2017
DOI: 10.17485/ijst/2017/v10i19/112258
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Application of Vector Error Correction Model (VECM) and Impulse Response Function for Analysis Data Index of Farmers’ Terms of Trade

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Cited by 15 publications
(17 citation statements)
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“…The impulse response function computes the compounding impact of a one-unit shock in one of the variables on the future values of the other endogenous variables (Winarno et al, 2021). It also attempts to determine the length of the shock's impact on one variable in comparison to the others (Usman, et al, 2017). Figure ( 4) displays the impulse response functions derived from VECM.…”
Section: Impulse Functionmentioning
confidence: 99%
See 1 more Smart Citation
“…The impulse response function computes the compounding impact of a one-unit shock in one of the variables on the future values of the other endogenous variables (Winarno et al, 2021). It also attempts to determine the length of the shock's impact on one variable in comparison to the others (Usman, et al, 2017). Figure ( 4) displays the impulse response functions derived from VECM.…”
Section: Impulse Functionmentioning
confidence: 99%
“…In this test, the skewness and kurtosis metrics were used. We compare the Jarque-Bera (JB) value to the value of chi-square with two degrees of freedom to see if the null hypothesis is rejected (Usman, Fatin, Barusman, & Elfaki, 2017). The test result shown in table (6) reveals that the residuals are normally distributed.…”
Section: Normality Testmentioning
confidence: 99%
“…Applying the impulse-response functions will make it possible to represent the response of the endogenous variable to the shock produced by the exogenous variables. Usman et al (2017) and Benkwitz et al (2001) exemplify the mathematical estimation of the impulse-response function in the VECM model, which is also applied in the case of the present analysis, due to the existence of cointegration relationships. Thus, the Eqs ( 12) and ( 17) are the basis of the application of the impulse-response function, which can be found in the following form:…”
Section: The Analysis Of the Var Processmentioning
confidence: 99%
“…Where y t is the element vector iy in time t, ∅ i is the order matrix n×n, whose elements are the coefficients of the vector y t−1 for i=1, 2., p, p is the lag length, c is the vector of the ordinate in origin, and ε t is the random shock vector (Usman et al, 2017).…”
Section: The Autoregressive Vector (Var)mentioning
confidence: 99%