# lagn¶

## Purpose¶

Lags (or leads) a matrix a specified number of time periods for time series analysis.

## Format¶

y = lagn(x, t[, fill])
Parameters:
• x (NxK matrix) – data

• t (scalar or Px1 vector) – number of time periods.

• fill (scalar or Px1 vector) – Optional input, the value to fill the newly missing observations. Default is a missing value, ..

Returns:

y (NxK matrix) – x lagged t periods.

## Examples¶

### Basic lag¶

// Set number of lags
nlags = 2;

// Define x
x = { 1.4, 2.7, 3.1, 2.9, 3.2, 2.5, 2.8 };

// Lag x, nlags number of lags
x_lag2 = lagn(x, nlags);


will assign x_lag2 to equal:

  .
.
1.4
2.7
3.1
2.9
3.2


### Basic lag with fill value¶

// Specify number of lags
nlags = 2;

// Define x vector
x = { 1.4, 2.7, 3.1, 2.9, 3.2, 2.5, 2.8 };

// Define fill value for missing values
fill = 0;

// Lag x, nlags number of lags
// and fill missings with 0
x_lag2 = lagn(x, nlags, fill);


will assign x_lag2 to equal:

  0
0
1.4
2.7
3.1
2.9
3.2


### Creating multiple lags¶

If the number of time periods to lag is a Px1 column vector, then the output matrix with be an NxP matrix where each column contains one of the lags. For example, changing the nlags variable from the example above to be a 3x1 column vector like this:

// Specify to compute 1, 2, and 3
// lags
nlags = { 1, 2, 3 };

// Define x vector
x = { 1.4, 2.7, 3.1, 2.9, 3.2, 2.5, 2.8 };

// Lag x 1, 2, and 3 times
lag_mat = lagn(x, nlags);


will assign lag_mat to equal:

  .        .        .
1.4        .        .
2.7      1.4        .
3.1      2.7      1.4
2.9      3.1      2.7
3.2      2.9      3.1
2.5      3.2      2.9


### Creating multiple lags with different fill values¶

If the fill value and the number of time periods to lag are both Px1 column vectors, then the output matrix with be an NxP matrix where each column contains one of the lags. For example, changing the nlags and fill variables from the example above to be a 5x1 column vector like this:

// Specify number of lags
nlags = { 1, 2, 3, 4, 5 };

// Specify a different fill value
// for each number of lags
fill = {  0.2270,
0.0488,
0.6927,
0.6478,
0.9160 };

// Define x to matrix of zeroes
x = zeros(5, 1);

// Compute lags of x using
// fill vector to fill missing values
lag_mat = lagn(x, nlags, fill);


will assign lag_mat to equal:

0.2270   0.0488   0.6927   0.6478   0.9160
0   0.0488   0.6927   0.6478   0.9160
0        0   0.6927   0.6478   0.9160
0        0        0   0.6478   0.9160
0        0        0        0   0.9160


## Remarks¶

If t is positive, lagn() lags x back t time periods, so the first t observations of y are filled with missing values. If t is negative, lagn() leads x forward t time periods, so the last t observations of y are filled with missing values.

For higher performance if you plan to trim of the first nlags rows, use lagTrim().

## Source¶

lag.src

Functions lagtrim()