• KDI Journal of Economic Policy
• /
• v.13 no.4
• /
• pp.117-140
• /
• 1991

The purpose of this paper is primarily to introduce a nonparametric statistical tool developed by Baek and Brock to detect a unidirectional causal ordering between two economic variables and apply it to interesting macroeconomic relationships among money, production and prices. It can be applied to any other causal structure, for instance, defense spending and economic performance, stock market index and market interest rates etc. A key building block of the test for nonlinear Granger causality used in this paper is the correlation. The main emphasis is put on nonlinear causal structure rather than a linear one because the conventional F-test provides high power against the linear causal relationship. Based on asymptotic normality of our test statistic, the nonlinear causality test is finally derived. Size of the test is reported for some parameters. When it is applied to a money, production and prices model, some evidences of nonlinear causality are found by the corrected size of the test. For instance, nonlinear causal relationships between production and prices are demonstrated in both directions, however, these results were ignored by the conventional F-test. A similar results between money and prices are obtained at high lag variables.

• Journal of Korea Port Economic Association
• /
• v.27 no.2
• /
• pp.217-240
• /
• 2011

In this study, vector autoregressive and vector error correction models in the short-run dynamics are considered to analyze the effect of the changes in international crude oil prices on Baltic dry index, Baltic Capesize index and Baltic Panamax index, and the intercorrelations between Capesize and Panamax prices, respectively. First, using the vector autoregressive model, the changes in international crude oil price have a statistically significant positive effect for Capesize at lag 1, for Panamax a significant negative effect at lag 3 and a significant positive effect for Baltic dry index at lag 1. From the impulse response analysis, the international crude oil price causes Baltic dry index to increase in the sort-run and the effect converges on the mean after 3 months. Second, using the vector error correction model, the empirical results for the spillover effects between Capesize and Panamax markets provide that in the case of the deviation from a long-run equilibrium the Panamax price is adjusted toward decreasing. The increases in freight rates of the Capesize market at lag 1 lead to increase the freight rates in Panamax market at present. The Panamax responses from the Capesize shocks increase rapidly for 3 months and the effect converges on the mean after 5 months. The Capesize responses from the Panamax shocks are relatively small, and increase weakly for 3 months and the effect disappears thereafter.

• Journal of Korean Society for Geospatial Information Science
• /
• v.21 no.4
• /
• pp.73-81
• /
• 2013

Ordinary least squares (OLS) regression is the primary statistical method in previous studies for vegetation distribution patterns in relation to landform. However, this global regression lacks the ability to uncover some local-specific relationships and spatial autocorrelation in model residuals. This study employed geographically weighted regression (GWR) to examine the spatially varying relationships between NDVI (Normalized Difference Vegetation Index) patterns and changing trends of landform (elevation, slope) and solar intensity (insolation and duration of sunshine) in Mt Geum-gang of North-Korea. Results denoted that GWR was more powerful than OLS in interpreting relationships between NDVI patterns and landform/solar intensity, since GWR was characterized by higher adjusted R2, and reduced spatial autocorrelations in model residuals. Unlike OLS regression, GWR allowed the coefficients of explanatory variables to differ by locality by giving relatively more weight to NDVI patterns which are affected by local landform and solar factors. The strength of the regression relationships in the GWR increased significantly, by showing regression coefficient of higher than 70% (0.744) in the southern ridge of the experimental area. It is anticipated that this research output will serve to increase the scientific and objective vegetation monitoring in relation to landform and solar intensity by overcoming serious constraints suffered from the past non-GWR-based approach.

• Environmental and Resource Economics Review
• /
• v.15 no.4
• /
• pp.673-692
• /
• 2006

In order to test for the dynamic optimality condition for the use of nonrenewable resource, it is necessary to estimate the shadow value of the resource in situ. In the previous literatures, a time series for in situ price has been derived either as the difference between marginal revenue and marginal cost or by differentiating with respect to the quantity of ore extracted the restricted cost function in which the quantity of ore is quasi-fixed. However, not only inconsistent estimates are likely to be generated due to the nonmalleability of capital, but the estimate of marginal revenue will be affected by market power. Since firms will likely fail to minimize the cost of the reproducible inputs subject to market prices under realistic circumstances where imperfect factor markets, strikes, or government regulations are present, the shadow in situ values obtained by estimating the restricted cost function can be biased. This paper provides a valid methodology for checking the dynamic optimality condition for a nonrenewable resource by using the input distance function. Our methodology has some advantages over previous ones: only data on quantities of inputs and outputs are required; nor is the maintained hypothesis of cost minimization required; adoption of linear programming enables us to circumvent autocorrelated errors problem caused by use of time series or panel data. The dynamic optimality condition for domestic coal mining does not hold for constant discount rates ranging from 2 to 20 percent over the period 1970~1993. The dynamic optimality condition also does not hold for variable rates ranging from fourth to four times the real interest rate.