The Fundamentals, the Polls, and the Presidential VoteAuthor(s): Christopher Wlezien and Robert S. EriksonSource: PS: Political Science and Politics, Vol. 37, No. 4 (Oct., 2004), pp. 747-751Published by: American Political Science AssociationStable URL: http://www.jstor.org/stable/4488900 .

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The Fundamentals, the Polls, and the

Presidential Vote

by Christopher Wiezien, Oxford University Robert S. Erikson, Columbia University

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

act science. Everybody knows that "the economy" matters, but simple projection from economic conditions at the time of the forecast is not enough. And the most impor- tant economic shocks to the economy are the late shocks, which may arrive too late to be measured by the forecaster. Other events also impact, such as (in 2004) the Iraq war. Incorporating presidential approval into the model helps to control for "other" events that economic indicators ignore, but obvi- ously only those that are observable by the time of the latest approval reading. They also don't reveal much about voters' compar- ative judgments of the two candidates. Of course one can forecast the presidential race from trial-heat polls available at the moment rather than trying to capture the fundamen-

tals that will matter on Election Day.1 But the whole pur- pose of forecasting is to present infor- mation about the voters' future behav- ior that is not yet evident in the trial- heat polls. Besides,

early polls only tell us little about the final election outcome.

In an earlier forecasting effort (Wlezien and Erikson 1996), we added one variable to the mix 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 we know about these indicators and their ability to predict the future economy and presidential ap- proval and, ultimately, the presidential vote. We then consider trial-heat poll results and what they contribute to our understanding leading up to Election Day. We apply our analysis to offer forecasts for 2004 using information available at the time of this writing (June, 2004). Our prediction is a close election with an edge to Bush, as signs of late growth in the economy offset Bush's mediocre poll numbers. Of course, forecasting is inherently dynamic. As we gain information, our forecast can change.

The Fundamentals and the Presidential Vote

When forecasting the presidential vote, scholars have relied on a "referendum" model of elections, where voters-at least those

largely non-partisan voters-are expected to either stay the course or change based on the performance of the incumbent administration.2 The different models typically include some measure of economic performance or percep- tions, usually measured during the middle of the election year. Most also include some measure of broad public judgments, the most popular of which is presidential approval it- self, following Brody and Sigelman (1983).3 Our approach over the years has been fairly typical.

In our ventures into electoral forecasting (Erikson and Wlezien 1994; 1996; Wlezien and Erikson 1996), we have relied on meas- ures of per capita disposable income growth during the current presidential term (following Hibbs 1987) and presidential approval.4 We find that these so-called fundamentals predict the vote well when measured shortly in ad- vance of the election, when the outcome is al- ready becoming clear in the polls (Erikson and Wlezien 1996). Their predictive power drops off quite quickly as one steps back from the election, i.e., values of income growth and approval taken early in the election year only modestly predict values of the same variables just prior to the election. To gain advance indi- cation of changes in the economy and ap- proval leading up to the election, we turned to the index of leading economic indicators (Wlezien and Erikson 1996). The index is de- signed to forecast economic turning points one or two quarters in advance (Rogers 1994).5 We found that it does tap future economic change during the election year, which allows us to at least partially forecast the Election Day econ- omy and approval using information from the first 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-election measures of income growth and approval as well 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 income growth, we measure LEI growth cumulatively during the current presidential term. Specifi- cally, the measure is the weighted average of quarterly summaries of monthly growth, with each quarter weighted 0.90 as much as the following quarter (1.11 the weight of the pre- ceding quarter). This weighting scheme maxi- mizes the correlation with the presidential vote, 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 economic shocks, which smooth out as the aggregation

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Table 1 Correlations, 1952-2000

Cumulative Income Final Final growth15 Approval Poll Vote

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

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

In Table 1 we can see that the quarter 13 measure of cumulative LEI growth nicely predicts the final, quarter 15 measure of cumulative income growth. The correlation is a healthy 0.90. The relationship partly reflects the strong correla- tion between the measure and quarterly income growth leading up to the election, especially in the 14th quarter of the cycle.7 It also reflects the fact that our measure of LEI summarizes income growth during the first 13 quarters, the correlation with which is 0.78. Notice as well in Table 1 that leading in- dicators predict the final, pre-election reading of presidential approval 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 sitting presidents reflect economics and other things. Perhaps most importantly, LEI growth correlates well with the incumbent party share of the final pre-election poll and, even more so, the presidential vote.8 Approval still is the better predictor: As we have noted, it captures other things besides economics and tells us quite a lot about what voters actually do. The polls tell most of all. Indeed, the correlation between the final pre- election poll and the vote is a near-perfect 0.96. Taking into account sampling error, it is indistinguishable from unity. This is not surprising; it nevertheless is satisfying.

Now, let us see what the variables tell us when measured at different points in the election cycle, following our previous research (especially Wlezien and Erikson 1996). Our dependent variable is the incumbent party share of the two-party vote. The independent variables include the current quarterly readings of cumu- lative income growth and presiden- tial approval during the election year.9 We also include the quarter 13 measure of cumulative LEI growth. Table 2 shows the results of regression analysis for the final four 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 growth and approval through the first quar- ter of the election year. The effect drops off as more updated informa- tion about income growth and ap- proval is used, which increasingly reflects the economic change pre- dicted by early leading indicators. By the end of the cycle, the three variables, though particularly cumu- lative LEI and income growth, are highly correlated with each other.

This is especially true in the final pre-election models, in the third and fourth columns in Table 2. These models use a measure of income growth cumulated through quarter 15, which we know from Table 1 has a 0.90 correlation with the measure of quarter 13 cumulative LEI. The results in the fifth column indicate that income growth in the final quarter (substantially) helps predict the vote. Indeed, the quarter 16 measure of cumulative income growth clearly dominates the measure of LEI growth, and the increase in explained vari- ance is significant (p < .01). The pattern implies that at least some portion of voters responds to the slope of the economy at the very end of the campaign. Regardless, the model is not useful for the purposes of forecasting, as quarter 16 data are not available until well after the election-specifically, not until the end of January, almost three months after the fact. Before the election, income growth adds absolutely nothing to our forecasting power (also see Wlezien and Erikson 1996).10

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

Vote = 37.83 + 17.50 LEI Growth13 + 0.24 Approvall4 (4.26) (7.35) (0.09)

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

where the subscripts 13 and 14 indicate the quarters of the election 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 election cycle), the value of LEI growth is 0.195. The number is above average compared to the previous 13 election years (mean = 0.14, s.d. = 0.15).11 This may surprise somewhat, as the economy did not perform well for Bush in his first three years. However, there were strong LEI gains in quarters 10-13, 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 2 Predicting the Presidential Vote, 1952-2000

Quarter of the Election Cycle

13 14 15 16apre 16bpost

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 2.56 LEI 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.

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predicts 52.8% of the two-party vote for Bush. Based on the model, therefore, it currently is advantage Bush. This advan- tage perhaps is best expressed as the probability of winning the majority share of the vote. We compute the p-value using the 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 the moment to be a little better than 2/3.

The Fundamentals and the Polls Not to be ignored is the information from trial-heat polls.

As the campaign evolves and preferences develop, these polls become 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 party vote from quarter 13 cumulative LEI growth, measures of ap- proval and the incumbent candidate's poll share in each of the last five months prior to Election Day and at the very end of the cycle.13 In the table we can see that the polls improve our forecasts quite a lot, though particularly as we move into the depths of summer, 3-4 months before the election. Early on, 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 of their effect on the vote increase markedly while the effect of presidential approval declines. The same is true for LEI growth, 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. Recall from above that the value of quarter 13 cumulative LEI growth is .195 and that the president's June approval is 48%. The June trial-heat poll share is approximately 49% for Bush.14 Inserting these numbers into the "5 months before" equation in the first column of Table 3 predicts 52.2% of the vote for Bush. This is only slightly lower than we predicted without the polls, and is further evidence of a Bush advantage at this point in time. In- deed, the probability of victory remains about .67.

Table 3 Forecasting 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 0.03 Approval, (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 0.95 Standard 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 1 Forecasting using Leading Economic Indicators and the Polls as the Election Campaign Unfolds, 1952-2000

00-

-o

0)

n?

70

to

-200 -150 -100 -50 0 Days Before Election

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 the R-squareds (across the 13 election years) from regressing the vote division on quarter 13 cumulative LEI growth and the poll division for each date starting 200 days before the elec- tion, again using data from Wlezien and Erikson (2002). That is, we do exactly what we did in Table 3 except that we esti- mate a model using polling numbers for each day-instead of each month-and exclude approval, which allows us to isolate the effects of new poll information over time.15 Given that the measure of LEI growth does not change, the pattern of the R-squareds from these 200 daily regressions tells us what the polls add from day to day.

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

improvement in predictability thereafter, especially during the last 100 days of the campaign, and this improvement is essen- tially linear, with perhaps a slight hint of decreasing mar- ginal returns. A ready interpreta- tion is that the early campaign is largely frozen in place, but then picks up by mid- summer-about the time the conventions occur-and contin- ues through Election Day.16 Regardless of the details, it is absolutely clear that the polls become more and more informa- tive as the election campaign un- folds. This partly reflects the fact that polls increasingly reflect final Election Day judgments about the economy and the sit- ting president. They also reflect other aspects of preferences, some of which are not known

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Table 4 Summary Statistics for Out-of-Sample Forecasts Using Leading Economic 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 1.2 Standard Error 2.9 2.8 2.8 2.3 1.8 1.7 Predictive 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

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 the additional information contained in the trial-heat polls. Table 4 reveals what happens when we simulate the past 13 elec- tions. The table summarizes out-of-sample forecasts from a model containing the measure of cumulative LEI growth and the polls measured at monthly intervals. For each election, the out-of-sample forecast represents the vote predicted from an estimated model that excludes the particular year. Table 4 presents summary statistics of the "forecast" errors for each monthly model. Here we can see that the mean absolute out-of-sample forecast error is 2.4 percentage points using poll data from 4-5 months before the election. The mean error drops sharply when using poll data for the last three months, especially numbers from the fall general election campaign. By Election Day, the average absolute error is only 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 really come into clear focus during the final 60 days. Using polls from 30 days out, the model correctly predicts 12 of the 13 winners. Using the final pre-election polls, the model cor- rectly predicts all 13.

Conclusions The foregoing analysis reveals the important impact of tem-

poral horizons on election forecasting. Forecasts made at differ-

ent points in time can differ meaningfully because of the available data. Well in advance of Election Day, the polls give us only a little insight about the fi- nal vote. Conditions in the coun- try early on also provide only limited information about ulti- mate Election Day preferences. Even leading economic indicators do not perfectly predict how the economy will evolve in the com- ing months. We also cannot an- ticipate all new events that will affect the course of the cam- paign. The different effects on voters' preferences do become in-

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

What then does this tell us about the 2004 election? As of this writing, five months in advance, the available information clearly is incomplete. We nevertheless can offer a tentative forecast. Our analysis of various combinations of economic conditions, leading indicators, presidential approval, and the latest polls themselves suggests a close race, with President Bush about a two-to-one favorite for reelection in November. Of course, to the extent past elections are any guide, this fuzzy prediction is subject to change.

We originally forecasted (on June 28) that Bush will receive 52.5% of the two-party presidential vote with a probability of election of about two-out-of-three. Given our modeling ap- proach, we can update our forecasts as we go, virtually on a daily basis using new trial-heat polls. We update now, just in advance of the Republican convention, using the latest num- bers available from pollingreport.com-specifically, the current Gallup presidential approval rating of 49% and the mean Bush two-party share in the four trial-heat polls reported in the last two 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 presidential approval, predicts 52.9% for Bush, almost exactly what we predicted (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 points lower than what we forecast without the trial-heats; it is 0.5 of a point lower than we forecasted two months earlier, using the corresponding June model. The implied probability of a Bush victory is about 75%.

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

Political Science Association, Chicago, September 2, 2004. We thank Cristina Adams, James Campbell, Mark Kayser, Des King, Pat Lynch, Iain McLean, Clive Payne, Laura Stoker, and Steve Yoder for comments on earlier incarnations.

1. In trial-heat polls, people are asked how they would vote "if the election 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 vote is 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 are objective: average weekly hours worked in manufacturing, average weekly claims for unemployment insurance, manufacturers' new orders for consumer goods and materials, manufacturers' new orders for nondefense capital goods, building permits for new private housing, Standard and Poor's 500 stock prices, the money supply (M2), the interest rate spread between 10-year Treasury bonds and federal funds, and vendor performance (slower deliveries diffusion index). The one perceptual component is the index of consumer

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

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

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

8. These political variables measure the incumbent party share of the two-party vote, ignoring third party candidates such as Nader. The poll data are from Wlezien and Erikson (2002), and are available through the Inter-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 which data is available. The quarter 16 measure is the percentage of the public that approves of the president in October or the most recent month for which 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 standard errors of the coefficients and thus is larger than the standard error of the estimate (also see Beck 2000).

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

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

15. Besides, approval does not add to our forecasting power during the final stages of the campaign, and decreasingly so as the cycle evolves (see Table 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 estimated a vote equation containing this predicted vote variable and the final pre-election polls. From this analysis we see that the effect of the polls is statistically significant (b = .49; s.e. = .16) and the effect of the predicted vote (b = .40; s.e. = .20) actually is not. The same is true when either the presidential incumbency variable or Abramowitz's "time for a change" is included in the equation.

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

References Abramowitz, 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 and Presidential Elections: An Update and Extension." Public Opinion Quarterly 47:325-328.

Brown, Lloyd B., and Henry W. Chappell Jr. 1999. "Forecasting Using History and the Polls." International Journal of Forecasting 15:127-135.

Campbell, James. E. 2000. The American Campaign: U.S. Presidential Campaigns 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: Sage Publications.

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: Political Science 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. New York: 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 Horizons and Presidential Election Forecasts." American Politics Quarterly 24:49-50.

. 2002. "The Timeline of Presidential Election Campaigns." Journal of 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.

Appendix: The Measure of Leading Economic Indicators There 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 this manuscript, we rely.primarily on the newer, more updated, and presumably better series, available from The Conference Board. 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 on analysis 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- LEIl1)/LEII*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 rates through quarter 13 and then calculate the average. To calculate the average, we divide the sum of the weighted growth rates by the 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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