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Management Research NewsVol. 32 No. 7, 2009pp. 670-682# Emerald Group Publishing Limited0140-9174DOI 10.1108/01409170910965260

Forecasting automobile salesSyed Shahabuddin

Department of Management Science and Operations Management,Central Michigan University, Mount Pleasant, Michigan, USA

Abstract

Purpose The purpose of this paper is to understand the behavior of the automotive industrywhich is very critical to avoid major economic disruptions in the economy. To understand thisindustry, one needs to understand its historical performance in relation to many economic factors thatmay affect the industry.Design/methodology/approach Data about automobile sales (in dollars and in units) and manyeconomic and demographic variables are collected from a variety of sources. Automobile sales arethe dependent variable. However, the variable of automobile sales is divided into foreign anddomestic car makers. The data are regressed using Statistical Package for the Social Sciences (SPSS)stepwise regression to obtain highly correlated variables.Findings The results indicate a strong relationship between the economic variables and foreigncar sales, but the relationship between the economic variables and domestic car sales is weak. Thedomestic cars sales relationship to the other economic variables should be explored further todetermine possible causes for the weak correlation. One of the possible reasons could be thatdomestic car makers use many incentives to influence sales, but data on incentives by model by yearare not available. The addition of this variable as a factor may improve correlation.Practical implications The results in this study could help the automobile companies betterunderstand their business, and the auto companies could use the results for possible strategicdecisions. In addition, legislatures in the impacted states could use the results to prepare forfluctuations in the industry that would result in profound effects on the states in question.Originality/value This type of analysis is not standard, and the use of multiple economicvariables correlated with domestic and foreign car sales is unique. The study provides a basis forfurther research.

Keywords Automotive industry, Sales forecasting, Economic conditions,United States of America

Paper type Research paper

IntroductionPlanning is an essential part of any business activity. However, business plans requireobjectives that are based on sales targets, which in turn require demand forecasts.Thus, forecasting is essential for planning. In addition, forecasts serve as input tomany other business decisions. Obviously, these decisions can be only as good as theforecast results used to make them.

Sales forecasts are the foundation of planning. The forecasts enable an organizationto have an optimum inventory level, to make appropriate purchasing decisions and tomaintain efficient daily operations. All these affect the profits of the organization.Therefore, forecasting is critical to profitability.

Demand planning involves the process of creating and affecting demand in thefuture. Regardless of method chosen (promotion, etc.), forecasting helps assess theimpact of each possible decision upon demand. Demand management integrates allaspect of an organizations strengths and weaknesses. It includes not only planningand forecasting but also coordinating all activities that affect customer demand, e.g.creating, shaping and fulfilling demand.

Demand forecasting requires projecting what will happen to demand in the future.Obviously, this requires statistical forecasting methods. Unfortunately, there is still agap between the statistical and economic techniques offered by forecasting and the

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judgmental technique most executives use to forecast demand (Shahabuddin, 1987;Sanders, 1997).

There are many techniques of forecasting, and they vary in complexity, ease of use,and the amount of data needed. Among the many forecasting techniques, manysurveys (Sanders, 1997; Mahmoud, 1984) have found the judgmental technique to bedominant. However, many studies (Armstrong, 1986; Dunn and Wright, 1991) havefound that the judgmental technique is less accurate, more biased, and more likely tolead to poor forecasts than other techniques.

Instead of using a statistical forecasting technique, some companies use intention-to-buy survey data to forecast sales. Consumer durable product manufacturers oftenuse purchase intention to forecast. The US government conducts surveys to forecastspending on durable goods. The survey results are presumed to help predict futuresales. A study (Morwitz and Schmittlein, 1992; Morrison, 1979; Armstrong et al., 2002)found a positive correlation between purchase intention and purchase behavior.Theory suggests that the best predictor of future behavior is past behavior. However,some social psychologists believe that a good predictor of what individuals will do istheir intention-to-perform the behavior (Fitzsimmon and Ajzen, 1975). Others suggestthat the intentions as predictors can work only under certain conditions (Armstrong,1985). The conditions are

. that the event is critical in the life of the intender;

. that the intender has the ability to fulfill the plan;

. that conditions which affect the intention do not change; and

. that the intender reveals accurate intention.

These conditions may be satisfied in the short-term for expensive items.In addition, the measurement of intention is key to the predictive ability of the data.

Studies ( Justin, 1996; Byrness, 1964) have shown that the type of questions used to askabout intention to predict intention-to-buy. They found that most purchases are madeby those who have reported no plans to buy ( Justin, 1996). This may occur becausenon-intenders are more likely to respond to surveys than intenders (Armstrong et al.,2002).

Despite the lack of reliable data, researchers do try to relate purchase intention topurchase behavior. Even though these attempts suggest that this relationship is useful,there has been no research testing the predictive accuracy of intentions and past sales.Lee et al. (1997) found very little relationship between buying intention and sales.

Due to the use of improper forecasting techniques, most forecasts give inaccurateresults. In addition to the use of inappropriate methods of forecasting, there are otherreasons for forecasting errors:

. Many forecasts rely on historical data without understanding the underlyingbasis of the data. For example, an unexpected jump in sales becomes part of thehistorical data instead of being considered as an outlier that may not happenagain.

. Forecasters tend to ignore likely changes that may influence the forecast, e.g.increases in population, increases in competition, technological changes, etc. Anyor all of these factors may affect the organizations sales and can easily beincluded.

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. Using inappropriate computational methods for the data. Each type of data (e.g.time series, cross-sectional data) requires different forecasting techniques.Incorrect computational techniques cause errors in forecasts.

. Forecaster bias affects results and should be kept to a minimum. Individualbiases as a result of personal optimism or pessimism have no place inforecasting. Bias increases error in forecasts.

Forecasters can choose from a variety of forecasting techniques. However, eachtechnique fits a limited set of situations, and thus methods appropriate to differentsituations result in the highest accuracy. The accuracy of forecasts is furthercomplicated when a forecaster uses available data that is not consistent and isstatistically unsound.

The use of regression to establish a relationship between the dependent variable andmany independent variables is an appropriate method of forecasting. However, theselection of the independent variables is a critical first step to accurate forecasts. Salesof many durable consumer goods correlate strongly with major economic indicators.

However, forecasting is especially complicated due to the changing economicfactors among which any business operates. The economic factors include the GrossNational Product (GNP), the employment rate, the discount rate, the population growthrate, and others. These economic factors have major effects on the manufacturers ofdurable goods. Their relationships are complicated by the possibility that some ofthese factors have lagged effects on the sales of durable products. In addition, sales ofmany products are affected by seasonal fluctuation. All these economic factors arerelevant when forecasting durable goods, e.g. automobile, sales. Forecasts can befurther complicated by sales promotions and advertising activities. Therefore,forecasters must be aware of these activities, although it is difficult to time theiroccurrences and to track the numbers of these activities. In addition, forecasters mustconsider activities that may cause problems in forecasting, and, if data are not availableon these activities, forecasters should at least be cognizant of the problems and take thepossibility of inaccurate results into consideration. Regardless of the difficulties,appropriate statistical models with relevant variables make for the best results.

Durable goodsThe cost of housing makes headlines, but transportation costs usually do not. Theaverage American spends almost as much on transportation as on housing. That is, anaverage household spent 20 cents of every dollar on transportation, or $7,697 perhousehold on average per year in 2002 (Department of Labor, 2004) to get around.Further, the average yearly transportation cost per household in some metropolitanareas is as high as $7,961 (e.g. the average cost of transportation in Chicago is $8,129 ayear or 16.9 percent of total household expenditure).

Even though the share of automobile output in GNP has declined from 4 percent to3.5 percent, it still accounts for 20 percent of the changes in GNP from quarter toquarter. In addition to its direct effect on the economy, automobile sales have a largespillover effect on the economy. Many other industries are directly or indirectly affectedby the auto industry. Further, consumer spending on purchase and maintenance ofautomobiles accounts for 10 percent of the GNP.

According to Fulton et al. (2000), in 1996 dollars, automobile output increased 51percent from 1987 to 2000 ($185.6 billion to $355.6 billion), and it contributed 3.5

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percent of the US Gross Domestic Product (GDP) in 2000 while employingapproximately 912,000 workers (McAlinden et al., 2003).

An analysis of the sales of consumer durable goods in Britain found that thedemand for cars increased by 90 percent from 1970 to 1978 while the disposable incomerose by 21 percent in the same period (Pickering, 1978). Obviously, this analysisindicates that income alone does not determine automobile demand. One possibleexplanation could be that consumers durables are purchased not with current incomebut are bought with savings or are financed. Further, consumer spending on durablegoods, especially automobiles, can be postponed to accommodate multiple factors asthe useful life of the goods can be extended through repair and maintenance. Inaddition, ownership of more than one car is also likely. These uncertainties makedemand for durable consumer goods difficult and challenging to forecast. Fauvel andSamson (1991) state that spending on durable goods in indeed the most volatilecomponent of total consumer expenditures.

In the USA, consumer durable goods purchases represent a huge market (e.g.$3,769,235 million in 2003), but not much research has been done to accurately forecastthe sales of these products (Fauvel and Samson, 1991). The forecast work that has beendone relates to new products or intention-to-buy methods. A library and GooglesScholar search on forecasting demand for durable goods generated 45 items thatmostly dealt with new products. Data for durable goods are hard to find and may beunreliable. However, due to its importance to consumption and the economy, forecastsof sales of durable goods such as automobiles are critical.

The automobile industry plays a critical role in many economies. Demand forautomobiles also determines trend for travel and tourism, roads, and patterns ofhousing (Abu-Eisheh and Mannering, 2002). That is, the more people own cars, themore they have the ability to travel and, thus, the higher the demand for more andbetter roads. The mobility of people also determines where and when they can locatetheir houses beyond congested cities, resulting in the expansion of communities. All ofthese activities expand economies and create jobs. In turn, the expansion puts pressureon politicians, urban planners and traffic engineers to be cognizant of trends inautomobile ownership. The demand for automobiles is a critical consumer decision andis influenced by sociological and economic factors, and automobile ownership affectsboth developing and developed countries (Wu, 1965; Abu-Eisheh and Mannering,2002).

Not much analytical work has been done relating to automobile sales, whichaccount for a large share of the durable goods market. Studies by Carlson and Umble(1980) and Harris (1986) tried to forecast demand for automobiles. Carlson and Umble(1980) predicted demand of automobile from 1979 to 1983 by segmenting automobileinto five classifications: sub-compact, compact, intermediate, standard and luxury. Theauthors were trying to determine the relationship between the price of gasoline andother major factors and the sale of cars. They found that the sales of compact cars grewfaster (from 35 to 45 percent) than the sales of other type of cars. They also establishedthat economic conditions were the major determinant of future automobile sales. Thestudy also found a relationship between the price of gasoline and the sales of cars.However, the study was limited to two independent variables trying to forecast salesduring a difficult political period (the oil embargo). Harris (1986) also studied therelationship of some economic variables to sales of automobiles and found a significantrelationship between demand and some economic variables.

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However, forecasting the overall market for automobiles in units as well as indollars is important to policy makers. Therefore, a comprehensive model relatingautomobile sales to as many relevant economic variables as possible is likely to be themost appropriate model (Carlson and Umble, 1980). Forecasting automobile salesrequires inclusive analysis that must consider personal and relevant national economicfactors to achieve good results. This study uses regression analysis to forecastautomobile sales in the US del (Carlson and Umble, 1980; Suits, 1958; Thompson andNoordewier, 1992).

ModelA common approach to forecasting is fitting a mathematical model to past data. Theresults are then used to forecast sales using the same explanatory variables that provedto be statistically significant.

Automobile forecasting methodology has evolved from the simple time seriesextrapolation used by Nerlove (1957) to the causal relationship models used by Dargayand Gately (1999) and Prevedouros and Ann (1998). Many other models have been usedto establish causal relationships between car sales and other socioeconomic variables:

. The LR 799 model (Tanner, 1977, 1979). This model tried to predict cars perperson in Great Britain using a very few explanatory variables, e.g. GDP perperson and cost of operating a car. However, Tanner found that this model wastoo complex and required a large amount of data.

. Autoregressive integrated moving average (ARIMA) (Garcia-Ferrer et al., 1997).Garcia-Ferrer et al. used variations of ARIMA to forecast automobile sales inorder to judge the forecasting performance of different methods. They did notfind any one variation to be better than others. According to the authors,Although there are differences in specific forecasting performance, all thosemethods yield comparable overall performance. They found that ARIMA islimited by its univariate nature and cannot fully account for the effects ofeconomic or social factors.

. The National Road Traffic Forecast (NRTF) models by Romilly (1998). TheNRTF models include household-based and explanatory models. Both modelsuse the combination of time series incorporating causal variables. Romilly foundthe results encouraging but susceptible to small-sample bias.

. The regression model. This is a commonly used model that relates variables anddetermines causal relationships. In a regression model, one can use statistical teststo determine the significance of the model as well as the variables.

DataTo forecast automobile sales in the USA, relevant demographic and economic variableswere selected, and data were collected from many sources, e.g. Bureau of LaborStatistics, Federal Reserve Bank and Moodys economy.com, economagic.com, USCommerce Department. Further, quarterly data from 1959 to 2006 were amassed ontotal automobile sales in units and in dollars, and automobile sales data were alsocollected on domestic cars and trucks as well as foreign brand cars. The variables andequations used are listed below. The independent variables are durable industrialdemand, durable personal consumption, discount rate, non-durable industrial goodsdemand, personal consumption, GNP, GDP, population, leading economic indicators,

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M1 (money that is liquid), M2 (M1 plus short-term invested assets), and M3 ( M2 and allinstitutional funds). All these variables are assumed to affect the purchase ofautomobiles and other consumer goods. However, some economic variables may maketake a long time to show their effects. Thus, in order to find whether variables have lagrelationships, variables were lagged one quarter. To understand whether any or all ofthese variables can predict automobile sales in units or value of shipment (in dollars),multiple regression method was used.

Two models were tested:

A model without a lag:

(1) Yt bo b1 X1,t b2 X2, t b3 X3, t b11 X11, tb12 X12,tand, a model with a lag:

(2) Yt v bo b1 X1, t1b2 X2, t1 b 3 X3, t1 b11 X11,t1 b12 X12, t1where Y is the sales of cars in units or in dollar value of automobile shipment eachquarter and Xs are independent variables:

X1 Durable industrial demandX2 Durable personal consumptionX3 PopulationX4 Discount rateX5 Non-durable industrial goodsX6 Non-durable personal consumptionX7 GNPX8 GDPX9 Leading economic indicatorsX10 M1 Money in cashX11 M2-M1 plus short-term institutional fundX12 M3-M2 and institutional fund

AnalysesData on automobile sales in units and in dollars and all the twelve independentvariables were regressed using Statistical Package for the Social Sciences (SPSS).Multiple regression models with stepwise method were run.

Regressing the 12 unlagged independent variables with total (domestic and foreign)automobile demand in units, the equation with significant variables is:

Y111922:95X12:48X20:08X328:84X40:3X61:8X811:72X91:69X10t 8:452:5 2:71 8:02 3:3 3:8 6:4 4:57 4:6

R2 0:75 and F30 Durban-Watsondwd1:56k5;n191

The selected variables have significant t values, and the R2 of 0.75 indicates that thesevariables (equation) can explain the automobile sales in units with 75 percent accuracy.In other words, 75 percent of the automobile sales in units can be explained by thisequation. Durban-Watson statistic is inconclusive about serial correlation and analysisof errors indicates no heteroscedasticity.

When demand for domestic cars (in units) alone is regressed with the 12 unlaggedindependent variables, the equationwith significant variables is:

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Y 8606 7:43 X1 0:22 X2 0:075 X3 47:31 X4 1:71 X8t 7:98 7:64 4:05 10:01 7:57 9:81R2 0:74 and F 44 Durban-Watson dw 1:70 k 5; n 191

The variables have significant t values, and the R2 of 0.74 indicates that this equation canpredict the sales in units with 74 percent accuracy. In other words, 74 percent of theautomobile sales in units can be explained by this equation. Durban-Watson statistic isinconclusive about serial correlation and analysis of errors indicates no heteroscedasticity

When demand for foreign cars (in units) alone is regressed with the 12 unlaggedindependent variables, the equationwith significant variables is:

Y 1172:7 0:85 X1 25:27 X4 0:195 X7 6:83 X9 0:23 X12t 15:5 5 13:6 14:3 18 14:06R2 0:91 and F 186 Durban-Watson dw 1:16 k 5; n 191

All the variables have significant t values, and the R2 of 0.91 indicates that thisequation can predict with 91 percent accuracy the sales. This indicates that the demandfor foreign cars (in units) is highly correlated with the significant variables. Durban-Watson statistic is inconclusive about serial correlation and analysis of errors indicatesno heteroscedasticity

Regressing the unit sales of trucks (from 1976 to 2006) only with unlaggedvariables, the equationwith significant variables is:

Y 768 1:34 X1 3:93 X2 32:4 X4 0:454 X11t 19:614:41 22:13 12:71 14:8R2 0:99 and F 2;036 Durban-Watson dw 1:2 k 4; n 123

All the variables have significant t values, and the R2 of 0.99 indicates that thisequation can explain with 99 percent accuracy the sales of trucks. Durban-Watsonstatistic is inconclusive about serial correlation and analysis of errors indicates noheteroscedasticity

In order to determine whether the value (in dollars) of automobiles sold couldprovide high predictive results, the sale of automobiles in dollars was regressed withthe same 12 unlagged independent variables. The analyses of the sales value ofautomobile with the unlagged independent variables resulted in:

Y 4432 53:45 X1 181:38 X2 9:08 X12t 4:34 5:7 20:61 11:99

R2 0:996 and F 5;955 Durban-Watson dw 1:30 k 4; n 191

The four variables have significant t values, and the R2 of 0.996 indicates that thisequation can predict with 99.6 percent accuracy the sales of total value of automobileshipment. Both R2 and F are very significant and indicate high predictive power.Durban-Watson statistic is inconclusive about serial correlation and analysis of errorsindicates no heteroscedasticity.

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When the sales of automobile in dollars is regressed with independent variableslagged one quarter, the resulting equation is:

Y 1235 213 X3 9:72 X12t 2:4 24:92 10:3R2 0:994 and F 7; 553 Durban-Watson dw 10:766 k 2; n 190

All variables have significant t values, and the equation has an R2 of 0.994, whichindicates that this equation can predict with 99.4 percent accuracy the sales ofautomobile in dollar. In other words, 99.4 percent of the automobile sales in dollar canbe explained by the equation. Durban-Watson statistic is inconclusive about serialcorrelation and analysis of errors indicates no heteroscedasticity

By lagging the variables by one quarter and regressing the total (domestic andforeign) units of automobile sales, the equation is:

Y 10776 3:18 X1 0:298 X2 0:08 X3 22:53 X4 2:25 X6 1:77 X8 9:62 X9 1:53 X10

t 7:81 2:61 3:6 7:7 2:5 2:4 6:1 3:7 3:9R2 0:72 and F 25 Durban-Watson dw 1:54 k 8;n 190

The variables have significant t values, but the R2 of 0.72 not a high value. Theequation can explain the automobile sale in units with only 72 percent accuracy.Durban-Watson statistic is inconclusive about serial correlation and analysis of errorsindicates no heteroscedasticity

Regressing the unit sales of just the domestic cars with variables lagged onequarter, the equation is

Y 8470 7:5 X1 0:074 X3 53:34 X4 1:5 X8 0:93 X10 0:14 X12t 6:48 5:01 7:5 7:5 5:52 4:30 2:18R2 0:73 and F 35:25 Durban-Watson dw 1:70 k 6; n 190

These variables have significant t values, but, again, the R2 of 0.73 is not a high value, andthe equation can predict with only 73 percent accuracy. Durban-Watson statistic isinconclusive about serial correlation and analysis of errors indicates no heteroscedasticity

Regressing the unit sales of foreign cars with variables lagged one quarter, theequation is

Y 904 1:66 X1 20:1 X4 0:235 X7 4:13 X9 0:238 X12t 9:3 4:2 8:67 15:55 6:4 13:45R2 0:91 and F 176 Durban-Watson dw 0:56 k 5; n 190

Here the variables have significant t values, and the R2 of 0.91 indicates that thisequation can predict with 91 percent accuracy the sales of foreign cars. Durban-Watsonstatistic is inconclusive about serial correlation and analysis of errors indicates noheteroscedasticity.

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When regressing the unit sales of trucks (from 1976 to 2006) with variables laggedone quarter, the equation is

Y 345 2:5 X2 36:62 X4 0:192 X8 0:367 X11t 1:77 4:8 10:46 2:92 9:4R2 0:98 and F 1; 070 Durban-Watson dw 1:5 k 4; n 122

The variables again have significant t values, and the R2 of 0.98 indicates that thisequation can predict with 98 percent accuracy the sales of trucks. Durban-Watsonstatistic is inconclusive about serial correlation and analysis of errors indicates noheteroscedasticity

The results of all the models are summarized in Table I.As can be seen, foreign cars sales (lagged or unlagged) has a high correlation with

significant variables, which means that it can be predicted with high accuracycompared to domestic cars. Domestic cars are harder to predict with the sameindependent variables. This obviously indicates that domestic cars sales are hard topredict due to many other factors, such as discounts, quality problem, and amount ofadvertising, are affecting sales. In contrast, demand for trucks can be predicted withhigh accuracy. However, the equation for trucks is based on data from 1976 to 2006, soit cannot be said with certainty that the future demand for trucks can be predicted withsuch a high accuracy, because the novelty and the competitive pressure of foreign carmanufacturers will affect the demand for domestic trucks as well. As a result, thedomestic truck market may eventually show the same weak relationship with thesignificant independent variables as the domestic cars.

ConclusionThe automobile industry is a major component of the US economy. Especiallyin states where automobiles dominate the manufacturing sector, decreases inautomobile sales can have dire economic consequences for the state and its people.Therefore, it is critical that the automobile industry plan its business carefully and tobe aware of the upturns and downturns that might be coming so as to prevent orameliorate any economic shock those changes might have on the countrys or thestates economy.

To accomplish this, automobile manufacturers and dealers must carefully developtheir business plans. For a plan to be effective, it must be reliable. The reliability of aplan increases if it is based on realistic goals. The goals can be realistic only if they arebased on an analysis of the conditions affecting the business. Such analysis requiresuse of a forecasting method that allows the incorporation of as many conditions asfeasible that might affect the business and that can be used to predict demand.

Table I.

Unlagged Lagged (one quarter)R2 F R2 F

Total units sold 0.75 30 0.72 25Domestic cars 0.74 44 0.73 35.25Foreign cars 0.91 186 0.91 175Truck 0.99 2,036 0.98 1,070Value of shipment 0.996 5,955 0.994 7,553

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Regression allows for such analysis. Therefore, to forecast automobile sales, twelvepossible economic factors were analyzed to establish a relationship between thevariables and the automobile sales. The data were analyzed using regression without alag and with a one quarter lag. Using the t-test, some variables were dropped due totheir insignificant relationship. Total automobile sales (domestic and foreign car sales)in units, domestic car sales, foreign car sales, truck, and value (in dollars) of automobileshipment were analyzed. Relationships of some of the independent variables with totalsales in dollars, foreign cars, and trucks were highly correlated with both lagged andunlagged independent variables.

Based on the analysis, foreign car sales also proved to be highly correlated to theeconomic variables, while domestic car and total car sales were not significant and hada very weak relationship. Therefore, it can be safely concluded that it is possible topredict foreign car sales, truck sales, and the total sales of automobile in dollar withhigh degree of confidence. However, these results and the domestic car sales canfurther be improved by segmenting cars and trucks by size and price range.

Some might counter that using current economic indicators to forecast current salescannot be theoretically supported. However, the purpose of analyzing the relationshipof sales of cars and trucks with both lagged and unlagged variables was to findwhether current information would indicate close relationships. Apparently, bothlagged and unlagged indicated the same strength of relationship with the dependentvariables. In other words, the strength of the relationships did not improve ordeteriorate depending onwhether variables were lagged or not.

In addition, some independent variables show high correlation with each other, e.g.GNP and personal consumption. However, removing one or two significantindependent variables did not change the strength of the correlation significantly;therefore, they were not eliminated in the study.

This study has correlated many economic and demographic independent variableswith the sales of automobiles in the USA. The results and relationships can further beimproved by the substitution of or the inclusion of subsets of the variables such asanalysis of each automobile company individually, of each model car, and of vehicles indifferent price ranges. However, such ses are not possible for this researcher withoutthe cooperation of each automobile manufacturer, as the required data is not availablepublicly. For now, this study can be used as a predictive model of the overallautomobile industry and as the basis for further study.

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Further reading

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About the author

Syed Shahabuddin, Professor of Management Science and Operations Management, received hisPhD from the University of Missouri, Columbia, Missouri. He has been with Central MichiganUniversity since 1980. Before joining Central Michigan University, he taught at the University ofNotre Dame, South Bend, Indiana. He has served as chairman of two departments at CentralMichigan University, and was a Fulbright scholar. Dr Shahabuddin has published more than 50articles and two books, entitled Management Science and Programming in Basic, and a thirdbook, Business Statistics, to be published shortly. He has chaired many sessions and presentedmany papers in the Decision Science Institute and the INFORMS national meetings. SyedShahabuddin can be contacted at: shaha1s@cmich.edu

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