Economics can be a challenging subject, especially when the topic of your study is something as technical as specification errors. But before you start working on your **specification errors homework answers**, it is important to understand the topic well.

**What is Specification Error?**

How often has it happened that you are working on a research project where one or more key feature, variable or assumption of a statistical model is incorrect, rendering the eventual outcome inaccurate? That’s exactly what specification error is.

A model that is free of specification errors is termed as robust. All results and outcomes from such a test are always accurate.

**Understanding Model Specification**

Specification is the process of coming up with a statistical model in regression analysis. The model specification in linear regression analysis has the following components:

- Choosing the relevant variables
- Choosing the correct functional form

Understanding the steps of model specification is important because it is directly linked to the presence of specification errors. Here we have only listed the two components, but you can find more detailed information on them by referring to **specification errors homework answers**.

**When does a Specification Error occur?**

A specification error typically happens in the following cases:

- Incorrect functional form
- Inclusion of irrelevant variable
- Exclusion of relevant variable

Let us take a look at each of these errors in some detail.

**Incorrect Functional Form**

Functional form is the type of model you choose for your regression analysis. It is the relationship between the dependent and independent variables. You need to explain why you have chosen specific independent variables (which is why they are also known as explanatory variables).

Choosing incorrect functional forms can lead to specification errors in regression.

**Inclusion of Irrelevant Variable**

When you choose an irrelevant variable in your regression model, the parameter of the variable will equal 0. More often than not, including an irrelevant variable will still to lead a correct hypothesis. This is because the β of that variable becomes 0 ensuring that the irrelevant variable does not effect the parameters of other variables and therefore the overall analysis.

So the inclusion of irrelevant variables is not as serious a concern as the other two specification errors. However, this does not mean you must include many variables thinking the consequences are not severe. Include only those variables that have an effect on the dependent variable.

**Exclusion of Relevant Variable**

Excluding a variable that actually has an impact on the dependent variable leads to a regression model becoming misspecified. This is a more serious error than including an irrelevant variable because the consequences of excluding a relevant variable are much worse than that of including an irrelevant variable.

The estimators of your regression model will be biased and inconsistent and will remain so no matter how big the sample size is.

**Tests for finding Specification**** Errors**

There are several tests for finding specification errors. Here are a few of them:

- Gujrati (an author of one the basic textbooks for econometrics) suggests F-test and T-test. However these are not particularly good and there are better tests available for finding specification errors.
- Looking at residuals in different kind of plots is another method. A pattern emerges in the residual terms when observe these plots.
- RESET (proposed by econometrician James B) is a popular test for checking a model for specification error.
- Some other tests include Likelihood Ratio test, Wald test, Lagrange Multiplier test and Hausman test.

If you want to study each of these tests in detail, you must refer to **specification errors homework answers**.

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Specification errors is one of the most vital problems econometricians face. As a student of economics, it is important for you to clear all doubts at this early stage.

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