Forecasting Methods Applied to Auditing

The UC articulates interdisciplinary concepts of mathematics and statistics, applied to quantitative modelling.

The classes are, according to what is determined in the curriculum, theoretical-practical. In the theoretical part, introducing concepts, fundamental results and methodologies, the expository method will be used predominantly. The practical part will be aimed at exemplifying procedures and solving problems under the guidance of the teacher, but encouraging autonomous work with the support of a computer tool. A strong interaction between theory and practice will prevail, giving, as much as possible, a central role to the visualization and treatment of concrete situations.

Classes will be taught according to the orientation given by the IPC presidency.

Goals to achieve:

• Awareness of quantitative modeling;
• Familiarization with computer tools that support the efficient application of the forecasting methods studied;
• Forecasting in the fields of Management, Accounting, Auditing and Economics.

Skills to develop:

• identify and apply the appropriate quantitative forecasting methods to specific cases;
• to know the assumptions and limitations of the methods applied to obtain models;
• to evaluate the predictive capacity of the models obtained.

1. Introduction to forecasting methods
    1.1. General considerations about forecasting methods
    1.2. Qualitative methods vs quantitative methods
    1.3. Causal and non-causal quantitative methods
    1.4. Exploratory analysis data and instruments
    1.5. Basic steps, concepts and tools in quantitative forecasting

2. Model-based forecasting for time series
    2.1. Forecasting with elementary methods
    2.2. Forecasting with exponential smoothing methods

3. Forecast based on econometric models
    3.1. Linear regression model: specification, extensions and assumptions
    3.2. Estimation by the method of least squares
    3.3. Model evaluation
    3.4. Selection of predictors
    3.5. Forecast: forecast of the isolated value and the average value; forecast ranges

NAO

Main:

• Albright, S.C., Winston, W.L. (2019). Business Analytics: Data Analysis and Decision Making, 7th Edition. Cengage Learning.
• Oliveira, M.M., Santos, L.D., Fortuna, N. (2018). Econometria, 2.ª Edição. Escolar Editora
• Richardson, V., Teeter, R., Terrel, K. (2019). Data Analytics for Accounting. McGraw-Hill Education.

Additional:

• American Institute of Certified Public Accountants (2017). Audit Guide: Analytical Procedures. AICPA.
• American Institute of Certified Public Accountants (2017). Guide to Audit Data Analytics. AICPA.
• Box, G.E., Jenkins, G.M., Reinsel, G.C. (2015). Time Series Analysis: Forecasting and Control, 4th Edition. Wiley.
• Caiado, J. (2016). Métodos de Previsão em Gestão com aplicações em Excel, 2.ª Edição (revista e aumentada). Edições Sílabo.
• Gee, S. (2014). Fraud and Fraud Detection: A Data Analytics Approach. Wiley.
• Stratopoulos, T.C., Shields, G.P. (2018). Audit Data Analytics with R. Waterloo University.
• Wooldridge, J.M. (2019). Introductory Econometrics: A Modern Approach, 7th Edition. Cengage Learning.