- Simple Random Sampling and Sampling Distribution
- Sampling Error
- Stratified Random Sampling
- Time Series and Cross Sectional Data
- Central Limit Theorem
- Standard Error of the Sample Mean
- Parameter Estimation
- Point Estimates
- Confidence Interval Estimates
- Confidence Interval for a Population mean, with a known Population Variance
- Confidence Interval for a Population mean, with an Unknown Population Variance
- Confidence Interval for a Population Mean, when the Distribution is Non-normal
- Student’s t Distribution
- How to Read Student’s t Table
- Biases in Sampling

# 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 | . | . | . |

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