouraging patent registration based on the patent.

• The Korean Journal of Applied Statistics
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• v.30 no.3
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• pp.323-334
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• 2017

We note that the dynamics of realized volatilities (RVs) are near the boundary between stationarity and non-stationarity because RVs have persistent long-memory and are often subject to fairly large outlying values. To forecast realized volatility, we consider a new method that adaptively use models with and without unit root according to the abnormality of observed RV: heterogeneous autoregressive (HAR) model and the Integrated HAR (IHAR) model. The resulting method is called the IHAR-O-HAR method. In an out-of-sample forecast comparison for the realized volatility datasets of the 3 major indexes of the S&P 500, the NASDAQ, and the Nikkei 225, the new IHAR-O-HAR method is shown superior to the existing HAR and IHAR method.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.11
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• pp.83-93
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• 2020

This empirical research aims to modeling and improving the forecasting accuracy of the volatility pattern by employing the Saudi Arabia stock market (Tadawul)by studying daily closed price index data from October 2011 to December 2019 with a number of observations being 2048. In order to achieve significant results, this study employs many mathematical functions which are non-linear spectral model Maximum overlapping Discrete Wavelet Transform (MODWT) based on the best localized function (Bl14), autoregressive integrated moving average (ARIMA) model and generalized autoregressive conditional heteroskedasticity (GARCH) models. Therefore, the major findings of this study show that all the previous events during the mentioned period of time will be explained and a new forecasting model will be suggested by combining the best MODWT function (Bl14 function) and the fitted GARCH model. Therefore, the results show that the ability of MODWT in decomposition the stock market data, highlighting the significant events which have the most highly volatile data and improving the forecasting accuracy will be showed based on some mathematical criteria such as Mean Absolute Percentage Error (MAPE), Mean Absolute Scaled Error (MASE), Root Means Squared Error (RMSE), Akaike information criterion. These results will be implemented using MATLAB software and R- software.

• Journal of Digital Convergence
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• v.10 no.9
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• pp.357-362
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• 2012

We have compared and predicted for non-linear time series data which are real data having different variences using GRCA(1) model and neural network method. In particular, using Korea Composite Stock Price Index rate, mean square errors of prediction are obtained in genaralized random coefficient autoregressive model and neural network method. Neural network method prove to be better in short-term forecasting, however GRCA(1) model perform well in long-term forecasting.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.2
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• pp.291-296
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• 2014

Most of the statistical signal analysis processed in the time domain and the frequency domain are based on the assumption that the signal is weakly stationary(wide sense stationary). Therefore, it is necessary to know whether the surface EMG signals processed in the statistical basis satisfy the condition of weak stationarity. The purpose of this study is to analyze the accuracy of the Run-test, modified Run-test, RA(reverse arrangement)-test, and modified RA-test for assessing surface EMG signal stationarity. Six stationary and three non-stationary signals were simulated by using sine wave, AR(autoregressive) modeling, and real surface EMG. The simulated signals were tested for stationarity using nine different methods of Run-test and RA-test. The results showed that the modified Run-test method2 (mRT2) classified exactly the surface EMG signals by stationarity with 100% accuracy. This finding indicates that the mRT2 may be the best way for assessing stationarity in surface EMG signals.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.1100-1102
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• 2000

Most systems in tile real world are non-linear and can be represented by the non-linear autoregressive moving average (NARMA) model. The extension of fuzzy system for modeling the system that is represented by NARMA model will be proposed in this paper. Here, fuzzy basis function (FBF) is used as fuzzy NARMA(p,q) model. Then, an approach to Identify fuzzy NARMA models based on fuzzy basis functions is proposed. The efficacy of the proposed approach is shown from experimental results.

• Journal of Korean Society of Rural Planning
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• v.26 no.3
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• pp.103-113
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• 2020

Land price can be affected by convenience or psychological repulsion like PIMFY (Please In My Front Yard) or NIMBY (Not In My Back Yard) for various facilities. The purpose of this study is to evaluate whether non-preferred facilities are related to NIMBY impact that negatively affect land prices using the spatial econometrics models which are spatial autoregressive models (SAR), spatial errors models (SEM), and general spatial model (SAC). The land price in urban area increases by 0.07-0.2% when the distance from aversion facilities increases by 1%. However, the land price in rural areas decreases when the distance from aversion or pollution facilities increase. Therefore, these facilities in rural areas located in the areas with higher land price because funeral homes located in center of rural administrative areas and charnel house or crematorium located in the fringe of urban areas. That is, this study explain the difference between land price and non-preferred facilities in urban and rural areas and why there are more N IMBY symptoms in urban areas.

• The Journal of Fisheries Business Administration
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• v.51 no.4
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• pp.47-67
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• 2020

I explore that South Korea's major import fishery product markets-frozen hairtail, frozen mackerel, frozen pollock and frozen squid-are integrated by testing whether there is favorable evidence of the law of one price (LOP). Unlike previous studies on the LOP for fishery product markets, I assume non-zero import costs and include them in a trade model. To explore whether LOP holds for major import fishery product markets in South Korea with non-zero import costs, I utilize a non-linear time-series model, Smooth Transition Autoregressive (STAR) model with the sample periods from January in 2002 to December in 2019. I find that the behaviors of home-foreign price (i.e., import price) differentials of all four major import fishery products are non-linear depending on whether trade occurs and favorable evidence of LOP for each import market in South Korea. These findings indicate that each of South Korea's major import fishery product markets is integrated. They imply that the supply of each major import fishery product-frozen hairtail, frozen pollock, frozen mackerel and frozen squid, and their prices are stable even if there is an economic shock on each market. When it comes to trade policy implications, the Korean trade policy including tariffs or quotas against their import countries for the four major import fishery products may not have influences on their price in the markets.

• Korean Journal of Childcare and Education
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• v.12 no.6
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• pp.249-270
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• 2016

The purpose of this study was to investigate the stability and reciprocal effects of maternal parenting stress and perceived social support in early childhood. Specifically, we compared these relations for working and nonworking mothers. The second through fourth wave data of the Panel Study of Korean Children (PSKC) were used in this study. Data were analyzed using t-tests, correlations, and autoregressive cross-lagged modeling analyses. First, parenting stress of non-working mothers was higher than that of working mothers and working mothers perceived higher levels of social support compared to nonworking mothers. Second, both maternal parenting stress and social support were stable over time. Third, there were significant reciprocal effects between maternal parenting stress and perceived social support. Differences between working and non-working mothers were found in the paths from parenting stress to social support. The implications of the stability and reciprocal effects of parenting stress and perceived social support and the difference between working and non-working mothers in the relationship of the two constructs have been discussed.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.591-599
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• 2020

This study examines how the health of the banks in ASEAN-3 countries namely Indonesia, Malaysia and Thailand respond to the change in exchange rates and foreign interest rates in four large economies. The transmissions of the two external factors through domestic factors in each ASEAN-3 countries eventually affects Non-Performing Loan (NPL) of commercial banks. This study uses the monthly time series data and the renowned Structural Vector Autoregressive (VAR) model comprising five variables, namely exchange rate, foreign interest rate, domestic interest rate, money supply, and non-performing loan (NPL). The results indicate that there are different effects between ASEAN-3 countries, which can be classified as short-run effect and long-run effect. In the long run effect, external factors have a dominant role in determining NPL in ASEAN-3 countries. Yuan has the biggest effect on Malaysia's NPL, while Indonesia is more affected by European interest rates rather than the fluctuation of the US currency and China's interest rates. Among ASEAN-3 countries, Malaysia is the one that is the most vulnerable to external factors. While Thailand's NPL is affected dominantly by domestic factors. This study shows that the Fed Funds Rate (US official interest rate) is not always the dominant factor affecting the health of domestic banks in ASEAN-3.