Article: “The Fundamentals, the Polls, and the Presidential Vote” Author: Christopher Wlezien, Robert S. Erikson Issue: Oct. 2004 Journal: PS: Political Science & Politics This journal is published by the American Political Science Association. All rights reserved. APSA is posting this article for public view on its website. APSA journals are fully accessible to APSA members and institutional subscribers. To view the table of contents or abstracts from this or any of APSA’s journals, please go to the website of our publisher Cambridge University Press (http://journals.cambridge.org). This article may only be used for personal, non-commercial, or limited classroom use. For permissions for all other uses of this article should be directed to Cambridge University Press at permissions@cup.org .

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The Fundamentals, the Polls, and thePresidential Vote

A s the experience of 2000 shows, fore-casting presidential elections is an inex-

act science. Everybody knows that “theeconomy” matters, but simple projectionfrom economic conditions at the time of theforecast is not enough. And the most impor-tant economic shocks to the economy are thelate shocks, which may arrive too late to bemeasured by the forecaster. Other events also impact, such as (in 2004) the Iraq war.Incorporating presidential approval into themodel helps to control for “other” eventsthat economic indicators ignore, but obvi-ously only those that are observable by thetime of the latest approval reading. Theyalso don’t reveal much about voters’ compar-ative judgments of the two candidates. Ofcourse one can forecast the presidential racefrom trial-heat polls available at the momentrather than trying to capture the fundamen-

tals that will matteron Election Day.1

But the whole pur-pose of forecastingis to present infor-mation about thevoters’ future behav-ior that is not yetevident in the trial-heat polls. Besides,

early polls only tell us little about the finalelection outcome.

In an earlier forecasting effort (Wlezien andErikson 1996), we added one variable to themix of predictors: leading economic indicators.The advantage of doing so is that leading indi-cators provide an advance reading of the econ-omy between the time of the forecast and Elec-tion Day. In this article, we retrace what weknow about these indicators and their ability topredict the future economy and presidential ap-proval and, ultimately, the presidential vote. Wethen consider trial-heat poll results and whatthey contribute to our understanding leading upto Election Day. We apply our analysis to offerforecasts for 2004 using information availableat the time of this writing (June, 2004). Ourprediction is a close election with an edge toBush, as signs of late growth in the economyoffset Bush’s mediocre poll numbers. Ofcourse, forecasting is inherently dynamic. Aswe gain information, our forecast can change.

The Fundamentals and the Presidential Vote

When forecasting the presidential vote,scholars have relied on a “referendum” modelof elections, where voters—at least those

byChristopher Wlezien, Oxford UniversityRobert S. Erikson, Columbia University

largely non-partisan voters—are expected toeither stay the course or change based on theperformance of the incumbent administration.2

The different models typically include somemeasure of economic performance or percep-tions, usually measured during the middle ofthe election year. Most also include somemeasure of broad public judgments, the mostpopular of which is presidential approval it-self, following Brody and Sigelman (1983).3

Our approach over the years has been fairlytypical.

In our ventures into electoral forecasting(Erikson and Wlezien 1994; 1996; Wlezienand Erikson 1996), we have relied on meas-ures of per capita disposable income growthduring the current presidential term (followingHibbs 1987) and presidential approval.4 Wefind that these so-called fundamentals predictthe vote well when measured shortly in ad-vance of the election, when the outcome is al-ready becoming clear in the polls (Erikson andWlezien 1996). Their predictive power dropsoff quite quickly as one steps back from theelection, i.e., values of income growth and approval taken early in the election year onlymodestly predict values of the same variablesjust prior to the election. To gain advance indi-cation of changes in the economy and ap-proval leading up to the election, we turned tothe index of leading economic indicators(Wlezien and Erikson 1996). The index is de-signed to forecast economic turning points oneor two quarters in advance (Rogers 1994).5 Wefound that it does tap future economic changeduring the election year, which allows us to atleast partially forecast the Election Day econ-omy and approval using information from thefirst quarter of the election year.

Table 1 shows bivariate correlations be-tween the measure of leading economic indi-cator (LEI) growth and our final pre-electionmeasures of income growth and approval aswell as the final pre-election polls and the ac-tual vote itself. The data relate to the 13 elec-tions between 1952 and 2000. As for incomegrowth, we measure LEI growth cumulativelyduring the current presidential term. Specifi-cally, the measure is the weighted average ofquarterly summaries of monthly growth, witheach quarter weighted 0.90 as much as thefollowing quarter (1.11 the weight of the pre-ceding quarter). This weighting scheme maxi-mizes the correlation with the presidentialvote, which peaks using quarter 13 indicators,through the first quarter of the election year.6

Measured in this way, cumulated leading indi-cators over a presidency represent the weigh-ted accumulation of projected economicshocks, which smooth out as the aggregation

748 PS October 2004

of actual economic shocks. For additional detail about themeasure, see the Appendix.

In Table 1 we can see that the quarter 13 measure of cumulative LEI growth nicely predicts the final, quarter 15measure of cumulative income growth. The correlation is ahealthy 0.90. The relationship partly reflects the strong correla-tion between the measure and quarterly income growth leadingup to the election, especially in the 14th quarter of the cycle.7

It also reflects the fact that our measure of LEI summarizesincome growth during the first 13 quarters, the correlationwith which is 0.78. Notice as well in Table 1 that leading in-dicators predict the final, pre-election reading of presidentialapproval from the Gallup poll. The correlation (0.64) is mod-est by comparison with what we see for income growth,which is as we would expect. That is, evaluations of sittingpresidents reflect economics and other things. Perhaps mostimportantly, LEI growth correlates well with the incumbentparty share of the final pre-election poll and, even more so,the presidential vote.8 Approval still is the better predictor: Aswe have noted, it captures other things besides economics andtells us quite a lot about what voters actually do. The pollstell most of all. Indeed, the correlation between the final pre-election poll and the vote is a near-perfect 0.96. Taking intoaccount sampling error, it is indistinguishable from unity. Thisis not surprising; it nevertheless is satisfying.

Now, let us see what the variables tell us when measured atdifferent points in the election cycle, following our previous research (especially Wlezien and Erikson 1996). Our dependentvariable is the incumbent party share of the two-party vote. Theindependent variables include thecurrent quarterly readings of cumu-lative income growth and presiden-tial approval during the electionyear.9 We also include the quarter13 measure of cumulative LEIgrowth. Table 2 shows the resultsof regression analysis for the finalfour quarters of the election cycle.These results are as expected given what we have already seen.The effect of LEI growth is greatest in the “quarter 13” model,using readings of income growthand approval through the first quar-ter of the election year. The effectdrops off as more updated informa-tion about income growth and ap-proval is used, which increasinglyreflects the economic change pre-dicted by early leading indicators.By the end of the cycle, the threevariables, though particularly cumu-lative LEI and income growth, arehighly correlated with each other.

This is especially true in the final pre-election models, inthe third and fourth columns in Table 2. These models use ameasure of income growth cumulated through quarter 15,which we know from Table 1 has a 0.90 correlation with themeasure of quarter 13 cumulative LEI. The results in thefifth column indicate that income growth in the final quarter(substantially) helps predict the vote. Indeed, the quarter 16measure of cumulative income growth clearly dominates themeasure of LEI growth, and the increase in explained vari-ance is significant (p < .01). The pattern implies that at leastsome portion of voters responds to the slope of the economyat the very end of the campaign. Regardless, the model isnot useful for the purposes of forecasting, as quarter 16 dataare not available until well after the election—specifically,not until the end of January, almost three months after thefact. Before the election, income growth adds absolutelynothing to our forecasting power (also see Wlezien and Erikson 1996).10

What about the 2004 election? Let us see what LEI growthand approval tell us now, at the end of the 14th quarter of this election cycle. To do so, we first estimate the followingequation, excluding income growth:

Vote = 37.83 + 17.50 LEI Growth13 + 0.24 Approval14(4.26) (7.35) (0.09)

Adjusted R-squared = 0.71Standard Error of the Estimate = 3.23,

where the subscripts 13 and 14 indicate the quarters of theelection cycle and standard errors are in parentheses. Then,to produce a forecast for 2004, we simply plug the 2004 val-ues of cumulative LEI growth and approval into the equation.Through the first quarter of 2004 (quarter 13 of the electioncycle), the value of LEI growth is 0.195. The number isabove average compared to the previous 13 election years(mean = 0.14, s.d. = 0.15).11 This may surprise somewhat, asthe economy did not perform well for Bush in his first threeyears. However, there were strong LEI gains in quarters10–13, and these bode well for the election year economy.As for approval, Bush’s most recent (June) number is 48%.Plugging this number and the LEI estimate into the equation

Table 1Correlations, 1952–2000

CumulativeIncome Final Finalgrowth15 Approval Poll Vote

Cum LEI growth13 .90 .64 .67 .77Cum Income growth15 — .52 .67 .70Final Approval — — .84 .85Final Poll — — — .96

Table 2Predicting the Presidential Vote, 1952–2000

Quarter of the Election Cycle

13 14 15 16aPre 16b

Post

Intercept 41.53** 39.47** 35.67** 34.62** 31.27**(4.24) (5.04) (5.02) (4.76) (2.36)

Cumulative 28.09* 28.54 6.52 8.62 2.56LEI Growth13 (10.49) (18.36) (15.60) (13.85) (6.63)Cumulative –3.53 –4.66 4.47 2.89 6.63*Income Growtht (6.06) (7.05) (6.14) (5.48) (2.29)Presidential 0.18 0.23* 0.26* 0.29** 0.32**Approvalt (0.08) (0.09) (0.09) (0.09) (0.06) Adjusted R-squared 0.64 0.70 0.73 0.76 0.87 Standard Error of the 3.63 3.32 3.13 2.97 2.17

Estimate

Note: N = 13. Numbers in parentheses are standard errors. LEI = leading economic indicators.

* p < .05; ** p < .01 (one-tailed).a Income growth cumulated through the 15th quarter of the election cycle. b Income growth cumulated through the 16th quarter of the election cycle.

Of course, things can change leading up to the election. In-deed, it is from this point on that the polls become increas-ingly informative. This is clear in Figure 1, which displays theR-squareds (across the 13 election years) from regressing thevote division on quarter 13 cumulative LEI growth and thepoll division for each date starting 200 days before the elec-tion, again using data from Wlezien and Erikson (2002). Thatis, we do exactly what we did in Table 3 except that we esti-mate a model using polling numbers for each day—instead ofeach month—and exclude approval, which allows us to isolatethe effects of new poll information over time.15 Given that themeasure of LEI growth does not change, the pattern of the R-squareds from these 200 daily regressions tells us what thepolls add from day to day.

In Figure 1 we can see that the model performs equallywell using poll data from about 125–200 days before ElectionDay. There is a good amount of information at that point intime, as R-squareds hover between 70 and 75%. We see stark

improvement in predictabilitythereafter, especially during thelast 100 days of the campaign,and this improvement is essen-tially linear, with perhaps aslight hint of decreasing mar-ginal returns. A ready interpreta-tion is that the early campaignis largely frozen in place, butthen picks up by mid-summer—about the time theconventions occur—and contin-ues through Election Day.16

Regardless of the details, it isabsolutely clear that the polls become more and more informa-tive as the election campaign un-folds. This partly reflects the factthat polls increasingly reflect final Election Day judgmentsabout the economy and the sit-ting president. They also reflectother aspects of preferences,some of which are not known

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predicts 52.8% of the two-party vote for Bush. Based on themodel, therefore, it currently is advantage Bush. This advan-tage perhaps is best expressed as the probability of winningthe majority share of the vote. We compute the p-value usingthe forecast itself, the standard forecast error, and the t dis-tribution (with 10 degrees of freedom).12 Based on the statis-tical history of the model, we estimate Bush’s chances at themoment to be a little better than 2/3.

The Fundamentals and the PollsNot to be ignored is the information from trial-heat polls.

As the campaign evolves and preferences develop, these pollsbecome increasingly more informative about the outcome(Erikson and Wlezien 1996; Brown and Chappell 1999;Campbell 1996; 2000). Let us see what they add to our fore-casting ability during the last five months of the campaign.Table 3 shows the regressions predicting the incumbent partyvote from quarter 13 cumulative LEI growth, measures of ap-proval and the incumbent candidate’s poll share in each ofthe last five months prior to Election Day and at the very endof the cycle.13 In the table we can see that the polls improveour forecasts quite a lot, though particularly as we move intothe depths of summer, 3–4 months before the election. Earlyon, the polls are highly correlated (0.68) with presidential ap-proval. Over the ensuing months, the correlation actually in-creases but the polls also incorporate other important informa-tion about the candidates. Thus, the size and significance oftheir effect on the vote increase markedly while the effect ofpresidential approval declines. The same is true for LEIgrowth, though note that the measure predicts the vote inde-pendently of the polls even in the final model. The result im-plies that the economy does not fully register with voters until Election Day itself.

This analysis affords us another forecast for 2004. Recallfrom above that the value of quarter 13 cumulative LEI growthis .195 and that the president’s June approval is 48%. The Junetrial-heat poll share is approximately 49% for Bush.14 Insertingthese numbers into the “5 months before” equation in the firstcolumn of Table 3 predicts 52.2% of the vote for Bush. This isonly slightly lower than we predicted without the polls, and isfurther evidence of a Bush advantage at this point in time. In-deed, the probability of victory remains about .67.

Table 3Forecasting During the Last Five Months, 1952–2000

Months Before the Election

5 4 3 2 1 0(June) (July) (August) (September) (October) (November)

Intercept 34.26** 32.86** 32.79** 28.51** 23.17** 16.54**(4.71) (4.51) (3.70) (3.33) (2.98) (3.78)

Cumulative 14.90* 13.11* 13.64* 15.77** 11.58** 8.28*LEI Growth13 (7.49) (6.62) (6.08) (4.39) (3.49) (3.39)Presidential 0.16 0.16 0.09 0.06 0.08 0.03Approvalt (0.11) (0.09) (0.09) (0.07) (0.05) (0.06)Trial-Heat 0.15 0.19 0.27* 0.35** 0.44** 0.61**Poll Resultst (0.10) (0.10) (0.11) (0.08) (0.07) (0.10)Adjusted R-squared 0.74 0.79 0.83 0.90 0.94 0.95Standard Error of the 3.04 2.75 2.47 1.91 1.43 1.37

Estimate

Note: N = 13. Numbers in parentheses are t values. LEI = leading economic indicators.

* p < .05; ** p < .01 (one-tailed).

Figure 1Forecasting using Leading EconomicIndicators and the Polls as the ElectionCampaign Unfolds, 1952–2000

750 PS October 2004

or even knowable in advance. To the extent the latter is true,election forecasting is inherently dynamic.

We can update our forecasts as we go along, using theadditional information contained in the trial-heat polls. Table4 reveals what happens when we simulate the past 13 elec-tions. The table summarizes out-of-sample forecasts from amodel containing the measure of cumulative LEI growth andthe polls measured at monthly intervals. For each election,the out-of-sample forecast represents the vote predicted froman estimated model that excludes the particular year. Table 4presents summary statistics of the “forecast” errors for eachmonthly model. Here we can see that the mean absoluteout-of-sample forecast error is 2.4 percentage points usingpoll data from 4–5 months before the election. The mean error drops sharply when using poll data for the last threemonths, especially numbers from the fall general electioncampaign. By Election Day, the average absolute error isonly 1.2 percentage points. The standard error shows a similar decline.

The third row of Table 4 summarizes the predictive accuracy of the out-of-sample forecasts in terms of the popular-vote winner. In this row we see that the accuracy re-mains largely unchanged using polls 2–5 months before Elec-tion Day. That is, we successfully forecast the winner in 10(or 9) of the 13 elections during the period. Things reallycome into clear focus during the final 60 days. Using pollsfrom 30 days out, the model correctly predicts 12 of the 13winners. Using the final pre-election polls, the model cor-rectly predicts all 13.

ConclusionsThe foregoing analysis reveals the important impact of tem-

poral horizons on election forecasting. Forecasts made at differ-

Notes * This article was presented at the Annual Meeting of the American

Political Science Association, Chicago, September 2, 2004. We thankCristina Adams, James Campbell, Mark Kayser, Des King, Pat Lynch, IainMcLean, Clive Payne, Laura Stoker, and Steve Yoder for comments onearlier incarnations.

1. In trial-heat polls, people are asked how they would vote “if theelection were held today.”

2. Also see Zaller (2004).3. For more detail on the various models, see Campbell and Garand (2000).4. See Erikson and Wlezien (1994). Our analyses at the time also in-

cluded incumbency, which we have since dropped, partly because of con-

cerns about its endogeneity and partly because its effect on the actual voteis not very reliable. It also is difficult to distinguish it from other variables,especially Abramowitz’s (1988) “time for a change”. See Wlezien (2001).

5. The LEI Index is a composite of ten components, nine of which areobjective: average weekly hours worked in manufacturing, average weeklyclaims for unemployment insurance, manufacturers’ new orders for consumergoods and materials, manufacturers’ new orders for nondefense capital goods,building permits for new private housing, Standard and Poor’s 500 stockprices, the money supply (M2), the interest rate spread between 10-yearTreasury bonds and federal funds, and vendor performance (slower deliveriesdiffusion index). The one perceptual component is the index of consumer

ent points in time can differmeaningfully because of theavailable data. Well in advanceof Election Day, the polls give usonly a little insight about the fi-nal vote. Conditions in the coun-try early on also provide onlylimited information about ulti-mate Election Day preferences.Even leading economic indicatorsdo not perfectly predict how theeconomy will evolve in the com-ing months. We also cannot an-ticipate all new events that willaffect the course of the cam-paign. The different effects onvoters’ preferences do become in-

corporated over time in the trial-heat polls, however, especiallyas Election Day approaches. This fact, while obvious, is impor-tant from the point of view of forecasting. Pre-election trialheats (increasingly) provide information about the “other things”that impact the ultimate vote.17

What then does this tell us about the 2004 election? As ofthis writing, five months in advance, the available informationclearly is incomplete. We nevertheless can offer a tentativeforecast. Our analysis of various combinations of economicconditions, leading indicators, presidential approval, and thelatest polls themselves suggests a close race, with PresidentBush about a two-to-one favorite for reelection in November.Of course, to the extent past elections are any guide, thisfuzzy prediction is subject to change.

We originally forecasted (on June 28) that Bush will receive52.5% of the two-party presidential vote with a probability ofelection of about two-out-of-three. Given our modeling ap-proach, we can update our forecasts as we go, virtually on adaily basis using new trial-heat polls. We update now, just inadvance of the Republican convention, using the latest num-bers available from pollingreport.com—specifically, the currentGallup presidential approval rating of 49% and the mean Bushtwo-party share in the four trial-heat polls reported in the lasttwo weeks, which is 49.9%, a coin flip.18 Our first model,containing the cumulative growth in leading economic indica-tors through quarter 13 and the August reading of presidentialapproval, predicts 52.9% for Bush, almost exactly what wepredicted (52.8%) using the model estimated in June. Our sec-ond model, which adds in late-August trial-heat poll results,predicts only 51.7% for Bush. This is 1.2 percentage pointslower than what we forecast without the trial-heats; it is 0.5 ofa point lower than we forecasted two months earlier, using thecorresponding June model. The implied probability of a Bushvictory is about 75%.

Table 4Summary Statistics for Out-of-Sample Forecasts Using LeadingEconomic Indicators and the Polls, 1952–2000

Months Before the Election

5 4 3 2 1 0(June) (July) (August) (September) (October) (November)

Mean Absolute Error 2.4 2.4 2.2 1.9 1.4 1.2Standard Error 2.9 2.8 2.8 2.3 1.8 1.7Predictive Accuracya 10/13 10/13 9/13 10/13 12/13 13/13

Note: For each of the separate quarters, the out-of-sample forecast for each election year represents the vote predicted from an estimated model that excludes the particular year.a As regards the popular-vote winner.

ReferencesAbramowitz, Alan I. 1988. “An Improved Model for Predicting Presiden-

tial Outcomes.” PS: Political Science and Politics 21:843–847.Beck, Nathaniel. 2000. “Evaluating Forecasts and Forecasting Models of

the 1996 Presidential Election.” In Before the Vote: Forecasting American National Elections, eds., James E. Campbell and James C.Garand. Thousand Oaks, CA: Sage Publications.

Brody, Richard, and Lee Sigelman. 1983. “Presidential Popularity andPresidential Elections: An Update and Extension.” Public OpinionQuarterly 47:325–328.

Brown, Lloyd B., and Henry W. Chappell Jr. 1999. “Forecasting UsingHistory and the Polls.” International Journal of Forecasting15:127–135.

Campbell, James E. 2000. The American Campaign: U.S. PresidentialCampaigns and the National Vote. College Station: Texas A&M University Press.

. 1996. “Polls and Votes: The Trial Heat Presidential Election Fore-casting Model, Uncertainty, and Political Campaigns.” American Poli-tics Quarterly 24:408–433.

Campbell, James E., and James C. Garand, eds. 2000. Before the Vote:Forecasting American National Elections. Thousand Oaks, CA: SagePublications.

Erikson, Robert S., and Christopher Wlezien. 1994. “Forecasting the Presidential Vote, 1992.” The Political Methodologist 5:10–11.

. 1996. “Of Time and Presidential Election Forecasts.” PS: PoliticalScience and Politics 29:37–39.

Hibbs, Douglas A., Jr. 1987. The American Political Economy. Cambridge,MA: Harvard University Press.

Holbrook, Thomas. 1996. Do Campaigns Matter? Thousand Oaks, CA:Sage Publications.

Rogers, R. Mark. 1994. Handbook of Key Economic Indicators. NewYork: Richard Irwin.

Wlezien, Christopher. 2001. “On Forecasting the Presidential Vote.” PS:Political Science and Politics 34:29–35.

Wlezien, Christopher, and Robert S. Erikson. 1996. “Temporal Horizonsand Presidential Election Forecasts.” American Politics Quarterly24:49–50.

. 2002. “The Timeline of Presidential Election Campaigns.” Journalof Politics 64:969–993.

Zaller, John. 2004. “Floating Voters in US Presidential Elections,1948–2000. In The Issue of Belief: Essays in the Intersection of Non-Attitudes and Attitude Change, eds., Paul Sniderman and Willem Saris.Princeton, NJ: Princeton University Press.

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expectations. For more information on the construction of the index, see theConference Board website: http://www.globalindicators.org/.

6. The weight (.90) of prior quarterly information about LEI is greater thanthat (.80) for income growth, which implies greater discounting of the latter.

7. The correlation is 0.88. The correlation with quarter 15 incomegrowth is a much more modest 0.25.

8. These political variables measure the incumbent party share of thetwo-party vote, ignoring third party candidates such as Nader. The polldata are from Wlezien and Erikson (2002), and are available through theInter-University Consortium for Political and Social Research (ICPSR):http://www.icpsr.org/.

9. Except for the last quarter of the election cycle, the measure of ap-proval is the average percentage of the public that approves of the presi-dent during each particular quarter or the most recent quarter for whichdata is available. The quarter 16 measure is the percentage of the publicthat approves of the president in October or the most recent month forwhich data is available.

10. The coefficient for income growth in Table 2 actually is negative,though not statistically significant, in the quarter 13 and 14 models.

11. However, it is virtually right in the middle, 7th highest of the 14.The median is .172.

12. The standard error of the forecast takes into account the standarderrors of the coefficients and thus is larger than the standard error of theestimate (also see Beck 2000).

13. As before, the approval data are from the Gallup poll and the pollresults from Wlezien and Erikson (2002). For more details see note 8.

14. This is based on polls from ABC/Washington Post, CBS/New YorkTimes, and CNN/USA Today/Gallup. See www.pollingreport.com.

15. Besides, approval does not add to our forecasting power during thefinal stages of the campaign, and decreasingly so as the cycle evolves (seeTable 3).

16. For a more in-depth analysis, see Wlezien and Erikson (2002).17. By the end of the campaign, the polls not only add to what we

know about the economy and approval measured right before the election,they add to what we know about these variables after the election. Follow-ing the post-election model in the fifth column of Table 2, we first predicted the vote from quarter 13 LEI growth, the final pre-election reading of approval, and income growth cumulated through the 16th quar-ter. We then generated the predicted vote using this equation and estimateda vote equation containing this predicted vote variable and the final pre-election polls. From this analysis we see that the effect of the polls isstatistically significant (b = .49; s.e. = .16) and the effect of the predictedvote (b = .40; s.e. = .20) actually is not. The same is true when either thepresidential incumbency variable or Abramowitz’s “time for a change” isincluded in the equation.

18. We rely only on the results from samples of registered voters. Thespecific survey organizations are: CBS, CNN/USA Today/Gallup, Investor’sBusiness Daily/Christian Science Monitor/TIPP, and the Los Angeles Times.

Appendix: The Measure of Leading Economic IndicatorsThere are two different series of Leading Economic Indicators (LEI). The first—the one we traditionally have relied on—began

in 1949 and ended in 1996. The second, newer series begins in 1959 and carries forward to the present. Beginning with thismanuscript, we rely primarily on the newer, more updated, and presumably better series, available from The ConferenceBoard. To preserve data for the years between 1952 and 1958, we use the original data as well. It was our original assump-tion that, although the series differed in levels and changes, percentage change measures would be comparable. Based onanalysis of the overlapping years, however, we discovered that this was not true. It thus was necessary to predict the new LEI data from the old using the overlapping years. Let us outline the full details.

To begin with, we created the percentage change in the monthly leading economic indicators, i.e., (LEIt – LEIt–1)/LEIt–1*100.Notice that the numbers are not annualized. Next, we calculated the quarterly mean of these monthly numbers. For 1949–1958,we generated predicted quarterly numbers by simply regressing the new numbers on the old in the overlapping years,1959–1996. Then we weight each quarter 0.90 as much as the following quarter (i.e., 1.11 the weight of the previous quarter),based on empirical analysis (Wlezien and Erikson 1996). Thus, LEI growth in quarter 13 counts approximately four times(1/.912) as much as LEI growth in the first quarter of the president’s term. Finally, we sum the weighted quarterly growth ratesthrough quarter 13 and then calculate the average. To calculate the average, we divide the sum of the weighted growth rates bythe sum of the weights for the 13 quarters, not the number of quarters (13) itself. The sum of quarterly weights is 7.46. The result-ing numbers are not that easy to interpret. For instance, the number (0.201) for 2000 indicates that the weighted average quar-terly growth rate in monthly leading economic indicators is 0.201%. (To be absolutely clear, the weight is 1.0 in quarter 13, .9 in quarter 12, .81 in quarter 11, .729 in quarter 10, and so on to .912 in the first quarter of a presidential term.)

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