259. VMA

Vector moving average

VMA(𝑞)𝒙𝑡=𝒄+𝚯(𝐵)𝜺𝑡𝚯(𝐵)=𝑰+𝚯1𝐵++𝚯𝑞𝐵𝑞

i.e.,

𝒙𝑡=𝒄+𝜺𝑡+𝚯1𝜺𝑡1++𝚯𝑞𝜺𝑡𝑞

Each component is a linear combination of past shocks across all series.

Parameters: 𝚯1,,𝚯𝑞 (𝐾2 each), 𝒄 (𝐾), 𝚺 (𝐾(𝐾+1)/2)
Orders: 𝑞, 𝐾 (series)

Example: VMA(1), 𝐾=2

Given

  • Order: 𝑞=1, 𝐾=2
  • Coefficient matrix:

    𝚯1=[0.40.20.20.4]
  • Intercept: 𝒄=[00]
  • Initial innovation: 𝜺0=[00]
  • Two series stacked into 𝒙𝑡=[𝑥1,𝑡𝑥2,𝑡]:
𝑡12345678910111213141516
x_(1\,)𝑡121081114129131614111518161317
𝑡12345678910111213141516
x_(2\,)𝑡89761011981213111014151312

Step 1 — formula

Substitute 𝑞=1 into the VMA(𝑞) recursion:

𝒙𝑡=𝒄+𝜺𝑡+𝚯1𝜺𝑡1

Forecast (set 𝜺𝑡=𝟎):

𝒙̂𝑡=𝒄+𝚯1𝜺𝑡1

Componentwise:

𝑥̂1,𝑡=𝑐1+0.4𝜀1,𝑡1+0.2𝜀2,𝑡1𝑥̂2,𝑡=𝑐2+0.2𝜀1,𝑡1+0.4𝜀2,𝑡1

Innovation:

𝜺𝑡=𝒙𝑡𝒙̂𝑡

Step 2 — apply at 𝑡=1

Plug in 𝒄=𝟎, 𝚯1, 𝜺0=[00]:

𝒙̂1=𝟎+𝚯1𝟎=[00]𝜺1=𝒙1𝒙̂1=[128][00]=[128]

Step 3 — iterate

Each row uses the previous innovation 𝜺𝑡1. Values rounded to 4 decimal places at every step (computation continues from the rounded value).

𝑡𝒙𝑡𝑥̂1,𝑡=0.4𝜀1,𝑡1+0.2𝜀2,𝑡1𝑥̂2,𝑡=0.2𝜀1,𝑡1+0.4𝜀2,𝑡1𝜺𝑡
1[128]0.4(0)+0.2(0)=00.2(0)+0.4(0)=0[128]
2[109]0.4(12)+0.2(8)=6.40.2(12)+0.4(8)=5.6[3.63.4]
3[87]0.4(3.6)+0.2(3.4)=2.120.2(3.6)+0.4(3.4)=2.08[5.884.92]
4[116]0.4(5.88)+0.2(4.92)=3.3360.2(5.88)+0.4(4.92)=3.144[7.6642.856]
5[1410]0.4(7.664)+0.2(2.856)=3.63680.2(7.664)+0.4(2.856)=2.6752[10.36327.3248]
6[1211]0.4(10.3632)+0.2(7.3248)=5.61020.2(10.3632)+0.4(7.3248)=5.0026[6.38985.9974]
7[99]0.4(6.3898)+0.2(5.9974)=3.75540.2(6.3898)+0.4(5.9974)=3.6769[5.24465.3231]
8[138]0.4(5.2446)+0.2(5.3231)=3.16250.2(5.2446)+0.4(5.3231)=3.1781[9.83754.8219]
9[1612]0.4(9.8375)+0.2(4.8219)=4.89940.2(9.8375)+0.4(4.8219)=3.8963[11.10068.1037]
10[1413]0.4(11.1006)+0.2(8.1037)=6.0610.2(11.1006)+0.4(8.1037)=5.4616[7.9397.5384]
11[1111]0.4(7.939)+0.2(7.5384)=4.68330.2(7.939)+0.4(7.5384)=4.6032[6.31676.3968]
12[1510]0.4(6.3167)+0.2(6.3968)=3.8060.2(6.3167)+0.4(6.3968)=3.822[11.1946.178]
13[1814]0.4(11.194)+0.2(6.178)=5.71320.2(11.194)+0.4(6.178)=4.71[12.28689.29]
14[1615]0.4(12.2868)+0.2(9.29)=6.77270.2(12.2868)+0.4(9.29)=6.1734[9.22738.8266]
15[1313]0.4(9.2273)+0.2(8.8266)=5.45620.2(9.2273)+0.4(8.8266)=5.3761[7.54387.6239]
16[1712]0.4(7.5438)+0.2(7.6239)=4.54230.2(7.5438)+0.4(7.6239)=4.5584[12.45777.4416]