Applied Econometrics – Steps to Carry Out an Empirical Study

Operating a business has become far more challenging than ever it was. Increasing competition, looming economic crisis, mounting operating costs and declining profit margins have added to the woes of business owners and managers.

They are often required to make predictions from various types of data, to support business strategies and fight competition. In such a scenario, they bank upon econometric applications to make inferences, and monitor and measure the impact of their decisions. A successful business strategy is, therefore, built upon the deductions made out from analyzing the data.

Applying econometrics models requires an in-depth understanding of the process for testing a hypothesis and various statistical methods. But before we get down to the nitty-gritty of the steps involved in conducting an empirical study, let us do a quick recap of what we studied in previous article. There are three reasons for this:

• You improve your understanding of the main concept;
• You consolidate the essential information; and
• You are able to integrate the central idea into the examples discussed.

What is Econometrics – A Quick Recap

Econometrics, as discussed in previous article, is about running the collected data or a given phenomenon through a number of mathematical and statistical methods/tools, in order to perform quantitative analysis. The purpose is to test the hypothesis or an observed trend to determine if it is economically viable or not. Other, but equally important, aims of the study include:

• Understanding the situation at the ground level
• Deriving economic relations between two or more phenomenon
• Predicting the value of economical variables
• Obtaining an optimal state for a trend or an initiative
• Determining the economic efficiency of an ongoing or soon-to-be-carried process

While an econometrical study provides you deep insights into a phenomenon, the results, however, shouldn’t be considered absolute. This is because econometrics too has its own limitations.

The totality of results is directly proportional to the authenticity of data. This means – lesser the errors in data collection, more realistic and reliable the results of the study.

Steps in Carrying Out an Empirical Study

Before we learn how to conduct an empirical study, let’s remember one thing – an econometric analysis is done to reduce uncertainties associated with a phenomenon/occurrence /situation.

This is not an easy task. And this is why econometricians go through a series of steps and use a variety of mathematical and statistical tools to unlock the power of information. Depending upon the results of the study, a business strategy is designed to meet a specific goal.

As we have already gone through econometrics’ definition in the previous segment, it’s time to go deeper and run down the econometric process. While we aim to simplify the process for you, we use the simplest and most usual examples to enhance your understanding.

Here are the steps that econometricians take to carry out an empirical study. Let’s go over them one-by-one.

1. Selection of a Hypothesis or an Observed Phenomenon

   The process begins with the selection of a hypothesis or a situation. The first step includes

   • defining a theory that needs to be tested
   • determining the variables for which the cause and effect relationship is to be conducted

   Say, for example, qualifications, experience and skills are the factors that affect productivity of a person and therefore the remuneration. This is an occurrence that needs to be tested and analyzed.

   In this phenomenon:

   • remuneration is the dependent variable
   • qualifications, experience and skills are independent variables

   These factors can be anything depending upon the hypothesis you want to test.

   The hypothesis, at this point, only establishes qualitative relationship and does not offer any concrete economic indicators.

2. Establishing the Objectives of the Study

   Deciding what you want to achieve from the study plays a crucial role. The golden rule is – do not invest time, money and effort into anything that doesn’t have any goal or objective.

   In this case, the objectives can be:

   • To determine the scope of this hypothesis
   • To examine the extent to which these factors affect remuneration
   • To identify how compensation affects an individual’s productivity

3. Developing an Economic Model

   Once you have set your goals, you can now develop an economic model. As you know, an econometrical analysis is carried out to derive economic relations or conduct quantitative analysis. This means, it’s essential to have an economic model for your hypothesis or situation.

   At this step, you don’t need to go much in detail. Rather develop a simple and less formal economic model. Here is how:

   Remuneration (Rem) = f(qualifications, experience, skills)

   whereas ‘f’ represents ‘the function of’

   This economic model shows that remuneration is a function of an individual’s qualifications, experience and skills.

4. Developing an Econometric Model

   In order to test this economical model, it’s essential to convert it into an econometric model. For this conversion to happen, we will have to:

   • Represent it mathematically
   • Take into account other factors that affect remuneration (Because it won’t be right saying that salary of an individual only depends on education, experience and skills. There could be many other factors, such as location, organization, university reputation)
   • Introduce random error or disturbance/con of a situation (another variable factors that can impact /disturb remuneration)

   Hence the corresponding econometric model will be:

   Remuneration = β1 + β2 (qualification) + β3 (experience) + β4 (skills) + u

   whereas

   β1: additional factors affecting remuneration
   β2: coefficient of qualification
   β3: coefficient of experience
   β4: coefficient of skills
   u: random error

   This is the simplest linear econometric model. An important point that you need to remember her is: β1, β2, β3, β4 and u will have numerical values.

5. Estimating the Values of Coefficients

   The next step is to obtain values for coefficients of the econometric model that we have derived above. In order to estimate their values, data are required. However, there are a number of considerations that you need to take into account at this step. It’s necessary to:

   • Prefer non-experimental* data over experimental** data to determine near-correct results
   • Select appropriate statistical method, keeping into account all pros and cons of it
   • Selecting the type of data from among time-series, cross-sectional and panel data to be used for the study

   *Non-experimental data are observed data collected by observing the real world.
   **Experimental data are collected in controlled environment.

   In this example, we will stick to linear econometric model, as this is the most frequently used statistical tool.

   Selection of Data Type

   Given the importance of data in deriving economical relations, it’s essential to carefully choose the type of data.

   Let’s look at the assumptions, advantages and limitations of each type of data.

   	Time-Series Data	Cross-Sectional Data	Panel Data
   Time	Variables are examined over a period of time	Variables are examined at a particular time	Variables are examined simultaneously for a period of time as well as at a particular time
   Assumption	Observation is not independent across time	Observation is obtained by random sampling	Sample population is independent of each other, but for an individual, observations are mutually dependent
   Sample-Size	Sample size can be small.	Sample size is generally large.	Sample population remains same over time
   Ordering of Data Set	Ordering matters because observation is not independent. It is dependent on previous observation.	Ordering of data doesn’t matter because it doesn’t take ‘period of time’ into account.	Ordering of observation doesn’t matter but the ordering in time dimension matters.

   
   

   This distinction will help you in selecting the right data type, depending upon the

   • size of your sample
   • type of study you want to carry out
   • time constraint

6. Data Analysis and Validation

   Once the data are collected and put in an econometric model, you can assess whether the estimates from the study are acceptable. The hypothesis is validated, if

   • The coefficients have expected magnitudes
   • The estimates have anticipated value
   • The results satisfy the established assumptions

   In this step, we also check the goodness-of-fit, which is about determining if the selected econometric model is the right fit for the type of data used in conducting the econometrical study.

   We also determine the coefficient of determination (R²), which shows to what extent an independent variable affects the dependent variable. The value of R² lays between 0 and 1 and higher the value of R², more apt the hypothesis is.

In the next article, we will understand this with the help of an example.