Â鶹Éç

Course Topics
1. Introduction to regression analysis for the public sector: The role of regression analysis in the context of the public sector. Formulating research questions and hypotheses. 2. The simple linear regression model: Model specification, interpretation, and assumptions. Estimation methods, least squares estimation, and assessment of model uncertainty. 3. Multiple linear regression: Inclusion of multiple predictors, variable selection, model building, model diagnostics. 4. Extensions of the linear regression model: Extending the multiple linear regression model by including non-linear terms and interaction effects. Linear regression methods for categorical output variables. 5. Methods for spatially and temporally correlated data: Linear methods for time series analysis, regression methods for spatially correlated data. 6. Recent developments in regression analysis: Robust estimation methods and outlier detection. Machine learning methods for high dimensional data from a regression perspective. Sparse regression models and penalized least squares methods.

Teaching format
Lectures and exercises will be in person, streaming and recordings will also be available.

Educational objectives
ILO (Intended Learning Outcomes) Applied Regression Analysis for Public Policy ILO1 Knowledge and understanding ILO1.1 The student acquires targeted knowledge of analytical techniques and tools necessary to understand and interpret in a quantitative manner economic and business phenomena related to public administration in order to support decision-making and management processes. Knowledge of statistical inference, linear models and their generalisations will be consolidated. Knowledge will also be acquired in the management of the main computer systems useful for the analysis, interpretation, visualisation and communication of data, commonly used in public administrations. ILO1.2 The student acquires knowledge of economic theory necessary to understand and analyse economic and business phenomena in the public sector in order to support decision-making processes. Knowledge of public policy and the tools necessary for the design of sustainable policies will be consolidated. Knowledge related to the labour market, education and health will also be deepened, functional to the development of public policy analysis and evaluation skills. ILO2 Ability to apply knowledge and understanding ILO2.1 ability to interpret market trends through the application of appropriate economic models and implement economic analysis tools, also using data ILO2.2 ability to apply economic models to describe the behaviour of economic agents and develop sustainable economic policies in various application domains of interest to companies and public bodies ILO2.3 ability to apply and implement statistical and econometric analysis techniques focusing on different types of datasets also of high dimensions ILO2.4 ability to interpret results deriving from statistical and econometric analysis in contexts of interest to companies and public bodies ILO3 Making judgements ILO3.1 ability to apply acquired knowledge to interpret economic and business phenomena in order to make managerial and operational decisions in the context of public administration ILO3.2 ability to select data and use appropriate information to describe a problem concerning the design, implementation and evaluation of public sector projects and policies, aiming at innovation and improvement of processes, products and results ILO3.3 ability to relate models and empirical evidence in the study of public policy phenomena ILO4 Communication skills ILO4.1 ability to communicate effectively in oral and written form the specialised contents of individual disciplines, using different registers according to recipients and communicative and didactic purposes, as well as to evaluate the formative effects of his/her communication ILO5 Learning ability ILO5.1 ability to use information technology autonomously to carry out bibliographic research and investigations and for one's own training and further education

Assessment
A. Modality "attending" and "non-attending" Assessment 1: Group Work (voluntary; 30%) : Attending and non attending students can participate in a data research project which counts 30% of the final grade. Students will work on a practical empirical project using real data and the statistical software R. The task will involve data management, writing R script files and the interpretation of results. Project work are valid for 1 academic year and cannot be carried over beyond that time-frame. (ILOs 1.1, 2.1, 2.3, 2.4, 3.2, 3.3, 4, 5) Assessment 2: Final written exam (70% if students participated in group work, 100% otherwise): students will have to solve theoretical, practical, and computational issues concerning a given concrete problem showing knowledge and understanding of the covered theories and methods. (ILOs 1.1, 2.3, 2.4, 3.3, 4) The assessment mode is the same for attending and non-attending students.

Evaluation criteria
All students must reach a passing grade on the combined grade of the written exam and the take home research project. The following aspects are relevant for the exam: correctness of answers, ability to interpret R outputs and a critical assessment of regression results considering econometric and economic theory. The following aspects are relevant for the take home research project: correctness of answers, ability to run successfully an econometric project in R, interpretation of R outputs and critical assessment of results.