# Estimate both the static and dynamic models using

## Estimate both the static and dynamic models using the time-series data over the period from 1950 to 2000 from ‘Macro_PEBLIF’ for Ghana, Argentina, South Korea and Australia.

The primary theme of the paper is Estimate both the static and dynamic models using the time-series data over the period from 1950 to 2000 from ‘Macro_PEBLIF’ for Ghana, Argentina, South Korea and Australia. in which you are required to emphasize its aspects in detail. The cost of the paper starts from \$79 and it has been purchased and rated 4.9 points on the scale of 5 points by the students. To gain deeper insights into the paper and achieve fresh information, kindly contact our support.

Econometrics assignment

The following are two macro production functions; the first is a static model, the second a dynamic model with a lagged dependent variable:

Static Model: Ln(yt) = β0 + β1 Ln kt + λ year + ut

Dynamic Model: Ln(yt) = β0 + β1 Ln (kt) + ρ Ln(yt-1) + λ year + vt

log(y) is the natural log of GDP per capita and log(k) is the natural log of capital per capita, Year is the trend variable.

The data for this exercise can be found in ‘Macro_PEBLIF’ (attached).

This macro data set is a merged file with data from PENN6.1, Barro and Lee (2000), the IMF Financial Statistics and measures of political and civil rights from the Freedom House annual ‘Freedom in the World’ survey over the period 1950 to 2000.

Answer the following in 2 pages:

1.  Estimate both the static and dynamic models using the time-series data over the period from 1950 to 2000 from ‘Macro_PEBLIF’ for Ghana, Argentina, South Korea and Australia.

2.  What are the assumptions that ensure the OLS estimates are unbiased and consistent and how can they be tested?

3.  What are the short- and long-run coefficients on capital and technical progress for both South Korea and Ghana?

4.  Establish the time-series properties of the arguments for the production function.

5.  Estimate the constant returns to scale production function and assess if the variables are cointegrated.

6.  The implications of variables having a unit root