# Lag, Lead values, and Differences of a Variable

To generate lag values of a variable in Stata, you can use the “l.” prefix followed by the number of lags you want to create. Here’s an example of how to generate one lag of a variable named “variable” in the below example:

Step-1: First need to declare your data as time series or panel data by using the following syntax:

`   tsset firm_id year`

Step-2: Now you can use the following syntax to generate the lagged values:

`   gen lag1 = l.variable`

This will create a new variable named “lag1” containing the lagged values of “variable”. By default, the “l.” prefix generates one lag, but you can also specify a different number of lags by adding a number after the “l.” prefix. For example, to generate two lags of “variable”, you can use:

`   gen lag2 = l2.variable`

## Difference between “l.” and “[ _n-1]”

##### Related Article: Use of System Variables, difference between _n and _N in Stata

Both “l.” and “[ _n-1]” are used to generate lag values of a variable in Stata, but they have bit of difference.

The “l.” prefix generates lagged values of a variable based on its previous time period, where “l.” stands for “lag”. For example, “l1.” generates the lagged value of a variable based on its value in the previous time period.

On the other hand, “[_n-1]” generates lagged values of a variable based on the observation that precedes the current observation, where “_n” stands for the current observation number. For example, “[ _n-1]” generates the value of a variable in the observation that immediately precedes the current observation.

`   bys firm_id (year): gen bylag = variable[_n-1]`

To better understand see the below example where “lag1” is generated by “l.” and “bylag” is generated by “[ _n-1]”.

##### Related Article: How to Create and use Business Calender in Stata

To generate lead values of a variable in Stata, you can use the “f.” prefix followed by the number of leads you want to create. Here’s an example of how to generate one lead of a variable named “variable” in the below example:

Step-1: First need to declare your data as time series or panel data by using the following syntax:

`  tsset firm_id year`

Step-2: Now you can use the following syntax to generate the lagged values:

`  gen lead1 = f.variable`

This will create a new variable named “lead1” containing the lead values of “variable”. By default, the “f.” prefix generates one lead, but you can also specify a different number of leads by adding a number after the “f.” prefix. For example, to generate two leads of “variable”, you can use:

`   gen lead2 = f2.variable`

Both “f.” and “[_n+1]” are used to generate lead values of a variable in Stata, but they have bit of difference.

The “f.” prefix generates leads values of a variable based on its subsequent time period, where “f.” stands for “lead”. For example, “f.” generates the lead value of a variable based on its value in the subsequent time period.

On the other hand, “[_n+1]” generates lead values of a variable based on the observation that subsequent the current observation, where “_n” stands for the current observation number. For example, “[_n+1]” generates the value of a variable in the observation that immediately subsequent the current observation.

`    bys firm_id (year): gen lead_n = variable[_n+1]`

To better understand see the below example where “lead1” is generated by “f.” and “lead_n” is generated by “[ _n+1]”.

## Generate Differences

To generate the first difference of a variable in Stata, you can use the “D.” prefix followed by the name of the variable. The “D.” prefix calculates the difference between consecutive observations of a variable. Here’s an example of how to generate the first difference of a variable named as “variable”:

`   gen diff4 =D.variable`

This will create a new variable named “diff1” containing the first difference of “variable”.

To generate the second difference of a variable, you can simply apply the “D2.” Prefix. Here’s an example of how to generate the second difference of the variable:

`   gen diff2 =D2.variable`
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