Managers use forecasting in budgeting time and resources. In this thesis, various advanced time series models are constructed, computed and tested for adequacy. This thesis serves as a practical guide to regression and time series analysis. It seeks to demonstrate how to approach problems according to scientific standards to students who are familiar with SPSS® but beginners in regression and time series analysis. Bibliographic notes of classical works and more recent academic advances in time series analysis are provided throughout the text.
The research question that this thesis seeks to answer can be formulated in its shortest version as: “How can the management of Dalian Chemson Chemical Products Co; Ltd. use existing company data to make short-term predictions about net sales, Cost of Goods Sold (COGS), and net contribution?” More specifically, this thesis seeks to provide different tools (models) for forecasting the P&L entries net sales, COGS, and net contribution a few months ahead. This author’s approach is based on various versions of two models: One model will forecast net sales and the other model will predict COGS. The expected net contribution is simply defined as the difference between the predictions of these two models.
In chapter 4.3 an ordinary least squares regression version of the two models has been computed. In chapter 4.6 a weighted least squares regression has been applied to the models. Autoregressions have been computed in chapter 4.7.1 and two Autoregressive Integrated Moving Average (ARIMA) versions have been constructed in chapter 4.7.6. The various versions of the models have then been compared against each other. The version that fits the data best will be used in forecasting. The statistical models in this thesis are computed using SPSS Base™, SPSS Regression Models™ and SPSS Trends™, versions 11.5.0. Each of the model versions constructed herein can be applied in a simple Excel spreadsheet. In the last chapter, a one-step-ahead forecast is produced via the in this thesis developed concept which consists of the most precise versions of the models to forecast net sales and COGS. The forecasting concept developed in this thesis is good in that it produces precise forecasts. Its simplified framework minimizes the effort and expertise required to obtain predictions.
Inhaltsverzeichnis (Table of Contents)
- Introduction
- Outline of the Historical Background of Forecasting
- Motivation
- Methodology
- Review of Literature
- Description of Data
- Analysis
- Building a Model for Forecasting Cost of Goods Sold
- Building a Model for Forecasting Net Sales
- Computation
- Assumptions of the Classical Linear Regression Model
- Validation of Assumptions
- Assumption 1
- Assumption 2
- Assumption 3
- Assumption 4
- Assumption 5
- Assumption 6
- Assumption 7
- Weighted Least Squares Regression
- Time Series Analysis
- The Autoregressive Process
- The Moving Average Process
- The Autoregressive Moving Average Process
- The Autoregressive Integrated Moving Average Model
- Model Identification
- Model Estimation
- Diagnosis
- Conclusion
- Forecasting
- Outlook
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
This thesis aims to develop and validate statistical models for short-term forecasting of key performance indicators, namely net sales, Cost of Goods Sold (COGS), and net contribution, using existing company data from Dalian Chemson Chemical Products Co; Ltd. (DCCP).- The impact of raw material prices, particularly lead prices, on the COGS.
- The effect of sales price adjustments on net sales.
- The application of classical linear regression and time series analysis techniques to forecast financial performance.
- The use of simplified models that are practical and easy to implement in a business setting.
- The importance of validating model assumptions and assessing model adequacy for reliable forecasting.
Zusammenfassung der Kapitel (Chapter Summaries)
- **Introduction:** This chapter provides a brief history of forecasting and its evolution, highlighting the significance of probability theory and modern statistical methods. It introduces the motivation behind the thesis, which is to address DCCP's need for accurate short-term forecasts of key financial indicators. The methodology employed in the thesis, involving various time series models for forecasting net sales, COGS, and net contribution, is outlined.
- **Review of Literature:** This chapter discusses the extensive literature on regression and time series analysis. It emphasizes the lack of similar published studies on predicting net sales, COGS, and net contribution for DCCP specifically, as well as the abundant resources available for understanding these statistical techniques.
- **Description of Data:** This chapter provides a detailed description of the data used in the analysis, including a list of variables, their definitions, and their characteristics. The data consists of 6 quantitative variables observed over 40 consecutive months, from January 2004 to April 2007. The author also calculated several additional series from the existing data using the SPSS Data Editor, such as average cost per unit sold, average net sales per unit, and lagged variables.
- **Analysis:** This chapter delves into the construction of two models, one for forecasting net sales and the other for predicting the Cost of Goods Sold. The models are designed to predict the average value per unit sold, which is then multiplied by the expected quantity sold to obtain the overall forecast. The chapter then explores the assumptions underlying the Classical Linear Regression Model (CLR) and examines the validity of these assumptions in the context of the two models.
- **Conclusion:** This chapter summarizes the key findings of the thesis, emphasizing the performance of the OLS version of model 1 and the ARIMA version of model 2 in forecasting net sales, COGS, and net contribution. The chapter highlights the practical applicability of these models and emphasizes the importance of balancing model complexity with forecasting accuracy and efficiency. The chapter also discusses potential areas for future research, including the exploration of more sophisticated variance-stabilizing transformations and dynamic models.
Schlüsselwörter (Keywords)
This thesis focuses on forecasting key performance indicators using time series models. Key concepts include linear regression, autoregressive integrated moving average (ARIMA) models, model identification, model estimation, and model validation. Other important themes are the impact of raw material prices, particularly lead prices, on the Cost of Goods Sold (COGS), and the effect of sales price adjustments on net sales. The study utilizes statistical software such as SPSS for data analysis and model building.- Quote paper
- Arno Palmrich (Author), 2007, Time Series Models for Short-Term Forecasting Performance Indicators, Munich, GRIN Verlag, https://www.grin.com/document/134834
