Liu - Evaluating Semi Industry Cycles

8/13/2019 Liu - Evaluating Semi Industry Cycles

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Forecasting the Semiconductor Industry Cycles

by Bootstrap Prediction Intervals

WEN-HSIEN LIU*

Institute of International Economics, National Chung Cheng University, Chiayi 621, Taiwan

ABSTRACT

In recent years, there has been a recognition that point forecasts of the semiconductor industry

sales may often be of limited value. There is substantial interest for a policy maker or an

individual investor in knowing the degree of uncertainty that attaches to the point forecast before

deciding whether to increase production of semiconductors or purchase a particular share from

the semiconductor stock market. In this paper, I first obtain the bootstrap prediction intervals of

the global semiconductor industry cycles by a Vector Autoregressive (VAR) model using monthly

U.S. data consisting of four macroeconomic and seven industry-level variables with 92

observations. The 24-step-ahead out-of-sample forecasts from various VAR setups are used for

comparison. The empirical result shows that the proposed 11-variable VAR model with the

appropriate lag length captures the cyclical behavior of the industry and outperforms other VAR

models in terms of both point forecast and prediction interval.

Keywords: Industry cycles; Out-of-sample forecast; Prediction interval; Semiconductor; Vector

Autoregressive (VAR) model.


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I. INTRODUCTIONSemiconductors are tiny electronic circuits that process, save, and transfer information. Since

they are essential to the operation of almost all electronic systems and are used in the production

of computers and consumer electronics, they are often mentioned as the crude oil of the

information age. However, it is estimated that a new state-of-the-art semiconductor fabrication

plant (or fab) may cost US$1 billion to $3 billions. Because of the cyclical nature of the

semiconductor industry, it is increasingly important for every CEO in the industry to predict the

future state of this industry before making such an enormous investment. Forecasting the

semiconductor industry cycle has thus been a popular topic and a challenging work to the

industry practitioners.

Using different methodologies, industry practitioners often have different views on what will

happen to the semiconductor industry business in the future. Table 1 provides the evidence that

the change in the point forecasts of the same target year by the same forecaster could be gigantic

sometimes. For example, WSTS announced a forecast in May 2001 that the industry would suffer

a 13.5 percent decline in 2001 and enjoy a 13.9 percent growth in 2002, but the numbers were

changed five months later to a 31.6 percent decline and a 1 percent growth.

In contrast to the industry practitioners point forecasts of the industry sales, this paper aims to


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cycles by a Vector Autoregressive (VAR) model using monthly U.S. data consisting of four

macroeconomic and 11 industry-level variables with 92 observations. The 24-step-ahead

out-of-sample forecasts from various VAR setups are used for comparison. The empirical result

suggests that the 11-variable VAR model with appropriate lag length does capture the cyclical

behavior of the industry and outperform other VAR models in terms of both point forecast and

prediction interval.

The rest of the paper is organized as follows. Section II reviews the current study on the

semiconductor industry cycle. In Section III, I examine the dynamics among 11 macroeconomic

and industrial-level variables using a VAR model. Section IV compares the results using different

VAR setups and a longer sample period. Section V briefly concludes this paper.

II. THE SEMICONDUCTOR INDUSTRY CYCLESThe fluctuations of the semiconductor business are best illustrated in a graph. Figure 1 shows the

growth rate of worldwide semiconductor shipments from 1957 to 2000. As Allan (2001) observes,

there were six semiconductor business cycles from 1965 to 1995, giving an average of five years

per cycle. Moreover, Leckie (2001) mentions that the semiconductor industry grew an average of

16 percent compounded annually from 1970 through 1995. However, from 1995 through 2000, it

had only grown 6 percent per year.


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and is also considered as a major source of cyclicality. In his model, firms invest in plant and

equipment, and R&D only if the return on investment (ROI) is sufficient. Because each firm has

different profit level and ROI, the level of new investments are different among firms. On the

other hand, the price is decided by the industry demand. Every firm is trying to reduce the

production cost to compete with the rest of the industry and maximize its profit. When the

industry demand decreases as a consequence of technology shocks, macroeconomic events or

inventory cycles, the price of semiconductors decreases and thus lowers the revenue, which in

turn reduces the profit and ROI. The investment to upgrade equipments or build new plants is

hence postponed. The output from existing capacity does not match the demand in the short run.

A shortage of semiconductors will force the price to rise and encourage long-term investment,

which might turn into an overcapacity and lower the price again.

In addition, Leckie (2001) believes that each cycle is somewhat different naturally, being caused

either by slowdowns in final demand, global economic recessions, excess inventories or

overcapacity, which in turn leads to price weakness. In some cycles, downturns were caused by a

combination of these factors. He claims that macroeconomic factors certainly influence the

business, but the main causes of the cycles are technological and financial factors. The

technology cycle is started when a new product design emergesfor example, a new processor

or memory chip. What happens next is that the market needs to be expanded by applying designs


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spending that has a good return on investments. However, when overcapacity exists, prices and

profit margins come under pressure and capital investment approval is difficult. This is when

only strategic technology spending is approved to position a company to a strong recovery from a

downturn.

Moreover, McClean (2001) creats and presents a "pinwheel" cycle chart in early 1999. The

pinwheel shows the contribution of various factors to the unending cyclical nature of the IC

industry. The logic behind this pinwheel chart is as follows. The natural lag time in getting a

new IC plant built and productive, together with the usual industry-wide over- or under-spending,

has created the history of unpredictability in IC capacity. This in turn has led to the sometimes

wildly fluctuating IC average selling prices (ASP), which have helped creat the famous

boom-bust cycle in the IC market. For example, when the price is softening and the market is

weak, IC manufacturers adapt conservative capital spending policy, which in turn results in little

capacity added. The undersupply due to lower capacity growth eventually increases the price of

ICs and results a strong market. When profit margin is up, IC manufacturers try to increase their

market shares and adapt aggressive capital spending to increase capacity. The oversupply soon

happens, thus softening the price again. The IC cycle then repeats itself.

III. DYNAMICS INSIDE THE INDUSTRY


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explained by its own lagged values and current and past values of the remaining N-1variables.2

In general, the VAR model is written as equation (1):

(1)

where ktY is aN 1 column vector ofNvariables at time t-k, 0C is aN 1 column vector of

constants, kA is a NN matrix of coefficients, p is the number of lags, and t is a N 1

column vector of white noise innovation terms with symmetric and positive definite

variance-covariance matrix .

In this paper, I first construct a VAR model to inspect the dynamics among five U.S.

macroeconomic variables, seven U.S. semiconductor industrial variables and worldwide

semiconductor revenues.3 I use impulses responses and forecast error variance decompositions to

obtain inferences on these dynamic relations. The 13 variables that I consider are: the U.S.

industrial production index (IP), the Federal Funds rate (FF), the consumer sentiment index from

University of Michigan (CS), the NASDAQ composite index (NDQ), the Philadelphia

semiconductor sector index (SOX), new orders of semiconductors (NO), total semiconductor

inventories (TI), the semiconductor capacity utilization ratio (UTL), North American

semiconductor equipment orders (EQO), the semiconductor producer price index (PPI), the

semiconductor industrial production index (SIP), the value of shipments created by the U.S.

=++= = )(, '

1

0 tttktk

p

k

t EYACY


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I decide the roles of the U.S. economy performance (DIP), the monetary polices (DFF), the

consumer confidence (DCS), the hig-tech stock index (DNDQ) and the sector stock index

(DSOX) by estimating a 13-variable VAR system as VAR(DIP, DFF, DCS, DNDQ, DSOX,

DNO, DTI, DUTL, DEQO, DPPI, DSIP, DVS, DWMB). I base this ordering on the idea that

exogenous macroeconomic supply shock (DIP) and monetary shock (DFF) influences the

consumer confidence (DCS) that in turn influences the demand for semiconductors. The change

in the NASDAQ composite index (DNDQ) and the semiconductor index (DSOX) can be

considered as the sensor of semiconductor industry that detects any changes in the overall

economic situation. The performance in the stock market (DSOX) and the demand for

semiconductors then affects the supply side of the industry. The U.S. semiconductor

manufacturers revenue and the worldwide industry revenue are finally affected. Table 2

summarizes the data descriptions and their sources. The May 1994 data are determined by the

availability of data on the Philadelphia semiconductor sector index (SOX) and the December

2001 data are decided by the availability of data on NO and TI.

Hypothesis Testing

The modified Dickey-Fuller t test (known as the DF-GLS test) proposed by Elliot, Rothenberg

and Stock (1996) is used to test if the series are stationary. Elliot, Rothenberg and Stock (1996)

and subsequent studies have suggested that this test has significantly higher power than the


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lag lengths quickly consume degrees of freedom, thus appropriate lag length selection is critical.

If the lag length is too small, the model misses the accuracy. If the lag length is too large, degrees

of freedom are wasted. I select one as the appropriate lag length for the 13-variable VAR system

based on SC and HQ values.

Table 4 presents the residual correlation matrix from the 13-variable VAR system. I notice that

the correlation between DUTL and DSIP is closed to 0.99 and the correlation between DNDQ

and DSOX is more than 0.84. To avoid the multi-collinearity problem, I decide to throw out SIP

and NDQ. The VAR system now becomes an 11-variable VAR model. The lag order selection

criteria (not reported here) still suggest the appropriate lag length is one. In addition, a block

exogeneity test (called the exclusion test) is used to decide whether to include a variable into a

VAR. The issue is to examine whether lags of one variable Granger-cause any other of the

variables in the VAR system. The result (not reported here) also supports that the lag and the

order of variables in the proposed VAR model are appropriate.

Table 5 provides information on roots of the 11-variable VAR(1) model. Ltkepohl (1993) and

Hamilton (1994) both assert that if the modulus of each eigenvalue is strictly less than one, then

the estimated VAR(P) is stable. Since no root lies outside the unit circle, the 11-variable VAR(1)

model satisfies the stability condition.


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the variation in worldwide semiconductor revenue growth (DWMB) when one-month-ahead

forecast is implemented. On the other hand, the growth of the U.S. industrial production index

(DIP) explains 0.240 percent of the variations in worldwide semiconductor revenue growth

(DWMB).

Figure 2 provides the impulse responses of worldwide industry revenue growth (DWMB). The

middle curve in each path traces out the point estimate of shock-over-time impact on a variable.

The upper and lower lines indicate two standard error bounds around the point estimate,

indicating the area of approximate 95 percent confidence level. These panels indicate the effects

from the macroeconomic and industry-level variables to worldwide industry revenue growth

(DWMB). For instance, an increase in the growth of new orders (DNO) signals an increase in the

worldwide semiconductor revenue growth (DWMB) for almost two months. Surprisingly, the

change in some macroeconomic variables (DFF and DSOX) does not have a clear influence on

the world industry revenue growth (DWMB). From the results of impulse response and variance

decomposition, one may find that DCS, DNO, DVS and DWMB have significant impacts on the

semiconductor industry cycle, i.e. worldwide semiconductor industry growth (DWMB).

IV. FORECASTING THE CYCLES

As De Gooijer and Hyndman (2005) point out in their survey paper, the use of prediction


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derived has not always been properly detected. In recent years, a series of papers on using the

bootstrap method to compute prediction intervals for time series models have been published (see

Masarotto (1990), Grigoletto (1998), Clements and Taylor (2001), Kim (1999, 2004), Lam and

Veall (2000), Pascual, Romo and Ruiz (2001, 2005) and Reeves (2005) for further discussions.).

In this paper, I compare prediction intervals of DWMB from four different VAR models. Table 7

details the available data period, the number of observations, the number of variables and the

names of variables of the four models. Model 1 is the 11-variable VAR(1) model I propose in the

previous section. Model 2 is a dimension-reduced model from Model 1 and consists of only four

variables, DCS, DNO, DVS and DWMB, which I believe may have significant impacts on

predicting DWMB. The lag order selection criteria indicate that the optimal lag length of Model 2

is three (see Table 8). Both Models 1 and 2 have the same data period from May 1994 to

December 2001. Model 3, on the other hand, enjoys a longer data period than the first two

models, ranged from January 1978 to August 2005. However, it suffers from the lack of balanced

data on DSOX, DNO, DTI, DEQO and DVS, and only consists of seven variables. Please note

that I replace SOX by NDQ here in order to keep a stock index in the VAR model. Model 4 is the

dimension-reduced VAR model from Model 3 and consists of DIP, DNDQ, DPPI and DWMB.

The four variables are chosen based on their weights in the forecast error variance decomposition

of DWMB in Model 3 (see Table 9). The lag order selection criteria (not reported here) indicate


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forecast performance of a VAR model is determined by the accuracy of both point forecast and

prediction interval. That is, the closer the forecast values to the actual (observed) values and the

more often the actual values falls inside the prediction intervals, the better the forecast

performance.

In this paper, I compute the 24-step-ahead forecasts using the dynamic forecast method. In

general, one-step-ahead forecasts are predictions of the values that the endogenous variables will

take next period, conditional on their current and lagged values and the values of any exogenous

variables in the VAR model. This type of prediction is most easily obtained. One-step-ahead

forecasts are in fact a special case of h-step-ahead forecasts or dynamics forecasts. A dynamic

forecast begins as a one-step-ahead forecast, but then it continues, using the forecasted values of

the endogenous variables as data to calculate the 2-step-ahead forecasts and so on. In order to

obtain the 24-step-ahead out-of-sample forecast over the period of 2000:01-2001:12, I use the

period of 1994:04-1999:12 (68 observations) as the observation sample for Models 1 and 2 and

the period of 1978:01-1999:12 (264 observations) for Models 3 and 4. Finally, I use the

parametric bootstrap simulation algorithm in which the innovations are drawn from a multivariate

normal distribution to estimate the prediction intervals.

Figure 3 summarizes the results of the 24-step-ahead out-of-sample forecasts. Different models


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3. Models 1 and 2 are estimated based on shorter observation period. But perform better than

Models 3 and 4, which have a longer observation period. It means that a longer observation

(sample) period does not guarantee a better forecast result. Instead, a careful selection of

variables is more important.

4. With the same variables in the models, the model with a larger lag length usually performs

better.

5. The number of variables may not matter. Model 3 that consists of 7 variables has a very

similar forecast performance as Model 4 that only has 4 variables.

V. CONCLUSIONS

In addition to construct a VAR model to conduct the out-of-sample point forecasts, this paper also

applies bootstrap prediction intervals on forecasting the semiconductor industry cycles. The

empirical result shows that the carefully-constructed 11-variable VAR model with appropriate lag

length (not optimal lag length) can capture the cyclical behavior of the industry and outperforms

other VAR models in terms of both point forecast and prediction interval. The comparison of

forecast performances from different VAR models also suggests that the selection of variables

into the model is more important than the length of observation period, the number of variables in

the model, and the implementation of optimal lag length.

ACKNOWLEDGMENT


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REFERENCES

Allan, A. (2001) Business cycle market/demand forecasts vs. TSCR cycle model,presentation at

Global Economic Workshop, organized by International SEMATECH, April 11, 2001,

Monterey, California.

Chatfield, C. and A. B. Koehler (1991) On confusing lead time demand with h-period-ahead

forecasts,International Journal of Forecasting, 7, 239-40.

Chung, S. (2001) The learning curve and the yield factor: the case of koreas semiconductor

industry,Applied Economics, 33, 473-83.Clements, M. P. and N. Taylor (2001) Bootstrapping prediction intervals for autoregressive

models,International Journal of Forecasting, 17, 247-67.

De Gooijer, J. G. and R. J. Hyndman (2005) 25 years of IIF times series forecasting: a selective

review, Tinbergen Institute Discussion Paper, TI2005-068/4.

Elliot, G., T. Rothenberg and J. Stock (1996) Efficient tests for an autoregressive unit root.

Econometrica, 64, 813-36.

Flamm, K. (2001) Economic model of investment, presentation at Global Economic Workshop,

organized by International SEMATECH, April 11, 2001, Monterey, California.

Grigoletto, M. (1998) Bootstrap prediction intervals for autoregressions: some alternatives,

International Journal of Forecasting, 14, 447-56.

Gruber, H. (1992) The learning curve in the production of semiconductor memory chips, AppliedEconomics, 24, 885-94.

Gruber, H. (1994) The yield factor and the learning curve in semiconductor industry, Applied

Economics, 26, 837-43.

Hamilton, J. (1994) Time series analysis. Princeton: Princeton University Press.

Kim, J. A. (1999) Asymptotic and bootstrap prediction regions for vector autoregression,

International Journal of Forecasting, 15, 393-403.

Kim, J. A. (2004) Bootstrap prediction intervals for autoregression using mean-unbiased

estimators,International Journal of Forecasting, 20, 85-97.

Koehler, A. B. (1990) An inappropriate prediction interval,International Journal of Forecasting,

6 557 58


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Pascual, L., J. Romo and E. Ruiz (2001) Effects of parameter estimation on prediction densities: a

bootstrap approach,International Journal of Forecasting, 17, 83-103.

Pascual, L., J. Romo and E. Ruiz (2005) Bootstrap prediction intervals for power-transformed

time series,International Journal of Forecasting, 21, 219-36.

Reeves, J. J. (2005) Bootstrap prediction intervals for ARCH model, International Journal of

Forecasting, 21, 237-48.

Sims, C. (1980) Comparisons of interwar and postwar cycles: Monetarian Reconsidered,

American Economic Review, 70, 250-57.Stock, J. and M. Watson (2001) Vector autoregressions,Journal of Economic Perspectives, 15(4),

101-15.


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Table 1

Point forecasts from research firms

Forecasts in growth rate ofForecaster Issuing date

2001 2002

Dataquest Aug. 2001

Sept. 2001

-26%

-35%

8.3%

3%

WSTS May 2001

Oct. 2001

-13.5%

-31.6%

13.9%

1%

SIA June 2001

Nov. 2001

-13.5%

-31%

20.5%

6%

Pathfinder Sept. 2001 -30% 13%

IC Insights Sept. 2001

Sept. 20, 2001

-27%

-35%

Single-digit recovery

VLSI Research October 2001 -34.3% 12.7%

Source: Current State of the Industry, November 2001 IEF, International SEMATECH


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Table 2

Data sources of the variables in the VAR system

Variable Description Coverage Source

(A) Macroeconomic variables

IP U.S. Industrial production index 1919:1-2005:08 Federal Reserve

FF Federal funds rate 1954:7-2005:08 Federal Reserve

CS U.S. consumer sentiment index 1978:1-2005:08 University of Michigan

NDQ NASDAQ composite index 1971:1-2005:08 NASDAQ Stock Market

SOX Philadelphia semiconductor index 1994:5-2005:08 Philadelphia Stock Exchange

(B) Industry-level variables

NO U.S. new semiconductor orders 1992:2-2001:12 Bureau of Census

TI U.S. total semiconductor inventories 1992:1-2001:12 Bureau of Census

UTL U.S. semiconductor capacity utilization 1967:1-2005:08 Federal Reserve

EQO North American equipment orders 1991:1-2005:08 SEMI

PPI U.S. semiconductor producer price

index

1967:1-2005:08 Bureau of Labor and

Statistics

SIP U.S. semiconductor industrial

production index

1954:1-2005:08 Federal Reserve

VS U.S. value of semiconductor shipments 1992:1-2005:08 Bureau of Census

WMB Worldwide semiconductor market 1978:1-2005:08 SIA


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Table 3Lag order selection criteria in the 11-variable VAR system

Lag LogL LR FPE AIC SC HQ0 1909.786 NA 0.000 -44.677 -41.714 -44.550

1 2066.043 268.395 0.000 -45.507 -44.361* -44.681*

2 2276.245 306.647 6.82e-35* -47.606 -40.335 -43.981

3 2404.616 154.046* 0.000 -47.779 -37.032 -43.456

4 2550.366 137.176 0.000 -48.362 -34.137 -42.640

5 2726.142 119.942 0.000 -49.650 -31.948 -42.530

6 2943.732 92.156 0.000 -51.923* -30.744 -43.404

* indicates lag order selected by the criterionLR: sequential modified LR test statistic (each test at 5% level)FPE: Final prediction errorAIC: Akaike information criterionSC: Schwarz information criterionHQ: Hannan-Quinn information criterion


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Table 4Variance-correlation matrix of residuals of the variables in the VAR system

DIP DFF DCS DNDQ DSOX DNO DTI DUTL DEQO DPPI DSIP DVS DWMBDIP 1.000 0.007 0.114 -0.080 -0.018 -0.059 0.001 0.393 0.160 -0.261 0.394 0.069 0.011

DFF 1.000 0.239 -0.248 -0.053 0.087 -0.090 0.163 -0.059 0.028 0.170 0.144 0.082

DCS 1.000 0.027 0.158 -0.092 -0.085 -0.025 -0.130 -0.143 -0.038 -0.086 -0.253

DNDQ 1.000 0.842 -0.026 -0.190 -0.083 0.127 0.022 -0.075 -0.103 -0.053

DSOX 1.000 -0.100 -0.161 0.007 0.117 -0.065 0.011 -0.091 -0.184

DNO 1.000 -0.139 0.193 -0.011 -0.310 0.204 0.437 0.338

DTI 1.000 -0.128 -0.030 -0.042 -0.099 -0.079 -0.139

DUTL 1.000 0.043 -0.145 0.991 0.360 0.143

DEQO 1.000 0.079 0.035 0.016 0.098

DPPI 1.000 -0.136 0.017 0.147

DSIP 1.000 0.361 0.167

DVS 1.000 0.391

DWMB 1.000


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Table 5Roots of the 11-variable VAR(1) model

Root Modulus

0.741297 0.741297

-0.454773 - 0.347575i 0.572387

-0.454773 + 0.347575i 0.572387

-0.269290 - 0.222183i 0.349116

-0.269290 + 0.222183i 0.349116

0.344604 - 0.053748i 0.348770

0.344604 + 0.053748i 0.348770

-0.299408 0.299408

0.113985 - 0.168997i 0.203845

0.113985 + 0.168997i 0.203845

-0.147565 0.147565


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Table 6Variance decomposition of DWMB in the 11-variable VAR(1) model

Period S.E. DIP DFF DCS DSOX DNO DTI DUTL DEQO DPPI DVS DWMB1 0.005 0.240 0.704 7.158 1.150 7.543 2.982 0.894 1.318 3.580 3.190 71.239

2 0.006 4.475 0.579 10.109 0.920 5.736 3.584 0.948 1.223 2.564 7.936 61.922

3 0.006 4.544 0.560 9.806 2.098 7.427 3.381 1.115 1.146 2.610 9.209 58.105

4 0.006 4.629 0.633 9.508 2.358 8.871 3.356 1.107 1.291 2.644 9.015 56.588

5 0.006 4.586 0.628 9.450 2.453 9.200 3.362 1.191 1.286 2.681 8.944 56.219

6 0.006 4.568 0.639 9.439 2.443 9.214 3.368 1.199 1.321 2.679 8.992 56.137

12 0.006 4.587 0.647 9.437 2.447 9.215 3.364 1.233 1.324 2.676 9.022 56.047

24 0.006 4.588 0.647 9.437 2.447 9.215 3.364 1.234 1.325 2.676 9.021 56.046


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Table 7

Comparisons of the four VAR modelsVariables

Model Data period# of

observations# of

variables DIP DFF DCS DSOX DNDQ DNO DTI DUTL DEQO DPPI DVS DWMB

1 1994:05-2001:12 92 11 2 1994:05-2001:12 92 4 3 1978:01-2001:12 288 7 4 1978:01-2001:12 288 4


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Table 8Lag order selection criteria of Model 2

Lag LogL LR FPE AIC SC HQ

269.911 NA 1.40E-08 -6.732 -6.612 -6.684

1 313.837 82.292 6.91E-09 -7.439 -6.839 -7.199

2 388.122 131.644 1.59E-09 -8.914 -7.835 -8.482

3 425.603 62.626 9.28e-10* -9.458* -7.899* -8.833*

4 437.541 18.740 1.04E-09 -9.355 -7.316 -8.538

5 447.535 14.674 1.25E-09 -9.203 -6.684 -8.194

6 466.932 26.517* 1.19E-09 -9.289 -6.290 -8.088

7 486.001 24.138 1.16E-09 -9.367 -5.888 -7.973

8 499.135 15.296 1.34E-09 -9.295 -5.335 -7.708

9 516.976 18.970 1.42E-09 -9.341 -4.902 -7.563

10 530.990 13.481 1.70E-09 -9.291 -4.372 -7.320

11 540.469 8.160 2.37E-09 -9.126 -3.727 -6.963

12 563.046 17.147 2.51E-09 -9.292 -3.414 -6.937

* indicates lag order selected by the criterionLR: sequential modified LR test statistic (each test at 5% level)FPE: Final prediction errorAIC: Akaike information criterionSC: Schwarz information criterionHQ: Hannan-Quinn information criterion


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Table 9Variance decomposition of DWMB in Model 3

Period S.E. DIP DFF DCS DNDQ DUTL DPPI DWMB

1 0.006 2.233 0.000 0.014 0.837 1.162 3.347 92.407

2 0.006 2.052 0.029 0.880 0.771 1.374 3.475 91.420

3 0.006 1.999 0.187 1.451 0.741 1.504 3.521 90.598

4 0.007 3.159 0.377 1.144 0.919 1.508 4.057 88.836

5 0.007 2.991 0.485 1.183 1.084 1.530 4.118 88.609

6 0.007 2.976 0.476 1.321 1.183 1.499 4.049 88.496

12 0.007 3.321 0.539 0.992 1.534 1.240 4.099 88.274

24 0.007 3.411 0.575 0.831 1.705 1.071 4.081 88.326


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Figure 1Worldwide semiconductor shipment annual growth (1958-2001)

-40%

-20%

0%

20%

40%

60%

80%

1958

1959

1960

1961

1962

1963

1964

1965

1966

1967

1968

1969

1970

1971

1972

1973

1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

Source: World Semiconductor Trade Statistics (WSTS)


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Figure 2Impulse responses of WMB to the variables in Model 1

-.10

-.05

.00

.05

.10

.15

1 2 3 4 5 6 7 8 9 10

Response of DWMB to DIP

-.10

-.05

.00

.05

.10

.15

1 2 3 4 5 6 7 8 9 10

Response of DWMB to DFF

-.10

-.05

.00

.05

.10

.15

1 2 3 4 5 6 7 8 9 10

Response of DWMB to DCS

-.10

-.05

.00

.05

.10

.15

1 2 3 4 5 6 7 8 9 10

Response of DWMB to DSOX

-.10

-.05

.00

.05

.10

.15

1 2 3 4 5 6 7 8 9 10

Response of DWMB to DNO

-.10

-.05

.00

.05

.10

.15

1 2 3 4 5 6 7 8 9 10

Response of D WMB to DTI

-.10

-.05

.00

.05

.10

.15

1 2 3 4 5 6 7 8 9 10

Response of DWMB to DUTL

-.10

-.05

.00

.05

.10

.15

1 2 3 4 5 6 7 8 9 10

Response of DWMB to DEQO

-.10

-.05

.00

.05

.10

.15

1 2 3 4 5 6 7 8 9 10

Response of DW MB to DPPI

-.10

-.05

.00

.05

.10

.15

1 2 3 4 5 6 7 8 9 10

Response of DWMB to DVS

-.10

-.05

.00

.05

.10

.15

1 2 3 4 5 6 7 8 9 10

Response of DW MB to DWMB

Response to Cholesky One S.D. Innovations 2 S.E.


26/27

Figure 3Bootstrap prediction intervals of DWMB

(a) Model 1, lag = 1

-.4

-.2

-5.551e-17

.2

.4

2000m1 2000m6 2000m11 2001m4 2001m9 2002m2time

95% CI f orecast

observed

Forecast for DWMB

(b) Model 2, lag = 1

-.4

-.2

-5.551e-17

.2

.4

2000m1 2000m6 2000m11 2001m4 2001m9 2002m2time

95% CI f orecast

observed

Forecast for DWMB

(c) Model 3, lag = 1

-.4

-.3

-.2

-.1

-2.776e-17

.1

.2

.3

2000m1 2000m6 2000m11 2001m4 2001m9 2002m2time

95% CI f orecast

observed

Forecast for DWMB

(d) Model 4, lag = 1

-.4

-.3

-.2

-.1

-2.776e-17

.1

.2

.3

2000m1 2000m6 2000m11 2001m4 2001m9 2002m2time

95% CI f orecast

observed

Forecast for DWMB

(e) Model 1, lag = 2

-.4

-.2

-5.551e-17

.2

.4

2000m1 2000m6 2000m11 2001m4 2001m9 2002m2time

95% CI f orecast

observed

Forecast for DWMB

(f) Model 2, lag = 2

-.4

-.2

-5.551e-17

.2

.4

2000m1 2000m6 2000m11 2001m4 2001m9 2002m2time

95% CI f orecast

observed

Forecast for DWMB

(g) Model 3, lag = 2

-.4

-.3

-.2

-.1

-2.776e-17

.1

.2

.3

2000m1 2000m6 2000m11 2001m4 2001m9 2002m2time

95% CI forecast

observed

Forecast for DWMB

(h) Model 4, lag = 2

-.4

-.3

-.2

-.1

-2.776e-17

.1

.2

.3

2000m1 2000m6 2000m11 2001m4 2001m9 2002m2time

95% CI f orecast

observed

Forecast for DWMB


27/27

27

Figure 3Bootstrap prediction intervals of DWMB (continued)

(i) Model 1, lag = 3

-.5

0

.5

2000m1 2000m6 2000m11 2001m4 2001m9 2002m2time

95% CI f or ec ast

observed

Forecast for DWMB

(j) Model 2, lag = 3

-.4

-.2

-5.551e-17

.2

.4

2000m1 2000m6 2000m11 2001m4 2001m9 2002m2time

95% CI f orecast

observed

Forecast for DWMB

(k) Model 3, lag = 3

-.4

-.3

-.2

-.1

-2.776e-17

.1

.2

.3

2000m1 2000m6 2000m11 2001m4 2001m9 2002m2time

95% CI f orecast

observed

Forecast for DWMB

(l) Model 4, lag = 3

-.4

-.3

-.2

-.1

-2.776e-17

.1

.2

.3

2000m1 2000m6 2000m11 2001m4 2001m9 2002m2time

95% CI forecast

observed

Forecast for DWMB

(m) Model 1, lag = 4

-.6

-.4

-.2

-5.551e-17

.2

.4

.6

2000m1 2000m6 2000m11 2001m4 2001m9 2002m2time

95% CI f orecast

observed

Forecast for DWMB

(n) Model 2, lag = 4

-.4

-.2

-5.551e-17

.2

.4

2000m1 2000m6 2000m11 2001m4 2001m9 2002m2time

95% CI f orecast

observed

Forecast for DWMB

(o) Model 3, lag = 4

-.4

-.3

-.2

-.1

-2.776e-17

.1

.2

.3

2000m1 2000m6 2000m11 2001m4 2001m9 2002m2time

95% CI f orecast

observed

Forecast for DWMB

(p) Model 4, lag = 4

-.4

-.3

-.2

-.1

-2.776e-17

.1

.2

.3

2000m1 2000m6 2000m11 2001m4 2001m9 2002m2time

95% CI f orecast

observed

Forecast for DWMB

Liu - Evaluating Semi Industry Cycles

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