White, Halbert L., 1980, A heteroscedasticity-consistent covariance matrix estimator and a direct test for heteroscedasticity, Econometrica, 48, 817-838.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....4.70 LAMBDA1 2 0.007 0.491 0.72 9.88 0.009 0.048 0.65 2.95 0.017 0.442 0.94 8.44 CONSTANT 0.615 8.92 0.243 3.97 0.371 4.02 Mean of log hourly earnings Sample size 1.848 4569 0.438 1.638 3932 0. 473 Note: absolute t ratios have been calculated with heteroscedastic consistent standard errors (see White, 1980). Table 1b Regression Results for Full Time Married Private Sector Employees: LFS 1993q3 1995q2 Males Females Gender Difference coefficient t stat coefficient t stat coefficient t stat EDCTN 0.038 12.84 0.42 8.92 0.004 0.68 EXPRN EXPRNSQx10 3 0.034 0.506 19.34 17.72 0.048 1.324 12.34 10.99 ....

....LAMBDA1 2 0.091 0.467 7.46 17.79 0.055 0.135 3.95 8.34 0.036 0.332 1.95 10.76 CONSTANT 0.476 8.86 0.197 2.55 0.278 2.96 Mean of log hourly earnings Sample size 1.671 13818 0.397 1.293 5988 0. 374 Note: absolute t ratios have been calculated with heteroscedastic consistent standard errors (see White, 1980). Table 1c Regression Results for Full Time Single Public Sector Employees: LFS 1993q3 1995q2 Males Females Gender Difference coefficient t stat coefficient t stat coefficient t stat EDCTN 0.019 2.11 0.035 5.46 0.015 1.37 EXPRN EXPRNSQx10 3 0.013 0.142 2.12 1.21 0.037 0.519 6.23 4.04 0.024 ....

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White, H. (1980) "A heteroscedasticity-consistent co-variance matrix estimator and a direct test for heteroscedasticity", Econometrica, vol. 48, pp. 817-838.

....#. Note, however, that the consistency of the variance estimate for the # does 12 GEE depend on proper specification of the working correlation structure. In such cases, Liang and Zeger (1986) propose a robust estimate of the variance covariance matrix of # , GEE analogous to that derived by White (1980), which is consistent even under misspecification of the correlation matrix. For reasons of brevity, I reserve treatment of robust standard errors in the GEE context for a future paper. 11 and Zeger 1986) Moreover, N (# #) is asymptotically multivariate normal, and the 1 2 GEE covariance ....

....improvement over other pooled models by allowing greater exploitation of information along one of those axes. Moreover, to the extent that heterogeneity in the observations remains due to the impact of the other, the GEE allows for the calculation of robust (heteroscedasticityconsistent, e.g. White 1980) standard errors for the model estimates. Thus the researcher may evaluate which specific problem he or she wishes to evaluate explicitly in the GEE context, then employ robust standard errors to mitigate the effects of heterogeneity due to the remaining source(s) of variability. With these ....

White, Halbert. 1980. A Heteroscedasticity-Consistent Covariance Matrix and a Direct Test for Heteroscedasticity. Econometrica 48:817-838.

....of 0.107. For Model 2 (multiplicative model) the null hypothesis of homoscedasticity was unrealistic for the three day and one day pre announcement periods, their respective p values being 0.066 and 0.043. Thus, where appropriate, the test statistics were corrected for heteroscedasticity using White (1980). insert Table 5 about here] The OLS results for the additive and multiplicative models based on trading volume are presented in Table 4. The corresponding results based on transactions are presented in Table 5. The tables indicate that announcement related factors are associated with the total ....

....periods, their p values being 0.045, 0.066, and 0.036. Again, for the models with squared values of UE and RANGE the null hypothesis of homoscedasticity seemed to be realistic for all the periods. Thus, where appropriate, the test statistics were corrected for heteroscedasticity using White (1980). insert Table 6 about here] The results based on trading volume are presented in Table 6 below and the results based on transactions are presented in Table 6. The upper part of the tables presents the regression results as based on the pre announcement periods and the lower part of the tables ....

White, H., (1980), "A heteroscedasticity-consistent covariance matrix estimator and a direct test for heteroscedasticity". Econometrica, 48:4, 817-838.

....variables. Hereafter, we will refer to this as our baseline regression specification. Note that the coefficient on lagged growth is statistically significant at the onepercent level in each decade (all standard errors used to judge significance are heteroskedastic consistent, following White, 1980). This is true for every alternative regression scheme we considered; thus we 15 focus exclusively on results obtained by including lagged growth in this discussion. Remarkably however, inferences regarding convergence divergence are largely invariant to the exclusion of lagged growth as a ....

White, H., 1980. "A Heteroscedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroscedasticity," Econometrica, vol. 48, pp. 817-838.

....V V 1 ) SELL BUY ( DPTH DPTH ( SPR ) BID log( ASK log( BID log( ASK log( The equation above was estimated using ordinary least squares. We anticipated the presence of heteroscedasticity in the residuals and so estimated the standard errors of the estimators in each equation using Whites (1980) method. This method provides standard errors that are robust to a wide variety of forms of heteroscedasticity. We also used Whites method to estimate the covariance between the estimators in the ask price equation and the estimators in the bid price equation. Having obtained this we are able to ....

White, H., 1980, A Heteroscedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroscedasticity,Econometrica, 48, 817-838.

....variances and the variance ratio should move closer to one 12 even with heteroscedastic disturbances. The asymptotic variance will depend on the degree and type of the heteroscedasticity present. Rather than specifying the form of heteroscedasticity in increments, Lo and MacKinlay (1988) followed White (1980) to derive variance ratio estimates, which allow for general forms of heteroscedasticity. Lo and MacKinlay (1988) made use of the relationship between the variance ratio and autocorrelation coefficients to derive the asymptotic distribution of the variance ratio estimator under heteroscedastic ....

White, H., 1980, "A Heteroscedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroscedasticity," Econometrica 48, 817-838.

....and statistically significant. 4 We define significance at the 10 percent level in the results reported below; the temporal patterns identified in this manner are robust to alternative choices. Standard errors used to evaluate significance throughout the paper are heteroscedasticity consistent (White, 1980). 5 Table 2 summarizes for each region our measure of integration obtained using the June 1994, June 1995, June 1998 and May 1999 sub intervals for the CPI data (the food price data yield similar results; specific comparisons between the integration measures generated by the two price ....

White, H. (1980). "A Heteroscedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroscedasticity." Econometrica 48: 817-838.

....the log of total expenditure. The coefficients from these regressions provide an estimate of how the best linear predictor of PTCLI changes as a result of changes in any of the household characteristics or log of total expenditure. Tables 8 and 9 present these best linear predictor functions and White (1980) heteroscedasticity consistent standard error estimates for the interior and boundary solution models for the local 20 and long distance 40 price changes scenario. The best linear predictor functions are very similar across the two tables. The negative coefficient on the log of total ....

White, H. (1980), "A Heteroscedasticity-Consistent Covariance Matrix Estimator and Direct Test for Heteroscedasticity," Econometrica, 48, 817-838.

....with shorter and longer sub periods produced results similar to those reported below. 5 This test is used by Parsley and Wei (1996) to analyze market integration in the United States. 6 Standard errors used to evaluate significance throughout the paper are heteroscedasticity consistent (White, 1980). 5 jumped in December of 1997 to 68.1 percent, and continued to rise throughout out 1998: 78.7 percent of the regions were integrated during December 1997 through May 1998. 7 3. Market Integration and the Aggregate Economy Our interest here is in analyzing how the temporal fluctuations in ....

White, H. (1980). "A Heteroscedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroscedasticity." Econometrica 48: 817-838.

....traffic composition is known. Non constant variance or heteroscedasticity may be present in the data since the variance of TIME will tend to increase in proportion to the number of headways vehicles observed. The data were analyzed for the presence of heteroscedasticity using White s test (White 1980). Essentially, TIME was first regressed against the variables defined in equation (2) The squares of this regression s residuals were regressed against the total number of vehicles in a queue. This regression was statistically significant only in the case of the right turning data. To accommodate ....

White, H. "A Heteroscedasticity-Consistent Covariance Matrix and a Direct Test for Heteroscedasticity." Econometrica, Vol. 48, May 1980, pp: 817-838.

....variance models GARCH M GARCH EGARCH GJR GQARCH # # 0.2062 0.2062 0.1767 0.2290 0.1572 #0.6611##0.6611## 0.5173##0.7367##0.4782# # # 0.8890 0.8890 1.0984 0.8688 0.9162 #5.1888##5.1889##5.7246##5.1398##5.1989# Notes. Heteroscedasticity consistent t ratios, calculated using the White (1980) estimator are reported as # . # parametric. The PNP model is of the form h t ###h t## # m # i## # i P it## (# t## i#) # m # # i## # i N it## (# t## #i#) 13) In this case the PNP model is estimated for m m# 4, with the kinks at # t## equal to 0, 0.5#, #, 1.5# ....

White, H. (1980) A heteroscedasticity-consistent covariance matrix estimator and a direct test for heteroscedasticity, Econometrica, 48, 817---38.

....coefficient implies a negative relationship between this variable and prices. One might presume that this means that low income families consume more PCS than higher income families (this is possible) but a better story is 19 A basic treatment of heteroskedasticity, the Goldfeld Quandt test, and White s (1980) correction of standard errors are given in Greene (1993) 15 that the fraction of households earning more than 35 thousand is capitalized in the C prices, which is positively related with the DE and F prices. On all of the demographic regressors the interpretation should be how the variable ....

White, Halbert (1980), "A Heteroscedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroscedasticity," Econometrica, 48, 817-838.

....t#i## )# 51 # ### that is as the standard deviation of the weekly changes in the LIBOR rate over the previous year. Hedge t is the net long hedging demand of large commercial traders in thousands of contracts. The t statistics are computed using heteroskedasticity corrected standard errors (White, 1980). The Nearby contract regression is also corrected for the negative first order autocorrelation of its residuals. #The number of observations for the nearby contract is less than the number of observations for the other contracts because of the exclusion of prices of the nearby contract on the ....

White, H. (1980) A heteroscedasticity-consistent covariance matrix and direct test for heteroscedasticity, Econometrica, 48, 817---38.

....based on annual and provincial observations and introducing some lag structures for fixed investment and areas affected by natural disasters. The regression was rejected by mis specification tests such as the residual normality test (Jarque Bera statistic) and the residual heteroscedasticity test (White 1980). In addition to the residual heteroscedasticity and non normality, undetected residual auto correlation for same province in different years may also be present. To address these problems, we process the data as follows. We take two year sums of farmland losses (FarmLD2) incremental ....

....HCSE represents the heteroscadasticity consistent standard errors . Large differences between the usual standard errors and HCSEs are indicative of the present of heteroscedasticity, in which case HCSEs provide consistent estimates of the regression coefficients standard errors, there the name (White, 1980). Data Sources: See Data Appendix. 18 To assess the proportional change of farmland loss when the driving forces vary, elasticities of farmland loss with respect to the explanatory variables at means are calculated and reported in Table 2 as well. The sum of elasticities of farmland loss with ....

White, H. (1980), "A heteroscedasticity consistent covariance matrix estimator and a direct test for heteroscadasticity," Econometrica: 48: 817-838.

....labeled Heckman and Newey Olsen contain GMM estimates of the selectivity models suggested by these authors. The OLS estimates are computed on the sample of firms reporting a positive R D intensity using the heteroskedasticity consistent estimate of the variance covariance matrix suggested by White (1980). Table 4 contains the estimation results for the R D intensity equation which includes in addition a set of 12 sector dummies for which no results are presented except for the Wald statistic indicating the joint significance of these variables in all three specifications. The estimation results ....

White, H. (1980): `A Heteroscedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroscedasticity', Econometrica, 48, 817-838.

....are, they are typically replaced by sample estimates. The resulting distribution is in that case no longer the true limiting distribution but only an approximation of it. It is interesting to investigate whether nonparametric covariance estimators, like the White covariance matrix estimator, see [17], can be used to overcome these di culties. These covariance matrix estimators can directly be used in the GMM objective function but expressions of the resulting limiting distributions are still unknown. 5 Cointegration with structural breaks In this section, we investigate the in uence of a ....

White, H., 1980, A Heteroscedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroscedasticity, Econometrica, 48, 817-838

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White, Halbert L., 1980, A heteroscedasticity-consistent covariance matrix estimator and a direct test for heteroscedasticity, Econometrica, 48, 817-838.

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White, H.(1980): "A heteroscedasticity-consistent covariance matrix estimator and a direct test for heteroscedasticity," Econometrica 48, 817-838. 30

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White, H. (1980) "A heteroscedasticity-consistent covariance matrix estimator and a direct test for heteroscedasticity", Econometrica, vol. 48, pp. 817-838.

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White, H. (1980): "A Heteroscedasticity-Consistent Covariance Matrix Estimator and Direct Test for Heteroscedasticity", Econometrica, 48, 817-838.

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White, H. (1980), A heteroscedasticity consistent covariance matrix estimator and a direct test of heteroscedasticity, Econometrica, 48, 817-838.

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White, H. (1980), `A heteroscedasticity-consistent covariance matrix and a direct test for heteroscedasticity', Econometrica, vol. 48, pp.721-746.

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White, H. (1980), "A Heteroscedasticity-Consistent Covariance Matrix Estimator and Direct Test for Heteroscedasticity," Econometrica, 48, 817-838.

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White, Halbert. 1980. "A Heteroscedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroscedasticity." Econometrica, Vol. 48, 817-838.

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White, H., 1981, "A Heteroscedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroscedasticity," Econometrica 21, 817-838.

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