Technical Paper Series

Congressional Budget Office

Washington, DC

FORECASTING DEPOSIT GROWTH:

Forecasting BIF and SAIF Assessable and Insured Deposits

Albert D. Metz

Microeconomic and Financial Studies Division

Congressional Budget Office

Washington, DC

(Email: ALBERT.METZ@CBO.GOV)

July 2003

2003-6

Technical papers in this series are preliminary and are circulated to stimulate discussion and critical

comment. These papers are not subject to CBO’s formal review and editing processes. The analysis

and conclusions expressed in them are those of the authors and should not be interpreted as those of

the Congressional Budget Office. References in publications should be cleared with the author.

Papers in this series can be obtained at www.cbo.gov.
2

FORECASTING DEPOSIT GROWTH

This paper develops a new method of forecasting growth in the deposits of financial institutions for

use by the Congressional Budget Office (CBO) in projecting receipts and expenditures of the Federal Deposit

Insurance Corporation (FDIC). Below I present an approach to forecasting the various deposit categories

and include an example of out-of-sample forecasting performance. I briefly discuss the limitations of the

approach and the results of some alternatives, including issues of regime change and cointegration. In the

end, I propose a simple linear regression relating deposit growth to growth in a larger money aggregate (M2)

and interest rate spreads, despite some statistical concerns.

1. Background

The FDIC distinguishes four classes of deposits, and it is necessary to forecast each separately.

These classes include assessable and insured deposits from both the Bank Insurance Fund (BIF) and the

Savings Association Insurance Fund (SAIF). Assessable deposits are essentially total domestic deposits,

some of which (in amounts of $100,000 or less) are insured by the FDIC. Table 1 reports their values as of

2001. BIF assessable deposits are almost exactly four times as large as SAIF assessable deposits. In 2001,

the insurance coverage ratio was about 67 percent for BIF and 90 percent for SAIF.

Table 1

2001 FDIC Deposits (millions of dollars)

BIF SAIF

Assessable Insured Assessable Insured

3,584,610 2,408,878 897,278 801,849

Source: FDIC
3

One requirement of these forecasts is that they be consistent with other long-term CBO

macroeconomic forecasts. It is therefore sufficient to specify contemporaneous relationships between

deposits and macroeconomic variables currently forecast by CBO. In applications, one can then use the CBO

forecasts to generate (conditional) deposit projections.

1.1 Available Data

The FDIC has provided a time series of annual (calendar year end) observations of BIF assessable

and insured deposits from 1947 through 2001. Unfortunately, I have observations of SAIF deposits (of both

types) only since 1989, also at an annual frequency. Deposit insurance for thrifts used to be managed by

FSLIC but was moved under FDIC when FSLIC was dissolved. While deposit data for thrifts during the

FSLIC era can be gathered, they correspond to such a different structural regime that they would be of little

use in the current analysis.

I use data on the M2 aggregate from the Federal Reserve and yields on the three month and ten year

Treasury securities. The M2 time series begins in 1959. As Table 2 shows, M2 is not a perfect substitute

for assessable deposits, though both are related.

Table 2

Definition of M2

Category Value as of December, 2001 (billions of dollars)

M1 $1,179.3

Currency a 579.9

Travelers Checks a 7.8

Demand Deposits 330.4

Other Checkable Deposits 261.2

Retail Money Market Mutual Funds a 997.8

Savings Deposits 2,370.6

Small Time Deposits 973.3

Total: $5,521.0 a

 Not included in assessable or insured deposit base.
1

 Specifically, "growth" is the first difference in the log level.

4

In contrast, "Assessable Deposits" are total domestic deposits (time deposits, demand deposits, and savings

deposits, including money market deposit accounts at banks or thrifts and interest-bearing checking accounts)

with some adjustments for the location of the insured institution relative to the economic agent who owns

the deposits.

2. BIF Assessable Deposits

I begin by positing that growth in BIF assessable deposits (Deposits) may be related to growth in the

M2 aggregate, since M2 partly includes these Deposits with some additions and modifications. CBO already

generates an implicit forecast of M2, and thus almost generates an implicit forecast of Deposits.

Figure 1 indicates that M2 grows faster on average than do Deposits. Put differently, the ratio of

Deposits to M2 is decreasing as other components of M2 become more significant. Forecasting Deposit

growth by M2 growth "one-for-one" would not generally lead to accurate forecasts, particularly recently.

Instead, I can at least project Deposit growth on M2 growth and obtain a better linear predictor.

I further find that given growth in M2, Deposits will grow faster if short-term interest rates (proxied

by the three-month Treasury bill) are rising relative to long-term interest rates (proxied by the ten-year

Treasury bond). This leads us to propose the following simple relationship:

dbift

 = " + $1dm2t + $2t3mt + $3t10yt + ,t, (1)

where dbif is BIF assessable deposit growth, dm2 is M2 growth, t3m is the log of the three month T-Bill, and

t10y is the log of the ten year T-Bond.1

 Call this specification the Basic Model. Using data since 1959, I

estimate (1) by OLS and obtain the following (see Figure 2):
2

 One could argue that the interest rates are set exogenously on a "world market."

5

Table 3

Regression Results: Basic Model, 1959-2001

Variable Coefficient Standard Error* T-stat

Constant 4.01 2.34 1.71

dm2 0.94 0.14 6.90

t3m 4.59 1.27 3.62

t10y -6.21 1.79 -3.46

Durbin-Watson = 1.98, F(3,38) = 27.65, = 0.66 R2

* Unless otherwise indicated, I use Newey and West’s estimator for variance, which is robust to

autocorrelation and heteroskedasticity.

Other economic factors (such as real GDP growth, M1 or M3 aggregates, price index growth,

corporate profits, other interest rates, as well as lagged values of the dependent variable and other variables)

have been tested. None enters significantly.

2.1 Statistical Concerns

A simultaneous dependence likely exists between M2 and Deposits in this specification.2

 To believe

that this is a statistically well-specified regression relationship, one must argue that the rate of M2 growth

is set exogenously such that shocks to Deposit growth represent transfers across categories within M2 and

not additions to or subtractions from M2. Examples might include people switching from demand deposits

into cash, or drawing down retail money market accounts and increasing deposits. Otherwise, OLS estimates

are biased and inconsistent estimates of the marginal effects of the regressors on Deposit growth. However:

C There are no unbiased estimators available by any technique (though there are techniques, e.g.

Instrumental Variables, which correct for consistency. See below.)

C The OLS estimates are biased and inconsistent estimates of the marginal effects - but I really
6

have no interest in the marginal effects. The OLS estimates are unbiased and consistent

estimates of "something else" - some linear combination of the marginal effects. As I will not

use this relationship to perform counter-factual policy experiments, I do not need to properly

identify the true marginal effects.

C It may be better for current purposes (to minimize the mean squared forecasting error) to trade

consistency for variance in the estimates. For example, if I use lagged values to instrument for

M2 and the interest rates, the results B though arguably consistent B have greater variance.

2.2 Instrumental Variables Estimation

The arguments above notwithstanding, I also estimate (1) by using dm2t-1 as an instrument for dm2t

.

The results are as follows:

Table 4

Regression Results: Basic Model, IV estimation for dm2, 1959-2001

Variable Coefficient Standard Error T-stat

Constant 3.19 2.34 1.36

dm2t (dm2t-1) 1.03 0.20 5.22

t3m 4.73 1.30 3.65

t10y -6.24 1.92 -3.24

Durbin-Watson = 2.04, F(3,37) = 6.64, = 0.66 R2

The coefficients on t3m and t10y are unchanged as is, essentially, the intercept. The coefficient on dm2 is

not statistically distinct from the OLS results. Using this IV adjustment for the simultaneity between

Deposits and M2 does not materially impact the results. Restricting to data since 1975 also does not change

the results.

If I wish to be more conservative and also control for simultaneity between Deposits and the interest
7

rates, I can use their lags as instruments as well:

Table 5

Regression Results: Basic Model, IV estimation for all, 1959-2001

Variable Coefficient Standard Error T-stat

Constant 0.24 5.86 0.04

dm2t (dm2t-1) 1.11 0.40 2.81

t3mt (t3mt-1) 1.17 7.65 0.15

t10yt (t10yt-1) -1.87 8.30 -0.23

Durbin-Watson = 2.11, F(3,37) = 19.20, = 0.58 R2

Clearly, these are dramatically different results. They are also less reliable, in that using the sub-sample

since 1975 yields:

Table 6

Regression Results: Basic Model, IV estimation for all, 1975-2001

Variable Coefficient Standard Error T-stat

Constant -5.40 7.26 -0.74

dm2t (dm2t-1) 0.71 0.35 2.05

t3mt (t3mt-1) 1.17 8.79 0.13

t10yt (t10yt-1) 1.96 10.30 0.19

Durbin-Watson = 2.16, F(3,18) = 12.05, = 0.61 R2

If one is willing to accept that the interest rates are statistically exogenous, then correcting for

simultaneity between Deposits and M2 is of no real consequence, and one may use either the OLS or the IV

results. If, however, interest rates are believed to be endogenous in this specification, the subsequent IV

results are too unstable to be useful, and one would prefer the reduced variance of the OLS results,

inconsistency notwithstanding.
3 The nominal insurance ceiling of $10,000 in 1959 was increased to $15,000 in 1966, $20,000 in 1969, $40,000 in

1974, and finally to its current level of $100,000 in 1980.

8

2.3 Regime Changes

Although there may have been structural shifts in monetary policy since 1959, the question in this

case is not whether there are different relationships governing Deposit growth, M2 growth or interest rates,

but whether there is a different relationship governing Deposit growth given M2 growth and interest rates.

The very fact which raises statistical concerns over this specification - the partial overlap between Deposits

and M2 - should help to make this specification somewhat robust to regime changes in the larger economy.

Taking as given M2 growth, I am almost taking as given Deposit growth, and any "regime change" must be

a change in that relationship, controlling for interest rates.

I nevertheless proceed to test for evidence of structural shifts. One source of such shifts could be

that the statutory insurance cap has changed several times since 1959.3

 Allowing fixed effects for each of

these insurance regimes (except the first), I obtain:

Table 7

Regression Results: Basic Model, different Insurance Regimes, 1959-2001

Variable Coefficient Standard Error T-stat

Constant 2.80 2.15 1.30

dm2 0.92 0.13 7.05

t3m 3.99 1.40 2.85

t10y -4.97 2.19 -2.27

D-15k 1.37 0.99 1.38

D-20k 0.96 1.41 0.68

D-40k -0.62 1.71 -0.36

D-100k -0.29 1.34 -0.22

Durbin-Watson = 2.20, F(7,34) = 11.77, = 0.65 R2

Taken individually, none of the insurance regimes offers a statistically significant level adjustment. Taken

together, the hypothesis that all levels are equal cannot be rejected at any reasonable confidence level
9

(F(4,38) = 0.64). Notice also that the coefficient on dm2 is essentially unchanged (numerically and

statistically) from the Basic Model.

Another source of shifts could be money market mutual funds (MMMF’s) which began to compete

with deposits about the middle of the 1970's. This corresponds closely with an apparent structural shift in

Figure 1. Indeed, if I search numerically for a single regime change that maximizes fit, I would ultimately

allow for an adjustment in 1975. A Chow test of the hypothesis that all coefficients (slope and intercept) are

equal pre- and post-1975 yields F(4,34) = 1.12, and hence I would not reject that hypothesis. However, if

I test only whether there are different level effects pre- and post-1975, I obtain F(1,37) = 3.54. I could reject

the hypothesis of equal level effects at the 10 percent confidence level. In other words, while there is no

evidence of different slope effects pre- and post- 1975, a case could be made that the "average" growth (as

captured by the intercepts) is different, perhaps because of MMMF’s.

This result, of course, is related to the fact that even in the Basic Model, a 95 percent confidence

interval around the intercept ranges from -0.58 to 8.60. This relative indeterminacy in relating the average

growth in Deposits to M2 is consistent with the results on cointegration and is discussed further below.

2.4 Cointegration and the Long-Run Relationship

Figure 1 suggests that Deposits and M2 do not grow "one-for-one", and this observation is supported

by the fact that their log levels are not related by the cointegrating vector [1 -1]. However, this does not

mean that they are not related by some other vector.

A preliminary to any discussion of cointegration is to establish that the series in question are

integrated to begin with. I perform the standard Dickey-Fuller tests on the following equations, using the

log level of Deposits (bif) and M2 (m2):

bift

 = " + $bift-1 + (t + ,t, and (2)

m2t

 = " + $m2t-1 + (t + ,t. (3)
10

In both cases, I wish to test first the hypothesis that $ =1 (unit root), and second the joint hypothesis

that $ = 1, ( = 0 (unit root and no time trend). For equation (2), I have for the first test that F(1,39) = 0.006

and for the second that F(2,39) = 4.256. Compared with the appropriate critical values, I cannot reject either

hypothesis. In other words, there is good evidence that the Deposits series has a unit root but no time trend.

For equation (3), I have first that F(1,39) = 0.019 and second that F(2,39) = 2.258. The conclusions are the

same: the evidence is consistent with the notion that M2 has a unit root but no time trend.

Having established that the individual series are (probably) integrated of order 1, I proceed to test

whether the vector [1 -1] is a cointegrating vector. That is, I construct the residuals ut

 from:

ut

 = bift - m2t, (4)

and perform a standard Dickey-Fuller test of the hypothesis that u has a unit root. Specifically, from the

regression:

ut

 = " + $ut-1 + et, (5)

I test the hypothesis $ = 1. From (5), I have that F(1,40) = 0.153, and hence cannot reject the hypothesis.

In short, the evidence suggests that the vector [1 -1] is not a cointegrating vector for Deposits and M2.

I can attempt to estimate a different cointegrating vector (if there is one) by running the following

regression:

bift

 = " + $m2t + dt. (6)
11

Table 8

Regression Results: Deposits on M2, log level, 1959-2001

Variable Coefficient Standard Error T-stat

Constant 7.22 0.14 52.64

m2 0.93 0.02 48.78

Durbin-Watson = 0.18, = 1.00 R2

Not surprisingly, the coefficient on m2 is less than one, and the hypothesis that it is one is rejected. It is

interesting to note that the coefficient of m2 is identical to that of dm2 in the Basic Model.

I proceed to apply an augmented Dickey-Fuller test to the residuals d from equation (6). That is, I

test the hypothesis that D = 1 from the equation:

dt

 = Ddt-1 + $1)dt-1 + $2)dt-2 + .... + $L)dt-L + <t. (7)

I estimate (7) with L = 3. For the hypothesis that D = 1, I have that F(1,35) = 1.306, and hence cannot

reasonably reject the hypothesis. For the joint hypothesis that D = 1 and all other coefficients are zero, I have

that F(4,35) = 0.514, and hence cannot reject this joint hypothesis either. Ultimately, the data suggest that

Deposits and M2 are not cointegrated since there is no linear combination which is stationary.

If, disregarding these test results, I assume the series are cointegrated with vector [1 -0.93] as

estimated in Table 8 and include an error-correction term in the Basic Model, I obtain:

dbift

 = " + ((bift-1 - 0.93@m2t-1) + $1dm2t + $2t3mt + $3t10yt + ,t. (8)

The implication of the error-correction model is that as the log level of Deposits and M2 deviate from

their long-run relationship, the growth rate of Deposits adjusts accordingly. For example, if Deposits are
4

 The hypothesis is that ( should be negative.

12

high relative to M2, then the subsequent growth of Deposits should be less than otherwise.4 The results of

estimating (8) (using a demeaned error-correction term) are:

Table 9

Regression Results: Basic Model with Error Correction, 1959-2001

Variable Coefficient Standard Error T-stat

Constant 2.42 3.37 0.72

(bif-0.93m2)t-1 -6.27 13.61 -0.46

dm2 0.96 0.12 8.27

t3m 4.51 1.18 3.83

t10y -5.37 2.15 -2.50

Durbin-Watson = 1.94, F(4,37) = 20.52, = 0.66 R2

The error-correction term does not enter significantly, and the other results are effectively unchanged. If I

repeat the above, but impose the cointegrating relationship [1 -1] instead, the coefficient on the errorcorrection term is 1.17 (nominally the wrong sign - forecasts would tend to diverge) with a variance of 6.19

and a T-statistic of 0.19. Again, the other results are unchanged from the Basic Model.

Finally, if I wish to simultaneously estimate the cointegrating vector and include the error-correction

term, I can estimate the following:

dbift

 = " + (1bift-1 + (2m2t-1 + $1dm2t + $2t3mt + $3t10yt + ,t. (9)
13

Table 10

Regression Results: Model (3), 1959-2001

Variable Coefficient Standard Error T-stat

Constant 0.36 3.48 0.10

bift-1 -10.40 12.27 -0.85

m2t-1 9.10 11.34 0.80

dm2 0.92 0.12 7.44

t3m 3.96 1.31 3.03

t10y -3.66 2.50 -1.47

Durbin-Watson = 2.01, F(5,36) = 16.70, = 0.66 R2

These results imply a cointegrating vector of [1 -0.87] - different from Table 8 - and the error-correction term

is still not significant.

There is no evidence in these data of a single, stable relationship between Deposits and M2 at low

frequencies (i.e., no evidence that they are cointegrated) - though there clearly is one at higher frequencies.

Since the Basic Model specification is defined over growth rates, it zeros out the lower frequencies in both

Deposits and M2 and emphasizes the higher frequencies. As a result, it is consistent with many different

hypotheses of the long-run relationship between Deposits and M2. For example, if one has a strong opinion

that over a ten year horizon, Deposits and M2 should grow at the same average rate (but not necessarily the

same in any particular year), then one could adjust the intercept from the Basic Model results accordingly.

The results presented in Table 3 are the best linear predictor of growth in Deposits given these

regressors. By themselves, they may yield a wide variety of implications for the long-run relationship

between Deposits and M2 (or anything else, for that matter) depending on the interest rate levels. If one has

an a priori opinion as to what that relationship should be over the forecast horizon, one can adjust the

intercept as needed. However, the available data by themselves do not identify any such relationship in

particular, and certainly not over a ten-year horizon.
14

2.5 Holdout Performance

One simple alternative for forecasting Deposit growth is to use nominal GDP growth (dgdp) one-forone. I wish to compare the forecasting performance of the specification presented above with this alternative.

In sample, the Basic Model has a measured fit of = 0.66, whereas the best fit between R dbif and dgdp has 2 R2

= 0.11, and that best linear relationship is not "one for one." Regressing dbif on dgdp I obtain:

Table 11

Regression Results: dbif on dgdp, 1959-2001

Variable Coefficient Standard Error T-stat

Constant 2.61 1.64 1.59

dgdp 0.53 0.22 2.43

Durbin-Watson = 1.46, = 0.11 R2

The joint hypothesis that the intercept is 0 and the slope is 1 is rejected at the 5 percent confidence level

(F(2,40) = 3.37).

To test performance out-of-sample, I estimate the coefficients of the basic model using data from

1959-1991, inclusive B thus holding out the last ten years of available data. I then apply the estimated

coefficients to the actual data on M2 growth and Treasury Bill rates from 1992-2001, inclusive. The result

is the forecast of Deposit growth which would have resulted in 1991 had I had perfect forecasts of M2 and

Treasury Bill rates.

I compare this deposit growth forecast, dbif_ols, with the actual deposit growth data dbif, where:

dbif_olst

 = + dm2t + T3mt + T10yt α$ . (10) $

β1

$

β2

$

β3
5

 Strictly, I present the root mean squared error in Table 12 and the root mean squared percent error in Table 13.

15

I also compare the actual data dbif with the forecast under the alternative which assumes Deposit growth

is exactly nominal GDP growth:

dbif_gdpt

 = dgdpt. (11)

Finally, I convert the growth rates into levels. The results are presented below and in Figures 3-6. Also

reported is the root mean squared forecasting error.5

Table 12

Forecasting Performance: Growth Rates

Year dbif dbif_ols dbif_gdp=dgdp

1992 -0.3% -1.0% 5.4%

1993 -0.7 -0.2 5.0

1994 -1.2 0.0 6.0

1995 4.5 5.0 4.8

1996 2.5 4.6 5.4

1997 5.3 6.2 6.3

1998 7.3 9.5 5.4

1999 1.4 6.1 5.5

2000 9.1 8.0 5.8

2001 7.5 4.8 2.6

rmse: 2.1 4.3

Table 13

Forecasting Performance: Levels (billions of dollars)

Year BIF BIF_ols BIF_gdp

1992 2,512 2,494 2,660

1993 2,494 2,489 2,796

1994 2,464 2,488 2,970
16

1995 2,576 2,617 3,116

1996 2,642 2,740 3,289

1997 2,786 2,916 3,502

1998 2,996 3,207 3,697

1999 3,038 3,409 3,904

2000 3,327 3,693 4,136

2001 3,585 3,875 4,244

rmse: 6.52% 21.43%

The model forecast outperforms the GDP forecast. Regressing dbif on dbif_ols yields:

Table 14

Regression Results: dbif on dbif_ols, 1992-2001

Variable Coefficient Standard Error T-stat

Constant -0.26 1.07 -0.24

dbif_ols 0.88 0.19 4.52

Durbin-Watson = 1.53, = 0.68 R2

The results are numerically and statistically close to the hypothesis that " = 0 and $ = 1 (F(2,8) = 0.88).

However, regressing dbif on dbif_gdp yields:

Table 15

Regression Results: dbif on dbif_gdp, 1992-2001

Variable Coefficient Standard Error T-stat

Constant 8.75 6.59 1.33

dbif_gdp -1.00 1.24 -0.81

Durbin-Watson = 1.36, = -0.04 R2
17

3. BIF Insurable Deposits

Define μt

 as the ratio of insured BIF deposits to assessable BIF deposits at time t. I expect that μ will

rise as the statutory insurance coverage rises, ceteris paribus. Furthermore, since the uninsured deposits

belong to high net worth individuals and institutions, I speculate that these are more "financially savvy" than

most deposit holders (insured). This would suggest that they are more responsive to the economy and

interest rates. In particular, I expect that uninsured deposits are more sensitive to marginal fluctuations in

M2 (thus μ should fall as M2 growth increases), and they are more sensitive to interest rates (hence μ should

move with long-term rates and against short-term rates).

With this in mind, I regress μ on the same factors as the Basic Model and dummy the level of μ with

different insurance coverage regimes. The results are:

Table 16

Regression Results: BIF Insurance Ratio, 1959-2001

Variable Coefficient Standard Error T-stat

M2 -0.004 0.002 -2.09

T3m -0.022 0.029 -0.76

T10y 0.053 0.030 1.75

D-10k 0.546 0.030 18.31

D-15k 0.566 0.034 16.47

D-20k 0.593 0.039 15.24

D-40k 0.624 0.045 14.03

D-100k 0.700 0.044 15.91

Durbin-Watson = 0.85, F(7,34) = 72.63, = 0.92 R2

In short, μ is almost constant within any insurance regime (dummy variables), but there are numerically slight

fluctuations at the margin. Using the more robust standard errors as reported above, the interest rates do not

enter significantly. However, using the traditional OLS errors, they are individually significant at the 5

percent confidence level.
18

4. SAIF Assessable Deposits

The available time series on SAIF deposits is short (1989 B present), probably too short to model

separately. One approach to obtaining earlier data under FSLIC might be to use Federal Reserve data on

"total deposits," and assume that Total Deposits = BIF Deposits + SAIF Deposits, and thus SAIF = Total B

BIF. Using the Federal Reserve’s time series on savings deposits, small time deposits, large time deposits,

demand deposits, and other checkable deposits, I obtain the following:

Table 17

Assessable Deposits Data: FDIC and Federal Reserve Proxy

I II (II-I)/I

Year FDIC Fed % Difference

1989 3414.1 3131.6 -8.3%

1990 3415.7 3142.4 -8.0

1991 3330.7 3138.6 -5.8

1992 3273.2 3124.0 -4.6

1993 3220.1 3130.8 -2.8

1994 3184.6 3127.6 -1.8

1995 3318.5 3250.0 -2.1

1996 3350.9 3411.2 1.8

1997 3507.5 3629.0 3.5

1998 3747.8 3856.5 2.9

1999 3802.7 4037.8 6.2

2000 4150.6 4302.5 3.7

2001 4481.9 4653.4 3.8

Average: -0.9%

This measure both under- and over-states "BIF + SAIF." While it is almost correct "on average", its

implications for SAIF deposit levels in particular are inaccurate:
19

Table 18

SAIF Assessable Deposits Data: FDIC and Federal Reserve Proxy

I II (II-I)/I

Year SAIF Fed-BIF % Difference

1989 948.1 665.7 -29.8%

1990 874.7 601.5 -31.2

1991 810.7 618.5 -23.7

1992 760.9 611.7 -19.6

1993 726.5 637.2 -12.3

1994 720.8 663.8 -7.9

1995 742.5 674.0 -9.2

1996 708.7 769.1 8.5

1997 721.5 843.0 16.8

1998 751.4 860.1 14.5

1999 764.4 999.4 30.8

2000 823.8 975.8 18.4

2001 897.3 1068.8 19.1

Average: -2.0%

The implications for SAIF deposit growth rates are even more misleading.

Another argument against attempting to model SAIF deposit growth before 1989 is the disruption

of the 1980’s savings and loan collapse. Any model would need to control for this unique set of

circumstances.

A different approach would be to establish some reasonable "rules of thumb" relating SAIF deposits

to BIF deposits. The most obvious method would be some statement that SAIF deposits tend to be X percent

of BIF deposits. Using FDIC data, the ratio of SAIF to BIF deposits seems to be moving from a higher level

at the end of the 1980’s to a (seemingly) stable 25 percent:
20

Table 19

FDIC Data: Ratio of SAIF to BIF Assessable Deposits

Year SAIF/BIF

1989 38.4%

1990 34.4

1991 32.2

1992 30.3

1993 29.1

1994 29.3

1995 28.8

1996 26.8

1997 25.9

1998 25.1

1999 25.1

2000 24.8

2001 25.0

Since I don’t have much data on FDIC classified SAIF deposits and since it would be difficult to

construct a reliable econometric model even if I had, it may be that using a rule of thumb such as "SAIF =

0.25 x BIF" would be no worse than any other approach. This 25 percent rule has held since 1998, a period

which includes both boom and bust conditions. Such a simple rule is also consistent with recent regulations

which require those institutions which hold BIF and SAIF funds to grow them at the same rate.

Of course, as a forecast, such a rule would imply that SAIF deposit growth would exactly equal BIF

deposit growth in all but the first year if one starts from a point in time at which SAIF is not exactly 25

percent of BIF. It might be more satisfactory simply to assume equal growth rates in all years, regardless

of the implication for the levels.
21

5. SAIF Insurable Deposits

The same reasons which make it difficult to forecast SAIF assessable deposits (i.e., short time series

and unique S&L disruptions) make it difficult to translate them into insured deposits. Let be the ratio μt

S

of SAIF insured to assessable deposits at time t. The available data are:

Table 20

FDIC Data: Ratio of SAIF Insured to Assessable Deposits

SAIF

Year Insured/Assessable

1989 93.1%

1990 94.9

1991 95.8

1992 95.9

1993 95.7

1994 96.1

1995 95.8

1996 96.4

1997 95.7

1998 94.4

1999 93.1

2000 91.7

2001 89.4

SAIF assessable deposits were contracting from 1990-1994 and again in 1996. This contraction corresponds

to high and generally increasing insurance coverage ratios. Once SAIF assessable deposits begin increasing,

the insurance coverage ratio falls.

These results suggest four strategies:

1) Use the sample average coverage ratio (94.4 percent) to translate assessable into insured SAIF

deposits.
22

2) If the early sample was influenced by holdovers from the S&L debacle, use instead the most

recent average coverage ratio (89.4 percent).

3) Try and relate these coverage ratios to deposit growth during this same period. That is, regress

μS

 on growth in SAIF assessable deposits (dsaif) using FDIC data:

Table 21

Regression Results: μS

 on saif, 1989-2001

Variable Coefficient Standard Error T-stat

Constant 0.944 0.007 139.796

dsaif -0.003 0.001 -2.154

Durbin-Watson = 0.65, = 0.49 R2

Note that this is a regression with 12 data points.

Since the forecast of dsaif is going to be essentially the forecast of dbif, if I use the sample mean

of dbif in the above equation, I come up with an average SAIF insurance coverage ratio of 92.7

percent, putting it midway between the first two strategies.

4) Accept different levels of insurance coverage between SAIF and BIF deposits but assume that

whatever forces cause μ to rise/fall also cause μS

 to rise/fall, such that I can (linearly) use μ to

forecast μS

. This intuition is bolstered by the fact that over the 13 years for which there are

overlapping data, μ and μS

 correlate at 0.87. Thus, regress μS on μ to obtain:
23

Table 22

Regression Results: μS

 on μ

Variable Coefficient Standard Error T-stat

Constant 0.549 0.080 6.873

μ 0.532 0.108 4.935

Durbin-Watson = 0.53, = 0.73 R2

Using sample means of m2, t3m, and t10y in the model of μ implies that = 0.73, and this in μ$

turn implies that = 94.0 percent, close to the sample average of μS μ$ . S

The differences which would result from using these options are probably well within the initial error

of forecasting SAIF assessable deposits. Having said that, option four is somewhat satisfying. One may still

make ad-hoc adjustments to the level of μS

 (by reducing the intercept), but this is no worse than any other

ad-hoc approach one might take.

6. Conclusion

The model for BIF assessable deposit growth outperforms some simple, reasonable alternatives. The

model may yet be refined further, but it remains the best performing out-of-sample forecasting model. While

the model is subject to valid statistical criticism, I believe that the alternatives, such as IV estimation and

corrections for cointegration, present other problems of greater practical concern. So long as the estimated

relationship is used only for forecasting and not for counter-factual analysis, this recommended approach

should be useful.

There has existed a strong linear relationship between BIF insured and assessable deposits ( = R2

0.92), and I am confident that this may be exploited going forward, given an insurance coverage ratio. To

test the effects of changing insurance coverage, another approach would be required.

Data limitations restrict any acceptable econometric modeling for SAIF assessable deposit growth.
24

However, very recent history (1998-2001, inclusive) which has included very strong and very weak macroeconomic conditions, indicates that the relationship between SAIF and BIF assessable deposits has been

fairly stable at SAIF = 0.25 x BIF. This "rule of thumb," or the related rule of thumb that SAIF and BIF

growth rates should be exactly equal going forward, is probably no worse than any other approach, and is

consistent with some new regulations governing organizations operating both BIF and SAIF deposits.

It is also difficult to have confidence in any model relating SAIF insured and assessable deposits

since the time series is short and arguably not representative of the true process. However, for the data that

are available, the insurance ratio of BIF deposits is a good linear predictor of SAIF deposits (correlation

0.87), and a regression can give us at least a point of departure.
25

BIF Assessable Deposits: Share of M2 Aggregate

65%

70%

75%

80%

85%

90%

95%

1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000

% Share

Regression Data and Fit: BIF Assessable Deposit Growth

-2%

0%

2%

4%

6%

8%

10%

12%

14%

16%

1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000

Annual Growth Rate

Data Fit

Figure 1

Figure 2
26

Model Holdout Test: BIF Assessable Deposits

2,400,000

2,600,000

2,800,000

3,000,000

3,200,000

3,400,000

3,600,000

3,800,000

4,000,000

4,200,000

4,400,000

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

$ (millions)

Data Forecast

Model Holdout Test: BIF Assessable Deposit Growth

-2%

0%

2%

4%

6%

8%

10%

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

Annual Growth Rate

Data Forecast

Figure 3

Figure 4
27

GDP Holdout Test: BIF Assessable Deposits

2,400,000

2,600,000

2,800,000

3,000,000

3,200,000

3,400,000

3,600,000

3,800,000

4,000,000

4,200,000

4,400,000

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

$ (millions)

Data GDP Forecast

GDP Holdout Test: BIF Assessable Deposit Growth

-2%

0%

2%

4%

6%

8%

10%

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

Annual Growth Rate

Data GDP

Figure 5

Figure 6
28

REFERENCES

CBO Paper, May 2002. "Raising Federal Deposit Insurance Coverage."

Federal Deposit Insurance Corporation (2001). FDIC 2000 Annual Report.

Greene, William H. (1993). Econometric Analysis Second Edition. Prentice-Hall, Inc.

Hamilton, James D. (1994). Time Series Analysis. Princeton University Press.