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A QUANTITATIVE ANALYSIS OF CBI & FED CHAIR HAWKISHNESS

By Hansen Wetsel

APRIL 23, 2019 POSC 3410: DR. BUSBY WORD COUNT: 4341 INTRODUCTION The is the central bank of the . It consists of ~2,000 commercial banks, 12 regional banks in major metropolitan areas, a Federal Advisory Committee, the Board of Governors, and the Federal Open Committee (Mishkin 2016). Since its formation in 1913, the Fed’s principle functions include: “maintaining full employment, price stability, and moderate long-term interest rates through control (Legal Information Institute 2019).” Most observers would conclude that the decentralized organizational structure of the Federal Reserve should prevent any one entity from disproportionately influencing how U.S. is determined. However, this is not the case in practice. Atop the U.S. central banking hierarchy sits the Chairman of the Federal Reserve. This individual is chosen by the President of the United States and confirmed by the Senate to serve a four-year term. During their tenure, it is the Fed Chair’s responsibility to regulate the U.S. through a combination of strategies, which include buying and selling government bonds through the FOMC, setting interest rates for inter-bank lending, and paying interest on reserves held by banks within the Federal Reserve system (Cowen and Tabarrok 2015). Since the Fed Chair is appointed by the President, an agent beholden to the ebb and flow of political sentiments, concerns naturally arise as to how independent the Chairperson will be in their implementation of monetary policy. Central bank independence (CBI) and political business cycles (PBC) are well-documented phenomena that have pertinent applications for understanding the intersection between and political science. PBCs refers to when expansionist monetary policies are implemented, thus lowering unemployment and interest rates immediately prior to an election and improving the incumbent’s chances of being re-elected (Mishkin 2016). By linking monetary policy with the political fortunes of any agent, i.e. the President, short-run success is won at the expense of long-run financial stability. We can envision monetary policy as an extension of Newton’s Laws of Motion: for every action undertaken, an equal and opposite reaction is necessitated. Whenever expansionist policies are implemented in the absence of extenuating circumstances, re. negative supply or demand shocks, contractionist policies must be enacted to prevent an unsustainable rise in inflation and interest rates. When we consider that the economy is a dynamic construct with ebbs and flows of its own, any extraneous meddling in the administration of monetary policy exponentially increases the difficulty of an already complex task in maintaining economic stability. Central bank independence is a multi-faceted concept that can be disaggregated into two encompassing categories: instrumental and goal independence. In the context of the U.S. Federal Reserve, instrumental independence mainly refers to the Fed’s ability to conduct open market operations, or “the buying and selling of U.S. Treasury and government agency securities” (Mishkin 2016). All open market operations directly affect interest rates and the monetary base, with the latter resulting in an increase or decrease in the money supply. This is the preferred policy tool of the Federal Reserve because it can be precisely calibrated to attenuate market volatility and its effects are reversible. Goal independence is when the administrative agents of a central bank, e.g. the Board of Governors and the Fed Chair, determine what policy outcomes should be pursued through the implementation of monetary policy. The United States Federal Reserve possesses a considerable amount of instrumental independence and a marginal amount of goal independence, yet it is ultimately accountable to Congress on account of its formation 1 under the of 1913. However, aside from the three core functions enumerated at the outset of this paper, Congress permits the Fed considerable leeway in determining which policy objective(s) to pursue at any point in time depending on the U.S. financial market’s macroeconomic trends. Given the Fed Chair’s central role in determining what monetary policy should be undertaken in response to economic contextual factors, their behavior is subject to a considerable amount of scrutiny and analysis. Fed Chairpersons are often characterized as being either ‘hawks’ or ‘doves.’ The terms hawkish and dovish traditionally refer to the Fed’s tolerance for inflation. Whereas hawks prefer aggressive policies, maintaining high interest rates, to prevent asset , doves prefer low interest rates that boost aggregate by incentivizing banks to lend money more readily. Striking a moderate balance between these two policy preferences is what enables the Federal Reserve to attenuate business cycle volatility. Basic monetary theory argues in favor of gradually raising interest rates during economic booms so that there is room to lower them during a financial crisis. Regardless of the Fed’s course of action, raising, lowering, or keeping interest rates at their pre-existing level sends a clear, discernable signal to the business community about how policymakers view the overall health of the U.S. economy; are they bullish (optimistic) or bearish (pessimistic)? The interplay between these ‘animal spirits’ can substantially affect the trajectory of market cycles; negative or positive narratives become self-fulfilling prophecies. The relationship between CBI and efficacious monetary policy is well-established. Ergo, it is imperative that we conduct a thorough analysis of Fed Chair behavior to determine if there is any correlation between a particular set of policy preferences and independence (or a lack thereof) from the deleterious influences of opportunistic politicians. My research question is best summarized as: Is there a relationship between the level of hawkishness exhibited by a Fed Chair and their independence from the President who appointed them? As of this writing, is the current Chair of the Federal Reserve, appointed by President Trump, and expected to complete his four-year term in 2022. But the current President’s unorthodox criticism of Federal Reserve Policy, combined with his penchant for disregarding long-established precedent, is contributing to speculation that Trump might fire Powell if he believes Fed Policy is inhibiting an economic boom, which was one of the President’s central campaign promises. Whether this undue pressure results in a volte-face by Mr. Powell in his administration of monetary policy remains to be seen, yet there is already unsettling prima facie evidence that the Fed is kowtowing to demands from the White House; see (Reuters 2019). THEORY & EXPECTATIONS My study addresses a gap in political science and economics literature concerning CBI, PBCs, and their relationship to the level of hawkishness exhibited by the Chairperson of the Federal Reserve. Previous studies focused on cross-sectional comparisons of central banks in different countries to evaluate an independence measure for each bank observed; values were calculated according to data primarily gathered from IMF databases (Alesina and Summers 1993; Arnone et al. 2006; Bade and Parkin 1982; Cukierman 2002). These measures for CBI would then be grouped according to their regional location or graphed against various statistical indicators reflecting aggregate economic performance to determine if there was a covariate relationship between CBI and macroeconomic outcomes. The prevailing consensus amongst researchers is 2 that there is significant and substantive empirical evidence in favor of rejecting the null hypothesis of no relationship between inflation rates and central bank independence (Alesina and Summers 1993). Geographically, there is empirical evidence that the effect of CBI on monetary policy is more substantial at the margin in countries with strong democratic institutions, which will have to be controlled for in my analysis of the Federal Reserve (Bodea and Hicks 2015). Nevertheless, these relationships do not causally explain long-run trends in macroeconomic performance for the periods observed. Researchers also examined how political business cycles relate to CBI (Alesina et al. 1992; Clark and Arel-Bundock 2013; Fair 1978; Grier 1987). Past studies analyzed whether a central bank chairperson’s background, re. political affiliation, affected their implementation of monetary policy and whether there was any correlation with an election cycle; were policies enacted to favor the candidate of one political party over another (Clark and Arel-Bundock 2013)? Although Fed Chair policy preferences were typically closer to the median Republican position, there was not enough evidence to conclude that monetary policy favored one party over another or affected electoral outcomes. However, studies did find evidence that a recurring cycle of money supply expansion would occur immediately prior to an election (Grier 1987). Despite this evidence, we cannot definitively reject the null that monetary policy is conducted independently of election cycles due to inconsistent findings in competing studies of the relationship between PBCs and CBI. There is a clear paucity of studies in which relationships between Fed Chair hawkishness, central bank independence, and political business cycles are analyzed. In the course of my study, I expect to find compelling evidence that comports with the theory originally posited by Kenneth Rogoff; there should be a statistically significant, positive correlation between an individual chairperson’s hawkishness and the level of CBI measured for the Federal Reserve in a given period (Rogoff 1985). Specifically, hawkish Fed Chairs will resist political overtures to implement expansionist monetary policies that artificially inflate asset prices by lowering interest rates (making credit more accessible), thus increasing the probability of an incumbent President being reelected. The hypotheses that I will test in the design section include the following:

: There is no relationship between a Fed Chair’s level of hawkishness and the CBI score for the Federal Reserve in a given period.

: The Federal Reserve’s CBI score will positively correlate with a Fed Chair’s level of hawkishness. As a Fed Chair’s hawkishness increases, the Federal Reserve’s CBI score will increase.

: The presence of an ‘activist’ President who publicly pressures the Fed Chair to pursue looser monetary policies will cause the Federal Reserve’s CBI score to decrease. DESIGN PERIOD My empirical study will be a time-series analysis of Fed Chairpersons during the period 1945 to 2018, which comprises ten individuals listed in the table below with the presidents under whom they served and their tenure in office: Table I.

3 Chair President(s) Tenure Marriner S. Eccles FDR/Truman 11/15/1934 – 02/03/1948 Thomas B. McCabe Truman 04/15/1948 – 04/02/1951 William M. Martin Truman/Nixon 04/02/1951 – 02/01/1970 Arthur F. Burns Nixon/Carter 02/01/1920 – 01/31/1978 G. William Miller Carter 03/08/1978 – 08/06/1979 Carter/Reagan 08/06/1979 – 08/11/1987 Reagan/H.W. Bush/Clinton/W. Bush 08/11/1987 – 01/31/2006 W. Bush/Obama 02/01/2006 – 01/31/2014 Obama/Trump 02/03/2014 – 02/03/2018 Jerome Powell Trump 02/15/2018 – Present

I restrict my range of observations to the post-World War II era because of the U.S. Federal Reserve’s central role in the of international finance; the U.S. dollar, tied to the price of , became the global reserve against which all others were measured. The reasoning behind the implementation of this system was to facilitate global price stability and free trade amongst countries, i.e. globalization (Gerber 2018). Bretton Woods lasted until President Nixon decoupled the dollar from the under Executive Order 11615 to combat a combination of high unemployment and inflation, i.e. (Cowen and Tabarrok 2015). CBI and responsible monetary policy became even more important after the ‘Nixon Shock’ because every currency in the world now floats against the dollar in a fiat system where value is based upon trust and institutional credibility. DATA The three principle indicators of macroeconomic health incorporated into this study are the annualized rate of inflation, seasonally adjusted unemployment, and gross domestic product. Economic data were principally gathered from the Federal Reserve of St. Louis’ FRED database (https://fred.stlouisfed.org/). Historical rates of inflation, re. pre-1960 data, were collected from the of Minneapolis’ website (https://www.minneapolisfed.org/). An annual unemployment rate prior to 1948 is calculated from estimates provided by the U.S. Department of Labor’s Bureau of Labor Statistics (https://www.bls.gov/data/). Data on the effective Fed funds rate for calculations, see following section, is also collected from the FRED database. Freedom House and Polity IV scores for measuring how democratic the U.S. was during the period observed are collected from (https://freedomhouse.org/) and (http://www.systemicpeace.org/polityproject.html) respectively. METHODS To calculate a measure of central bank independence, I utilize the Cukierman and GMT methods (Cukierman et al. 1992; Grilli et al. 1991). CBI is a multi-faceted concept, and each of these methods emphasizes different factors in their respective operationalization of CBI. Ergo, a holistic analysis is only possible if we incorporate the most reliable and valid measures from prior studies. What follows is an overview of the coding values, their ranges, and how they correspond with the various components of CBI.

4 Cukierman recognizes that de jure laws and de facto practices are equally important in establishing a precedent of respect for the institutional inviolability of central bank independence. The first component of his CBI measure is legal independence, which consists of four thematic issues subdivided into sixteen constituent categories. Each issue type is part of a weighted index detailing how the bank chair is appointed and dismissed, how conflicts between the bank and executive branch are resolved, the bank’s objectives, and limitations on central bank lending (Cukierman et al. 1992). Each of the sixteen variables is then coded along a range from (0) to (1) where a value of (1) signifies the highest level of CBI. A reproduction of Cukierman’s original table with index weights and variable descriptions is included in the Appendix of this study. De facto CBI, i.e. whether a central bank operates independently in practice, is measured with expert survey data for seven variables with the same value range (0-1) as the metric for legal independence. Unfortunately, this survey was not administered to Federal Reserve specialists, which would be necessary for my study. Grilli, Masciandaro, and Tabellini disaggregate CBI into two primary components: political and economic independence. Political independence is synonymous with our understanding of goal independence, whereas economic independence is analogous to instrument independence. Grilli and his colleagues divide the variable for political independence into three parts pertaining to how the central bank’s administration is appointed, their relationship with the government, and legal statutes (Grilli et al. 1991). There are eight further variable subdivisions within each of the three parts previously enumerated; four in ‘appointments,’ two in ‘relationship with government,’ and two in ‘constitution.’ Each variable is dichotomous (1 = yes; 0 = no) and denoted by an asterisk if the central bank can be characterized as possessing the attribute in question. The number of asterisks is then summed into an index ranging from (0) to (8) where a value of (8) signifies complete political independence. This same type of dichotomous variable index is also applied to the authors’ measure of economic independence. However, economic independence is divided into two parts, ‘monetary financing of the budget deficit’ and ‘monetary instruments,’ with five variable subdivisions within the former and two variable subdivisions in the latter; the highest economic independence score is a value of (7). For a verbatim description of the variable subdivisions, please refer to the Appendix. My measure for Fed Chair hawkishness is predicated upon principles first elucidated by John Taylor’s formula for setting the optimal Fed funds rate (FFR) in relation to inflation and the output gap. The Fed funds rate is the interest charged on interbank lending that is set by the FOMC, and the output gap refers to the difference between potential and actual economic output (Mishkin 2016). A formulaic expression of this policy goal can be represented as: = + 0.5 + 0.5( − 2) + 2 Variable () signifies the optimal Fed funds rate, () is the annual inflation rate, and () is the output gap as represented by the following formula: = 100( − ∗)/∗ Variable () is the actual rate of GDP growth, and (∗) is the potential growth rate of GDP. Historically, the Fed sets a target inflation rate of two percent for optimal price stability, hence the inclusion of the integer value (2) in our formula for the Taylor Rule. For the purposes of this study, I modified Tom Wilson’s method of ranking Fed Chair dovishness according to an average of the prescribed Taylor Rule FFR and actual FFR; whereas he calculated one average

5 value for a Fed Chair’s entire tenure, I would calculate a hawkishness ranking for each year a Fed Chair was in office. This modification produces more sample observations ( = 73) and allows us to determine whether a chairperson who served under multiple presidents altered how they implemented monetary policy in response to political pressure. MODEL I will conduct a time-series OLS regression analysis to determine if there is a substantive and statistically significant relationship between my principal independent variable, Fed Chair hawkishness (), and dependent variable of interest, the Federal Reserve CBI-score (); how do changes in () affect ()? My goal in this study is to establish a line of best fit that minimizes the residuals of the stochastic component () when plotting () against () for each year in the period observed. Since Fed Chair hawkishness varies and the number of cases () exceeds the maximum number of estimated parameters, plus one, by a factor of nine (73 > 8), we can assume that () is normally distributed along a ‘68-95-99’ bell curve (Kellstedt and Whitten 2013). This normal distribution enables us to construct a range of variables, each with an equal probability of representing the true population value of interest, within a confidence interval (CI) at the (0.1), (0.05), or (0.01) percent-level. A (0.05) CI is the preferred level of significance for studies in the social sciences because it minimizes the likelihood that either a Type I or II Error occur: As we increase our CI from (0.1) to (0.01), we decrease the probability of rejecting a true null hypothesis, yet we increase the potential for not rejecting the null when some alternative is likely true (Hanna 2018). This study utilizes a multi-variate regression model that is formulaically expressed as: Model 1: = ′ + ′ + ′ Model 2: = ′′ + ′′ + ′′ + ′′ + ′′ + ′′ ∗ ∗ Model 3: = ′′′ + ′′′ + ′′′ + ′′′ + ′′′ + + + ′′′ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ Model 4: = + + + ( ∗ ) + + + + + + ∗ Model 1 is the simple bivariate regression model upon which I expound. Parameter () is the constant, i.e. y-intercept, CBI-score when Fed Chair hawkishness () is neutral, i.e. neither hawkish nor dovish. Parameter () is the slope, or how much () changes per 1-unit increase (decrease) of (). Parameter () represents the difference between the true population dependent variable and the sample dependent variable for each observation. Model 2 adds control variables for inflation (), unemployment (), and real GDP () to account for changes in Fed Chair behavior due to macroeconomic trends. Model 3 includes controls for democratic confounds () and () that are related to () and (); CBI and Fed Chair hawkishness could be attributed to a combination of de jure democratic statutes that safeguard institutional independence and a society’s de facto respect for precedent and the rule of law. This alternative explanation is consistent with constructivist theory and quantitative analyses of the relationship between democracy and CBI. Model 4 incorporates a dichotomous dummy variable to account for the presence of an ‘activist’ President who publicly exerts political pressure on the Fed Chair, whether through speeches, pronouncements, or some other mode of official communication, to pursue looser monetary policies. This interaction term allows for the

6 relationship between hawkishness and the Federal Reserve CBI score to differ depending on a given president’s modus operandi. If an activist president were the incumbent, we would expect to observe a diminished substantive relationship between () and () that lacks statistical significance because politicizing monetary policy would erode the Fed Chair’s credibility. Moreover, this would create a persistent market distortion, whereby any attempt to reassert CBI through the implementation of sound monetary policy would be discounted; public trust in the Federal Reserve would have to be re-established over the long run. Below is a variable index for reference purposes and to clarify how each variable is operationalized:

() Fed Chair hawkishness: difference between Taylor Rule FFR and Actual FFR; potential value range from a max (-100) to min (100); a negative sign indicates when short term interest rates were set above core inflation and vice versa for a positive sign. We assume that the Federal Reserve respects the zero-bound limitation, i.e. Actual FFR cannot be negative.

() Inflation rate: potential values range from min (0) to max (100); negative inflation (-100 to 0) known as deflation.

() Unemployment rate: potential values range from min (0) to max (100).

() Real GDP: consumer spending, plus firm investment, plus government spending, plus net exports (exports minus imports); measured in USD; potential values range from min (0) to max (∞).

() Polity IV Score: 21-point measure of democracy in a country with potential values ranging from most autocratic (-10) to most democratic (10).

() Freedom House Score: 7-point scale combining measures of civil rights and civil liberties to calculate the level of freedom enjoyed by individuals within a country from most free (1) to least free (7).

() Activist President Score: dichotomous dummy variable where (1) signifies that the President attempted to publicly influence monetary policy and (0) signifies that they did not. This score would be assigned after a historical analysis of media reports and official statements made by a President during their tenure. LIMITATIONS There are several methodological limitations inherent in an OLS time-series regression analysis that need to be tested for, lest our results be rendered spurious and invalid due to an unreliable process. Autocorrelation, “when the stochastic components in two or more cases are systematically related,” is especially prevalent in time-series analyses because the effect of an IV in one year, e.g. inflation in 1992, is not independent of past levels of the same measure; inflation in 1991, 1990, etc. (Kellstedt and Whitten 2013). To control for autocorrelation, I will incorporate a weighted geometric ‘Koyck’ lag term () indexed to 1913, the year when the U.S. Federal Reserve was chartered, for each economic IV during my period of observation; values for the lagged term will exponentially decrease from (1) to (0). Issues of multicollinearity, when two or more IVs are highly correlated and measure a nearly identical relationship with the DV, will be controlled for with a ‘VIF’ test in Stata; any variables with a VIF score above (5) will

7 undergo further evaluation to determine whether they should be dropped from the model. Heteroscedasticity is another potential problem during my period of interest because the standard errors for measuring CBI pre- and post-Nixon Shock (1971) could be unequally varied, i.e. () ≠ (). To control for heteroscedasticity, I will conduct a Breusch-Pagan Test, and if the results are statistically significant, then I will repeat the regression with Huber-White robust standard errors that are equivalent to “the square root of the elements along the diagonal of a covariance matrix” (Zaiontz 2019). RESULTS I would report my findings a regression table like this: CBI & FED CHAIR HAWKISHNESS Variables Model 1 Model 2 Model 3 Model 4

()

() Coefficient Coefficient Coefficient Coefficient SE SE SE SE

() Coefficient Coefficient Coefficient SE SE SE

() Coefficient Coefficient Coefficient SE SE SE

() Coefficient Coefficient Coefficient SE SE SE

() Coefficient Coefficient SE SE

() Coefficient Coefficient SE SE

() Coefficient SE

( ∗ ) Coefficient SE Observations ------ ------Residual SE --- (df = __) --- (df = __) --- (df = __) --- (df = __)

8 [Legend for Statistical Significance: p = (0.1)*, p = (0.05)**, p = (0.01)***] The substantive and statistical significance of Models 1, 2, and 3 can be inferred from the sign and/or values of the coefficients and whether there are asterisks next to them. We would expect to find a positive, statistically significant relationship between Fed Chair hawkishness () and CBI (). However, the interaction term ( ∗ ) distinguishes between Presidents that publicly pressured the Fed to lower the FFR and those that did not, thus yielding different trend lines in our regression model; the sign of our primary IV should change from (+) to (–) if the interaction term value is equal to (1). Moreover, we would not be able to determine whether the results were statistically significant in Model 4 without graphing a margins plot. If there are overlapping confidence intervals present, we would not be able to reject the null hypothesis that a President who interferes in how monetary policy is implemented does not change the CBI score of the Federal Reserve. CONCLUSION: My study on the relationship between a Fed Chair’s level of hawkishness, political business cycles, and central bank independence addresses a gap in the field of : Does tighter monetary policy correlate with higher levels of CBI? Furthermore, would an ‘activist’ President who pressures a Fed Chair to pursue looser monetary policies result in decreased levels of CBI, or would institutional norms be resilient enough to attenuate the deleterious effects of political pressure? To test my hypotheses, I constructed a research design that sufficiently addresses each of the four causal hurdles: 1) A cursory literature review of past studies confirms that a country’s inflation rate is inversely related to how independent its central bank is. We can expound upon this supposition and posit that Fed Chair hawkishness, i.e. inflation aversion, would positively correlate with increased levels of CBI. 2) Endogeneity is a potential issue; de jure and de facto institutional norms that promote CBI could dictate how a Fed Chair implements monetary policy, which is why I control for this possibility in my regression model. 3) A time-series OLS regression model would provide me with empirical data that either corroborate or reject the null hypothesis. 4) Monetary policy is not conducted in a vacuum, which is why my study controls for potential confounds, re. dynamic processes in day-to-day economic activity. If there is compelling evidence in favor of confidently rejecting the null hypothesis, this would suggest that Fed Chair hawkishness positively correlates with higher levels of CBI, and the presence of an activist President negatively correlates with higher levels of CBI. Regardless of my findings, multiple iterations of this study would be required before arriving at any definitive conclusions about a relationship between Fed Chair hawkishness and CBI. Future studies should consider alternative measures, besides the Taylor Rule, for operationalizing Fed Chair hawkishness, in addition to constructing a continuum by which we could measure a President’s level of influence in the implementation of monetary policy. Furthermore, this research is important given recent political developments under the Trump Administration that suggest Federal Reserve Policy is becoming increasingly politicized.

9 REFERENCES Alesina et al. 1992. “Macroeconomic Policy and Elections in OECD Democracies.” NBER Working Paper No. 3830. Cambridge, MA. Alesina, Alberto and Lawrence H. Summers. 1993. “Central Bank Independence and Macroeconomic Performance: Some Comparative Evidence.” Journal of Money, Credit, and Banking. 25 (2):151-162. Arnone et al. 2006. “Measures of Central Bank Autonomy: Empirical Evidence for OECD, Developing, and Emerging Market Economies.” International Monetary Fund. Working Paper. Bade, R. and M. Parkin. 1988. “Central Bank Laws and Monetary Policy.” University of Western Ontario. Ontario, Canada. Bodea, Cristina and Raymond Hicks. 2015. “Price Stability and Central Bank Independence: Discipline, Credibility, and Democratic Institutions.” International Organization. 69 (1):35-61. Clark, William Roberts and Vincent Arel-Bundock. 2013. “Independent But Not Indifferent: Partisan Bias In Monetary Policy At The Fed.” Economics & Politics. 25 (1):1-26. Cowen, Tyler and Alex Tabarrok. 2015. Modern Principles: . Worth Publishers. New York, NY. Cukierman et al. 1992. “Measuring the Independence of Central Banks and Its Effect on Policy Outcomes.” The World Bank Economic Review. 6 (3):353-398. Cukierman et al. 2002. “Central Bank Reform, Liberalization, and Inflation in Transition Economies: An International Perspective.” Journal of . (49):237 264. Fair, Ray C. 1978. “The Effect of Economic Events on Votes for the President.” The Review of Economics and Statistics.” 60 (2):159-173. Gerber, James. 2018. . 7th ed. Pearson. New York, NY. Grier, Kevin B. 1987. “Presidential Elections and Federal Reserve Policy: An Empirical Test.” Southern Economic Journal. 54 (2):475-486. Grilli et al. 1991. “Political and Monetary Institutions and Public Financial Policies in the Industrial Countries.” . 6 (13):342-392. Hanna, Lee. 2018. Stat 2300. Clemson University. Clemson, SC. Kellstedt, Paul M. and Guy D. Whitten. 2013. The Fundamentals of Political Science Research. 2nd ed. Cambridge University Press. New York, NY. Legal Information Institute. 2019. “Maintenance of Long-Run Growth of Monetary and Credit Aggregates.” U.S. Code Title 12. Cornell Law School. https://www.law.cornell.edu/uscode/text/12/225a. Accessed on January 30th, 2019. Mishkin, Frederic S. 2016. The Economics of Money, Banking, and Financial Markets. 11th ed. Pearson. New York, NY. Rogoff, Kenneth. 1985. “The Optimal Degree of Commitment to an Intermediate Monetary Target.” Quarterly Journal of Economics. 100 (4):1169-1190. Schneider, Howard and Jason Lange. 2019. “In A Shift, Fed Says Will Be ‘Patient’ On Future Rate Hikes.” Reuters. https://www.reuters.com/article/us-usa-fed/in-a-shift-fed-says-will be-patient-on-future-rate-hikes-idUSKCN1PO0DO. Accessed on April 4th, 2019. Wilson, Linus. 2018. “A Dove to Hawk Ranking of the Martin to Yellen Federal Reserves.” Working Paper. University of Louisiana at Lafayette. Lafayette, LA. Zaiontz, Charles. 2019. “Robust Standard Errors.” Real Statistics Using Excel. http://www.real

10 statistics.com/multiple-regression/robust-standard-errors/. Accessed on April 15th, 2019.

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