Most of the test for heteroscedasticity are based on the following strategy. OLs estimator is a consistent estimator of B even in the presence of heteroscedastic- ity. As such, the OLS residuals will mimic, albeit imperfectly because of sam ling variability, the heteroscedasticity of the true disturbance. Therefore, tests designed to detect heteroscedasticity will, in most cases, be applied to the o residuals 2.1 Nonspecific Tests for Heteroscedasticity There may be instance when the form of the heteroscedasticity is not known, but nevertheless, it is known that the disturbance variance in monotonically related to the size of a known exogenous variables z by which observation on the depen dent variable y can be ordered. One frequently used test in this instance are the Goldfeld-Quandt test Perhaps it is also believed that the broader class of heteroscedasticity is i= h(ia), where h( is a general function independent of i, is applicable (such as o?=za, 02=(a)2 and 0?= exp(z(a)). If so, the Breush-Pagan test is appropriate. If nothing is known a priori other than the heteroscedastic variance are uniformly bounded, White general test is applicable 2.1.1 The Goldfeld-Quandt Test a very popular test for determining the presence of heteroscedasticity which is monotonically related to an exogenous variables by which observations on the de- pendent variables can be ordered is the Goldfeld-Quandt(1965)test. The steps of this test are as follow 1. Order the observations by the values of the variables z 2. Choose p central observations and omit them. 3. Fit separate regression by OLS to the two groups, provides(N-p)/2> k 4. Let SSEl and SSE2 denote the sum of squared residuals based on the small variance(which you suppose they do) and the large variance group, respectively Form the statistics SSEl FMost of the test for heteroscedasticity are based on the following strategy. OLS estimator is a consistent estimator of β even in the presence of heteroscedastic￾ity. As such, the OLS residuals will mimic, albeit imperfectly because of sam￾pling variability, the heteroscedasticity of the true disturbance. Therefore, tests designed to detect heteroscedasticity will, in most cases, be applied to the OLS residuals. 2.1 Nonspecific Tests for Heteroscedasticity There may be instance when the form of the heteroscedasticity is not known, but nevertheless, it is known that the disturbance variance in monotonically related to the size of a known exogenous variables z by which observation on the depen￾dent variable y can be ordered. One frequently used test in this instance are the Goldfeld-Quandt test. Perhaps it is also believed that the broader class of heteroscedasticity is σ 2 i = h(z 0 iα), where h(·) is a general function independent of i, is applicable (such as σ 2 i = z 0 iα, σ 2 i = (z 0 iα) 2 and σ 2 i = exp(z 0 iα)). If so, the Breush-Pagan test is appropriate. If nothing is known a priori other than the heteroscedastic variance are uniformly bounded, White general test is applicable. 2.1.1 The Goldfeld-Quandt Test A very popular test for determining the presence of heteroscedasticity which is monotonically related to an exogenous variables by which observations on the de￾pendent variables can be ordered is the Goldfeld-Quandt (1965) test. The steps of this test are as follow: 1. Order the observations by the values of the variables z. 2. Choose p central observations and omit them. 3. Fit separate regression by OLS to the two groups, provides (N − p)/2 > k. 4. Let SSE1 and SSE2 denote the sum of squared residuals based on the small variance (which you suppose they do) and the large variance group, respectively. Form the statistics F = SSE1 SSE2 = e 0 1e1/N1 − k e 0 2e2/N2 − k , 3