Our objective is to provide future analysts with the tools, methodologies, interpretations and statistical data interpretations of economic phenomena. The teaching model is geared towards providing students with the tools to develop data sets, choose the most appropriate statistical methodology for the objectives that the analyst wants to achieve, draw and interpret statistical reports, and finally, translate the statistical result obtained into policy actions.
The course is strongly oriented with regard to simulations of real cases and provides students with new competitive capabilities in specialized training. The setting of the course is aimed at facilitating the understanding of the potential analysis and application of various techniques that are used to achieve a suitable balance between the technical rigor of the methods and the illustration of the potential applications of the same by using Excel.
1. Knowledge and understanding (knowledge and understanding):
The course requires a suitable knowledge of descriptive statistics. The course setting’s objective is to facilitate the understanding of the potential application of different analytical and statistical techniques that are applied to economics and to achieve a suitable balance between the technical rigor of the methods and illustration of the potential applications of the same through the use of common software. The educational activities towards the development of the knowledge of the theoretical (preparatory) and practical (application) aspects. Learning and understanding will be tested during the course (ongoing testing) and at the end of the series of lectures and exercises (ex-post verification).
2. Through the development of a balanced quantity of simulation and Excel as well as through the presentation of a wide panel of case studies, the students will have topics on which to reflect to identify specific methods regarding the case to solve, process and interpret the results. This approach allows an understanding tangimile of the utility of statistical tools and the consistency between the objective to be achieved and the methodology to be adopted.
3. Making judgments (Making judgments):
Through continuous feedback, the student, with the teacher, will be encouraged to improve his ability to process and describe interpretative models with precision and clarity to statistically analyse economic phenomena.
Thus, this study will be useful in the working groups’ comparisons. In fact, the data allows for more effective interactive teaching and the development of leadership skills.
Through the comparison between the working group and the teacher and within the working group, the students acquire autonomy in a critical capacity in their preparation compared to the parameters of the evaluation, which leads them to the final exam.
4. Communication skills (communication skills):
At the end of the course, the students will be able to transfer to other courses, with technical language and practical information and assessments related to the economic analysis that is also assigned by the use of Excel. Communication skills are acquired and verified during the course specific study groups, preparation of statistical reports on economic phenomena, classroom discussions and presentation of results. The acquisition of communication skills is entrusted to the discussion of the results with the study groups and the final test. Students are invited to take action at any time of the lesson for clarification or to gather more information regarding the topics covered or are urged to verify the practical understanding of the theoretical aspects treated. In addition, there will be weekly meetings, outside of school hours, for suggestions, discussions and review of the documents (usually during office hours).
5. Learning skills (learning skills):
At the end of the training, as characterized by constant stress by the teacher regarding the submission time of entries per module; by open discussions in the classroom, from meetings during the teacher’s office hours; and by comparison within a single workgroup and among the working groups, the student will have acquired the knowledge and expertise to analyse and solve problems through the selection of statistical tools in practical cases.
MODULE I - Basic statistical methods for analysis of economic fluctuations
Index numbers *: classification of index numbers; index numbers thunderstorms; numbers elementary indices, fixed-base, a mobile base; Synthetic index numbers; Choice of the base; Choosing the method of calculation; properties and formal conditions of index numbers; Index numbers calculated by ISTAT; Temporal comparisons of economic aggregates; Spatial comparisons of economic aggregates.
MODULE II - Statistical methods for the analysis of time series
Series analysis *; classical analysis of time series *; models for economic time series *; the approach based on deterministic functions; approach with stochastic components; stochastic processes; the lag operator; processes AR (p); processes MA (q); processes ARMA (p, q); processes ARIMA (p, d, q); Verification of the model; test of normality; test of the absence of autocorrelation and homoskedasticity; constancy of parameters and structural change; The forecast in the economic *; predictive inference *; predicting in strategic decision-making; the role of information in the forecast; The forecast with the regression model; forecasting economic trends; prediction with little information.
MODULE III - Quantitative analysis of the processes of growth and transformation of production systems
The labor market *; sources of labor force statistics *; synthetic indices and specific employment and unemployment Labour *; Consumption analysis *; the function of aggregate consumption; specification and estimation of the parameters of a function of consumption; The production function and the measurement of productivity; production function; the production function of Coob Douglas; productivity indicators; the production function of Solow; Series analysis space; spatial autocorrelation; measure of economic distances; Hints on multivariate analysis.
1. Renato Guarini – Franco Tassinari “Statistica economica” ed. il Mulino
Testi di approfondimenti:
2. Predetti, I numeri indici, Teoria e pratica, Giuffrè Ed. Milano, 1996
3. A. Guizzardi, La previsione economica
4. David M. Levine, Timothy C. Krehbiel, Mark L. Berenson (2011) Statistica II ed. edizione Apogeo