•GUIDA ECONOMIA 07-08 - Università degli studi di Udine

prospectus udine
243
- D.M. LEVINE, T.C. KREHBIEL, M.L. BEREN-
SON, Statistica, Apogeo, Milano, 2002.
- G. CICCHITELLI, Probabilità e statistica,
second edition, Maggioli Editore, Rimini,
2001.
Exam
Evaluation: To receive credits students
must complete two written assignments,
a midterm examination, and a final examination.
STATISTICS
(CdL SCSBM)
Prof. Ruggero Bellio
Aims
The course provides an introduction to
basic methods of statistical inference.
Contents
1. Complementary elements of probability
theory. Basic sampling theory.
2. The concept of a statistical model.
Model specification and model checking.
3. Point estimation.
4. Testing.
5. Confidence intervals and regions. Prediction
problems.
Bibliography
Main text
- L. PACE, A. SALVAN, Introduzione alla Statistica,
II, Inferenza, verosimiglianza, modelli,
Cedam Padova, 2001.
Supplementary texts
- M. GRIGOLETTO, L. VENTURA, Statistica
per le scienze economiche, Giappichelli,
Torino, 1998.
- G. CICCHITELLI, Probabilità e Statistica,
second Edition, Maggioli Editore, 2001.
STATISTICS FOR
ECONOMIC ANALYSIS
Prof. Gian Pietro Zaccomer
Aims
The course is the logical continuation of
Statistical Measurement of Economics
Phenomena. While the first course illustrates
the elements to understand and
measure economic values, the second
course focuses on the main methods of
statistical analysis, in time and space, of
economic phenomenon.
Contents
1 Analysis of economic values in time
1.1 Introduction to time series analysis.
Variation of economic time aggregates.
Graphic analysis of time series. Classical,
stochastic and structural analysis.
Hypothesis of decomposition model.
1.2 Stationarity of series. Linear and nonlinear
trends. Variable transformations.
Regression and moving averages.
1.3 Economic cycle and turning points.
The concept of economic hysteresis.
1.4 Season and methods of seasonal
adjustment for time series.
1.5 Residual component analysis.
1.6 Outlines on time series forecasting.
2 Analysis of economic values in space
2.1 Introduction of analysis and characteristics
of spatial data. Analysis for
points, lines and areas. Regular and irregular
grids. Administration divisions. Triangulation
methods and the creation of
gravitational areas.
2.2 Indicators for socioeconomic territorial
analysis. Classic shift-share analysis.
2.3 Contiguity and connection matrices.
Spatial shift-share analysis.
2.4 Spatial autocorrelation and its measures.
2.5 The main problems of statistical modeling
with spatial data. Territorial reorganization
by means of indicators, flows
and cluster analysis.