4.2.8 At the end of a month, a large retail store classifies each receivable account according to 0: Current 1: 30-60 days overdue 2: 60-90 days overdue 3: Over 90 days Each such account moves from state to state according to a Markov chain with transition probability matrix 0 1 2 3 0 0.95 0.05 0 0 1 0.50 0 0.50 0 P = 2 0.20 0 0 0.80 3 0.10 00 0.90 In the long run, what fraction of accounts are over 90 days overdue? 4.2.8 3 || 8 51.

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4.2.8 At the end of a month, a large retail store classifies each receivable account
according to
0: Current
1: 30-60 days overdue
2: 60-90 days overdue
3: Over 90 days
Each such account moves from state to state according to a Markov chain with
transition probability matrix
0
1
2
3
0 0.95 0.05
0
0
1 0.50 0
0.50
0
P =
2 0.20
0
0
0.80
3 0.10
00
0.90
In the long run, what fraction of accounts are over 90 days overdue?
Transcribed Image Text:4.2.8 At the end of a month, a large retail store classifies each receivable account according to 0: Current 1: 30-60 days overdue 2: 60-90 days overdue 3: Over 90 days Each such account moves from state to state according to a Markov chain with transition probability matrix 0 1 2 3 0 0.95 0.05 0 0 1 0.50 0 0.50 0 P = 2 0.20 0 0 0.80 3 0.10 00 0.90 In the long run, what fraction of accounts are over 90 days overdue?
4.2.8 3
||
8
51.
Transcribed Image Text:4.2.8 3 || 8 51.
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