## Establishing a relationship between FDI and air pollution in India

In India, in the last two decades, the inflow of FDI has grown significantly. Similarly, its environmental pollution has also been rising since 1991 due to an increase in economic activity. This article empirically investigates the impact of FDI on air pollution in India.

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## How to test normality in STATA?

Time series data requires some diagnostic tests in order to check the properties of the independent variables. This is called ‘normality’. This article explains how to perform normality test in STATA.

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## How to test time series multicollinearity in STATA?

The problem of multicollinearity arises when one explanatory variable in a multiple regression model highly correlates with one or more than one of other explanatory variables. It is a problem because it underestimates the statistical significance of an explanatory variable (Allen, 1997).

## How to test time series autocorrelation in STATA?

This article shows a testing serial correlation of errors or time series autocorrelation in STATA. Autocorrelation problem arises when error terms in a regression model correlate over time or are dependent on each other.

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## How to perform Granger causality test in STATA?

Applying Granger causality test in addition to cointegration test like Vector Autoregression (VAR) helps detect the direction of causality. It also helps to identify which variable acts as a determining factor for another variable. This article shows how to apply Granger causality test in STATA.

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## How to perform Heteroscedasticity test in STATA for time series data?

Heteroskedastic means “differing variance” which comes from the Greek word “hetero” (‘different’) and “skedasis” (‘dispersion’). It refers to the variance of the error terms in a regression model in an independent variable.

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## Slacks based measure or SBM analysis in DEA

Slacks based measure or SBM analsysis is a non-radial model to solve the problem in the “additive model” developed by Charnes, Cooper, & Rhodes in 1978. This model can discriminate between efficient and inefficient Decision-Making Units (DMU).

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