Question

(a) In regression analysis, often the researcher will encounter issues of omitted variable bias (OVB) or their included variables are too closely related (multicollinearity). In you own words,
(i) explain what is meant by OVB?

2. (ii) what is multicollinearity

3. (iii) How do these problems lead to type1/type 2 errors?

(b) In your own words, describe your understanding of linear regression analysis. What

is the causal fallacy?
(c) How is the model fit measured? In your answer describe both the R-squared and

SER.

4. (d) What are dummy variables and how are they interpreted?

5. (e) As an economist you are interested in diminishing returns. Describe how you would

   go about modelling this.

Answer 1

a.1. OVB

In statistics,ommitted-variable bais occures when a statistical model leaves out one or more relevent variable .

OVB is the bais that appears in the estimates of parameters in a regression analysis,when the assumed specification is incorrect in that it omits an independent variable that is a determinant of the dependent variable and correlated with one or more of the included independent variables.

2. Multicollinearity

In statistics, multicollinearity (also collinearity) is a phenomenon in which one predictor variable in a multiple regression model can be linearly predicted from the others with a substantial degree of accuracy.Multicollinearity does not reduce the predictive power or reliability of the model as a whole,at least within the sample data set;it only affects calculations regarding individual predictors.

Multicollinearity is a characteristic of the data matrix, not the underlying statistical model.

3. Multicollinearity is just a fact to be mentioned,just one of the characteristics of the data to report.

The notionof omitted variable bias does not apply to descriptive modelling.In contrast to causal modelling ,the causal notion of relevance of variables does not apply for description .You can freely choose the variables you are interested in describing probabilistically and you evaluate your model w.r.t the chosenset of variables ,not variables not chosen.

b. Linear Regression

In statistics,linear regression is a linear apporoach to modeling the relationship between a scalar response and one or more explanatory variables. The case of one explanatory variable is callled simple linear regression.For more than one explanatory variable,the process is called multiple linear regression.

Causal fallacy

The questionable cause also known as causal fallacy,false cause ,or non cause pro causa is a category of informal fallacies in which a cause is incorrectly identified.

d. Dummy variables

In statistics and econometrics , particularly in regression analysis, a dummy variable is one that takes only the value 0 or 1 to indicate the absence or presence of some categorical effect that may be expected to shift the outcome.

e. Diminishing returns,also called law of diminishing returns or principle of diminishing marginal productivity,economic law stating that if one input in the production of a commodity is increased while all other inputes are held fixed, a point will eventually be reached at which additions of the input yield progressively smalles,or diminishing ,increses in output.