# Overview

When you are investigating the relationship between series, it can happen that their movements are not synchronized. This is where the *Lag* analysis becomes useful. It moves values of a series backward or forward in time by the number of observations that you specify. This functionality relates to another analysis, Correlation*,* which helps you identify the optimal lag setting to get the highest correlation between two series.

# Settings

## Method

You decide in which direction you want to move the values of the series:

**Lag:**moves a series forward by specified length.**Lead:**moves a series backwards by specified length.

## Base

Here you specify by how many observations you want to move the series.

# Example

In this example, we used the output from the Correlation analysis to decide which settings to apply in the Lag analysis. As a result, we lagged the US ISM PMI by 3 observations the. In the chart, we can now make assumptions on the future movements of Industrial Production, based on the current values of the ISM PMI.

# Questions

## How do I lag or lead a series?

There are two main possibilities to lead or lag a time series.

**In Lag analysis:**

Set the direction as 'Method' and length as 'Base'.

NOTE: 'Lead' moves values backwards in time, while 'Lag' moves values forward.

**With formula:**

You can also use the formula:

Lag(series, length)

- The function returns the series lagged by the number of observations specified with the variable 'length'. The 'length' variable is rounded to an integer.

#### Examples:

Lag(sek, -2)

This will move the series 2 observations backward (meaning 'Lead').

Lag(sek, YearLength())

This will move the series 1 year forward (meaning 'Lag').

For more about Formula and how it's working see Formula analysis.