# Heteroskedastic

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## Heteroskedastic

A sequence of variables in which each variable has a different variance. Heteroskedastics may be used to measure the margin of the error between predicted and actual data. See also: ARCH.
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Since the interpretation of the model is solely based on t-statistics, it is important to note that the interpretation of t-statistics might not be robust if the model's residuals exhibit autocorrelation and heteroscedasticity problems (Liew et al., 2006).
There are a lot of software with which you can identify heteroscedasticity. These are professional packages (SAS, BMDP), universal packages (STADIA, OLIMP, STATGRAPHICS, STATIST1CA, SPSS) and specialized packages (DATASCOPE, BIOSTAT, MESOSAUR).
The LMh test shows the presence of heteroscedasticity at a .01 significance level.
The Heteroscedasticity results are highlighted in the following table.
However, after conducting several diagnostic tests, there is no evidence of multicollinearity, the data are stationary and heteroscedasticity but has problem of serial correlation.
Thus, it can be proven that there are no symptoms of heteroscedasticity or in other words, the regression equation satisfies the assumption of non-heteroscedasticity.
In order to control unspecified differences between banks and years, we examine the data with firm fixed effects and year fixed effects while correcting for heteroscedasticity and autocorrelation.
Under the assumption of heteroscedasticity of the variances, it is considered that [[epsilon].sub.ij] ~ N (0, [[sigma].sup.2.sub.i]) where [[sigma].sup.2.sub.i] = [[sigma].sup.2.sub.i] [x.sup.[lambda].sub.j] is the parameter that characterizes the variance of the response variable in the jth explanatory variable.
Mean Median Mode std Min Max Bole 27.20 27.00 26.5 2.07 21.40 34.20 Tamale 28.69 28.40 27.8 2.47 21.40 35.60 Skewness Kurtosis Hurst [chi P value Exponent square] Bole 0.41 2.77 0.8212 615.01 0 Tamale 0.31 2.34 0.7643 567.85 0 Table 2: Engle test for residual heteroscedasticity of Bole and Tamale.
Pagan (1979) A Simple Test for Heteroscedasticity and Random Coefficient Variation.
Generalized autoregressive conditional heteroscedasticity, Journal of Econometrics, Vol 31 pp.307-326
To correct for any heteroscedasticity and autocorrelation errors in the residuals, Newey and West (1987) methodology is adopted for the analysis.

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