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.
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 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.
GM (1,1) modeling is a popular grey forecasting method because of its computational efficiency. There are many challenges in GM (1,1) modeling, but they are solvable using MS Excel. This article is a detailed guide on how to overcome these challenges.
This article explains the application of PINDIS separately in Hamlet II. It also presents an example using PINDIS analysis to understand the application in depth. Accessing PINDIS separately is possible only after creating the input file using ‘Select’ function.
PINDIS method applies a series of variations by increasing flexibility in each iteration. The aim is to maximize the optimization. It provides better specifics when compared to the INDSCAL results.
This article focuses on the application and interpretation of non-metric Multidimensional Scaling (MDS) method Michigan-Nijmegen Integrated Smallest Space Analysis (MINISSA) in Hamlet II.