• Journal of the Korean Statistical Society
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• v.24 no.1
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• pp.149-160
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• 1995

We consider the problem of optimal adaptive estiamtion of the euclidean parameter vector $\theta$ of the univariate non-linerar autogressive time series model ${X_t}$ which is defined by the following system of stochastic difference equations ; $X_t = \sum^p_{i=1} \theta_i \cdot T_i(X_{t-1})+e_t, t=1, \cdots, n$, where $\theta$ is the unknown parameter vector which descrives the deterministic dynamics of the stochastic process ${X_t}$ and ${e_t}$ is the sequence of white noises with unknown density $f(\cdot)$. Under some general growth conditions on $T_i(\cdot)$ which guarantee ergodicity of the process, we construct a sequence of adaptive estimatros which is locally asymptotic minimax (LAM) efficient and also attains the least possible covariance matrix among all regular estimators for arbitrary symmetric density.

• Management & Information Systems Review
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• v.27
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• pp.131-147
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• 2008

The study examines common component existing in five Asian countries from 1991 to 2007. To do this, the daily stock market indices of Korea, Malaysia, Thailand, Indonesia, and the Philippines were used. Using a Vector Autoregressive Model this paper analyzes causal relations and dynamic interactions between five Asian stock markets. The findings in this study indicate that level of five Asian stock markets' stock return linkages are low. First, from the statistics for pair-wise Granger causality tests, I find Granger-causal relationship between Korea and Indonesia and between Malaysia and and Indonesia. Second, from the results of response function and the statistics of variance decomposition, I find that week shocks to Korean stock market return on Malaysia, Indonesia, Thailand, and the Philippines stock market returns. The results indicate increased Asian stock market linkages but the level is very low. This implies that the benefits of diversification within the five Asian stock markets are still existed.

• The Korean Journal of Applied Statistics
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• v.25 no.6
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• pp.925-932
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• 2012

The article introduces a method to estimate a cointegrated vector autoregressive model, using mixed-frequency data, in terms of a state-space representation of the vector error correction(VECM) of the model. The method directly estimates the parameters of the model, in a state-space form of its VECM representation, using the available data in its mixed-frequency form. Then it allows one to compute in-sample smoothed estimates and out-of-sample forecasts at their high-frequency intervals using the estimated model. The method is applied to a mixed-frequency data set that consists of the quarterly real gross domestic product and three monthly coincident indicators. The result shows that the method produces accurate smoothed and forecasted estimates in comparison to a method based on single-frequency data.

• Journal of Korean Society of Forest Science
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• v.95 no.5
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• pp.539-542
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• 2006

This study confirmed econometrically the causality of forest inventory and roundwood supply using Korean data. In general, forest inventory is included as explanatory variable in roundwood supply function. We checked whether each series is stationary or not before using it in the model, and determined whether the combination of the series is comtegrated. The relationship between forest inventory and roundwood supply was represented by bivariate vector autoregressive model. The causality of forest evidence of the causal relationship between change in forest inventory and change in roundwood supply in Korea. That is, change in forest inventory does not cause change in roundwood supply in Korea. It seems reasonable not to include forest inventory as explanatory variable in roundwood supply function in Korea.

• Knowledge Management Research
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• v.16 no.4
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• pp.109-132
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• 2015

We examine offline and online channel sales of experience goods, and compare and contrast the sales patterns of existing products and new products between channels. To this end, we obtain the channel-specific time-series sales data from the leading company selling beauty products, both offline and online. By applying the Vector Autoregressive Model, we empirically find out how the relationship between existing products and new products changes between the shopping channels. Our empirical findings are as follows. First, the sales effects from existing products to new products are significantly positive at both offline and online channels, and this positive effect is greater in the offline channel than in the online channel. Second, the influence of new products on existing products is more positive in the offline channel than in the online channel. Third, the impact of existing products sales on new products sales is greater than that of new products on existing products. Lastly, the inertia effect, the effect within the same shopping channel and the same selling product, is significantly positive in the offline channel but not in the online channel, and this asymmetric inertia effect emerges as we focus on experience goods. Moreover, the impulse response function analysis provides the three important implications. First, companies should pay attention to the same channel but different types of products. Second, the offline channel is more vulnerable to market shock than the online channel. Third, new products sales vary by existing products sales to the greater extent, compared to the opposite relationship. We believe our study contributes theoretically and practically to the fields of marketing and knowledge management.

• Economic Analysis
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• v.25 no.3
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• pp.100-137
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• 2019

In this study, I analyzed the effects of government spending on macro variables and on each industry by using a factor augmented vector autoregressive model (FAVAR) and 167 macro-variables in Korea since 2000. The results reveal that the effects of two types of government spending - government consumption and government investment - greatly differ, therefore it is better to consider the two types of spending separately for a more precise analysis. The stimulus effects of government consumption are clear, but those of government investment are not. In addition, the crowding-out effects of government spending take place through the current account deficit channel rather than the traditional crowding-out channel, reducing private consumption and investment. Both types of government spending show a positive effect on the construction industry. Also, an increase in government consumption stimulates output in various manufacturing and service sectors.

• Smart Structures and Systems
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• v.5 no.1
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• pp.95-117
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• 2009

In this paper, the time series based damage detection algorithms developed by Nair, et al. (2006) and Nair and Kiremidjian (2007) are applied to the benchmark experimental data from the National Center for Research on Earthquake Engineering (NCREE) in Taipei, Taiwan. Both acceleration and strain data are analyzed. The data are modeled as autoregressive (AR) processes, and damage sensitive features (DSF) and feature vectors are defined in terms of the first three AR coefficients. In the first algorithm developed by Nair, et al. (2006), hypothesis tests using the t-statistic are applied to evaluate the damaged state. A damage measure (DM) is defined to measure the damage extent. The results show that the DSF's from the acceleration data can detect damage while the DSF from the strain data can be used to localize the damage. The DM can be used for damage quantification. In the second algorithm developed by Nair and Kiremidjian (2007) a Gaussian Mixture Model (GMM) is used to model the feature vector, and the Mahalanobis distance is defined to measure damage extent. Additional distance measures are defined and applied in this paper to quantify damage. The results show that damage measures can be used to detect, quantify, and localize the damage for the high intensity and the bidirectional loading cases.

• The Journal of Fisheries Business Administration
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• v.51 no.4
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• pp.69-83
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• 2020

The purpose of this paper is to empirically investigate whether asymmetric price transmission exists in the distribution stage of farmed olive flounder market. For the analysis, time series data were used for the producer prices of Jeju and Wando, and the wholesale prices of Incheon, Hanam and Busan. Through the Granger causality test, the causal relationship from the producer price to the wholesale price was derived and the asymmetric price transmission was analyzed using the autoregressive distributed lag model (ARDL). As a result of the analysis, it was found that there is a phenomenon of 'positive asymmetric price transmission' from the producer price to the wholesale price. This result can be one evidence that excess profits are received in the intermediate distribution stage, and can be said to be a result showing the incompleteness and inefficiency of the distribution structure of the farmed olive flounder. In the future, it is required to establish an information-sharing system in all stages of production, distribution, and consumption that can create a competitive environment for distribution participants and resolve information asymmetry. Also, it is necessary to review the distribution center specializing in live fish from the viewpoint of the establishment of new distribution channels and sales diversification strategy under the rapidly changing fisheries environment.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.9
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• pp.1788-1795
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• 1992

Numerical simulations have been performed to investigate various spectral estimators used in LDV signal processing. In order to simulate a particle arrival time statistics known as the doubly stochastic poisson process, an autoregressive vector model was adopted to construct a primary velocity field. The conditional Poisson process with a random rate parameter was generated through the rescaling time process using the mean value function. The direct transform based on random sampling sequences and the standard periodogram using periodically resampled data by the sample and hold interpolation were applied to obtain power spectral density functions. For low turbulent intensity flows, the direct transform with a constant Poisson intensity is in good agreement with the theoretical spectrum. The periodogram using the sample and hold sequences is better than the direct transform in the view of the stability and the weighting of the velocity bias for high data density flows. The high Reynolds stress and high fluctuation of the transverse velocity component affects the velocity bias which increases the distortion of spectral components in the direct transform.

• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.839-847
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• 2011

In this study, we derive an estimator based on autocovariance for the regression coefficients vector in the multiple linear regression model. This method is suggested by Park (2009), and although this method does not seem to be intuitively attractive, this estimator is unbiased for the regression coefficients vector. When the vectors of exploratory variables satisfy some regularity conditions, under mild conditions which are satisfied when errors are from autoregressive and moving average models, this estimator has asymptotically the same distribution as the least squares estimator and also converges in probability to the regression coefficients vector. Finally we provide a simulation study that the forementioned theoretical results hold for small sample cases.