**can be done by utilizing formal statistical methods employing**

*Forecasting**time series, cross-sectional or Panel data*, or alternatively to less formal judgmental methods.

For instance, to *forecast *data by Time, Time Series Analysis can be used; and to *forecast* *data *from different places, Cross Sectional Data Analysis will be helpful.

In order to simplify the theory, let’s use an example of wildlife. Wild animals are perishing due to many natural and artificial reasons from planet earth which is a huge matter of concern.

In order to analyse similar data, we need to make use of specific tools. Here, I am sharing some specific codes and commands of __R-studio__.

- Fixed Effect Models
- Random Effect Models

First step is data extraction, once data is extracted; programming can be done to derive the Fixed Effect & Random Effect Models.

The programming is given below:

Data("Gasoline")

Gasoline

Names(Gasoline)

> scatterplot(lgaspcar~year|country, boxplots=FALSE, smooth=TRUE, reg.line=FALSE, data=Gasoline)

ols <-lm(lgaspcar ~lincomep+lrpmg+lcarpcap , data=Gasoline)

summary(ols)

fixed <- plm(lgaspcar ~lincomep+lrpmg+lcarpcap , data=Gasoline, index=c("country", "year"), model="within")

summary(fixed)

fixef(fixed) # Display the fixed effects (constants for each country)

pFtest(fixed, ols) # Testing for fixed effects, null: OLS better than fixed

random <- plm(lgaspcar ~lincomep+lrpmg+lcarpcap , data=Gasoline,index=c("country", "year"), model="random")

summary(random)

We need to review the results in order to find out which model is better suited…

“If the p-value is significant (for example <0.05); then use Fixed Effects.

If not; then use the Random Effects.”

> phtest(fixed, random)

# Regular OLS (pooling model) using plm > >

pool <- plm(lgaspcar ~lincomep+lrpmg+lcarpcap, data=Gasoline,index=c("country", "year"), model="pooling")

summary(pool)

Similarly, different tests can be utilized in order to check which model is better:

# Breusch-Pagan Lagrange Multiplier for Random Effects. Null is no Panel Effect (i.e. OLS is better).

plmtest(pool, type=c("bp"))

Data Science will remain the sexiest job of the 21st century despite the problems scientists face with preparation of data. However, it’s best for a user to decide which *forecasting *method will suit their problem.

Panel data lately & largely been used in:

1.Determining of public expenditure on health

2.Health and Growth

A. Childhood mortality

B. Economic growth

C. Income on child health, etc.

3.Determinant of Investment Pattern in different states for different years

4.Rural Poverty Study

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