All articles by Priya Chetty

Application of applied statistics in the Pharma industry

Applied statistics in the pharmaceutical industry is traced to Kefauver-Harris Drug Amendments in 1962. According to the amendment, firms must prove the safety of the drug and also present substantial evidence of its efficiency (Burger et al, 2012). This led to an increased need for statisticians, especially with the adoption of genomics and proteomics for drug discovery. Read more »

Common water pollution indicators and their use in economic studies

The previous article reviewed the indicators of air pollution and their use in economic studies. This article focuses on a number of water pollution indicators and their use in economic studies. This is because indicators of water pollution help analyse the impact of economic growth on the environment. Water pollution refers to the contamination of water by toxic elements and harmful chemical agents. It implies an alteration in the natural state of water including its physical, chemical and biological properties. Water pollution due to chemicals from industrial toxic releases. Read more »

Panel data regression: random effect model in STATA

The previous article (Pooled panel data regression in STATA) showed how to conduct pooled regression analysis with dummies of 30 American companies. The results revealed that the joint hypothesis of dummies reject the null hypothesis that these companies do not have any alternative or joint effects. Therefore pooled regression is not a favourable technique for the panel data sets. It is also due to the fact that inclusion of too many dummies can lead to consequent loss of degrees of freedom. Therefore pooled regression is not the right technique to analyze panel data series. Therefore the present article intends to introduce to the concept of random effect model in STATA. Read more »

Pooled panel data regression in STATA

Before applying panel data regression, the first step is to disregard the effects of space and time and perform pooled regression instead. In this, a usual OLS regression helps to see the effect of independent variables on the dependent variables disregarding the fact that data is both cross-sectional and time series. The underlying assumption in pooled regression is that space and time dimensions do not create any distinction within the observations and there are no set of fixed effects in the data. This article explains how to perform pooled panel data regression in STATA. Read more »

Introduction to panel data analysis in STATA

The previous articles in this module showed how to perform time series analysis on a dataset where observations are present for days, weeks, months, quarters or years. This article of the module explains how to perform panel data analysis using STATA. In the case of panel data, the observations are present in time and space dimensions. For instance, a survey of the same cross-sectional unit such as firm, country or state over time. Read more »

ARCH model for time series analysis in STATA

The previous article showed how to initiate the AutoRegressive Conditional Heteroskedasticity (ARCH) model on a financial stock return time series for period 1990 to 2016. It showed results for stationarity, volatility, normality and autocorrelation on a differenced log of stock returns. The article concluded that the series has an ARCH effect. In continuation, this article presents the ARCH model of the same series. Read more »

Identifying ARCH effect for time series analysis in STATA

The previous articles showed how to apply Vector Auto Regression (VAR) and Vector Error Correction Model (VECM) based on the assumption that the variables either have a long run or short run causality among them. Some financial time series such as stock returns show wide swings for an extended period of time. Such behaviour is known as volatility. Volatility only represents a high variability in a series over time. Read more »

Testing and diagnosing VECM in STATA

The previous article estimated Vector Error Correction (VECM) for time series Gross Domestic Product (GDP), Gross Fixed Capital Formation (GFC)Private Final Consumption (PFC ). This article explains testing and diagnosing VECM in STATA to ascertain whether this model is correct or not. Among diagnostic tests, common ones are tested for autocorrelation and test for normality. Read more »

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