Significance of econometrics in business affair and software used for statistical analysis

Significance of Econometrics in business affair and software used for statistical analysis 

According to Qu (2020), econometrics is the combination of these three view-points that of statistics, economic theory, and mathematics and it helps to real understanding of the quantitative relations in modern economic life with what we call general economic theory, although a considerable portion of this theory has a definitely quantitative character. The scholar Greene (2000) has claimed it is an integration of economics, mathematical economics and statistics with an objective to provide numerical values to the parameters of economic relationships. The econometric tools are helpful in explaining the relationships among variables. Econometrics is the quantitative application of statistical and mathematical models using data to develop theories or test existing hypotheses in economics and to forecast future trends from historical data. It subjects real-world data to statistical trials and then compares and contrasts the results against the theory or theories being tested (Gujarati, Porter & Gunasekar, 2012; Dougherty, 2011; Mukras, 1993).

The researchers Clinch and Murphy, (2001) noted that econometrics is the use of statistical methods using quantitative data to develop theories or test existing hypotheses in economics or finance. Econometrics relies on techniques such as regression models and null hypothesis testing. Econometrics can also be used to try to forecast future economic or financial trend. Different types of methods as follows;

Source; Public trust of in Basic Social and Political institutions (2005)

 

Significance of Econometrics in Management and Business field

According to Groot and Garcia (2006), econometric is concerned with the quantitative relationships between economic variables with applied business data in various fields of business management and it can provide an important input into the decision-making process. The econometric problem is how to use these tools along with measurement to answer business cycle questions Econometric enhanced our understanding of the way the managerial decision works by using quantitative analysis of actual economic phenomena based on theory, observations and set of assumptions to simplify the complex economic phenomena (Terng, 2017).

Dima and Man (2015) claimed econometrics is differs from other aspects of management science in that it considers problems primarily, though not exclusively, from a background of economics rather than of other disciplines and behavior is usually dealt with at higher levels of data aggregation than the individual firm. 

Econometrics has developed models for cross-sectional data, and panel data and dynamic panel data. To different types of data, different estimation methods can be chosen according to their advantages and disadvantages of business firm (Johnston, & Reeves, 2019).

In the field of business and management, the practical applications of econometrics are;

·            Forecasting macroeconomic indicators

·            Estimating the impact of immigration on native workers

·            Identifying the factors that affect a firm’s entry and exit into a market

·            Determining the influence of minimum-wage laws on employment levels

·            Finding the relationship between management techniques and worker productivity

·            Measuring the association between insurance coverage and individual health outcomes

·            Deriving the effect of dividend announcements on stock market prices and investor behavior

·            Predicting revenue increases in response to a marketing campaign

·            Calculating the impact of a firm’s tax credits on R&D expenditure

·            Estimating the impact of cap-and-trade policies on pollution levels.

Significance of the use of Software for Statistical Analyses

Jascaniene et al. (2013), noted statistical software as a tools that assist in the statistics based collection and analysis of data to provide science-based insights into patterns and trends. Statistical analysis software products are specialized programs designed to allow users to perform complex statistical analysis. These products typically provide tools for the organization, interpretation, and presentation of selected data sets. IBM SPSS Statistics, RStudio, Stata, Minitab etc. are the some mostly used software.

Importance of SPSS in Research & Data Analysis

SPSS -Statistical Package for the social sciences is a software program combined in a single package. The main application of SPSS is analyzing data. They are widely used in a social science-related research project to analyze enormous data. That can be used in surveys, market research, data mining, etc.; with the help of this, researchers can easily identify the particular product’s demands in the market. Based on the result, they can change the business or research strategy work. Generally, SPSS is like a store or organize the provided data, which can be used later to produce the desired output. It is designed in a way to handle a huge set of data (Arkkelin, 2014; Verma, 2012; Connolly, 2007).

Importance of STATA in Data Analysis

Stata is one of the most common and widely used statistical software among researchers in the world. Stata enables one to write their own code or use menus to achieve their analysis. It supports importing data in various formats, including CSV and spreadsheet (including Excel) formats. Its file formats are platform-independent, allowing users of various operating systems to share their datasets and programs easily (Kothari, 2015; Hamilton, 2012).

It has data management features as well. Stat’s graphical user interface (GUI) includes menus and dialogue boxes. Through these dialogue boxes, users can access various useful features, such as data management, data analysis, and statistical analysis. The details, graphics, and statistical menus are all easily accessible (Farhat & Robb, 2014).

Conclusions

By the above discussion, it is clear that Econometric analysis is concerned with the quantitative relationships between economic variables and it can provide an important input into the decision-making process. The range of areas in which econometric models are successfully applied has steadily widened including finance and business management. Econometrics has enhanced our understanding of the way the managerial decision works. Econometrics is used in doing quantitative analysis of actual economic phenomena based on theory and observations.  The tools for the organization, interpretation, and presentation of selected data sets have crucial value in econometrics.

 

References

Arkkelin, D. (2014). Using SPSS to understand research and data analysis.

Clinch, J., & Murphy, A. (2001). Modelling winners and losers in contingent valuation of public goods: appropriate welfare measures and econometric analysis. The Economic Journal, 111(470), 420-443.

Connolly, P. (2007). Quantitative data analysis in education: A critical introduction using SPSS. Routledge.

Dima, I. C., & Man, M. (2015). Econometrics and Scientific Management. In Modelling and simulation in management (pp. 69-96). Springer, Cham.

Dougherty, C. (2011). Introduction to econometrics. Oxford university press.

Farhat, J. B., & Robb, A. (2014). Applied survey data analysis using Stata: The Kauffman firm survey data. Available at SSRN 2477217.

Greene, W. H. (2000). Econometric Analysis. International edition, New Jersey: Prentice Hall, 4^th edition, 4201-215.

Groot, T., & García-Valderrama, T. (2006). Research quality and efficiency: An analysis of assessments and management issues in Dutch economics and business research programs. Research policy, 35(9), 1362-1376.

Gujarati, D. N., Porter, D. C., & Gunasekar, S. (2012). Basic econometrics. Tata McGraw-Hill Education.

Hamilton, L. C. (2012). Statistics with Stata: version 12. Cengage Learning.

Jascaniene, N., Nowak, R., Kostrzewa-Nowak, D., & Kolbowicz, M. (2013). Selected aspects of statistical analyses in sport with the use of STATISTICA software. Central European Journal of Sport Sciences and Medicine, 3(3), 3-11.

Johnston, J., & Reeves, A. (2019). Research in UK universities: a tale of two subjects–Economics and Econometrics; and Business and Management Studies. European Journal of Higher Education.

Kothari, P. (2015). Data analysis with STATA. Packt Publishing Ltd.

Mukras, M. S. (1993). Elementary econometrics: Theory, application and policy. East African Publishers.

Qu, Z. (2020). Introduction to Econometrics.

Terng, C. (2017). Price Determinants of Scholarly Business Books: An Econometric Model. Library Collections, Acquisitions, & Technical Services, 40(3-4), 106-113.

Verma, J. P. (2012). Data analysis in management with SPSS software. Springer Science & Business Media.