What is Econometrics?
Econometrics is an understanding of relations of economic data by using statistical model referencing and getting an observation or pattern from provided data for developing the approximation future trend. Econometrics is simply economic with the additive of Mathematics and Statistics and helps in forecasting and estimation by applying statistical methods.
Methods of Econometrics
The more common methods are:
- Multiple Linear regression
- Estimation theory
- Linear programming
- Frequency Distribution
- Probability distribution
- Correlation & Regression
- Time-series Analysis
- Simulation equation
Examples of Econometrics for Finance
Below are examples of econometrics for Finance
Econometrics Example #1
Michael has an income of $50000. Spending pattern of his income is 10000 – Fixed rent and other household expenses is 50% of his gross income earned during the period.
Multiple linear regression is one of the best tools to develop a relationship on the basis of past trends.
By using the equation one can get the amount Michael will spend on the basis of his earned income.
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- Expense = B0 (Fixed rent) + B1 (other household exp.) + e (Error term)
- = 10000 + 50% (50000)
- = 35000
Error term shows that there can be little up or down deviation from the result arrived by applying the statistical tools.
Econometrics Example #2
Let’s find out the salary of the person on the basis of his working experience
Minimum wages: $10K
On the basis of regression on the person salary its find out that B1 = 2000
So by applying the method, it can be understood as a person will get the minimum wages of 10000 + (2000*No. of years of experience)
These 10K and 2K are hypothesized values and are to be tested on statistical tools like t-test & F-test. If they are not significantly different from 0, then the hypothesized value has no relevance and test need to be done again to get the different value.
How does Econometrics Works in Finance?
Advantages of Econometrics
Here are the advantages of Econometrics.
- By using the tools or applied econometrics one can be able to convert data into a specific model for the purpose of making the decision that supports empirically data.
- Help to getting the specified pattern or result from the scattered data.
- Benefitted to enable us to retrieve the relevant information from the basket of information.
Disadvantages of Econometrics
There are some disadvantages of Econometrics.
- Sometimes the relation build-up by the economic tools is spurious i.e. even no relation exists between two variable but model is showing a pattern on the basis of past information. Ex. Correlation between rain and Dividend paid
- This shows that whenever the rain comes in a quarter then only the company declare the dividend in that period. Even the rain has no relation of dividend paid but as per the trend establish it can provide the false signals which can lead to the wrong decision.
- There is always a choice between simplicity and accuracy. Model specification is a very important task in applied economics. Choosing less variable can help in simplicity and provide faster result but it can be inaccurate due to insufficient information and if one goes for the high no. of variable then the model can be critical, uneconomical or gigantic.
- There can be a problem of multicollinearity between the variables used in the data. It’s very important that the variable chosen should have been a low correlation between two explanatory variables. The model left this section on the model user.
Important Points
- Tools of econometrics are very judgmental. Final conclusion can vary from user to user.
- Result depending on the type and specification of the model. Results are model-oriented.
- Data Economical, Feasible, Time to get results to be considered while applying the model.
- It can be applied both on Cross-sectional or time-series data.
- There should be perimeter or test required to conduct the resulting effectiveness like f-test, T-test, Statistics table, ANOVA table analysis by using tool packs.
Conclusion
- Always remember to check that whether the result comes out are statistically significant for taking decisions
- It evolves out of the model or perimeter under consideration
- The result must be empirically as well as futuristic favorable.
- It is a reiterative exercise and different model can also be applied to the single problem to get better insights.
- Overfitting or underfitting of results can be diluted by an improved model specification.
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