The course aims to provide the basic elements, logical and conceptual, to understand, measure and analyse the phenomena and dynamics of an economic system, by means of main concepts and knowledge of descriptive and inferential statistics. The primary objective of this course is to perform data analysis, to identify relationships between variables and to describe and anticipate the performance of an economic phenomenon in the time and space.
Basic knowledge of mathematics
The program of the course can be divided into more parts, as follows:
part I (about 16 hours): univariate and bivariate descriptive statistics
part II (about 6 hours): probability and probability distributions
part III (about 10 hours): statistical inference
part IV (about 3 hours): statistical methods for the analysis of economic fluctuations
part V (about 6 hours): national account and main economic aggregates
part VI (about 9 hours): production structure analysis
part VII (about 6 hours): economic growth measures
part VII (about 8 hours): production and productivity measures
The program is structured in two parts, the first based on methods and techniques used to collect and elaborate statistical data, the second based on development of models for macroeconomic phenomena.
Statistical surveys. Sources and techniques for data collection. Sample surveys and censuses. Sampling techniques.
Organization and data representation. Tables and graphs for qualitative and quantitative data. The frequency distribution of absolute, relative, and cumulative percentage.
Summary and description of quantitative data. Average (arithmetic and geometric), median, mode and quantiles. Measures of variability: dispersion and inequality. Measures of dispersion: range, interquartile range, variance, standard deviation and coefficient of variation.
Bivariate statistical analysis. Bravais-Pearson correlation coefficient and simple linear regression model.
Probability. The basic concepts. Conditional and joint probability. Cumulative probability.
Random variables and probability distributions. Probability distribution of a discrete random variable: Binomial and Poisson. Probability distribution of a continuous random variable: Normal (Gaussian) and Student’s-t.
Sampling distribution. Distribution of the sample mean. Central Limit Theorem. Distribution of sample proportion.
Confidence intervals: mean (variance known and not known) and proportion.
Test for the mean (variance known and not known) and for the proportion.
Statistical methods for the analysis of economic fluctuations. The construction of index numbers, simple and complex. Index numbers of prices and quantities calculated for comparisons in time and space. The main Official Numbers Indices calculated by ISTAT. Inflation and deflation.
Time series analysis. Classic approach: additive and multiplicative models. Moving average method for the estimation of the trend-cycle, estimation of seasonally. Forecasts. Measures of the goodness of prediction.
National Accounts and main economic aggregates. Methods of estimating the Gross Domestic Product (GDP).
Analysis of input-output table.
Measures of economic growth. The estimation of material capital. Estimation methods of production capacity: Wharton school, Ratio Capital Product, the Bank of England. Measures of output and productivity: Cobb-Douglas and method of estimation of productivity, partial and total.
During the course, the exercises will focus on issues related to the field of interest of the degree program and all applications statistics will be executed by means of Excel (and sometimes even with the use of the SPSS statistical software).
Lectures and practical excercises
Levine D.M., Krehbiel T.C., Berenson M.L. (ult. ed), Statistica, Apogeo.
R. Guarini, F. Tassinari, Statistica Economica, Il Mulino.
During the course the teacher will give additional material useful to understand the covered arguments.
Written exam about 2 hours, including 3 exercises of statistics and 3 of economic statistics and 1 theoretical question of both parts. The oral exam is optional.