- Analytical Methods EViews and Panel Data. - The first part is the name of the variable, and the second part of the name is the cross-section identifier that indicates which cross-sectional unit the variable belongs to. - It is good practice (though not necessary) to begin cross-section identifiers with an underscore mark to make the full variable names more readable.. - Example: Suppose we want to work with a panel data set on the USA, Canada, and Mexico.. - It is easy to see that panel data sets can quickly become very large. - A window will open with space for you to list your cross-section identifiers:. - We will describe each element of the specification window.. - By default, EViews will use the largest sample possible in each cross-section. - An observation will be excluded if any of the explanatory or dependent variables for that cross-section are unavailable in that period.. - If the box for Balanced Sample is checked, EViews will eliminate an observation if data are unavailable for any cross-section in that period.. - In this field you list all explanatory variables that you assume have the same slope coefficient for every cross-sectional unit. - Keep in mind that your panel data set should have a rather long time series dimension in order to get reliable estimators of the autocorrelation coefficients.. - Cross-Section Specific Coefficients. - In this window you type the names of all explanatory variables that you assume have different slope coefficient values for different cross-sectional units. - Cross-section weights. - To use this, the time-series dimension must exceed the cross-section dimension (T >. - 1 If you check this option you will see that the EViews output does not report standard errors, t-stats, or p-values for the estimates of the fixed effects. - If you are interested in these (especially for conducting Wald test on these coefficients), you can enter C in the Cross section specific coefficients edit field, and estimate a. - In panel data models (as in single-equation multiple-regression models) we are interested in testing two types of hypotheses: hypotheses about the variances and covariances of the stochastic error terms and hypotheses about the regression coefficients. - Before testing hypotheses about the regression coefficients, it is important to have a good specification of the error covariance matrix so that the test statistics for the regression coefficients are reliable.. - Testing Hypotheses About The Error Covariance Matrix. - It is helpful to think about restricted and unrestricted error covariance matrices.. - An error covariance matrix is a square matrix with the error variances of the individual cross- sectional equations along the diagonal and with the contemporaneous error covariances on the off-diagonal elements. - covariance matrix for a panel model with five cross-sectional units we have a (5 x 5) matrix with five diagonal units and ten off-diagonal units:. - On the other hand, if we believe that the cross-sectional units do not have any contemporaneous cross-equation error covariances, we would click the button for Cross-Section Weighting and EViews would impose zero restrictions on all of the off-diagonal elements of the matrix. - Finally, if we assumed that our stochastic disturbances were free of cross-sectional. - This is an estimator of the joint probability of the observed sample, given the point estimates of the parameters. - As such, it is a number bounded by zero and one.. - Our interest here is in the extent to which imposing restrictions on the error covariance matrix reduces the log-likelihood statistic.. - If we form a ratio of the likelihood of a restricted model ˆ L R divided by the likelihood of an unrestricted model ˆ L U , we expect the ratio to be less than 1 because the maximum likelihood subject to a restriction can be no greater than the maximum likelihood of the unrestricted model.. - Define the likelihood ratio: ˆ ˆ R. - The distribution theory of the likelihood ratio is a bit. - However, it is well known that the distribution of 2 log. - we reject the null hypothesis if the realized value of the likelihood ratio statistic exceeds an appropriate critical value or if the p-value of the test is smaller than the pre-selected significance level.. - While testing hypotheses about restrictions on the error covariance matrix, some specification of the panel data regression model must be maintained. - It is recommended that the maintained model be "general". - Testing Restrictions on the Panel Data Model. - After a sound specification for the error covariance structure has been established, tests. - Keep in mind that when the cross-section weights or SUR methods or any AR(p) specification is used, the results all asymptotically based so that the t-stats are approximately standard normal and the Wald F-stats are approximately Chi-Squared.. - In this model, the intercepts and the partial regression coefficients vary across cross-sectional units. - If either the no-weighting or cross-sectional weights option is chosen for the error covariance structure, then the results will be exactly the same as applying OLS to the data for each cross sectional unit.. - In many panel data sets the time-series dimension is quite short so it is impractical to estimate the model for which all parameters vary across cross-sectional units. - general feasible model is the fixed-effects model: only the intercepts vary across cross-sectional units. - the partial regression coefficients are the same for all cross-sectional units.. - Of course, there are models in which some partial regression coefficients are identical across cross-sectional units while others vary.. - The most restrictive model is the one in which the intercepts and the partial regression coefficients are identical for all cross-sectional units.. - As you move through the general-to-simple modeling strategy it is sensible to re-check the error covariance structure as you impose restrictions on the model's partial regression coefficients and intercepts. - Even though you may fail to reject the hypotheses that represent restrictions that you impose, the hypotheses may not be perfectly true, and that may affect the estimators and tests of the error covariances.
Xem thử không khả dụng, vui lòng xem tại trang nguồn hoặc xem
Tóm tắt