• KDI Journal of Economic Policy
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• v.37 no.4
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• pp.1-20
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• 2015

We examine the validity of popular exchange rate models such as the purchasing power parity (PPP) hypothesis and the monetary model for Korean won/US dollar exchange rate. Various specification tests demonstrate that Korean data are more favorable for both models based on time-varying cointegration coefficients as compared to those based on constant cointegration coefficients. When the abilities to predict future exchange rates between those models based on time-varying cointegration coefficients are compared, an in-sample analysis shows that the time-varying PPP (monetary model) has better predictive power over horizons shorter (longer) than one year. Results from an out-of-sample analysis indicate that the time-varying PPP outperforms models based on constant cointegration coefficients when predicting future exchange rate changes in the long run.

• KDI Journal of Economic Policy
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• v.31 no.2
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• pp.1-13
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• 2009

The monetary model suggests that nominal exchange rates between two countries will be determined by important macroeconomic variables. The existence of a cointegrating relationship among these fundamental variables is the backbone of the monetary model. In a recent paper, Rapach and Wohar (2002, Journal of International Economics) advance the literature by testing for linear cointegration in the monetary model using a century of data to increase power. They find evidence of cointegration in five or six of ten countries. We extend their work to the nonlinear framework by performing threshold cointegration tests that allow for asymmetric adjustments in two regimes. Asymmetric adjustments in exchange rates can occur, for example, if transactions costs are present or if policy makers react asymmetrically to changing fundamentals. Moreover, whereas Rapach and Wohar (2002) found it necessary to exclude the relative output variable in some cases to maintain the validity of their cointegration tests, we can include this variable as a stationary covariate to increase power. Overall, using their same long-span data, we find more support for cointegration in a nonlinear framework.

• Communications for Statistical Applications and Methods
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• v.18 no.1
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• pp.13-21
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• 2011

This paper studies the analysis of multivariate nonstationary time series with seasonality. Three types of multivariate time series models are considered: seasonal cointegration model, nonseasonal cointegration model with seasonal dummies, and vector autoregressive model in seasonal differences that are compared for forecasting performances using Korean macro-economic time series data. The cointegration models produce smaller forecast errors in short horizons; however, when longer forecasting periods are considered the vector autoregressive model appears preferable.

• Journal of the Korean Data and Information Science Society
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• v.16 no.3
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• pp.695-703
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• 2005

This paper extends the maximum likelihood seasonal cointegration procedure developed by Johansen and Schaumburg (1999) for daily time series. The finite sample distribution of the associated rank test for dally data is also presented.

• Journal of the Korean Data and Information Science Society
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• v.18 no.1
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• pp.73-80
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• 2007

Cointegration(together with VARMA(vector ARMA)) has been proven to be useful for analyzing multivariate non-stationary data in the field of financial time series. It provides a linear combination (which turns out to be stationary series) of non-stationary component series. This linear combination equation is referred to as long term equilibrium between the component series. We consider two sets of Korean bivariate financial time series and then illustrate cointegration analysis. Specifically estimated VAR(vector AR) and VECM(vector error correction model) are obtained and CV(cointegrating vector) is found for each data sets.

• Korean Journal of Agricultural Science
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• v.43 no.1
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• pp.136-143
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• 2016

Garlic and onion are staple agricultural products to Koreans and also are important with regard to agricultural producers' income. These products' acreage responses are highly correlated with each other. Therefore, it is necessary to test whether there is a cointegration relationship between garlic acreage and onion acreage when one tries to estimate the acreage response's function. Based upon the test result of cointegration, it is confirmed that there is no statistically significant cointegration relationship between garlic acreage and onion acreage. In this case, vector autoregressive model is preferred to vector error correction model. This study investigated the dynamic relationship between garlic and onion acreage responses using vector autoregressive (VAR) model. The estimated results of VAR acreage response models show that there is a statistically significant relationship between current and lagged acreage of more than one lag. Therefore, it is recommended that government should consider the long-run period's relationship of each product's acreage when it plans a policy for stabilizing the supply and demand of garlic and onion. For the price variables, garlic price only affects garlic acreage response while onion price affects not only onion acreage response but also garlic acreage response. This implies that the stabilizing policy for onion price could have bigger effects than that for garlic price stabilization.

• Communications for Statistical Applications and Methods
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• v.15 no.3
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• pp.411-420
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• 2008

This paper considers a feasible two-step estimator for seasonal cointegration as the extension of $Br{\ddot{u}}ggeman$ and $L{\ddot{u}}tkepohl$ (2005). It is shown that the reducedrank maximum likelihood(ML) estimator for seasonal cointegration can still produce occasional outliers as that for non-seasonal cointegration even though the sizes of them are not extreme as those in non-seasonal cointegration. The ML estimator(MLE) is compared with the two-step estimator in a small Monte Carlo simulation study and we find that the two-step estimator can be an attractive alternative to the MLE, especially, in a small sample.

• Korean Business Review
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• v.20 no.2
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• pp.1-13
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• 2007

The purpose of this study is to investigate a simple present value model Involving earnings (i.e., the earnings discount model) that presumes a relationship between stock prices and earnings. The model suggests a simple linear equilibrium relationship between stock prices and earnings. The tests for cointegration render strong support for the cointegration hypothesis between stock prices (Pt) and earnings (Xt) even at the one-percent significance level. The tests are based on residuals from a cointegrating regression of Pt on Pt+l + Xt. This suggests that there is a stable long-nu equilibrium relationship between stock prices and earnings. The results of the tests lead to the acceptance of the present value model of stock prices involving earnings.

• The KIPS Transactions:PartB
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• v.14B no.7
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• pp.531-540
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• 2007

In this research, we proposed a two-phase hybrid stock price forecasting model with cointegration tests and artificial neural networks. Using not only the related stocks to the target stock but also the past information as input features in neural networks, the new model showed an improved performance in forecasting than that of the usual neural networks. Firstly in order to extract stocks which have long run relationships with the target stock, we made use of Johansen's cointegration test. In stock market, some stocks are apt to vary similarly and these phenomenon can be very informative to forecast the target stock. Johansen's cointegration test provides whether variables are related and whether the relationship is statistically significant. Secondly, we learned the model which includes lagged variables of the target and related stocks in addition to other characteristics of them. Although former research usually did not incorporate those variables, it is well known that most economic time series data are depend on its past value. Also, it is common in econometric literatures to consider lagged values as dependent variables. We implemented a price direction forecasting system for KOSPI index to examine the performance of the proposed model. As the result, our model had 11.29% higher forecasting accuracy on average than the model learned without cointegration test and also showed 10.59% higher on average than the model which randomly selected stocks to make the size of the feature set same as that of the proposed model.

• Proceedings of the Korean Statistical Society Conference
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• 2005.05a
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• pp.13-15
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• 2005

In this paper, we propose an extension of the maximum likelihood seasonal cointegration procedure developed by Johansen and Schaumburg (1999) for daily time series. We presented the finite sample distribution of the associated rank test statistics for daily data.