– Monday, 12:15 – 13:45 (Teams)

– Thursday, 12:15 – 13:45 (Teams)

– Friday, 12:15 – 13:45 (Teams)

Abstract: The course offers a review of the main Small Area Estimation Methods and teach how to apply them to European survey data to have a local monitoring of the Sustainable Development Goals and to estimate deprivation and inequality indicators, focusing also in aggregating Multidimensional Indicators and on conceptualizing, defining and measuring poverty under the capability approach. The course will be structured in the following parts

1) Analysis of the collected data for estimation and testing for the phenomenon under study; definition of planned and unplanned domains.

2) Direct and indirect estimates for unplanned domains; R codes for the application of the SAE estimators (EURAREA and SAMPLE project libraries)

3) quality issues in SAE and usage of SAE in European Statistical System.

At the end of the module student will be able to deal with small area estimation both at the theoretical and empirical level and to apply aggregation methods to indicators from the European survey data.

Students will learn the fundamental small area methods and what might be the problems that arise in the application of them and in the definition of their statistical quality.
Syllabus: The course introduces a range of quantitative tools commonly used to provide indicators of poverty and living conditions at local level.

It covers the definition of poverty and living conditions indicators (see Laeken Indicators of Poverty and/or Multidimensional Indicators of poverty, as an example), design based and model based estimates using survey data with an emphasis on the ways in which they are applied to obtain local data and indicators when the domains of study are not planned in current surveys and there is the need to have statistically sound estimates (with acceptable Coefficient of variation).

The course (6ECTS) will be structured in the following parts

1) Analysis of the collected data for estimation and testing for the phenomenon under study; definition of planned and unplanned domains.

How the small area method works: rationale of the small area method, data sources and statistical modeling
Data requirements to produce small area estimates (introduction to the data available in Europe: annual survey, censuses, administrative data), European Poverty Data (data sources), Level of analysis in different countries (European countries)
Sample design and estimation

2) Direct and indirect estimates for unplanned domains; R codes for the application of the SAE estimators (EURAREA and SAMPLE project libraries) 3) quality issues in SAE and usage of SAE in European and USA Statistical System.

How to use R functions to produce small area estimates
Interpretation of the results: point estimates and their accuracy – Quality of the small area estimates
Operational aspects to put the method in practice in Developed and Developing Countries (flow chart of the data production process, institutions involved, dissemination of the estimates)

3) quality issues in SAE and usage of SAE in European Statistical System
Link: https://esami.unipi.it/programma.php?c=51609&aa=2021&cid=120&did=17

Unit 1
Memory
Three Important Distinctions
Sensory Memory
Working Memory
Long-term Memory
Implicit Memory
Constructive Memory
Spatial Memory
Improving Memory

Unit 2
Emotion
Components of Emotion
Cognitive Appraisal and Emotion
Subjective Experiences and Emotion
Thought and Action Tendencies and Emotion
Positive Psychology and Emotion
Bodily Changes and Emotion
Communication and Emotion
Emotion Regulation
Emotion, Gender, and Culture

Unit 3
Stress, Health, and Coping
Characteristic of Stressful Events
Psychological Reactions to Stress
Physiological Reactions to Stress
Coping Skills
Managing Stress

Unit 4
Perception
The use of perception
Attention
Localization
Recognition
Abstraction
Perceptual constancies

Unit 5
Consciousness
Aspects of consciousness
Sleep and dreams
Meditation
Hypnosis
Psychoactive drugs
PSI phenomena

Unit 6
Language and thought
Language and communication
The development of language
Concepts and categorization
Reasoning
Imaginal thought
Problem solving

READING LIST
– SUSAN NOLEN-HOEKSEMA, BARBARA L. FREDRICKSON, GEOFF R. LOFTUS, WILLEM A. WAGENAAR, Atkinson & Hilgard's Introduction to Psychology 16th Edition. Wadsworth Pub Co, 2014 (Chapters excluded: 2,3,13,15,16,17,18)
Slides and articles uploaded on Blackboard are considered as additional course materials

TEACHING METHOD
The classroom lessons – which will consist of explanations, examples, and practical activities – will be enriched by the materials made available online on the Blackboard platform.

ASSESSMENT METHOD AND CRITERIA
The exam is split in two mandatory steps:
1. a written test composed by the following sections: section A- 10 multiple-choice questions and Section B- 2 open questions. Multiple-choice questions are scored with 0 points for wrong or missing answers and 2 points for the correct response. Therefore Section A score ranges from 0 to 20 points. Open questions receive a score ranging from 0 (for missing response or response completely wrong) and 5 points (for exemplary responses). Section B score, indeed, ranges from 0 and 10 points. The written test is passed when the student reaches a sufficient score in both the sections (12/20 in A; 6/10 in B). The sum of scores obtained in Section A and Section B is the starting grade with which the student accesses the oral exam
2. an oral exam upon passing the written test. The final grade is calculated as follows: written test score +/- 3 points.
For the purpose of the evaluation, the relevance of the answers, the appropriate use of specific terminology, the reasoned and coherent structuring of the speech, the ability to create connections, and the critical re-reading of the topics will be crucial.

NOTES AND PREREQUISITES
Given the introductory nature of the course, previous knowledge of the contents is not required.
In case the current Covid-19 health emergency does not allow frontal teaching, remote teaching will be carried out following procedures that will be promptly notified to students.

CONTACTS
Teachers meet students by appointment before or after lessons (to make an appointment please write an email to silvia.serino@unicatt.it or giuseppe.riva@unicatt.it

Link:

1) the European Statistical System (ESS), the Data Production Model (DPM) and the main economic aggregates to study poverty and on the surveys on households (poverty and living conditions; EUSilc survey, Household Budget Survey, Labour Force Survey).

2) the main survey designs and their estimation strategy for planned domains of study (Regions); R codes for the application of the main direct estimators of the indicators (EURAREA and SAMPLE project libraries)

At the end of the course student will be able to deal with the ESS and to criticize the statistical quality of the published indicators of poverty and living conditions (Laeken Indicators).
Syllabus: The contents of the course are:

the European Statistical System and its Data Production Model,
Definition of poverty and living conditions (e.g. Laeken Indicators of Poverty and/or multidimensional indicators of poverty),
survey methods and estimation strategies (Horvitz -Thompson estimator, Hayek estimator),
introduction to economic official data and aggregates to study poverty and the main european surveys on households (EUSilc: European Survey on Income and Living Conditions, Household Budget Survey, Labour Force Survey). Issues on data integration and new data sources as Big data.

Link: http://sampieuchair.ec.unipi.it/

#Bibliography:
I. Musu, Introduzione all’economia dell’ambiente. Il Mulino, 2020.
R. Perman, Y. Ma, J. McGilvray, M. Common. Natural Resource and Environmental Economics, Pearson, 2003.
T. Tietenberg, L. Lewis. Environmental and Natural Resource Economics, Pearson Education, 2012.
R.K Turner, D. Pearce, I. Bateman, Environmental Economics: an elementary introduction, John Hopkins Univ. Press, 1994.

#Prerequisites:
No prerequisites. The course is understandable by a broad audience. Technical and economic aspects will be treated gently.
Link:

Module 1 introduces students to the fundamental principles of structured programming, with basic applications to data processing. It starts from basic notions of programming (variables, data types, collections, control & repetition structures, functions & modules), and progresses to basic data processing functionalities (loading, manipulation, and visualization of CSV data).

Module 2 introduces students to the components of typical data analysis processes and machine learning pipelines. It first builds the necessary toolset by introducing popular Python libraries for data manipulation/visualization (NumPy, Pandas, Seaborn, scikit-learn) with simple applications. The toolset is then applied to a more complex case study on the classification of benign and malignant breast cancer, including aspects of data preprocessing, dimensionality reduction, clustering, and classification. The module also presents KNIME, a popular python-integrated workflow-based language for data analysis.
Prerequisites: Knowledge of computer programming obtained attending Module 1.

Prerequisites: No prerequisites.
Evaluation Group project with final presentation and Jupyter notebook documentation.
Materials: Learning Python (M. Lutz). Statistics and Machine Learning in Python (E.Duchesnay, T.Löfstedt, F.Younes).
Syllabus: Module 1: The module starts from basic notions of programming (variables, data types, collections, control & repetition structures, functions & modules), up to basic data processing functionalities (loading, manipulation, and visualization of CSV data).
Module 2: first builds the necessary toolset by introducing popular Python libraries for data manipulation/visualization (NumPy, Pandas), applied to simple applications. The toolset is then applied to a more complex case study on the classification of benign and malignant breast cancer, including aspects of data preprocessing, dimensionality reduction, clustering, and classification. The module will also present KNIME, a popular python-integrated workflow-based language for data analysis.
Link: https://github.com/EMbeDS-education/StatsAndComputing20212022/wiki

— Evaluation of Options. Models of Cox-Ross-Rubinstein and of Black-Scholes. Risk Neutral evaluation (European Options, American Options, Exotic Options). Dynamic evaluations. Market premium and change of numeraire. Affine processes in continuous time and valuation formulae. Models of Merton and Bates.

— Volatility. Volatility surfaces. Extensions of the Black and Scholes Formula and local volatility models. Stochastic Volatility models in continuous time. Estimation of volatility. Stable convergence and infill asymptotic. Realized Measures of Volatility: Asymptotic properties.

— Numerical methods for the estimation of models. Maximum Likelihood Methods: Estimation of coefficients of SDE.
Link: https://www.sns.it/it/corsoinsegnamento/quantitative-finance

Module 1
– Unsupervised classification; Clustering methods
– Unsupervised dimension reduction; Principal Components Analysis and related techniques
– Supervised classification methods
– Non-parametric regression methods
– Resampling methods, Cross Validation, the Bootstrap and permutation-based techniques.

Module 2
– Feature selection and regularization techniques for high-dimensional Linear and Generalized Linear Models
– Feature screening algorithms for ultra-high dimensional supervised problems
– Supervised dimension reduction; Sufficient Dimension Reduction and related techniques
– Subsampling/partitioning approaches for ultra-high sample sizes
– Under- and oversampling approaches for data rebalancing

Evaluation: Group project with final presentation and written report.

Materials: An Introduction to Statistical Learning – with Applications in R (James, Witten, Hastie, Tibshirani; Springer). Computer Age Statistical Inference (B. Efron, T. Hastie). Links to further material will be provided as needed.

Prerequisites: a working knowledge of basic statistical inference (point estimation, confidence intervals, testing) and linear and generalized linear models. E.g., this may be obtained, or refreshed, through course Applied Statistics.
Syllabus: Module 1
– Unsupervised classification; Clustering methods
– Unsupervised dimension reduction; Principal Components Analysis and related techniques
– Supervised classification methods
– Non-parametric regression methods
– Resampling methods, Cross Validation, the Bootstrap and permutation-based techniques.

Module 2
– Feature selection and regularization techniques for high-dimensional Linear and Generalized Linear Models
– Feature screening algorithms for ultra-high dimensional supervised problems
– Supervised dimension reduction; Sufficient Dimension Reduction and related techniques
– Subsampling/partitioning approaches for ultra-high sample sizes
– Under- and oversampling approaches for data rebalancing
Link: https://github.com/EMbeDS-education/StatsAndComputing20212022/wiki

1) data collection methods

2) the use of secondary sources in data production

3) the use of R

At the end of the course student will be able to deal with traditional and modern data collection methods and will be able to present a simple policy paper using data from official surveys or other sources.
Syllabus: 1) Data collection methods

Official statistics: meaning and implications; Data collection procedure. The general model GSBPM; Data collection solutions. Introduction to difference model based| model assisted; Reference population, Survey population, Survey frame, Field organization, Contact of reporting units., Questionnaires, Direct observation, Electronic data reporting, Administrative data, Big data. Data quality.

2) The use of secondary sources in data production

Definitions and nature of secondary data, framework for access to the data, quality, data linkage and matching, supporting statistical surveys with administrative data, administrative data in the production of statistical register, a register based statistical system.

3) R software

Data structure and functions; Data elaboration; Plot and indicators
Link: https://esami.unipi.it/programma.php?c=51614&aa=2021&cid=120&did=17