Microeconometrics in Business Management E-Book

Jerzy Witold Wisniewski

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch
• Veröffentlichungsjahr: 2015

Seitenzahl: 326

Beschreibung

This book introduces the application of microeconometric methods for modelling various aspects of economic activity for small to large size enterprises, using methods that are based on both time-series and cross-section approaches. The information obtained from using these estimated models can then be used to inform business decisions that improve the efficiency of operations and planning. Basic models used in the modelling of the business (single-equation and multiple-equation systems) are introduced whilst a wide range of economic activity including major aspects of financial management, demand for labour, administrative staff and labour productivity are also explored. Microeconometrics in Business Management: * Introduces econometric methods which can be used in the modelling of economic activity and forecasting, to help improve the efficiency of business operations and planning. * Describes econometric entities through multiple-equation and single-equation microeconometric models. * Explores the process of building and adapting basic microeconometric tools. * Presents numerous micromodels based on time-series data and statistical cross-sectional sequences, which can be used in any enterprise. * Features numerous real -world applications along with examples drawn from the authors own experience. * Is supported by a companion website featuring practice problems and statistical data to aid students to construct and estimate micro models. * Features end of chapter exercises with examples present in free software GRETL. This book serves as a valuable resource for students, business management practitioners and researchers in econometric micro-model construction and various decision-making processes.

Leseprobe

Table of Contentsx

Cover

Title Page

About the Author

Preface

Acknowledgment

1 A single-equation econometric model

1.1 The essence of an econometric model

1.2 Specification of an econometric model

1.3 Estimation of an econometric model’s parameters

1.4 Verification of the model

1.5 Multiplicative econometric models

1.6 The limited endogenous variables

1.7 Econometric forecasting

2 Multiple-equation econometric models

2.1 Classification of multiple-equation models

2.2 A reduced form of the model

2.3 Identification of the model

2.4 Estimation of the parameters of a multiple-equation econometric model

2.5 Forecasts estimation based on multiple-equation models

3 Econometric modeling of a large- and medium-sized enterprise’s economic system

3.1 Specification of a large- and medium-sized enterprise’s econometric model

3.2 Structural form of an econometric model of a large- and medium-sized enterprise

3.3 Empirical econometric model of a medium-sized enterprise

3.4 Application of the company’s model during a decision-making process

4 An empirical econometric model of a small-sized enterprise

4.1 Specification of a small-sized enterprise’s econometric model

4.2 The structural form of a small-sized enterprise’s econometric model

4.3 Equation of the cash inflows

4.4 Equation of the sales income

4.5 Equations of ready-made production

4.6 Equation of labor efficiency

4.7 Equations of the average wage

4.8 Equations of the net payroll

4.9 The employment equation

4.10 Equations of the fixed assets

4.11 Equations of wage effectiveness

4.12 Equations of the efficiency of implementing the fixed assets

4.13 Practical applicability of a small-sized enterprise’s model

5 Econometric modeling in management of small-sized enterprise

5.1 The concept of financial liquidity and its measurement in a small-sized enterprise

5.2 econometric modeling of monthly financial liquidity

5.3 econometric modeling of quarterly financial liquidity

5.4 Econometric modeling of debt recovery efficacy

5.5 Econometric model describing interdependencies between the financial liquidity and the debt recovery efficacy in an enterprise

5.6 Econometric forecasting of financial liquidity

6 Econometric model in the analysis of an enterprise’s labor resources

6.1 A study of a mechanism of the demand for labor

6.2 Econometric modeling of labor intensity of production

6.3 Econometric model in selection of an efficient worker

6.4 Econometric model in the selection of an efficient white-collar worker

Conclusion

Bibliography

Index

End User License Agreement

List of Tables

Chapter 03

Table 3.1 Basic statistical data of the ENERGY enterprise.

Table 3.2 An empirical equation of the sales income.

Table 3.3 An empirical equation of employment.

Table 3.4 An empirical equation of labor productivity.

Table 3.5 An empirical equation of the average wage.

Table 3.6 An empirical equation of the fixed assets.

Table 3.7  An empirical equation of the technical labor equipment.

Table 3.8 Forecasts of labor efficiency in the company ENERGY obtained for three quarters of 2013.

Table 3.9 Forecast of the sales income (SNET

Tp

) in the company ENERGY obtained for the first quarter of 2013.

Chapter 04

Table 4.1 Quarterly forecasts of investments for the year 2003 (in thousands PLN).

Table 4.2 The company’s quarterly forecasts of the values of the fixed assets, for the year 2003 (in thousands PLN).

Chapter 05

Table 5.1 An empirical dynamic econometric model of the company’s financial liquidity liq, based on the monthly data for the years 1996–2006.

Table 5.2 An empirical dynamic econometric model of the company’s financial liquidity, based on monthly data from the years 1996–2006.

Table 5.3 An empirical dynamic econometric model of the company’s financial liquidity (liq), based on quarterly data from the years 1996–2006.

Table 5.4 An empirical dynamic econometric model of the company’s financial liquidity, based on quarterly data from the years 1996–2006.

Table 5.5 Statistical characteristics of the efficacy measures of monthly debt recovery in the company REX, during the years 1996–2006.

Table 5.6 Annual results of debt recovery in the company REX during the years 1996–2006.

Table 5.7 An empirical econometric equation describing the company’s debt recovery efficacy measure (evind), based on monthly data from the years 1996–2006.

Table 5.8 Monthly forecasts of ready-made production for the year 2007, in thousands PLN.

Table 5.9 Monthly forecast estimations of relative liquidity (liqproc

Tp

), of average prediction errors (

V

T

) and of relative prediction errors (

) as well as forecast ranges at a confidence level of 95%

t

(115, 0.025) = 1.981.

Chapter 06

Table 6.1 An empirical monthly econometric model describing the changes in the company REX’s employment dynamics.

Table 6.2 An empirical monthly econometric model describing the changes in the VAT tax per one employee, in the years 1996–2006, in the company REX.

Table 6.3 The empirical monthly econometric model describing the changes in the company REX’s employment dynamics, depending on the fiscal factors.

Table 6.4 The company REX’s labor intensity of production in monetary units (annually).

Table 6.5 The company’s labor intensity of production in natural units (annually).

Table 6.6 The parameter estimation results of the model for the labor intensity of the ready-made production, in monetary units (LABCD), based on monthly data.

Table 6.7 The parameter estimation results of the model for the labor intensity of the ready-made production in natural units (LABCD), based on the monthly data.

Table 6.8 The worker’s efficiency and labor quality, as well as some of their personal characteristics.

Table 6.9 The effectiveness of claim recovery, the net sales income as well as the selected personal characteristics of the traders in the company MAX, annually for the years 2010–2013 (in thousands PLN annually).

List of Illustrations

Chapter 01

Figure 1.1 A linear model of the limited dependent variable.

Figure 1.2 A basic transformation of the limited dependent variable.

Figure 1.3 A logit transformation of the both-sides limited dependent variable.

Chapter 03

Figure 3.1 Economic interrelations in a large-sized (medium-sized) enterprise.

Figure 3.2 The actual monthly net sales income, the theoretical values, and the residuals calculated based on the equation from Table 3.2.

Figure 3.3 Actual quarterly employment, theoretical employment, and residuals calculated based on the equation from Table 3.3.

Figure 3.4 Actual quarterly team-labor productivity, theoretical volumes, and the residuals calculated based on the equation from Table 3.4.

Figure 3.5 The actual average quarterly net wages, their theoretical values, and the residuals calculated on the basis of the equation from Table 3.5.

Figure 3.6 The actual quarterly value of the fixed assets, the theoretical values, and the residuals calculated using the equation from Table 3.6.

Figure 3.7 Actual quarterly labor technical equipment, its theoretical volumes, and the residuals calculated based on equation from Table 3.7.

Figure 3.8 Forecasts of EMP.

Figure 3.9 Forecasts of labor efficiency (EFEMP

Tp

) in the company ENERGY obtained for three quarters of 2013.

Figure 3.10 Forecast of the sales income (SNET

Tp

) in the company ENERGY obtained for the first quarter of 2013.

Chapter 04

Figure 4.1 Economic interdependencies in a small-sized enterprise.

Figure 4.2 The actual and the theoretical monthly amounts of the cash inflows as well as the residuals calculated on the basis of Equation 4.12.

Figure 4.3 Distribution of the residuals of Equation 4.12.

Figure 4.4 The actual and theoretical quarterly values of the cash inflows and the residuals of Equation 4.13.

Figure 4.5 Distribution of the residuals of Equation 4.13.

Figure 4.6 The actual monthly gross sales income, the theoretical values, and the residuals calculated on the basis of Equation 4.14.

Figure 4.7 Distribution of the residuals of Equation 4.14.

Figure 4.8 The actual quarterly values of the gross sales income, the theoretical values and the residuals calculated on the basis of Equation 4.15.

Figure 4.9 Distribution of the residuals of the empirical equation of the gross sales 4.15.

Figure 4.10 Monthly seasonal fluctuations of the ready-made production’s dynamics index, in the years 1996–2002.

Figure 4.11 Empirical values of monthly ready-made production, the theoretical values and the residuals, calculated on the basis of Equation 4.16.

Figure 4.12 The actual quarterly values of finished production, the theoretical values and the residuals, calculated on the basis of Equation 4.17.

Figure 4.13 The actual monthly values of team-labor efficiency, the theoretical values, and the residuals calculated on the basis of Equation 4.18.

Figure 4.14 The actual values of quarterly team-labor efficiency, its theoretical values, and the residuals calculated on the basis of Equation 4.19.

Figure 4.15 The actual average monthly net wages, their theoretical values and the residuals, calculated on the basis of Equation 4.20.

Figure 4.16 The actual average monthly net pay, the theoretical values and the residuals calculated on the basis of Equation 4.21.

Figure 4.17 The actual monthly values of the net payroll, the theoretical values and the residuals calculated on the basis of Equation 4.22.

Figure 4.18 The actual quarterly values of the net payroll, the theoretical values, and the residuals calculated on the basis of Equation 4.23.

Figure 4.19 The actual monthly employment, theoretical employment, and the residuals calculated on the basis of Equation 4.24.

Figure 4.20 The actual employment, the theoretical employment volumes, and the residuals calculated on the basis of Equation 4.25 (quarterly data).

Figure 4.21 The actual initial values of the tangible fixed assets in use, the theoretical values, and the residuals calculated on the basis of Equation 4.26 (monthly).

Figure 4.22 The actual initial values of the fixed assets, the theoretical values, and the residuals calculated on the basis of Equation 4.27 (quarterly).

Figure 4.23 The actual initial values of machinery and equipment, the theoretical values and the residuals calculated on the basis of Equation 4.28 (quarterly).

Figure 4.24 The actual initial values of machinery and equipment, the theoretical vales, and the residuals calculated on the basis of Equation 4.29 (monthly).

Figure 4.25 The actual quarterly investment outlays, the theoretical values of investments, and the residuals calculated on the basis of Equation 4.30.

Figure 4.26 The actual monthly investments, the theoretical values, and the residuals calculated on the basis of Equation 4.31.

Figure 4.27 The actual quarterly investment outlays for machinery and equipment, its theoretical values and the residuals calculated on the basis of Equation 4.32.

Figure 4.28 The actual monthly ESAL values, the theoretical values, and the residuals calculated on the basis of Equation 4.34.

Figure 4.29 The actual quarterly EPL values, the theoretical values, and the residuals calculated on the basis of Equation 4.35.

Figure 4.30 A single-base dynamics indexes of the variable SALDE in the years 1996–2002 (monthly, 1996, I = 100).

Figure 4.31 The actual monthly ESBSL values, the theoretical values, and the residuals calculated on the basis of Equation 4.36.

Figure 4.32 The actual quarterly values of EFSAL, the theoretical values, and the residuals calculated on the basis of Equation 4.37.

Figure 4.33 The actual monthly EPLW values, the theoretical values, and the residuals calculated on the basis of Equation 4.38.

Figure 4.34 The actual quarterly EPLW values, the theoretical values, and the residuals calculated on the basis of Equation 4.39.

Figure 4.35 The actual monthly EFMACH volumes, the theoretical values, and the residuals calculated on the basis of Equation 4.40.

Figure 4.36 The actual monthly EFBFIX volumes, the theoretical values, and the residuals calculated on the basis of Equation 4.41.

Figure 4.37 The actual quarterly values of EFMACH, the theoretical values, and the residuals calculated on the basis of Equation 4.42.

Figure 4.38 Quarterly forecasts of investments for the year 2003 (in thousands PLN).

Figure 4.39 The company’s quarterly forecasts of the values of the fixed assets in use, for the year 2003 (in thousands PLN).

Chapter 05

Figure 5.1 The links between cash inflows, gross sales income, and ready-made production in a small-sized enterprise.

Figure 5.2 Monthly financial liquidity of the company REX during the years 1996–2006 (in thousands PLN).

Figure 5.3 The structure of monthly cumulated financial liquidity of the company REX during the years 1996–2006 (in thousands PLN).

Figure 5.4 Cumulated monthly financial liquidity of the company REX during the years 1996–2006 (in% %).

Figure 5.5 The structure of cumulated relative monthly financial liquidity in the company REX during the years 1996–2006 (in%%).

Figure 5.6 Empirical and theoretical values of monthly financial liquidity liq in the company REX as well as the residuals during the years 1996–2006 (in thousands PLN), calculated on the basis of the equation given in Table 5.1.

Figure 5.7 Empirical and theoretical values of monthly financial liquidity in the company REX (calculated on the basis of the equation from Table 5.2) as well as the residuals, during the years 1996–2006 (in%%).

Figure 5.8 The structure of quarterly financial liquidity in the company REX during the years 1996–2006 (in%%).

Figure 5.9 Empirical and theoretical values of relative monthly financial liquidity in the company REX as well as the residuals (calculated on the basis of the equation from Table 5.3), during the years 1996–2006 (in%%).

Figure 5.10 Empirical and theoretical values of relative cumulated quarterly financial liquidity in the company REX as well as the residuals (calculated on the basis of the equation from Table 5.4) during the years 1996–2006 (in%%).

Figure 5.11 Measures of debt recovery efficacy in the company REX, during the years 1996–2006 (in thousands PLN).

Figure 5.12 The structure of the efficacy measure of monthly debt recovery evind

t

in the company REX, during the years 1996–2006.

Figure 5.13 The share of the measure evind

t

in the value of monthly ready-made production in the company REX, during the years 1996–2006 (%).

Figure 5.14 The structure of the share of monthly debt recovery’s efficacy measure ewind

t

in the value of ready-made production in the company REX, during the years 1996–2006.

Figure 5.15 Annual results of debt recovery in the company REX during the years 1996–2006 (in thousands PLN).

Figure 5.16 The actual and the theoretical values of the variable liq and the residuals from the empirical reduced-form’s equation (

).

Figure 5.17 The actual and the theoretical values of the variable ewind and the residuals from the empirical reduced-form’s equation (

).

Figure 5.18 The actual and the theoretical monthly values of the variable liqproc (calculated on the basis of model 5.12) during the years 1996–2006.

Figure 5.19 Monthly forecasts of ready-made production for the year 2007, in thousands PLN.

Figure 5.20 Forecast estimations of relative liquidity (liqproc

Tp

) and forecast rages at a confidence level of 95% t (115, 0.025) = 1.981.

Chapter 06

Figure 6.1 The monthly dynamics of employment in the company REX, in the period from January 1996 to December 2006.

Figure 6.2 The quarterly dynamics of the company REX’s employment in the period from the first trimester of 1996 to the fourth trimester of 2006.

Figure 6.3 The monthly changes of the average net wage in the company REX, in the years 1996–2006.

Figure 6.4 The monthly changes in the technical devices into machinery and equipment, in the years 1996–2006.

Figure 6.5 The monthly changes of the VAT tax, in PLN per one employee, in the years 1996–2006.

Figure 6.6 The actual and the theoretical volumes of the labor dynamics indexes (of the model from Table 6.1) and the model’s residuals.

Figure 6.7 Formation of the average VAT rate for the company REX’s sales income, in the years 1996–2006 (%).

Figure 6.8 The value of the input VAT tax per one employee in the company REX, in the years 1996–2006 (in thousands PLN).

Figure 6.9 The actual and the theoretical values of VAT (of the model from Table 6.2) per one employee as well as the residuals.

Figure 6.10 The actual and the theoretical employment volumes as well as the residuals, calculated on the basis of the model 6.1.

Figure 6.11 The actual and the theoretical employment volumes as well as the residuals, calculated on the basis of the model 6.2.

Figure 6.12 The monthly values of the fiscal labor costs in the company REX, in the years 1996–2006 (in PLN per one employee).

Figure 6.13 The quarterly values of the company REX’s fiscal labor costs, in the years 1996–2006 (in PLN per one employee).

Figure 6.14 The annual fiscal labor costs in the company REX, in PLN per one employee.

Figure 6.15 The empirical and the theoretical (calculated on the basis of the model from Table 6.3) employment volumes (EMPL) as well as the residuals.

Figure 6.16 The company REX’s labor intensity of production in monetary units.

Figure 6.17 The company REX’s labor intensity of production in natural measures.

Figure 6.18 The actual and the theoretical monthly values of the company’s labor intensity of the ready-made production (in thousands PLN) as well as the residuals.

Figure 6.19 The actual and the theoretical monthly values of the company’s labor intensity of the ready-made production (in persons/100 thousand PLN) as well as the residuals.

Guide

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Table of Contents

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Microeconometrics in Business Management

Jerzy Witold Wiśniewski

This edition first published 2016© 2016 John Wiley & Sons, Ltd

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A catalogue record for this book is available from the British Library.

About the Author

Full professor of Nicholas Copernicus University (NCU) in Toruń (Poland), the Head of Econometrics and Statistics Department, the author of over 100 scientific papers in the field of econometrics and statistics, who has extensive experience in business management, the former Dean of the Faculty of Economics and Management and the Vice-Rector of NCU. Jerzy Witold Wiśniewski is a published author with extensive (over 40 years) experience in teaching and in leading research in the field of econometrics and statistics. Since 1991 he has led his own business, which has enabled him to combine theoretical knowledge with practice. All examples of application of various numerical methods are based on real data collected during his practice in business management.

University of Gdanńsk (1972–1976), Assistant, Department of Econometrics, University of Gdanńsk, Poland

University of Gdanńsk (1976–1977), Associated Professor, Department of Econometrics, University of Gdanńsk, Poland

Nicolaus Copernicus University, Toruń, Associated Professor (1977–1987), Head of the Department of Mathematics, Statistics, and Econometrics (1978–1983)

University of Gdanńsk (1987–1990), Professor

Nicolaus Copernicus University, Toruń, Professor (from 1990)

Nicolaus Copernicus University, Toruń, Head of Doctoral Studies at the Faculty of Economics and Management (1994–1996)

Nicolaus Copernicus University, Toruń, Dean of the Faculty of Economics and Management (1996–2002)

Nicolaus Copernicus University, Toruń (2002–2005) – The Vice-Rector of Economic Issues at Nicholaus Copernicus University, Poland

Nicolaus Copernicus University, Toruń, The Head of Econometrics and Statistics Department (from 1999)

The owner of the enterprise (Publishing and Printing Company (1991–2006))

Chairman of the Supervisory Board of The PIK Radio (1993–1999)

Member of the Supervisory Board of PZU (biggest insurance company in Poland) (1998–1999)

Expert of the National Science Centre (2012).

Preface

Decision-making requires having access to various increasingly effective tools serving preparation of such processes. Econometrics provides economists with efficient decision-making instruments.1 The world economy crisis between 1929 and 1933 has triggered necessity of continual analysis and prediction of the business cycle. In answer to the demand, first econometric tools, the so-called economic barometers, emerged.

Econometrics is a branch of economic studies. As Oskar Lange2 wrote, “econometrics is a science concerned with assessment of specific quantitative regularities occurring in the economy, through the use of statistical methods. (…) it combines economic theory with statistics, and seeks to use mathematical-statistical methods for assigning a specific quantitative expression to general diagrammatic regularities determined by economic theories.”