In time series analysis of a given set of variables，practitioners often have to decide whether to use monthly，quarterly,or annual data.They usually try to use the time series data of the higher frequency in order to increase the number of observations. However，time series data of different frequencies and different time spans are often available to empirical studies. They are usually changed to a common time interval through temporal aggregation of systempling，depending on whether the variables are flow variables or stock variables respectively. Several papers have documented the fact that time aggregation potentially distorts the relationship between variables. The objective of this paper is to investigate the impact on the correlation of four kinds of variables which are additive,multiplicative，systematicdally sampled and temporal aggregated. We show that the n-period correlation between variables decreases monotonically or approaches to one-period correlation as n increases. These results in this paper can be applied in finance，economics，and other fields where correlation or regression analyses are employed.