**Quantitative Methods: Application**

**Reading 10. Sampling and Estimation**

**Learning Outcome Statements**

d. distinguish between time-series and cross-sectional data;

*CFA Curriculum, 2020, Volume 1*

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### Subject 2. Time-Series and Cross-Sectional Data

Data come in many different shapes and sizes, and measure many different things at different times. Often, financial analysts are interested in particular types of data, such as time-series data or cross-sectional data.

**Time-series data**is a set of observations collected at usually discrete and equally spaced time intervals. The daily closing price of a certain stock recorded over the last six weeks is an example of time-series data. Note that a too-long or too-short time period may lead to time-period bias. Refer to subject g for details.**Cross-sectional data**are observations that come from different individuals or groups at a single point in time. If one considered the closing prices of a group of 20 different tech stocks on December 15, 1986, this would be an example of cross-sectional data. Note that the underlying population should consist of members with similar characteristics. For example, suppose you are interested in how much companies spend on research and development expenses. Firms in some industries, such as retail, spend little on research and development (R&D), while firms in industries such as technology spend heavily on R&D. Therefore, it's inappropriate to summarize R&D data across all companies. Rather, analysts should summarize R&D data by industry and then analyze the data in each industry group.