Relevance of Econometric Applications for Managers

Relevance of Econometric Applications for Managers 1 Relevance of Econometric Applications for Managers Srinivas Kolluru and R.K. Mishra Institute of Public Enterprise (IPE) Osmania University Campus, Hyderabad “Econometrics is about how we can use theory and data from economics, business and the social sciences, along with tools from statistics, to answer “how much” type questions.” (Hill, Griffiths, and Judge, Introduction to Econometrics, 2nd edition, John Wiley & Sons, Inc., 2001). ABSTRACT Econometric applications have become an integral part of training in modern economics and business management. Modern managers in number of sectors are increasingly incorporating econometric applications into their businesses to establish healthy economic strategies, to develop insight, create value, optimised solutions, and outperform competition. Econometric applications provide organisations with a potent set of tools to unlock the power of information and in effective decision making. The present paper focuses on emphasising the manner in which econometric applications can serve the development of business, and enhance performance of a firm by helping it go ahead of its competitors in this globalised world. 1. INTRODUCTION The continuous increasing competition, exposure to global markets, increasing costs, and declining profit margins, etc. are making modern business far more challenging than it ever was. In these circumstances, econometric applications and quantitative modeling are being used by managers in firms as a powerful means of beating the 2 Econometric Applications for Managers competition. Econometric applications provide organisations with a potent set of tools to unlock the power of information and in effective decision making. The empirical studies in the area of econometrics are mainly associated with mainstream research, but we have chosen to introduce the present discussion on the relevance of econometric applications with reference to managers, because number of questions arises in the minds of modern managers that how would the sale of a company’s product change during booms and recessions? What exactly the role played by past sales in determining the future sales? How exactly do the mathematical forecasting tools predict the future sales? How would a manager infer the monthly and seasonal fluctuations in sales? How would a manager decide on the rate of growth of the company in a booming trajectory? What kind of tools are required for the financial sectors?, etc. Answers to the aforementioned questions require good understanding of econometric techniques and applications. Managers of the business world are often asked to make qualitative and quantitative inference from various types of data such as primary, secondary, cross section, panel, etc. A successful business strategy would come when there is the basic understanding of the objective prediction coming out of the data. In the modern business world, it is becoming of more and more significance to use the state of the art econometric model to analyse data. These highly powerful models essentially supplement one with the subjective knowledge of the domain. 2. ECONOMETRIC APPLICATIONS Econometrics applications involve the formulation of mathematical/statistical models to represent real-world economic systems, whether the whole economy, or an industry, or an individual business. Econometric modeling is used to analyse complex market trends (the demand function) to determine the variables driving the growth or shrinkage of demand for a product or service. Econometric models are used to decipher the economic forces that affect supply and costs (the supply function) within an industry or firm. Few companies really understand the external forces that drive their industries, their companies, or their brands. Understanding these forces provides the foundation for strategy development and business planning. Managers use economic indicators as tools in the decision-making process. Economic indicators include the Gross Domestic Product, inflation rate, exports/imports, unemployment rate, personal income data, etc. Business strategists use these numbers to make decisions, such as increasing purchasing orders, issuing layoffs or increasing/ decreasing production, etc. For instance, if a business analyses that fewer jobs were added to the economy during a particular period of time, it may be less likely to hire candidates with the belief that fewer people have money to spend on its products. On the other hand, an increase in the sale of durable consumer goods, such as cars, might compel a firm to increase its production if it is in a closely-related industry, such as steel. 3 Relevance of Econometric Applications for Managers Thus, economic indicators are a useful tool though they can create a self-fulfilling prophecy. To understand clearly the role of econometrics for managers, it may be useful to first describe the general process of modern econometric research. Like most other sciences, the general methodology of modern economic/econometric research can be briefly summarised in four steps (Table 1). Table 1: Steps in Econometric Research Steps Summary Step 1: Data and summary of empirical stylised facts The stylised facts are often summarised from observed data. For instance, in microeconomics, a well-known stylised fact is the Engel’s Curve, which characterises that the share of a consumer’s expenditure on a commodity out of total income will eventually decline as the income increases; in macroeconomics, a well-known stylised fact is the Phillips Curve, which characterises a negative correlation between the inflation rate and the unemployment rate in an aggregate economy; and in finance, a well-known stylised fact about financial markets is volatility clustering, that is, a high volatility today tends to be followed by another high volatility tomorrow and vice versa. The empirical stylised facts often serve as a starting point for managerial econometric research. For example, the development of unit root and cointegration econometrics was mainly motivated by the empirical study of Nelson and Plossor (1982) who found that most macroeconomic time series are unit root processes. Step 2: Development of models With the empirical stylised facts in mind, managers can develop a model in order to explain them. This usually calls for specifying a mathematical model of economic theory. In fact, the objective of economic modelling is not merely to explain the stylised facts, but to understand the mechanism governing the economy and to forecast the future evolution of the economy. Step3: Empirical verification Economic theory only suggests a qualitative economic relationship. It does not offer any concrete functional form. In the process of transforming a mathematical model into a testable empirical econometric model, one often has to assume some functional form, up to some unknown model parameters. One need to estimate unknown model parameters based on the observed data, and check whether the econometric model is adequate. An adequate model should be at least consistent with the empirical stylised facts. Step 4: Applications After an econometric model passes the empirical evaluation, it can then be used to test hypotheses, to forecast future evolution of the economy, and to make policy decisions. Source: Gujarati (2006). Econometric Applications for Managers 4 3. ECONOMETRIC METHODS AND APPLICATIONS IN BUSINESS Nowadays, applied work in business requires a solid understanding of econometric methods to support decision-making. Combining a solid exposition of econometric methods (statistics, simple and multiple regression, non-linear regression, maximum likelihood, and generalized method of moments) with an application-oriented approach provides managers to enhance the performance of a firm and help to stay ahead of its competitors. The applications of econometrics of choice (logit and probit, multinomial and ordered choice, truncated and censored data, and duration data) and the econometrics of time series (univariate time series, trends, volatility, vector autoregressions, panel data, and simultaneous equations) show how econometrics can solve practical questions in modern business and management. Various econometric methods and their implications have been shown in Table 2. Table 2: Applications of Econometrics Applications What it does Generalised Linear Modelling Determination of independent drivers, degree of causality and preparation of forecasts with cross section data. Segmentation and Clustering Analysis Identification of homogenous customer and product groups for strategic marketing and pricing initiatives. Time Series Modelling Preparation of forecasts by building various time series models with a variety of distributional assumptions. Constrained Optimisation Creation of business rules by accounting for dynamic business constraints for an effective solution. GARCH (Generalised autoregressive conditional heteroscedasticity) Identification of independent drivers, direction and degree of causality for parameter estimation in volatile environments. Neural Network Techniques Development of machine learning based estimation techniques to help in pattern identification, sequence recognition and knowledge discovery in databases. Game Theoretic Applications Identification of dominant and next-best strategies in a dynamic business environment with realistic asymmetric information assumptions. Non Linear Modelling Key parameters estimations requiring high degree of precision. Response Modelling Estimation of response probabilities to key marketing, pricing and operation strategies. The ultimate application of econometrics in management is the creation of a comprehensive model of a market, an industry, or a company, so that the interaction of all economic indicators can be understood and predicted. For instance, an example Relevance of Econometric Applications for Managers 5 from managing an Information Technology (IT) firm; many IT firms managers (being of a technical background) inherently focus on the product by nature and have less interest in other social sciences and econometric analysis. In such a case an IT firm would carry the risk of failing to relate to the reality of economic issues such as demand, competition, client, markets and its dynamics all of this would fill through its business model and the decision making process. Economics is a decision science. A business manager is always making decisions. The available literature on the subject highlights that not only that manager will have to be very well informed about the macroeconomics, and the economic environment of his or her firm, but the basics of supply chain management (that’s economy-driven), the most advanced economics field nowadays (applied econometrics, games theory for negotiation, neuroeconomics for marketing), and of course, finances. 4. ADVANTAGES FROM ECONOMETRICS Bennion (1961) averred that there are at least two highly valuable contributions that an econometric model can make to many business decisions; and, in one sense, these contributions are virtually inseparable. First, the user gains a qualitative-quantitative insight that a manager is unlikely to be able to get from any other method. In order to derive a predicted set of numbers from the econometric model, first it is necessary to specify clearly the full set of variables to be included in the equations constituting the model. Whatever set of predicted numbers the model yields are surely a powerful asset in the intelligent use of any decision-making process. The second contribution of an econometric model is closely related to the first one. There are some times when the econometrician will have much greater confidence in the predicted set of numbers than at other times. Usually on those occasions when his confidence is low, he will be able to pinpoint the reasons (i.e., the areas in his model) for his low confidence. It is then a simple matter to substitute alternative assumptions— about assumed values for autonomous variables. It is obvious that any other known means of prediction can begin to compete successfully with an econometric model as a basis for testing assumptions and for evaluating the sensitivity of one’s results to alterations in those assumptions. 5. LEARNING FROM EACH OTHER What econometrics can learn from management is the problem solving attitude. What management can learn from econometrics is in particular its accumulated experience in the area of economic relationships as well as the imagination displayed in deriving them. Needless to say, these are broad statements which need appropriate qualifications. But apart from such qualifications, which to some extent deal with priorities and are 6 Econometric Applications for Managers therefore not really important, the main thing is simply that they can learn from each other, the main reason being that they have so much in common. Essentially, they have two common features. One is the object of investigation, which is usually a problem in economics—either microeconomics or macroeconomics. The other is the tool of analysis, which is statistical/mathematical to a large extent. 6. FINAL OBSERVATIONS Making good decisions has always been the ne plus ultra (the highest point capable of being attained) function of top managers. To fortify this function with the best available econometric techniques is not to weaken its foundations but to give them the greatest possible strength. The above discussion clearly shows that econometric applications can be increasingly used in management in general and by managers in particular. The advanced econometric techniques are indeed providing the intended benefits. With data bases getting more extensive and reliable, and new techniques and technology becoming available, this game has just begun. Clearly, those managers or businesses that are early movers in this arena will discover new ways of outperforming their competitors and enhancing their competitive position. Undoubtedly their existing strategic intent and objectives will drive the initial set of applications. It is possible that over time, firms will identify Blue Ocean areas, using econometric applications in their businesses, which could potentially lead to new business models or sources of competitive advantage. REFERENCES Bennion, Edward G. (1961). “Econometrics for Management”, Harvard Business Review, Vol. 39(2), pp. 100–112. rd Gujarati, D.N. (2006). Essentials of Econometrics, 3 Edition, McGraw-Hill: Boston. Ichniowski, Casey and Shaw, Kathryn L. (2009). “Insider Econometrics: Empirical Studies of how Management Matters”, NBER Working Paper Series 15618, National Bureau of Economic Research, USA. Kapoor, Harsha. (2007). “Advanced Analytics Powering Indian Business”, BSENSEX, October – November, pp. 31–32. Nelson, C.R. and Plosser, C.I. (1982). “Trends and Random Walks in Macroeconomic Time Series: Some Evidence and Implications”, Journal of Monetary Economics, Vol. 10 (1), pp. 139–162. Thiel, H. (1965). “Econometrics and Management Science: Their Overlap and Interaction”, Management Science, Vol. 11(8), pp. 200–212.