Greetings to all gretl users and fans,

There is a new package GlobalFactors 0.1 now available.

The package deals with PC estimation of multilevel factor models, in which the factors are not only pervasive (i.e. common to all groups) but also semi-pervasive (i.e. common to a subset of groups only). Multilevel factor models make it possible to distinguish between factors and explore their hierarchical influences.

The main ideas behind the package (so far) are: (a) to consistently estimate the number of global factors (b) Conditional on that, consistently estimate the number(s) of local factors. Conditional on these, consistently estimate global factors and loadings and local factors and loadings (all these are based on Principal Components estimates so there are no significant computational "complexities").

(Of course) there is a catch. Special care should be taken to initially "identify" the global factors through a pairwise canonical correlation procedure. Identification here, means to avoid choosing a pair of countries - or more generally of groups - that strongly correlate not due to global factors but due to regional or local or within group factors.

However, the package is on its way for extension to include two ultra new approaches:

(1) Chen (2022). Circularly Projected Common Factors for Grouped Data.  develops a consistent selection criteria for identifying the number of the global factors


(2) Lin & Sin (2022). Generalised Canonical Correlation Estimation of the Multilevel Factor Model, that deals (a) with consistent estimation of the number of global factors and (b) consistent estimation of global and local factors (c) also providing inference theory. LS(2022) do not require either the orthogonality between the global and local factors or the selection of any tuning parameters. As LS (2022) state "this makes the GCC criterion more general than existing studies".

Hopefully, the fully extended/updated package will be presented at the coming 8th Gretl Conference GdaƄsk, Poland.

Please send me an email for any bugs, naïve code writing etc. for this package version and/or ideas for extending the package.

(well hopefully there are no errors...)

Thank you for your time.