Econ2206 Notes
ECON2206 Revision Notes W2 – SIMPLE REGRESSION MODEL MOTIVATION Much of applied econometric analysis are interested in “explaining y in terms of x” and confront three issues: 1) Since there is never an exact relationship between y and x, how do we account for the “other unobserved” variables? 2) What is the function relationship between y and x? 3) How do we invoke a ceteris paribus relationship or a causal effect between y an x?
The simple linear regression model is: y = β0 + β1x + u
-‐ “u” is the stochastic error or disturbance term and represents all those unobserved factors or other factors other than x. -‐ If all other factors (u) is held fixed, so that change in u is zero, we can observe the function relationship between y and x. -‐ If we take the expected value of the model, (Δu = 0 and Δβ0), then we can see that x has a linear effect on y.
We will only get reliable estimates of β0 and β1 if we make restricting assumptions on u. As long as β0 is included in the model, nothing is lost by making the assumption that the expected value of u in the population is zero; E(u) = 0. ZCM Our
The simple linear regression model is: y = β0 + β1x + u
-‐ “u” is the stochastic error or disturbance term and represents all those unobserved factors or other factors other than x. -‐ If all other factors (u) is held fixed, so that change in u is zero, we can observe the function relationship between y and x. -‐ If we take the expected value of the model, (Δu = 0 and Δβ0), then we can see that x has a linear effect on y.
We will only get reliable estimates of β0 and β1 if we make restricting assumptions on u. As long as β0 is included in the model, nothing is lost by making the assumption that the expected value of u in the population is zero; E(u) = 0. ZCM Our