The file LIFEEXPECTANCY contains data for Life Expectancy across 145 countries (

Question

The file LIFEEXPECTANCY contains data for Life Expectancy across 145 countries (average for the years 1960-2015) and It is shared at

Data File Link :https://drive.google.com/file/d/0BwrnNVBanbKXRWU5RWxNYTFNbGs/view?usp=sharing

The variables used in this file are described below:

Lifeexp - Life expectancy at birth, total (years)

Literacy - Adult literacy rate, population 15+ years, both sexes (%)

GDPpc - GDP per capita (constant 2010 US$)

Hosbed - Hospital beds (per 1000 people)

Sanit - Improved sanitation facilities (% of population with access)

Water - Improved water source (% of population with access)

Physician - Physicians (per 1000 people)

Question : Use these data to examine the determinants of the Life Expectancy at birth. In doing so, first present your preferred specification results, explaining why you prefer this specification over the other specifications you may have considered, and then proceed to explore the hypothesis in question using your preferred specification.

If you want to use anyother software , you can , please post the answer as soon as possible. Can you please provide me below details:

1. Which variables should I omit ?

2. Which model should I use to do the analysis?

Share stata ( commands and results) with the answer. Do not directly copy and paste the commands and results here. if possible.

Explanation / Answer

you should run a linear multiple rregression model.

Use command

xtset country year

Then check for the stationarity for the variable

using command dfuller var_name

if the p-value is small close to zer than the variables are stationary. If variables are not stationary then use differenced variables.

To create differenced variable use command diff_varname = D.varname

For regression

xtreg var1 var2 var3 var4 .... , fe

or for non- stationary variables use the differenced variable

Now, check the results if the adjusted R2 is higher and if the the coefficients are significant then use this model. Then try using different combinations and stick with the model showing best results.

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