Recently we studied about marketing techniques and the main motive of marketing is to increase the profits for a company. Before spending the big bucks the marketing manager needs to be sure about how the campaign is going to work and how the audience would react to it.
For such situations, Econometrics is used to determine the relationship between marketing efforts and sales, such as;
How much additional revenue is generated from an additional hundred rupees spent on advertising?
Which type of advertising (digital, TV, newspaper, etc.) yields the largest impact on sales?
These types of questions can be solved with econometric techniques.
Econometrics is the quantitative application of statistical inferences, economic theory and mathematical models using data to develop theories or test existing hypotheses in economics and to forecast future trends from the huge amount of data acquired over time.
Its function is to convert real-world data to statistical trials and then compares the findings against the theory or theories being tested for similar patterns.
In other words, it analyses theoretical economic models and uses them for economic policymaking.
Econometrics prime function is to convert the qualitative statements into quantitative statements.
According to the book by stock and watson(2007), “Econometric methods are used in many branches of economics, including finance, labor economics, macroeconomics, microeconomics, and economic policy.”
Lawrence Klein, Ragnar Frisch, and Simon Kuznets are considered to be the pioneers of econometrics and also won the Nobel Prize in economics in 1971 for their contributions. Today, it is used extensively by the likes of scholars as well as practitioners such as the Wall Street traders and analysts.
Depending on whether you are interested in testing an existing theory or in using existing data to develop a new hypothesis based on those inferences, econometrics can be further categorised into: theoretical and applied econometrics.
It is the study of the properties of existing statistical models and procedures for finding out the unknown values in the model. In this we seek to develop new statistical procedures that are valid despite the nature of economic data to change itself simultaneously.
Theoretical econometrics relies heavily on the likes of mathematics, theoretical statistics, and numerical quantities to prove that the new procedures have the ability to draw correct inferences.
(Also check: Statistical Data Analysis)
The theoretical econometrics focuses on issues such as the general linear model, simultaneous equations models, distributed lags and ancillary related topics. Most of these problems were encountered while working on empirical research.
Types of econometrics
It is the special use of econometric techniques to convert qualitative economic statements into quantitative ones, unlike the theoretical approach. Because applied econometricians acquire a closer experience with the data, they often face problems regarding data attributes that point to errors with existing set of estimation techniques and also alert their theoretical econometricians about the anomalies.
The applied econometrics deals with topics of production of goods and their productivity, demand for labour, arbitrage pricing theory, demand for housing related issues.
For example, the econometrician might discover that the variance of the data (how much individual values in a series differ from the overall average) is always rotating and is never fixed over time.
The main tool of econometrics is the linear multiple regression model, which helps in estimating how a change in one of the explanatory variables affects the working of the model, from the variable being explained to the changes occurring on the dependent variable. In modern econometrics, many statistical tools have come to the limelight, but simple linear regression is still the most routinely used starting point for an analysis.
This step is necessary because a regression tends to estimate the marginal impact of a specific explanatory variable after taking into account the variance caused by the impact of the other explanatory variables to the model.
For example, the model may try to differentiate the effect of a 1 percentage point increase in taxes on average household consumption expenditure, assuming other consumption factors, such as pretax income, wealth, and interest rates to be static.
The methodology of econometrics is fairly straightforward.
Stages of econometrics
The first step is to suggest a theory or hypothesis, in order to start examining a particular piece of data. The explanatory variables in the model are specified before-hand, and the sign and/or magnitude of the relationship between every single explanatory variable and the dependent variable are clearly established so as to not cause any confusion.
At this stage, applied econometricians come into play and they rely heavily on economic theory to successfully formulate a hypothesis out of the provided data.
(Suggested read: 7 Major Branches of Discrete Mathematics)
For example, a philosophy of international economics is that prices across open borders go hand in hand after purchasing power parity is allowed. The empirical relationship between domestic prices and foreign prices (adjusted for nominal exchange rate scenarios) should always be positive, and they should try to maintain parity at all times.
The second step is to specify a statistical model that captures the essence of the theory. The economist tries to propose a unique relationship between the dependent variable and the explanatory variables through the model.
By far the most easy approach is to assume linearity—meaning that any change in an explanatory variable will always induce a similar change in the dependent variable. It is certainly impossible to account for every little influence on the dependent variable, hence a variable is added to the statistical model to nullify the external disturbances.
The role here of the new variable is to represent all the determinants of the dependent variable that cannot be accounted for. Mostly caused by the complexity of the data.
Simply to be consistent and make all conditions hold true for the statistical model, economists usually assume that this “error” term averages to zero and is unpredictable.
(Recommended read: Types of statistical analysis)
The third step is to estimate the unknown variables of the model using economic data at disposal. It usually involves using an appropriate statistical procedure and an econometric software package to carry out this process.
This is termed as the easiest part of the analysis thanks to easy availability of abundant economic data and excellent econometric techniques and software. The econometrics still rely on the principles of the famous GIGO (garbage in, garbage out)style of computing.
This is the fourth step and also the most important out of all. This step involves asking the right questions to ourselves. For example,
Are the signs and the relationship of the estimated parameters that bridge the dependent variable to the explanatory variables consistent with the predictions of the economic theory?
If the estimated parameters do not make sense, how should the statistical model be edited by an econometrician so as to yield appropriate results?
And does a more accurate estimate guarantee an economically significant model?
This step, in particular, tests the econometrician’s skill and expertise in the field.
The main tool of the fourth stage is hypothesis testing, a statistical procedure in which the researcher remarks regarding the true value of an economic parameter, and a statistical test is carried out to finds out whether the estimated parameter is synonymous with the particular hypothesis.
If it is not, the researcher must either reject the hypothesis or make changes in the statistical model and start all over again.
If all four stages proceed successfully, the result is a model that can be used as a tool to assess the empirical validity of an economic model.
The empirical model may also be used to predict the dependent variable, potentially helping policymakers to make critical decisions about changes in monetary and/or fiscal policy to keep the economy on an even platform.
Students of econometrics are often fascinated by the ability of linear multiple regression to forecast economic relationships.
Three fundamentals of econometrics are worth remembering;
First, the quality of the parameter findings depends on the current working condition of the economic model.
Second, if a relevant explanatory variable is excluded from the model, it is most likely for parameter estimates to become unreliable and inaccurate.
Third, the parameter estimates have a very slim chance of being on similar lines with the actual parameter values that are generated by the statistical data, even if the econometrician identifies the process as the source of the original data.
Eventually the estimates are used because they will become precise as more data is available and estimates are in accordance to the vastness of coverage.
Econometrics has basically three closely interrelated functions.
The first function of Econometrics is to test out economic theories or hypotheses laid out by the coveted econometricians. For example, is consumption directly related to income? Is the quantity demanded of a certain commodity inversely related to its price?
The second function of Econometrics is to provide numerical estimates for the variables of economic relationships. These are essential in decision making.
For instance, a government policy maker needs to have an accurate estimate of the coefficient of the relationship between consumption and income in order to understand the stimulating effect of a proposed tax reduction and make a sound decision.
The third function of econometrics is to predict economic events. This, too, is necessary in order for policy makers to take economically appropriate action if the rate of unemployment or inflation is predicted to rise in the future.
It is no secret that economics runs the world and with econometrics, new theories are proved, new inferences are made and statistical models are rejected or approved, everyday.
Econometrics divides the world into infinite possibilities of forming new theories which is complemented by the data that is provided to the econometricians.
Policy-makers find this data to be very insightful and depend on these inferences to form very important policies and decisions.
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