Indian Seafood Exports: Issues of Instability, Commodity ...
Indian Seafood Exports: Issues of Instability, Commodity ...
Indian Seafood Exports: Issues of Instability, Commodity ...
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INDIAN SEAFOOD EXPORTS ISSllES OF INS1AHII.II Y. ('OMMODITY CONCFNTHATION 213<br />
Aliisfex =~,,+fi,fblcc,., t ~ ,f Alngc,, + fi,f~lliif~d~,,, +p,fAli!nfgdp,,<br />
to, i~ltish~r,, +BECM(-I)+ c,<br />
. . , (7)<br />
Whcre A is the difference operator. ECM(-I) is error-correction term lagged by one<br />
period In the cotntegrating regression, for integrating short term dynamics tn the<br />
long-run seafood export functton, This functlon allows to estimate short-run<br />
relationships betwccn <strong>Instability</strong> index <strong>of</strong> <strong>Indian</strong> seafood exports and its<br />
determinants, e,, the error term follows normal ltidependent and identically<br />
distributed (i.i.d) properties. The coefficient 6 measures the response <strong>of</strong> instability<br />
Index <strong>of</strong> seafood expons In each prriod from the long-ru11 equiltbrium with the<br />
co~ntegration equation normaliscd on ~yfex. The coefficient 6 represents the<br />
propartton <strong>of</strong> the d~sequllihrium in iisfex in one period corrected in the next period 6.<br />
is ehpected to have a negative sign and be statistically significant.<br />
The modelling strategy adopted to estimate VECM involvcs three steps<br />
Step I Tesrjor Srarronorrh<br />
Before conducting cointegration tests, it is necessary to establi5h the univariate<br />
time series properties <strong>of</strong> the variables lo confirm all the variables are non-stationary<br />
and integrated <strong>of</strong> the same order. This is performed by unit-root test, viz., Augmented<br />
Dickey-Fuller (ADF) test. This test fitids out the order <strong>of</strong> integration, which is the<br />
minimal number <strong>of</strong>times a series has to be differenced until 11 becomes stationary.<br />
Step 2: Derermrnal~on <strong>of</strong> Oprimum Lag Lengrh<br />
The cointcgration test is based on vector auto regression and is sensitivc to the<br />
number <strong>of</strong> lags included in the model, therefore first we should determine the optimal<br />
number <strong>of</strong> lags used in the cointegration test One way to determine the number <strong>of</strong><br />
lags is to select the model with minimum information criterion which are based 011<br />
log-likelihood and penalise the inclusion <strong>of</strong> additional regressors (Greene, 1993). The<br />
study utilises Akaike ~nformation criterion (AIC) to choose the optimum lag length<br />
Step:3 Coinregrarion Tesr and VECM<br />
The purpose <strong>of</strong> cointegration test is to determine whether a group <strong>of</strong> nonstationary<br />
series is cointegrated or not. The presence <strong>of</strong> cointegration enables to<br />
form a vector error correction mechanism to analyse both the short and long-run<br />
nlationship among cointegrated series.