In a previous post, we explored the intricacies of panel data regression. We unveiled a range of panel models and demonstrated their application in estimating cigarette demand by using the CPANEL procedure. However, achieving reliable insights in the realm of panel data regression requires addressing practical challenges. These would include
Panel data are commonly used in today’s economics research. Panel data regression stands out as a powerful tool that aids in unraveling trends and patterns that evolve over time. This tool is particularly valuable when considering hidden factors in the investigations of cause-and-effect relationships. In this post, you will be
Learn how the %FiniteHMM macro can automatically pre-process input data as well as post-process output tables for finite Hidden Markov Models (HMMs) using PROC HMM.
SAS' Gunce Walton introduces to you a new scoring capability, how it utilizes Deep Neural Networks (DNNs) and shares use cases with PROC DEEPCAUSAL.
SAS' Kelly Fellingham, an advanced analytics software developer, reveals how SAS software's new SASEBEA interface helps you identify patterns in US economics data.
In a Q&A with SAS' Udo Sglavo, Xilong Chen of SAS parses the work of 2021 Nobel Prize winners for Economics.
SAS' Rajesh Selukar introduces you to a new scoring feature.
SAS' Udo Sglavo and Jan Chvosta discuss the power of a regression framework and choosing the correct regression model.
SAS' Udo Sglavo interviews colleague Jan Chvosta, director of Scientific Computing at SAS, on regression analysis and how it works.
SAS' Xilong Chen introduces the new DEEPCAUSAL procedure in SAS Econometrics for causal inference and policy evaluation and much more.
