# mls: MIDAS lag structure In midasr: Mixed Data Sampling Regression

## Description

Create a matrix of selected MIDAS lags

## Usage

 `1` ```mls(x, k, m, ...) ```

## Arguments

 `x` a vector `k` a vector of lag orders, zero denotes contemporaneous lag. `m` frequency ratio `...` further arguments used in fitting MIDAS regression

## Details

The function checks whether high frequency data is complete, i.e. `m` must divide `length(x)`.

## Value

a matrix containing the lags

## Author(s)

Virmantas Kvedaras, Vaidotas Zemlys

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```## Quarterly frequency data x <- 1:16 ## Create MIDAS lag for use with yearly data mls(x,0:3,4) ## Do not use contemporaneous lag mls(x,1:3,4) ## Compares with embed when m=1 embed(x,2) mls(x,0:1,1) ```

### Example output

```Loading required package: sandwich
X.0/m X.1/m X.2/m X.3/m
[1,]     4     3     2     1
[2,]     8     7     6     5
[3,]    12    11    10     9
[4,]    16    15    14    13
X.1/m X.2/m X.3/m
[1,]     3     2     1
[2,]     7     6     5
[3,]    11    10     9
[4,]    15    14    13
[,1] [,2]
[1,]    2    1
[2,]    3    2
[3,]    4    3
[4,]    5    4
[5,]    6    5
[6,]    7    6
[7,]    8    7
[8,]    9    8
[9,]   10    9
[10,]   11   10
[11,]   12   11
[12,]   13   12
[13,]   14   13
[14,]   15   14
[15,]   16   15
X.0/m X.1/m
[1,]    NA    NA
[2,]     2     1
[3,]     3     2
[4,]     4     3
[5,]     5     4
[6,]     6     5
[7,]     7     6
[8,]     8     7
[9,]     9     8
[10,]    10     9
[11,]    11    10
[12,]    12    11
[13,]    13    12
[14,]    14    13
[15,]    15    14
[16,]    16    15
```

midasr documentation built on Feb. 23, 2021, 5:11 p.m.