. - Quantitative Methods
PROFF. PAOLO SCKOKAI- CLAUDIA LANCIOTTI
Applied Agricultural and Food Economics
PROF.PAOLO SCKOKAI
COURSE AIMS
The course aims to introduce students to some basic econometric tools applied
to food and agricultural data. Special attention will be given to those models that
can be applied in a business environment.
COURSE CONTENT
The multiple regression model. Review of the multiple regression
model. The use of dummy variables. F-tests on model specification.
Heteroscedasticity and serial correlation. Definition, test and
correction for heteroscedasticity. Definition, test and correction for
serial correlation.
Forecasting. Use of the regression model for forecasting. Forecast
error, forecast error variance and confidence intervals. Forecasting
with serially correlated errors.
Models of qualitative choice. Definitions: binary and multiple choice
models. Binary Linear Probability Model, Probit Model and Logit
Model.
Panel data models. OLS estimation on panel data. Fixed-effects
models: definition and estimation problems.
Instrumental Variable estimation. Correlation between
explanatory variables and error term. IV estimation, endogeneity and
2SLS estimation.
Estimation of systems of equations. Simultaneity, OLS estimation
and the identification problem. Seemingly unrelated regressions
(SUR) and Three-stage least squares (3SLS).
Tutorial sessions
READING LIST
Selected readings from the following textbook
ECTS
1.0
0.5
0.5
1.0
0.5
0.5
1.0
1.0
R.S PINDYCK-D.L. RUBINFELD, Econometric Models and Economic Forecasts, 4a ed., McGraw-Hill,
1998.
Further readings on specific topics will be provided by the instructor.
TEACHING METHOD
The course consists of five credits of lectures and one credit of tutorial computer
sessions.
ASSESSMENT METHOD
There will be one final exam, integrated by some group work carried out during the
course.
NOTES
Further information can be found on the instructor's webpage or on the Faculty notice
board.
Prof. Paolo Sckokai is available to meet with studentes after class in the SMEA offices.
Applied Statistics for the Agri-Food System
PROFESSOR CLAUDIA LANCIOTTI
COURSE AIMS
The course develops the student’s ability to use descriptive and inferential
statistics in data analysis, elaboration of hypotheses and in reaching conclusions in
the food sector applied research.
COURSE CONTENT
Descriptive statistics.
Presentation and summary of univariate data, presentation and
summary of bivariate data, graphical depiction. Measures of
central tendency and measures of variability, covariance and
correlation, concentration analysis. Index numbers.
Probability and probability distribution.
Theories of probability, theorems. Binominal distribution,
uniform distribution, normal distribution and central limit
theorem.
Sampling and sampling distributions.
CFU
0.5
0.5
0.5
Techniques, sample size, sampling and non-sampling errors.
Random variables, estimators and their properties.
STATISTICAL INFERENCE
Point estimate and interval estimate of population parameters, t,
2 and F distributions. Hypothesis tests for single populations.
Hypothesis tests about the comparison of two independent or
related populations. Chi-squared goodness-of-fit test and chisquared test of independence.
Analysis of variance.
One-way ANOVA. Tukey’s and Tukey-Kramer’s multiple
comparison tests. Two-way ANOVA.
Simple linear regression analysis.
Least squares estimates. Test of the assumptions and residual
analysis. Hypothesis tests for the regression coefficient and
testing the overall model. Confidence intervals for y|x and
prediction intervals for y.
Multiple linear regression analysis.
Hypothesis tests for the regression coefficients and partial F
test,adjusted R2, residual analysis. Non-linear regression models.
Dummy variables. Multicollinearity and model-building
procedures.
Tutorials.
1.0
0.5
1.0
1.0
1.0
READING LIST
K. BLACK, Business Statistics for Contemporary Decision Making, 5e, John Wiley & Sons, USA,
2007.
Slides and classnotes.
TEACHING METHOD
Lectures, team work.
ASSESSMENT METHOD
Written and oral exam.
Professor Claudia Lanciotti will receive students by appointment.