66056 - Laboratory of Economic Statistics and Market Analysis

Learning outcomes

At the end of the course, the student acquires knowledge of statistical software suitable for processing and analyzing economic data sets. In particular, the student is able to: integrate methodological knowledge with the interpretation of economic phenomena; carry out the various stages of statistical analysis of economic phenomena; present the results of statistical analyses both in written and oral form.

Course contents

• Introduction to the course
  Presentation of the course, syllabus, and assessment methods.

• Design and management of statistical surveys
  From planning to analyzing the results of statistical surveys using the LimeSurvey platform.

• Measuring changes in economic aggregates over time
  Building, calculating, and interpreting spatial and temporal index numbers for the analysis of economic phenomena. Definition of functions in R.

• Estimation of indicators from sample surveys
  Applying design-based inference methods for the estimation of parameters and variances from sample surveys. Use of the survey and sampling R packages for the analysis of official microdata from ISTAT, EUROSTAT, and the WORLD BANK.

• Composite indicators for summarizing complex phenomena in the economic and business fields
  Designing and computing composite indicators to represent multidimensional phenomena in economics and business through data normalization, weighting, and aggregation procedures. Definition of functions in R.

• Analysis of behavioral models
  Applying and interpreting models of economic behavior based on the Theory of Reasoned Action (TRA) and the Theory of Planned Behavior (TPB), using regression techniques and structural equation models implemented in R.

Readings/Bibliography

The materials (R scripts, slides, and online resources) will be provided by the instructor and made available on Virtuale.

Suggested readings for further study

Wickham, H., & Grolemund, G. (2017). R for Data Science (Vol. 2). Sebastopol: O’Reilly.

Teaching methods

Lectures held in the computer laboratory.

Assessment methods

The final assessment consists of producing a reproducible report, either individually or in groups, based on the analysis of a real dataset. The report, written in R Markdown, will be discussed during the oral examination. Detailed submission instructions and guidelines are available on Virtuale.

During the oral exam, students must demonstrate an adequate knowledge of the basics of the R language, with particular attention to the code and functions used in the report. They must also be able to explain and justify the methodological choices made in their analyses. Successful completion of the exam will result in the award of a pass grade.

Teaching tools

Scripts, exercises, and other resources will be made available on Virtuale.

Students with disabilities or specific learning disorders (SLD) are kindly asked to inform the instructor of their condition in order to identify the most appropriate arrangements to meet their needs.

Office hours

See the website of Lorenzo Mori