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.
Table of Contents
1 Introduction
1.1 Outline of the Historical Background of Forecasting
1.2 Motivation
1.3 Methodology
2 Review of Literature
3 Description of Data
4 Analysis
4.1 Building a Model for Forecasting Cost of Goods Sold
4.2 Building a Model for Forecasting Net Sales
4.3 Computation
4.4 Assumptions of the Classical Linear Regression Model
4.5 Validation of Assumptions
4.5.1 Assumption 1
4.5.2 Assumption 2
4.5.3 Assumption 3
4.5.4 Assumption 4
4.5.5 Assumption 5
4.5.6 Assumption 6
4.5.7 Assumption 7
4.6 Weighted Least Squares Regression
4.7 Time Series Analysis
4.7.1 The Autoregressive Process
4.7.2 The Moving Average Process
4.7.3 The Autoregressive Moving Average Process
4.7.4 The Autoregressive Integrated Moving Average Model
4.7.5 Model Identification
4.7.6 Model Estimation
4.7.7 Diagnosis
5 Conclusion
5.1 Forecasting
5.2 Outlook
Research Objectives and Key Topics
The primary research objective is to develop and evaluate statistical forecasting tools using existing company data to predict short-term key performance indicators—specifically net sales, Cost of Goods Sold (COGS), and net contribution—for Dalian Chemson Chemical Products Co; Ltd. (DCCP). The thesis aims to replace reliance on mere assumptions with evidence-based mathematical models to improve budget accuracy and management decision-making.
- Application of linear regression and advanced time series analysis in a corporate environment.
- Evaluation of Ordinary Least Squares (OLS) versus Weighted Least Squares (WLS) regression models.
- Implementation of Autoregressive Integrated Moving Average (ARIMA) models for short-term prediction.
- Diagnostic testing for model validity (linearity, heteroscedasticity, and autocorrelation).
- Development of practical forecasting frameworks for implementation in spreadsheet software.
Excerpt from the Book
4.7 Time Series Analysis
Modern forecasting has its roots in different scientific disciplines. For many years, different concepts and methods evolved independently in these areas. In Mathematics, for example, the approaches usually involved linear stochastic systems, whereas, in Physics, nonlinear deterministic systems were investigated (Schelter et al., 2006). The academic literature on forecasting indicates that there is a gap between the theoretical and applied aspects of forecasting. The choice of a model for forecasting involves trade-offs among complexity, reliability, and the cost of generating forecasts. That is why most corporations are still reluctant to employ sophisticated models. Balancing the cost against the expected gains to be obtained from a versatile academic model often leads to the use of simple models despite their theoretical limitations.
The apparently widest gap in forecasting, however, is due to the fact that the analysis of economic data has been approached from two different philosophies. Until not so long ago time series analysts following the Box-Jenkins approach tended to ignore the role of explanatory variables, whereas, the other group followed the more classical econometric approach, paying little attention to stationarity: These econometric models attempt to describe the relationship among variables by use of traditional regression equations. During the last three decades, a number of steps to bridge the gap between the econometric and the Box-Jenkins approach have been taken, and cross-fertilization between different scientific disciplines took place. The reason behind this was that disquieting studies claimed that econometric forecasts were inferior, because running regressions on nonstationary data can lead to erroneous conclusions (Kennedy, 2003).
Summary of Chapters
1 Introduction: Provides historical context on forecasting theory and defines the motivation and methodology for improving DCCP's budgeting through statistical modeling.
2 Review of Literature: Surveys standard statistical textbooks and recent advancements in regression and time series analysis relevant to the study.
3 Description of Data: Details the quantitative variables and longitudinal data collected from internal company reports for the purpose of empirical modeling.
4 Analysis: Documents the construction, validation, and computational testing of various regression and time series models (OLS, WLS, ARIMA) to predict COGS and net sales.
5 Conclusion: Evaluates the performance of the developed models against actual P&L figures and discusses the practical application for future forecasting.
Keywords
Forecasting, Time Series Analysis, Linear Regression, Cost of Goods Sold, Net Sales, ARIMA, Ordinary Least Squares, Weighted Least Squares, Heteroscedasticity, Autocorrelation, Stationarity, Statistical Modeling, Budgeting, Performance Indicators, Dalian Chemson Chemical Products
Frequently Asked Questions
What is the fundamental purpose of this research?
The study aims to provide the management of Dalian Chemson Chemical Products Co; Ltd. with a robust, evidence-based method to generate short-term forecasts for key financial metrics, moving away from subjective assumptions.
What are the primary thematic areas covered in this work?
The paper focuses on the intersection of industrial financial reporting and quantitative statistical methods, specifically emphasizing regression analysis and time series modeling.
What is the central research question?
The research asks how existing company data can be leveraged via statistical models to make accurate, short-term predictions for net sales, COGS, and net contribution.
Which scientific methods are employed throughout the analysis?
The author utilizes Ordinary Least Squares (OLS) regression, Weighted Least Squares (WLS) regression to address heteroscedasticity, and Box-Jenkins ARIMA time series modeling to account for temporal dependencies.
What content is addressed in the analytical main section?
The analysis covers the derivation of forecasting equations, the rigorous validation of model assumptions (such as linearity and normality of residuals), and the comparative evaluation of different model versions.
Which keywords characterize this thesis?
Key terms include forecasting, time series analysis, regression modeling, COGS, net sales, and statistical model identification.
How is the impact of raw material costs integrated into the model?
The author identifies the market price of lead as a critical regressor and uses lagged values of this price to anticipate changes in the cost of lead-containing raw materials, which significantly impact COGS.
Why did the author conclude that OLS and WLS models perform adequately?
The author observed that the predicted values closely track the actual data series, suggesting that despite simplifications, these models provide sufficient predictive utility for management decision-making.
- Citar trabajo
- Arno Palmrich (Autor), 2007, Time Series Models for Short-Term Forecasting Performance Indicators, Múnich, GRIN Verlag, https://www.grin.com/document/134834
