Observation and Analysis for the Semiconductor Cycle with a State ...

The 31st Annual International Symposium on Forecasting, June 26-30, Prague, University of Economics
Observation and Analysis for
the Semiconductor Cycle with
a State Space Model
Takashi Ogawa
Graduate School of Business Science
University of Tsukuba
ogawa@gssm.otsuka.tsukuba.ac.jp

Agenda
1. Motivation and Background
2. Semiconductor Market
3. Modeling
4. Analysis and Consideration
5. Summary of results
Graduate School of Business Science/University of Tsukuba
2

Motivation and Background:
Semiconductor Cycles
Change %
100
80
60
40
20
0
-20
-40
-60
Semicondoctor market
Semiconductor manufacfuring equipment market
1990 1995 2000 2005
• Cyclical behaviors in
semiconductor markets have
been well-known as “Silicon
Cycle”
• Influential factors for the
behaviors from observations
– Business cycles
– Unbalance of supply and
demand
– Memory cycles with periodical
technology shifts
• Expectation for modulations
– Consolidation of industry
– Diversification in electronics
demand
Graduate School of Business Science/University of Tsukuba
3

Motivation and Background:
Reviews for the Past Research
• Academic research for “Silicon Cycle” has been limited in the past
– Many analysts have given the qualitative view and speculation for the
market behaviors time to time
– Okada(1993): Speculated “Semiconductor Waves” in semiconductor revenue at a
technology shift of DRAM with a drastic bit price decline
• In these days, the accumulated market data during over 20 years
in the past becomes available for the quantitative analysis and
modeling
– Chow(2007): Constructed a leading indicator for the electronics cycle with Bayesian
VAR model
– Ogawa(2010): Simulated “Periodic behaviors” in DRAM market with SD model and
evaluated effects of time-delay and over expectation in the capacity investment
• The purposes of this research are;
– Investigate the feasibility for quantitative model with standard statistical approach
(State Space Model)
– Through the analysis, withdraw the influential factors and characteristics for the
cyclical behaviors to understand the dynamics
Graduate School of Business Science/University of Tsukuba
4

Semiconductor Market:
Data Categories
Semiconductor
market
1.Analog
2.Discrete
3.Logic
4.Memory
5.Micro
component
6.Optoelectronics
• Data source: Worldwide
Trading Semiconductor
Statistics
• Period for analysis: 1985
January – 2008 December
• Six major categories for
semiconductor devices
• Monthly revenue data is
incorporated into the
model and analyzed
Graduate School of Business Science/University of Tsukuba
5

Modeling:
Structural Time Series Model
• Structural Time Series Model (Harvey 1989) represents Time
series observations (y n : n=1,..,T) by a linear combination of
Trend(t n ), Seasonality(s n ), and Cycle(c n )
y
t
s
c
n
n
n
n
t
i1
q
i1
i
s
n n
k
(k)
ci
tni
i1
p1
s
c
ni
ni
c
n
n2
n1
n3
,
,
,
n
~
~
~
~
Graduate School of Business Science/University of Tsukuba
,
n
n1
n2
n3
N(0,
N(0,
2
2
1
N(0,
N(0,
)
)
2
2
2
3
)
)
6

Modeling: Structural Time Series Model
for Semiconductor Market
• Multivariate structural time series model could be
applied for semiconductor market data
y
y
t
s
c
i, n
i,n
i,n
n
Cycle factor
n
:Time
t
2t
q
j1
i,n
p1
j1
s
j
s
i,n 2
c
series
n
n
j
n
j
t
c
observations
i,n 1
n
n2
,
n3
,
n
,
n1
,
n
n2
n3
for device category (i),
~
~
~
N(0,
n1
~
N(0,
N(0,
N(0,
2
2
2
i
2
3
),
)
)
2
i, 1
)
p 12
i 1,....,6
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Analysis and Consideration: Analysis for
Semiconductor Market
400
200
0
Std. Residuals
Actual
Predicted
ANALOG M$
4,000
3,000
2,000
1,000
0
400
200
0
Std. Residuals
Actual
Predicted
MEMORY M$
8,000
6,000
4,000
2,000
0
-200
-200
-400
-400
-600
200
0
-200
88 90 92 94 96 98 00 02 04 06 08
Std. Residuals
Actual
Predicted
DISCRETE M$
400 -500
3,000
2,500
2,000
1,500
1,000
500
0
-600
2,000
1,000
0
-1,000
88 90 92 94 96 98 00 02 04 06 08
Std. Residuals
Actual
Predicted
MICROCOMPONENT M$
10,000
8,000
6,000
4,000
2,000
0
-400
88 90 92 94 96 98 00 02 04 06 08
Std. Residuals
Actual
Predicted
LOGIC M$
8,000
6,000
-2,000
88 90 92 94 96 98 00 02 04 06 08
Std. Residuals
Actual
Predicted
OPTOELECTRONICS M$
4,000
3,000
2,000
4,000
1,000
800
400
0
2,000
0
800
400
0
0
-1,000
-400
-800
88 90 92 94 96 98 00 02 04 06 08
Graduate School of Business Science/University of Tsukuba
-400
-800
88 90 92 94 96 98 00 02 04 06 08
8

Analysis and Consideration:
Changes in Cyclical Behaviors with the Lag Orders
in Cycle Factor
Cycle factor
c
n
q
j1
j
c
n
j
n3
,
n3
~
N(0,
2
3
)
q=6~9
200
Smoothed SV3 State Estimate
200
Smoothed SV3 State Estimate
600
Smoothed SV3 State Estimate
300
Smoothed SV3 State Estimate
150
500
200
100
50
100
400
300
100
0
0
200
0
-50
100
-100
-100
-100
0
-200
-150
-100
-200
-200
-300
-200
88 90 92 94 96 98 00 02 04 06 08
88 90 92 94 96 98 00 02 04 06 08
88 90 92 94 96 98 00 02 04 06 08
88 90 92 94 96 98 00 02 04 06 08
q=10~13
SV3 ± 2 RMSE
SV3 ± 2 RMSE
SV3 ± 2 RMSE
SV3 ± 2 RMSE
Smoothed SV3 State Estimate
Smoothed SV3 State Estimate
Smoothed SV3 State Estimate
Smoothed SV3 State Estimate
400
400
400
300
300
300
300
200
q=11
200
200
100
200
100
100
0
100
0
0
-100
0
-100
-100
-200
-100
-200
-300
-200
-200
-300
-400
-300
88 90 92 94 96 98 00 02 04 06 08
88 90 92 94 96 98 00 02 04 06 08 -300
88 90 92 94 96 98 00 02 04 06 08
88 90 92 94 96 98 00 02 04 06 08
SV3 ± 2 RMSE
SV3 ± 2 RMSE
SV3 ± 2 RMSE
SV3 ± 2 RMSE
Graduate School of Business Science/University of Tsukuba
9

Analysis and Consideration:
Observation of Cyclical Behavior in Semiconductor
Market
Smoothed SV3 cycle State Estimate (C State SV3 Estimate
n : q=11)
Filtered cycle (C n : q=11)
400
200
300
150
200
100
100
50
0
0
-100
-50
-200
-100
-300
88 90 92 94 96 98 00 02 04 06 08
-150
88 90 92 94 96 98 00 02 04 06 08
SV3
± 2 RMSE
SV3
± 2 RMSE
Graduate School of Business Science/University of Tsukuba
10

Analysis and Consideration:
Interpretation for Cyclical Behavior
Smoothed cycle
YoY change %
.4
.2
400
200
0
YoY change % of semiconductor unit shipment
.0
-.2
-.4
-200
-400
Smoothed Cycle (C n : q=11)
86 88 90 92 94 96 98 00 02 04 06 08
SV3F
PCHY_SEMI_UNIT
Graduate School of Business Science/University of Tsukuba
11

Analysis and Consideration:
Principal Components Analysis of Semiconductor
Market
5
4
3
2
1
0
-1
-2
-3
Principal Components Scores
1st component
Proportion of Variance:90.4%
2 0.975
60,000
50,000
40,000
30,000
20,000
10,000
Semiconductor Unit Shipment
-4
1.5
86 88 90 92 94 96 98 00 02 04 06 08
0
9
86 88 90 92 94 96 98 00 02 04 06 08
1.0
0.5
2 0.800
8
7
0.0
6
-0.5
-1.0
-1.5
2nd component
Proportion of Variance: 5.8%
5
4
3
Unit Price of Microcomponent
-2.0
1.5
86 88 90 92 94 96 98 00 02 04 06 08
3rd component
2
10
86 88 90 92 94 96 98 00 02 04 06 08
1.0
0.5
Proportion of Variance: 2.9%
2 0.720
9
8
7
Unit Price of Memory
6
0.0
5
4
-0.5
3
-1.0
2
86 88 90 92 94 96 98 00 02 04 06 08
1
86 88 90 92 94 96 98 00 02 04 06 08
12
Graduate School of Business Science/University of Tsukuba

Analysis and Consideration:
Incorporation of Explanatory Variables into Cycle
Factor
• Based on considerations from principal components analysis,
the explanatory variables could be incorporated into the
cycle factor
c
n
U
n
MP
MR
U
: YoY change
n
n
,
,
n
MP
: YoY change
: YoY change
: regression
n
of
MR
of
semiconductor unit shipment
of
n
unit price of
unit price of
coefficient
n3
,
n3
microcomponent
memory
~
N(0,
2
3
)
Graduate School of Business Science/University of Tsukuba
13

Analysis and Consideration:
Cyclical Behavior with Explanatory Variables
Smoothed cycle
300
200
100
0
-100
-200
YoY change % of semiconductor revenue
Smoothed Cycle (C n wih explanatory variables)
YoY change %
.8
.6
.4
.2
.0
-.2
-.4
-.6
-300
86 88 90 92 94 96 98 00 02 04 06 08
SV3FC3
PCHY_SEMI_REV
Graduate School of Business Science/University of Tsukuba
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Summary of Results
• Multivariate structural time model was applied for
semiconductor market for the first time
• Analysis successfully reproduced the historical market
trends for the six device categories
• Observed cyclical behaviors were consistent with the
changes of semiconductor market
• The explanatory variables from principal components
analysis supported the cyclical behavior by the structural
time series model well
• These results implied that this approach had a potential
to provide a leading indicator of semiconductor cycle
with further research for the structure of cycle factor and
explanatory variables
Graduate School of Business Science/University of Tsukuba
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