The economic questions raised throughout the course are based on the assumption that students possess a basic knowledge of micro- and macroeconomic theory, since it is the goal for students to learn the quantitative methods useful to conduct empirical analysis in economics. A basic background in statistics is also suggested (but not required).

GOALS - The goal of this course is to equip students with the knowledge of quantitative methods frequently used by economists to make connections between economic theory and empirical observation. The methods examined are divided into two categories: descriptive statistics used to measure individual economic variables, and statistical inference used to measure relationships between economic variables and to test hypotheses about them.

INTENDED LEARNING OUTCOMES - Upon successful completion of this course, students will have the skills needed to generate numerical results with each of the methods by applying them to specific economic questions. Successful completers will be also able to provide economic interpretations of the statistical results and to explain the strengths and limitations associated with each method.

The course covers the following parts and topics:

Part I - Introduction

• The role of statistics in economics
• Visual presentations of economic data

Part II - Descriptive statistics of an economic variable

• Observations and frequency distributions
• Measures of central tendency
• Measures of dispersion

Part III - Temporal descriptive statistics

• Measuring changes in price and quantity
• Descriptions of stability: short-run changes
• Patterns of long-term change

Part IV - Statistical inferences about a single variable

• Statistical hypothesis testing of a mean

Part V - Relationships between two variables

• Correlation analysis
• Simple linear regression analysis: descriptive measures
• Simple regression analysis: statistical inference
• Simple regression analysis: variable scales and functional forms

Part VI - Relationships between multiple variables

• Multiple regression analysis: estimation and interpretation
• Multiple regression analysis: hypothesis tests for partial regression coefficients and overall goodness of fit
• Multiple regression analysis: dummy variables and statistical problems

(A); Lewis M.; Applied Statistics for Economists; Routledge, Abingdon, United Kingdom; 2012; Pages/Chapters: chs. 1, 2, 3, 5, 6, 7, 8, 11, 12, 13, 14, 15, 16, 17, 18; 9780415777988, 9780203808450

Students are encouraged to regularly check the course website for ongoing information. Lecture slides and additional teaching materials will be accessible via the course’s Microsoft Teams channel. The password required to access the materials can be requested from the instructor, exclusively via the institutional UniMC email address.

• The course includes a theoretical and a laboratory part. The theoretical part is taught in the form of lectures with the help of slides prepared by the teacher. The laboratory part consists of practical activities of data acquisition, extraction and processing using Excel.

• The assessment of learning will be through a written exam composed of three open-ended questions and three problems (similar to those included in the textbook) to be solved with explanations. The sum of the individual response scores will determine the final grade. For the open-ended questions, each response will be scored (max. 5 points) according to the following criteria:

  • relevance to the question (max. 2 points);
  • thoroughness to the question (max. 2 points);
  • use of specific language and clarity of exposition (max. 1 point).

  For problems, max. 5 points will be awarded based on the following criteria:

  • accuracy of the solution procedure (max. 2 points);
  • motivation for the problem-solving procedure (max. 1 point);
  • accuracy of calculation (max. 1 point);
  • use of specific language and clarity of exposition (max. 1 point).

  There are no ongoing assessments.

English is the only language used for teaching activities.

English