Panel Data Analysis is a systematic procedure of studying a challenging research objective through multiple sites, periodically tracked over a well-defined time frame. Panel data comprise of repeated observations over a period of time with similar respondents and a set of cross-sectional units. These respondents can be individuals, institutions, expert panel, or any collection of units a marketer can follow over time. A well-structured statistical method has been designed by Chromatus to identify and explore the first-hand information available in panel data sets. Chromatus strongly believes that the time factor is a key feature of our panel data sets, addressing problems of serial correlation and dynamic effects that need to be considered. Panel data sets help in projecting the systematic, overlooked data-points across respondents whose effects are to be measured. Chromatus has successfully overcome the challenges for interpreting estimates from panel data models with the systematic data modelling approach.
Panel data statistical methods are used in estimating the key attributes of the product, concept, services, and to quantify its dynamic linkages, in order to perform valid inferences. For Chromatus, the basis for data models is ordinary least squares applied to suitably transformed data. We have overcome the key challenge in developing a projection model to ensure that statistical output is valid. The key attributes for the Panel Data are –
Panel data give more informative data, more variability,less co-linearity among the variables,more degrees of freedom and more efficiency.
Panel data are better able to study the dynamics of adjustment.
Panel data are better able to identify and measure effects that are simply not detectable in pure cross sections or pure time-series data.
Panel data model allow us to construct and test more complicated behavioral models than purely cross -section or time-series data.
Panel data are usually gathered on micro units, like individuals ,firms and households – More accurate inference of model parameter.
Constructing and testing more complicated behavioral hypotheses.
Controlling the impact of omitted variables.
Panel data can minimize estimation biases that may arise from aggregating groups into a single time series.
Simplifying computation and statistical inference along with Uncovering dynamic relationship.