Applied Econometrics - Statistics Project Example

Q.3. (10) What does the sign of the estimate on ln area in model (2) tell you about the sign of the correlation between slave exports and the size of the country? Hint: think about omitted variable bias. Due to omitted variable bias, there will be a bias as the coefficient of In exports picks up the part of the influence of ln area that was correlated with In exports. The sign of the estimate on ln area in model (2) is positive telling us that the expected sign of the correlation between slave exports and the size of the country is also positive.

In model 1, the value of R2 is given as 0.25 similarly in model 3, the value of R2 is given as 0.25 this shows no change in the value of R2 implying that the included variable (population) has no effect on the model; the variable is irrelevant. Q.6. (10) Note that the standard error on ln exports in model (2) is higher than the standard error on ln exports in model (1). Comment (in detail) on what information this provides you regarding the specification of Model (2). Q.7. (15) While the idea of Nunn is interesting, it is unlikely that slave exports alone can explain why economic output is so low amongst African countries.

Consider population density in 1400 AD as an additional explanatory variable. Acemoglu, Johnson and Robinson (2002) have shown that population density has a positive impact on economic prosperity. Comment (in detail) on the impact of the omission of this variable from Nunn’s empirical model.  Each observation (variable) affects the fitted regression equation differently and has a different influence on each variable; this may result to what we term as omitted variable bias (OVB). OVB occurs when a model is created which incorrectly leaves out one or more important causal factors.

The "bias" is created when the model compensates for the missing factor by over- or underestimating the effect