Time Series and Cross Sectional Data

In investment analysis, we observe two types of data, namely, time-series data and cross-sectional data.

Time-series data refers to observations made over a period of time at regular intervals. For example, when we take daily closing prices of a stock for 1 year, it is time-series data. The time unit of observation could be anything such as day, week, month, or year.

Note that time-series data contains observations on a single phenomenon (prices of one stock) over multiple periods of time.

Cross-sectional data on the other hand, contains observations on multiple phenomena observed at a single point of time. For example, closing price of 100 stocks at the end of a year.

The following tables illustrate the difference.

Time Series Data

Day	Closing Stock Price of Stock A
1	.
2	.
3	.
4	.
5	.

Cross Sectional Data

Closing Stock Price	Stock A	Stock B	Stock C	Stock D	Stock E
Dec 31, 2013	.	.	.	.	.

As you can see, both time-series data and cross-sectioned data are one-dimensional.

We can combine time-series and cross-sectional data to form two-dimensional data sets.

Panel Data

Observations on multiple phenomena over multiple time periods are called panel data. The following table shows closing price of 5 stocks for years. This is an example of panel data. Note that it contains multi-period data (5 years) of a single characteristic (closing price) of multiple entities (5 different stocks).

Closing Stock Price	Stock A	Stock B	Stock C	Stock D	Stock E
Year 1	.	.	.	.	.
Year 2	.	.	.	.	.
Year 3	.	.	.	.	.
Year 4	.	.	.	.	.
Year 5	.	.	.	.	.

Longitudinal

We can also have a data set that contains multi-period data (say 5 years) of multiple characteristics (say closing price, P/E ratio, and Market cap) of a single entity (Say a single stock – Stock A).

Stock A	Closing Price	P/E Ratio	Market Cap
Year 1	.	.	.
Year 2	.	.	.
Year 3	.	.	.
Year 4	.	.	.
Year 5	.	.	.