Economics | Econometrics

The course is of particular interest to researchers and professional working in social sciences, psychology, business administration, marketing and management.

Production frontier models have over the years become an indispensable tool of analysis for both scholars and practitioners interested in the measurement of performances through efficiency scores, in academia, business and government. This course provides participants with both the knowledge and requisite applied toolset for applying frontier methods to cross-section and panel data in Stata.

This course offers an introduction to the visual analysis of spatial data using the statistical software Stata. The course begins with an overview of the peculiar characteristics of spatial data and the implications of such for the analysis of spatial data, before moving on to discuss the concept of spatial proximity and the centrality of this particular concept to spatial data analysis.

This course provides a rigorous overview of existing DPD techniques, thus offering students the opportunity to acquire the more advanced technical capabilities currently available for panel data analysis.

This introductory course offers participants the opportunity to acquire the necessary theoretical background and the applied skills to enable them to: i) independently employ micro panel data techniques to their own research topics, and ii) to understand and evaluate micro panel data analysis published in the academic literature.

This course follows on from our Linear Panel Data Models in Stata course to offer the necessary theoretical background and the applied skills to enable participants to: i) independently employ non-linear micro panel data techniques to their own research topics, and ii) to understand and evaluate micro panel data analyses published in the academic literature.

Our “Introduction to Spatial Panel Data analysis using Stata” course offers participants the opportunity to acquire the necessary theoretical and empirical toolset for modelling data which are correlated in time and space using both official and community written Stata spatial estimation commands. The opening session reviews Stata’s inbuilt sp command suite and illustrates how one prepares data for a spatial longitudinal analysis, before moving on to discuss different estimation techniques for both spatial fixed- and random-effects “static” models and for dynamic models with additive and/or interactive fixed-effects.

TStat’s introduction to micro data analysis course focuses, from both a theoretical and applied point of view, on the following methodologies: count models, binary dependent variable models, multinomial models, Tobit and Interval Regression models, models with treatment variables and Sample Selection and the Control function approach.

Many phenomena in the fields of economics, medical and social science, such as unemployment, crime rates or infectious diseases tend to be spatially correlated. Spatial econometrics has developed to include techniques and methods to model the spatial characteristics of such data, by taking into account both spillover effects and spatial heterogeneity.

This intensive introductory course offers therefore an introduction to the standard machine learning algorithms currently applied to social, economic and public health data in order to illustrate (using a series of both official and user written Stata commands), how Machine Learning techniques can be applied to search for patterns in large (often extremely “noisy”) databases, which can subsequently be used to make both decisions and predictions.