preliminary evidence on the determinants of federal reserve open market operations

Public Choice 74: 317-338, 1992. © 1992 Kluwer Academic Publishers. Printed in the Netherlands.

Preliminary evidence on the determinants o f Federal Reserve

open market operations ~

HARINDER SINGH Department of Economics, San Diego State University, San Diego, CA 92182

PAUL ZAK Department of Economics, University of Pennsylvania, Philadelphia, PA 19104

Accepted 15 February 1991

1. Introduction

In the absence of a fixed policy rule, the Federal Reserve Bank (Fed) has significant discretion in setting monetary policy. The existence of professional "Fed- watchers" attests to the fact that Fed policy actions are difficult to interpret. Recent literature has focused on the motivations behind Fed policy making and the need for secrecy and ambiguity. We investigate one aspect of Fed behavior: the propensity of the Fed to buy and sell government securities beyond that required to implement stated policies. Milton Friedman (1982: 26) called attention to this issue when he wrote:

In the year 1980, the Federal Reserve made gross open market purchases of securities of something over $800 billion, and gross transactions, including sales or maturities being rolled over, of more than double that amount. The net change in the portfolio was $4.5 billion. The open market desk therefore made $184 worth of purchases gross and roughly twice that amount of transactions (purchases plus sales) in order to add one dollar to its portfolio. Why all this churning? It accounts for something like one-quarter to one-half of all the transactions of U.S. government securities dealers other than the Fed itself. It generates millions of dollars of fees for the dealers involved.

In 1989 the gross open market purchases of securities by the Fed reached nearly 1.5 trillion dollars, an increase of 84°7o in 9 years. Figure 1 depicts the accelera- tion of Fed open market purchases in the last few decades. The question Fried- man asked in 1982 is all the more relevant today: Why all this churning?

* We are grateful to Marcelle Chauvet, Marvin Goodfriend, Stefano Manzocchi, Jiannis Venieris

and an anonymous referee for comments. The usual caveat about errors applies.

318

billions

1600 1

1400 I

1200

1000

800

60O

40O

j 0 M ~ i ' ' l ' ' l ' ' l ' r ~ l ~ l l l ] r l l l l l l l [ l l l l

59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89

Figure 1. Federal Reserve open market purchases.

In an effort to address this question, we draw from two strands of the economics literature to construct a comprehensive framework. The first strand, developed by public choice economists, views the Fed as a utility maximizing bureau, subject to political and economic constraints. For instance, Mark Toma (1982) has investigated the connection between inflation and the institu- tional arrangements of the Fed. Shughart and Tollison (1983) focus on the inflationary bias generated by Fed's desire for amenities and more employees. Skaggs (1984) developed a utility maximizing model in which the Fed attempts to obtain "discretion" subject to various political/economic constraints. The public choice perspective (as it applies to Fed behavior) is discussed in detail by Toma and Toma (1986). From this viewpoint, churning enables the Fed to obtain more discretion by "muddying the waters" and may be positively related to public choice variables such as higher Fed earnings and expenses.

The second strand of the economics literature focuses on the informational content of Fed policies and the resultant effects on expectations formation. Cukierman and Meltzer (1986) model the Fed's preference for ambiguity in policy actions in order to facilitate monetary surprises. In this model, an optimal level of ambiguity balances the desire for credibility with the need for monetary surprises. Stein (1989) develops a model in which the Fed engages in "cheap talk" (restricts itself to ambiguous statements), so that it can manipu- late expectations and (possibly) pursue time-inconsistent policies. Time inconsistency allows the Fed to modify existing policies in response to changes in political pressures or other constraints. The Fed's need for secrecy, both in its decision-making and policy implementation is evaluated by Goodfriend (1986). He cites congressional pressure, as well as private law suits seeking (under the Freedom of Information Act) a more open decision-making process by the Fed.

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In an environment where secrecy is difficult to maintain, a more subtle approach may be to provide excessive and/or conflicting information. Generat- ing "noise" by churning the open market portfolio could be a convenient mechanism for this purpose. While discussing the (theoretical) possibility of a monetary authority profiting from private information by buying (selling) government securities at lower (higher) prices, Goodfriend (1986: 88, fn. 54) points out,

If a monetary authority responds to a single type of private information, as in this example, then it must also disguise its net open market interventions, so that the market can't infer the private information by directly observing open market operations.

Consequently, if the Fed's open market operations are partly motivated by increased earnings, churning may be necessary to ensure that the market does not infer the Fed's private information from its actions.

In summary, if the Fed churns its portfolio, the following goals could be ac- complished. (1) It "muddies the waters" making congressional supervision difficult, allowing the Fed to obtain discretion at a low cost. (2) Private information (known to the Fed) is not revealed by direct policy actions, instead it is obfuscated by churning, while the policy can still be enacted. (3) It increases the Fed's ability to generate monetary surprises and pursue time-inconsistent policies. (4) A constituency of security dealers is cultivated as pointed out by Friedman (1982). (5) It facilitates the realization of bureaucratic objectives (such as higher earnings on the government securities portfolio). It is clear, however, that open market operations are motivated by conventional economic reasons as well. In particular, the Fed operates so as to (1) offset changes in reserves caused by other market forces (defensive operations), and (2) implement policy changes to control inflation or stimulate the economy. One indica- tion of churning would be Fed purchases of government securities beyond that required to implement stated objectives (henceforth referred to as excess purchases). In Section 3 we examine the relationship between excess purchases and bureaucratic variables, but first a schematic of the Fed's decision-making process is presented.

2. A model of Federal Reserve decisions

Benjamin Friedman (1978) developed a dynamic model of Fed open market operations. He concludes that the controversy about the disclosure of information is not an economic but a political issue. However, his model is confined only to economic variables. We generalize the model to incorporate both

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bureaucratic and economic goals. Let U t represent the preferences of the Fed- eral Reserve Board, where

Ut = Ut (Et' Et + 1" • " ; Bt' Bt + 1" • ") (1)

is continuous, strictly increasing and strictly quasi-concave. U t is maximized by two sets of goals: E t is a vector of economic goals such as sustained and stable growth of real output, control of inflation, etc. B t is a vector of bureaucratic goals such as greater discretion and more organizational power. These objectives are subject to two constraints:

Et = gt (MPt, M P t - I ' ' "; Xt, X t - l ' " "; E t - l , Et-2"" "; r/t), (2)

Bt = ht (It, I t - l " "; Et-1, E t - 2 " "; Xt, Xt- l " " "; Bt-1, Bt-2; et), (3)

where:

MP t

Xt

I t

r/t

is a vector of monetary policy variables such as commercial bank reserves, federal funds rate, prices, etc. is a vector of exogenous factors beyond the Fed's control which affect economic/bureaucratic goals and may require defensive actions. is a vector of bureaucratic instruments used to achieve bureaucratic goals. These instruments include Fed earnings on its securities portfolio, higher Fed expenses, payments to the U.S. Treasury, developing a constituency of government security dealers, etc. and et are white noise error terms.

Lagged values available at time t are included in each function to incorporate the dynamic structure of the economic system. The behavioral relations Ut(. ), gt(') and ht(.) summarize the Fed's (subjective) expectation of the relationship between the objective variables and state variables, given all information available at time t. The relations are subscripted since optimal policies need not be time consistent. Following Kydland and Prescott (1977) time inconsistency may occur because current decisions depend on expectations of future policies. Moreover, changes in the open market committee or the political climate may induce discretionary deviations from a policy rule.

The maximization procedure leads to optimal growth paths for the economic variables {Et*} and bureaucratic variables IBt* I. From this the Fed can deduce the desired level of open market purchases required to obtain { Et* ] and {Bt* } . A third behavioral equation summarizes this relation as

Pt d = ft (MPt' M P t - I ' " ; Xt' X t - l " ' ; It' I t - l ' " ; ) , (4)

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where Pt d is the desired Fed purchases of government securities. Observe that bureaucratic instruments (It) directly influence Pt d. One clear implication is that the dynamic optimization problem faced by the Fed may not yield optimal economic goals if these conflict with bureaucratic goals - though these goals may often be in agreement.

3. Empirical analysis

The desired level of Fed purchases [Pt d } is modeled as a partial adjustment process for several reasons. First, the Fed requires time to recognize the need for policy changes and to develop a consensus for change. Second, the bureaucratic nature of the board may be a source of delay. For instance, Mounts and Sowell (1986) have provided evidence of changes in the monetary constitution, tenure and inertia affecting the targeted level of monetary growth. Third, Cukierman and Meltzer (1986) have shown that the Fed's desire to build credibility requires persistence in policy actions. Credibility is defined as the speed with which the public recognizes changes in the objectives of the policy maker. Finally, the dynamic nature of the economy and the empirical evidence of lag structures also suggest the need for a partial adjustment model. The policy feedback rule can be represented by a simple partial adjustment model:

Pt = Pt-1 + ~'(Pt d - Pt- l ) + et, (5)

where ;~ e (0,1) and e t is a random error term. A linear approximation of equation (4) (with current and previous period lags) is combined with equation (5). 1 Substituting defensive operations (DPt) needed to offset exogenous factors beyond Fed's control (Xt), we obtain:

Pt = Bo + B1MP t + B2DP t + B3I t + B4Pt_ l + #t" (6)

We estimate equation (6) by assuming that/x t is normally distributed and developing proxies for MP t, DP t and I t. The period of analysis is 1959-1989. Monthly observations are employed in order to capture rapid adjustments which may be subsumed in a longer observation period. Details of the variables are presented in Appendix 1. Nelson and Plosser (1982) have pointed out that an inappropriate procedure for detrending the data can cause serious specification biases. Following convention, the models are estimated in log form to reduce heteroscedasticity. A test is conducted to determine whether log of Fed gross purchases of government securities (LP) is trend stationary (the process can be expressed as a fixed function of time) or difference stationary (only the

322

first and higher differences of the process are stationary). We utilize the method proposed by Dickey and Fuller (1979) to estimate the equation

LP t = C o + CIT t + OlLPt_I + e t, (7)

where LP t is log (Pt) and T t is a linear time trend. For the process to be difference stationary the null hypothesis is that Pl =

1 and C 1 = 0. The test statistic for (Pl - l) is 9.23 and for C 1 is 8.69. Contrast- ed with the critical values cited by Fuller (1976), both test statistics are significant at the one percent level. Consequently, we accept the alternative hypothesis that the process is trend-stationary. The inclusion o f a time trend variable is the appropriate procedure for detrending.

Initially, the relationship between Fed purchases of government securities (LP) and Fed earnings on its government securities portfolios (LSEC) is analyzed. The Pearson correlation coefficient between LP and LSEC is 0.9609 (for the period 1959-1989). Details of the models estimated to investigate the relationship between excess purchases and LSEC are presented in Tables 1 and 2.

In M o d e l 1, the proxies for defensive operations are the percentage change in reserves and the percentage change in the federal funds rate (relative to the previous month). The Fed is known to utilize both the federal funds rate and reserves as operating targets. Consequently, greater volatility in these targets (within a month) require more defensive operations. Since the Fed can fine- tune the actual purchases and sales of government securities, the net monthly change in the portfolio is employed as the policy proxy. 2 Other control variables are log of consumer price index (LCPI), log of industrial production (LIP) and a time trend variable (Trend). 3 The lagged dependent variable (LP1) is highly significant across all specifications, indicating that a partial adjustment model is appropriate. LP1 also (indirectly) captures potentially omitted variables that may have influenced purchases in the previous period. Diagnostic checking revealed the presence of heteroscedasticity, particularly in the 1970s and 1980s (for instance the Breusch-Pagan test statistic for Model 1 was 48.66 compared to a criticial Chi-square value of 20.9). 4 Note that a relatively more heteroscedasticity error term in recent years may be a by-product of greater churning and coincides with the recent pressures on the Fed to eschew secrecy in its proceedings. In order to interpret the t-statistics correctly, the results are reported after White's (1980) heteroscedasticity correction. In addition, almost all equations exhibited first order autocorrelation. The reported Durbin's h- statistic (for equations with lagged dependent variables) and Durbin-Watson statistic (for other equations) indicate that 1 st order autocorrelation is statistically insignificant after correction.

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The null hypothesis (Ho: BLSEC < 0) of no significant positive relationship between excess purchases and Fed earnings on the securities portfolio is rejected in favor of the alternative hypothesis (HA: ]]LSEC ) 0) of a positive relationship at the 1%o level (t value of 3.01 vs. critical value of 2.33). In order to test the robustness of the result alternative specifications are estimated as follows:

Model 2: The possibility of a simultaneous equation bias is examined with a Hausman test, which involves including predicted security earnings (PSEC) as an additional explanatory variable in the basic equation (Model 1). PSEC is generated by regressing LSEC on industrial production, reserves, federal fund rate and time trend (R E -- 0.994). The t-value for PSEC is 1.41, implying that the null hypothesis of no simultaneity bias cannot be rejected at the 5% level. As an additional check, a two-stage least squares model (with 1st and 2nd order autoregressive variables as instruments) is estimated. The results indicate that the existence of a simultaneity bias (if any) does not significantly alter the results of Model 1.

Model 3: This specification requires some background. Levin and Meulen- dyke (1982) commenting on Friedman's article contend that his estimate of

open market purchases of $800 billion for 1980 is exaggerated because it also includes some "matched transactions" which are performed with foreign central banks. The breakdown for matched transactions arranged in the open market and those arranged directly with foreign central banks is not readily available. However, we peform a conservative test by constructing an alternative dependent variable: Net purchases (Net LP) = log (total purchases - all matched transactions). 5

Model 4: A different proxy for intra-month variations in Federal Funds Rate and reserves is employed to capture defensive operations: percentage changes relative to the annual mean.

Model 5: A third proxy for defensive operations, the range of changes in the Federal Funds Rate and Reserves during the month, is utilized. To test the robustness of the results, additional specifications to the basic equation (Model l) are reported in Table 2 (Models 6 to 12).

Model 6: One issue of concern is the temporal stability of parameter estimates. As Figure l indicates, there has been a major increase in the growth rate of Fed's open market purchases after the late nineteen sixties. After this period Fed purchases have risen sharply and consistenly with the exception of a $200 million decline in 1981-83. 6 In order to test the stability of the coefficient of Fed security earnings, an intercept and slope dummy (for lsec) is introduced in Model 6 in Table 2. This dummy variable (D1) is defined as one during the period in which Fed purchases grew rapidly (Jan. 1969-Dec. 1980 and Jan. 1984-Dec. 1989) and zero otherwise. 7 It can be seen from the results in Table 2 that both the intercept (D1) and the slope (DSEC1) dummy are statistically

324

insignificant. Fed security earnings (LSEC) is significant at the one percent level.

Model 7: In this model an alternative dummy variable (D2) specification is employed. D2 is defined as one for the period Jan. 1969 to Dec. 1989 and zero otherwise. The intercept and slope dummy are statistically insignificant in this specification. Fed security earnings is significant at the five percent level when the insignificant dummy variable is included in Model 7.

Model 8: It can be observed that the coefficient of the consumer price index (LCP1) is not very stable across different model specifications. An alternative specification is to estimate the equation in real values. The results of Model 8 reveal that real Fed security earnings (RLSEC) is significantly and positively related with real Fed purchases.

Model 9: An issue which needs further investigation is the dynamic lag structure of the independent variables. If the Fed is reacting defensively to the control variables, enough time has to be allowed for the Fed to recognize the changes and respond. Since theory does not provide any guidelines regarding the dynamic lag structure, all the independent variables are initially lagged for six quarters and the lagged variables with a test statistic of less than one are subsequently dropped (in conformity with the basic rule for maximizing R- square). The final reduced model is presented in Table 2. We do not seek to interpret the coefficients of the lagged independent variables because of mul- ticollinearity. Note that Fed security earnings is significant at the one percent level.

Model 10: Another issue of concern is the possibility of measurement error in the Fed security earnings variable (LSEC) since it is the monthly average of annual Fed profits on the sale of securities. Inquiries to the Fed and a perusal of Fed publications indicate that an alternate data set is not readily available. We test the possibility of measurement error by employing a special case of the more general Hausman's test (see Pyndick and Rubinfeld, 1990: 174-177 for details). The test involves a two-stage process. In the first stage, as in Model 2, predicted values of LSEC are generated by employing industrial production, CPI, Reserves, Federal Funds Rate and a time trend as explanatory variables. In the second stage, the residuals from this regression (RES) are included in our primary equation (Model 1). The results of Model 10 indicate that the measurement error is statistically significant (t-value of -2.71 for the RES variable). However, note that correcting the possibility of measurement error by the inclusion of the RES variable has marginally increased the coefficient on the Fed earnings variable (from 0.31 to 0.36) and Fed earnings continues to be highly significant (t-value of 3.19). This indicates that the measurement error bias, if any, favors the rejection of the null hypothesis, BLSEC = 0.

Model 11: Another specification for testing the direction of potential measurement error bias is to substitute predicted Fed security earnings (PSEC) as

325

a proxy for actual Fed earnings. The results of Model 11 indicate that the proxy for Fed earnings is significant (t-value of 3.33).

Model 12: One other issue that needs to be explored is that of causality. Un- fortunately, a statistical test of "causality" merely implies testing the lead-lag relationship. As Leamer points out, Granger causality only indicates that one variable precedes another. We do not regard this notion of precedence- causality as very important for our hypothesis. From the Public Choice viewpoint, the critical notion is that Fed purchases may be motivated by the desire to earn profits on its security portfolio. In other words, Fed security earnings are one rationale behind inflated Fed purchases of government securities. However, this does not necessarily imply that Fed security earnings should pre- cede or Granger-cause Fed purchases, since the planning horizon of the Fed is not known and may be subject to change over time. Nevertheless, following convention, we perform Granger causality tests for both the actual and proxy Fed earnings variable (PSEC) with total Fed purchases. In both cases, the results indicate that Fed earnings Granger-cause Fed purchases. The F-tests for the proxy variable are reported in Table 2.

In summary, out of eleven specifications in Tables 1 and 2, all but one indicate that the Fed security earnings variable is significantly and positively related to Fed security purchases at the one percent level. The only exception is Model 7 in which Fed earnings is significant at the five percent level. Note that in Model 7 the intercept and slope dummy are statistically insignificant but nevertheless retained in order to maintain proper control. As a general procedural rule, we include all the control variables of Model 1 in each specification for proper control, regardless of their significance level. This procedure results in a more rigorous test of the null hypothesis since (generally) the inclusion of irrelevant variables reduces efficiency and results in lower t-values.

Next, the relationship between Fed purchases (LP) and Fed expenses (LEXP) is investigated. The Pearson correlation coefficient between LP and LEXP is 0.961. Following the framework used for Fed security earnings specifications, we proceed to estimate the same twelve specifications for Fed expenses. The results in Tables 3 and 4 indicate that Fed expenses are statistically significant at the one percent level for nine specifications (Models, 1, 4, 5, 6, 7, 8, 9, l0 and 11), the five percent level for one specification (Model 2) and the ten percent level for the remaining specification (Model 3). The test for causality also indicates that Fed expenses Granger-cause Fed purchases (Model 12). 8

4. Concluding remarks

Our empirical investigations indicate that excess purchases of government securities by the Fed appear to be positively and significantly related to Federal

326

Reserve earnings on its securities portfolio. This relationship is robust across eleven model specifications. Excess purchases and Fed expenses also appear to be positively related, although the significance pattern is relatively less robust across the same eleven specifications. These preliminary results are consistent with public choice theory and provide indirect evidence supporting the theoretical models of information signaling such as those proposed by Cukierman and Meltzer (1986). Shughart and Tollison (1983) have analyzed the positive relationship between expansionary monetary policy (net Fed purchases) and Fed employment. Expansion is preferred because contractionary open market policies reduce Fed profits and available funds for hiring. On the other hand, our results show that excessive Fed transactions (after accounting for net Fed purchases and defensive operations) are positively related to Fed profits and expenses. These two results complement each other.

However, two caveats are in order. First, the data (particularly in the 1970s and 1980s) are subject to a high degree of volatility. Although we have attempt- ed to control for this variation by employing dummy variables and correcting for heteroscedasticity, the results are contingent on the success of these efforts. Second, the analysis is subject to the usual reservation about omitted variables and specification biases. We have employed a variety of specifications to assess the robustness of the results. It is possible that the proxies we have utilized for defensive operations, policy changes and bureaucratic variables may not capture the true relationship between these variables and Fed purchases. Conse- quently, the results should be regarded as tentative.

We do not regard public choice variables as a substitute for traditional monetary policy variables. Rather they are (potentially) additional determinants of Fed open market operations. In order to avoid specification errors, the relevance of these public choice variables should not be ruled out on a priori grounds, their significance needs to be investigated with alternative specifications. Moreover, (possible) adverse effects of churning on financial markets and investments, caused by the relatively higher levels of uncertainty, should also be analyzed.

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Notes

1. An equation incorporating two lags (of the dependent variable) was also derived and estimated. Various model specifications of this equation yielded results similar to those reported.

2. It is conceivable that the Fed could implement net policy changes on the basis of a different periodicity. However, seasonal dummies for quarterly changes are statistically insignificant and do not change the relevant coefficients.

3. The time trend variable may indirectly capture some elements of churning. However, its inclu-

sion in each estimated equation is desirable for control purposes. 4. In this connection, note that a shift of the data in terms of higher variability in the mid-1960s

and beyond is also noted by other researchers: Friedman and Schwartz (1982) and Klein (1978). We estimated the relationships after controlling for heteroscedasticity. Dummy variables included to capture a shift in the 1960s are statistically insignificant. See Models 6 and 7 in Table

2 for details. 5. The inclusion of a dummy variable (defined as zero for months when reported matched transac-

tions are zero and one otherwise) for model 3 leads to relatively less significant results for the public choice variables. Without the dummy variable Fed profits and Fed expensese are significant at the 1% level. With the inclusion of the dummy, Fed expenses is significant only at the 10% level and Fed security earnings is significant at the 1°70 level.

6. Note that the higher growth of Fed purchases after the late 1960s coincides with the relatively greater volatility in monetary variables and the need for more defensive action. The drop in the growth rate during 1981-1983 coincides with the 1982 recession. However, this period is also associated with greater pressures on the Fed to be less secretive in its deliberations/operations and an increased scrutiny implied by the public choice approach. These pressures may induce the Fed to employ more subtle methods to obtain discretion, such as greater churning. This paper attempts to disentangle the economic factors and bureaucratic incentives associated with

higher Fed purchases. 7. The move to higher growth rates in Fed purchases is initially gradual and occurs some time in

the late 1960s. The shift is identified to be in 1969 by finding the minimum of the log likelihood ratio (for alternative specifications of the dummy variables). See Brown, Durbin and Evans (1975) for more details about the search for a shift in structure.

8. The relationship between Fed purchases (LP) and Fed payments to the Treasury (LTR) was also investigated for models in Table 1. The Pearson correlation coefficient between LP and LTR is 0.953. The coefficients and the t-values (in parenthesis) of LTR for the exact five model

specifications (as in Table l) are as follows:

Model 1 [0.15 (LTR)]; Model 2 [0.13 (LTR)]; (1.52) (1.15)

Model 3 [0.75 (LTR)]; Model 4 [0.09 (LTR)] (2.35) (1.00)

Model 5 [0.11 (LTR)]. (1.21)

Note that all coefficients are positive. However, LTR is significant at the 1 °7o level only in Model 3 and at the 10°70 level in Model 1. Consequently, we focus attention on Fed security earnings and expenses.

References

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Appendix 1. Data specifics

Variable Definition Units/type Source of variable

LP Log of total purchases $ million of U.S. govt. securities

LP1 LP of previous month $ million Net LP Log of (total purchases less $ million

all matched transactions)

Fed. Res. Annual Report

Fed. Res. Annual Report Fed. Res. Annual Report

Appendix 1. (continued)


337

Policy

LSEC

Net monthly change in $ billion system open market acct. Log (monthly average of dollars annual profits on sale of securities)

LEXP Log (monthly average of dollars annual Fed expenses)

LTR Log (monthly average of dollars annual payments made to U.S. Treasury)

RESP Percentage change in reserves percentage (from previous month)

RESA Percentage change in reserves percentage (from annual average)

RESR Range of changes in $ million reserves (monthly)

FFP Percentage change of Federal percentage Funds Rate (from previous

month) FFA Percentage change of Federal percentage

Funds Rate (from annual average)

FFR Range of changes in Federal $ million Funds rate (monthly)

LCPI Log of Consumer Price Index Index LIP Log of Industrial Production Index D1 Jan. 1959-Dec. 1968, D1 = 0 Dummy

Jan. 1969-Dec. 1980, D1 = 1 Jan. 1981-Dec. 1983, D1 = 0 Jan. 1984-Dec. 1989, D1 = 1

DSEC1 Dl x LSEC Slope dummy DEXP1 D1 x LEXP Slope dummy D2 Jan. 1959-Dec. 1968, D2 = 0 Dummy

Jan. 1969-Dec. 1989, D2 = 1 DSEC2 D2 x LSEC Slope dummy DEXP2 D2 x LEXP Slope dummy PSEC log (predicted securities) Fitted regression

values RES LSEC-PSEC Regression

residuals PEXP log (Predicted Fed. Fitted regression

Expenses) values RES 1 LEXP-PEXP Regression

residuals





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Citbase data bank

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Construction Construction Construction

Construction Construction Estimation

Estimation

Estimation

Estimation

338

Append ix 1. (continued)


RLP log (Fed. Purchases/CPI) $ million Citibase data bank RPOLI- CY log (Policy/CPI) $ billion Citibase data bank RLSEC log (Security Earnings/CPI) dollars Citibase data bank REXP log (Fed. Expenses/CPI) dollars Citibase data bank RRESP 070 change in (reserves/CPI) percentage Citibase data bank

preliminary evidence on the determinants of federal reserve open market operations

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