# Statistical Treatment of Data

## Objectives

The curricular unit of "Treatment of Statistical Data" aims to provide skills in the domain of evolution of economic time series, in particular in the field of index numbers, seasonal correction and outliers and missing values treatment.

## General characterization

400018

6.0

##### Responsible teacher

Jorge Morais Mendes

##### Hours

Weekly - Available soon

Total - Available soon

##### Teaching language

Portuguese. If there are Erasmus students, classes will be taught in English

Not applicable

### Bibliography

Please check couse contents; 0; 0; 0; 0

### Teaching method

The course is based on theoretical and practical classes. The classes are aimed at solving problems and exercises.

### Evaluation method

(100%) Final exam (1st or 2nd round dates)

## Subject matter

1.    Introduction to índex number
1.1.    Index numbers problem
1.2.    Taxonomy of approaches to índex number
1.3.    Implicit and direct number indexes
1.4.    Index number formulation
1.5.    Basic concepts of índex number calculation
1.6.    Contemporary issues
2.    Statistical/axiomátic approaches
2.1.    Bilateral indexes
2.2.    Fixed base and chain indexes
3.    Productivity indicators
3.1.    Partial productivity indexes of production factors
3.2.    Total productivity indexes of production factors
4.1.    Bried introduction to time series
4.2.    Seasonality and its determinants
4.3.    Decomposition models
4.4.    Exploition tools
4.6.    Outliers, calendar effects and its components
4.7.    European Statistical System guidelines
5.    Treatment of outliers
6.    Tratamen of missing data

a.    Index numbers
•    SNA08, Chapter 15: Price and volume measures
http://unstats.un.org/unsd/nationalaccount/docs/SNA2008.pdf

•    International PPI Manual
http://www.imf.org/external/pubs/ft/ppi/2010/manual/ppi.pdf

•    International CPI Manual
http://www.imfbookstore.org/ProdDetails.asp?ID=CPIMFT
•    Practical Guide to Producing CPI
•    RECENT DEVELOPMENTS IN INDEX NUMBER THEORY AND PRACTICE
http://78.41.128.130/dataoecd/49/2/35619491.pdf
•    MEASUREMENT OF AGGREGATE AND INDUSTRY-LEVEL PRODUCTIVITY GROWTH, OECD manual
www.oecd.org/std/productivity-stats/2352458.pdf
•    THE MEASUREMENT OF AGGREGATE TOTAL FACTOR PRODUCTIVITY GROWTH
www.economics.ubc.ca/.../pdf_paper_erwin-diewert-02-05-measurementofaggregatetotalfactorproductivitygrowth
b.    Missing data
•    Allison, P. D. (2002). Missing Data. Thousand Oaks, CA: Sage
•    Bryk, A. S. And Randenbush, S. W. (1992). Hierarchical linear models, thousands oaks, CA: Sage
•    Cleveland, W. S. (1983), Visualizing data. Summit.
•    Enders, C. K. (2010) – Applied missing data Analysis, New York, the Guilford Press
•    Little, R. J. A. and Rubin (1987) – Statistical analysis with missing data, New York, Willey
•    McKnight P., McKnight K., Sidani S. and Figueredo A. (2007) Missing data – A Gentle introduction, the Guilford Press
•    Shaffer, J. L. and Graham, J.W. (2002) Missing data: our view of the state of the art.
•    Shafer, J. L. (1997) – Analysis of incomplete data, Chapman and Hall, London
c.    Outliers
•    Barnett V. and Lewis T (1995) – Outliers in statistical data, 3rd edn. Wiley
•    Brauman M. (1994), Sobre testes de deteção de outliers em populações exponenciais. Dissertação de douturamento, Universidade de Évora
•    Cook, R. D. and Weisberg S. (1982) - Residuals and influence in Regression. Chapman and Hall
•    David, H. A. (1981), Order Statistics, Wiley
•    Hoaglin, D. C., Mosteller, F. and Tukey J.W. (1992), Análise exploratória de dados. Técnicas robustas. Edições Salamandra.
•    Murteira, B., Ribeiro, C.S., Silva J.A., Pimenta C. (2002), Introdução à estatística, MCGrawhill
•    BCE: