Economics. Applied empirical methods. Ans 1). a). The v

Economics
Applied empirical methods
Ans 1)
a)
The variables which show low p – values are statistically significant at the 10 % significance
level. Here, income and _cons terms are significant.
b)
The regression coefficient of income has a value of 0.1010786
c)
The coefficient of female in this regression is 0.0234885
d)
Heteroscedasticity refers to a situation where a variable’s variability is not equal for a given
range of values for another variable which is used for its prediction. Variance can be used to
measure it.
The estimated values of variance and covariance for the OLS model can be corrected to make
them consistent. If this OLS regression suffered from Heteroscedasticity, then it can be
corrected by using any other estimator than the OLS estimator for the parameter estimation
for the model.
Ans 2)
a)
The equation is given by :
Y = b0 + b1 [t] + b2 [i] + b3 [t*i] + b4 [cv] + e

Here, t = time, i = intervention, cv = covariate
The regression coefficient measures the difference-in-difference impact is ‘b3’ which shows
the difference between any changes which time.
b) The assumptions to be met for the difference-in-difference method to yield unbiased
estimates of the impact of a policy on a treated group, when a policy has not been randomized
are : positivity, exchangeability and SUTVA ( Stable Unit Treatment Value Assumption ) or
absence of any spill over effect and parallel trend in the outcome. The difference between the
groups ( treating and controlling ) must not change w.r.t. time.
Ans 3)
The propensity score matching is used to estimate the probability of participating into a
policy. OLS is not most suitable regression because if random experiment is done, then for
every covariate it is assumed that the participating group will be balanced but it is not true
practically.
The regression which must be used is the logistic regression as it consists of 2 outcomes – 0
( for not participating ) and 1( for participating ) \.
Ans 4)
a) Multicolinearity in the OLS concept is seen amongst the various regressors. It follows
the assumptions of OLS. It shows big values of standard error, R – square value,
correlation for the coefficients estimated and variables which are independent.
It affects the validity of OLS coefficients. If a minor change is done in the model, the
coefficients show large sensitivity. The precision for the coefficients estimated
decreases. This makes the strength of the regression model in statistical terms very
small. The p – values of the statistically significant variables which are independent
cannot be relied upon.
Since, the design matrix becomes degenerated. Also, the there will be no possible outcome
with a unique solution for any linear algebra problem in the OLS. There are a lot of good
solutions.
.
b) Detection and correction for multicollinearity