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Contemporaneous Correlation Demystified: Know the Techniques

What is Contemporaneous Correlation? 1. Contemporaneous correlation is a statistical concept that measures the correlation between the realizations of two time series variables in the same time period. 2. It is often used in models that involve multiple equations, such as the seemingly unrelated regression models. 3. Contemporaneous correlation implies that the equations are interrelated and cannot be estimated separately. 4. Contemporaneous correlation implies that there is dependence across the cross-sectional units in a panel data. 5. When errors across sectional units are not independent. In essence, correlations exist. 6. Correlation of errors across panels. Contemporaneous Correlation Technique Suitable under the following conditions: 1. N less than T panel data structure – when the number of cross-sections is LESS than the time dimensions. 2. Evidence of cross-sectional dependence in the data. 3. Recommended for PG research – MSc and PhD Contemporaneous Correlation Techniques 1. Panel-Corrected Standard Errors, PCSE 2. Feasible Generalised Least Squares, FGLS. This video provides PG students and researchers with information about contemporaneous correlation techniques (PCSE and FGLS) that controls for cross-sectional dependence, autocorrelation and heteroscedasticity. Panel-Corrected Standard Errors (PCSE)  xtpcse calculates panel-corrected standard error (PCSE) estimates for linear cross-sectional time-series models where the parameters are estimated by either OLS or Prais-Winsten regression.  When computing the standard errors and the variance-covariance estimates, xtpcse assumes that the disturbances are, by default, heteroskedastic and contemporaneously correlated across panels. Features of the PCSE Technique:  Controls for cross-sectional dependence.  Assumes different forms of autocorrelation in the panel.  Computes different methods of autocorrelation.  Controls for heteroscedasticity. Feasible Generalised Least Squares (FGLS)  xtgls fits panel-data linear models by using feasible generalized least squares.  This command allows estimation in the presence of AR(1) autocorrelation within panels and cross-sectional correlation and heteroskedasticity across panels. Features of the FGLS Technique:  Controls for cross-sectional dependence.  Computes different error structures to control for heteroscedasticity.  Assumes different forms of autocorrelation in the panel.  Computes different methods of autocorrelation. Practical Application of PCSE & FGLS Techniques  Detailed and practical videos will show you the step-by-step approach to estimating models using these two techniques. Some of these videos will be posted to YouTube Channel.  Full videos will on Teachable paid platform https://cruncheconometrix.teachable.com. There are currently 85 top-notch videos mostly suitable for PG students, early-bird and experienced researchers.  Practical Econometrics for Researchers, Beginners, and Advanced-Level Users (P.E.R.B.A). One-time enrolment fee is US$200.00. CrunchEconometrix videos should be supported by relevant readings from econometrics textbooks, journal articles and other resources to properly harness the simplicity of the video tutorials.