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Published in:
Single-Equation Econometric Model Read chapter Read first chapter

2023 | OriginalPaper | Chapter

4. Forecasting from Simple Econometric Micromodels

Author : Jerzy Witold Wiśniewski

Published in: Forecasting from Multi-equation Econometric Micromodels

Publisher: Springer Nature Switzerland

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Abstract

Three empirical simple models based on real data from enterprises were presented. The first model describes the cost volatility mechanism of a company listed on the stock exchange. Forecasts of production, sales, and management costs were built, observing the rule of sequential forecasting. Another simple model was based on cross-sectional data. It describes the influence of personal characteristics of workers on their individual work efficiency and quality of production. The use of an empirical model to predict the performance and quality of candidates for a vacant worker position is presented. The third of the empirical simple models, also based on cross-sectional data, describes the mechanism of influence of the personal characteristics of traders on sales effectiveness and the effectiveness of debt collection. Forecasts of the variables described in this model and their use in the decision to employ candidates for the vacant position of a trader in the enterprise are presented.

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previous chapter Econometric Forecasts
next chapter Forecasting from Recursive Econometric Micromodels
Footnotes
1
The calculations performed for the purpose of this monograph were carried out using the GRETL package primarily.
 
2
The most important employee characteristic is his/her individual labor productivity. The more productive a worker is, at the right quality of workmanship, the more desirable he/she is from the enterprise perspective.
 
3
Forecasting is generally associated with the use of statistical data in the form of time series. In the case under consideration, the model used to estimate the forecasts is based on statistical observations in the form of cross-sectional data. The forecasts constructed using such models, however, apply to potential future events.
 
4
Most of the sales representative’s personal traits have been measured by dummy variables, as presented in Table 4.15.
 
5
Let us assume that the study concerns the results of sports equipment sales by the company’s sales representatives. As such, the fact that a given sales representative was involved in competitive sports in the past may be of relevance. Competitive sports participation can prove relevant not only in the sale of sports equipment. This type of activity develops a will to fight, perseverance in pursuit of a goal, which is conducive to all kinds of activities.
 
6
The variable “seniority” (xi3) is positively correlated with the variable xi6—sales representative’s age. Both of these variables proved to be statistically significant in the final model configuration, in which age turned out to be a variable negatively affecting sales representative efficiency, whereas seniority became a stimulator of that efficiency. Such cases arise every so often when examining the combined impact of a bundle of explanatory variables.
 
7
Negative frequencies do not exist; therefore, when a negative empirical result emerges for the frequency forecasted, it should be assumed that (\( {y}_{1 Tp}^{(l)}=0 \) (l = 1, 2, …, 10).
 
Literature
Barczak A (1968) Ekonometryczna analiza kosztów w przemyśle piwowarsko-słodowniczym, “Przegląd Statystyczny”, No. 4
Barczak A (1971) Ekonometryczne metody badania kosztów produkcji. PWN, Warszawa
Barczak A, Dziembała L (1969) Ekonometryczny model kosztów wydobycia rudy żelaza w Polsce, „Przegląd Statystyczny” No. 1
Dobrodolac T (2011) Forecasting by econometric models as support to management. Perspect Innovat Econ Bus 7(1):1–5
Juszczyk M, Zima K, Lelek W (2019) Forecasting of sports fields construction costs aided by ensembles of neural networks. J Civ Eng Manag 25(7):715–729CrossRef
Pawłowski Z (1976) Ekonometryczna analiza procesu produkcyjnego. PWN, Warszawa
Wiśniewski JW (2010) Prognozowanie w modyfikacjach struktur małego przedsiębiorstwa. In: P. Dittmanna, E. Szabela-Pasierbińskiej (red.), Prognozowanie w zarządzaniu przedsiębiorstwem, Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu No. 103, Wrocław, pp 240–249
Metadata
Title
Forecasting from Simple Econometric Micromodels
Author
Jerzy Witold Wiśniewski
Copyright Year
2023
Book
Forecasting from Multi-equation Econometric Micromodels

Print ISBN: 978-3-031-27491-6

Electronic ISBN: 978-3-031-27492-3

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-3-031-27492-3_4