Data Come in various shapes and sizes, and measure different things at different TIMES. Financial analysts are often interested in particular types of data, such as time-series data or cross-sectional data or panel data.

• Time Series Data: A time series dataset is one where the observations are time-dependent. For instance, let us now suppose that a researcher collects salary data across a city on a month-by-month basis. The observations in the dataset will now differ at various  time points.
• Cross – Sectional Data: A cross-sectional dataset is one where all data is treated as being at one point in time. Let's consider that you have a dataset of salaries across a city - they have all been gathered at one point in time and thus we refer to the data as cross-sectional.
• Panel Data: Pooled (or panel) data is where the two are combined together. i.e. a salary dataset can contain observations collected at one point in time, as well as across different time periods.

Few ADDITIONAL points to bear in mind in this regard – The most common issues when working with cross-sectional data are multicollinearity and HETEROSCEDASTICITY. Multicollinearity is where two or more independent variables are correlated with each other. Heteroscedasticity is where the variance of the error term is not constant (e.g. salaries are typically higher in bigger vs. smaller cities, skewing results towards bigger cities).

For time series data, serial CORRELATION (also known as autocorrelation) is an issue. This happens when correlations exist across the error term across different time periods. e.g. if salaries are growing across time as a worker gets more experience, this does not allow us to identify IMPORTANT differences between salaries across different observations. 

Various methods and techniques are there to deal with each of these problems.