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The global economic crash of financial markets in 2008 resulted in widespread international turmoil. Central Banks were forced into making decisions in relation to their policies and regulations; one being monetary policies. Monetary policies play a huge role in how financial markets fluctuate. The Taylor Rule is a model used to estimate what the interest rates of a countries economy should be and will be, depending on how certain changes in the economies occur. This review will highlight some of the key aspects of the Taylor rule and how it is an integral part of monetary policies.
The financial crisis has had a long-lasting effect on financial markets. Many countries today remain in a state of economic recovery. Ireland is a prime example of how the financial crash had a detrimental effect on a country’s economy. Since 2008, many of the worlds largest economies have made changes to their monetary policies and many people have attempted to conclude what is the optimal set of rules for an efficient monetary policy. Real exchange rates are linked with a set of fundamentals when the nominal interest rate reaction function (Taylor Rule) is applied to monetary policies. (Mark, 2009) There is evidence that the Taylor rule based model is a successful measure of the importance of specific factors in relation to real exchange rate fundamentals (Kim and Park, 2018). Monetary shocks and real exchange rates have a correlated relationship. Many studies have shown that policy shocks implemented within a one-year period have had a desired effect on the real exchange rates (Benigno, 2004). Interest rate smoothing policies and sticky relative pricing help provide an explanation for the correlation between monetary shocks and real exchange rates. In comparison to the work of (Bergin and Feenstra, 2001; Chari et al., 2002; Kollmann, 2001), (Benigno, 2004) shows results “that there is no relationship of proportionality between the time during which prices remain sticky and the persistence of the response of the real exchange rate”. Other results show that serial uncorrelated monetary policies with high nominal price stiffness are not sufficient in generating any form of continuity from monetary shocks.
Many studies that have been carried out over the years attempt to understand why monetary policies that have been put into action do not always follow the Taylor Rule forecasts or recommendations. McCallum’s Rule is another model that challenges the basis of the Taylor Rule. Central banks were at first mainly focused on the increase in money supply they needed at a yearly rate to meet price stability objectives. (Friedman, 1968). McCallum (1988) proposed the monetary base as an instrument and Taylor (1993) proposed the policy rate as an instrument. In comparison McCallum’s Rule does not incorporate real interest rates and output gaps into its model. (Jung, 2018). Although Taylor’s Rule has been a major component of Central bank’s monetary policy positions for many years, (Asso, Kahn, & Leeson, 2010) McCallum’s rule has been proven to be a suitable alternative in assessing Central Bank’s monetary policies (Esanov, Merkl, & de Souza, 2005; Patra and Kapur, 2012; Sun, Gan, & Hu, 2012). The recession forced many policymakers into adopting the use of monetary base in the decision-making process (McCallan’s Rule). Evidence shows that the addition of information perceived from the use of monetary aggregates can aid policymakers in achieving the objective of price stability (Masuch, Nicoletti-Altimari, Rostagno, & Pill, 2003). Additionally, after the financial crash in 2008 policies adopting the approach of money growth outperformed the Taylor Rule and it was strongly recommended that the money growth factor was to be considered by policymakers (Scharnagl, Gerberding, & Seitz, 2010). Although Taylor Rule has been a global standard for many years, it has faced criticism for its limitations. Hofmann & Bogdanova (2012) finds that actual interest rates since the turn of the century have not been at the levels estimated by the Taylor Rule. These results were caused by the increased level of uncertainty in forecasting output and inflation.
Over the year’s policymakers have attempted to gain a better insight into what is the optimal form of Taylor Rule. Roskelley (2016) claims that an augmented form of Taylor Rule gives a better estimation of inflation and output levels instead of a linear or non-linear approach. Taylor (1993) which was a monetary policy guideline for central banks for many years, does not take into account all of the variables that are included in the decision-making process associated with monetary policies. Branch (2014) incorporates the main principles of Taylor Rule along with the ability of policymakers to make calculated estimates based on the information they have access to. Branch calls it “nowcasting” Taylor Rule. With reference to the methods used by Engleberg, Manski, and Williams (2009), Clements (2010), and D’Amico and Orphanides (2008) he finds the average estimate of forecasted uncertainty within the Survey of Professional Forecasters (SPF). The results of the study are quite similar to those of Capistran (2008). Taylor Rule should be adapted to suit “policymakers averse to overpredicting inflation and the output gap.” (Branch, 2014). Siklos & Bohl (2009) views Taylor Rule with the same approach. They believe in forecasted evidence of what inflation and output should be and not just “forward-looking” estimates. Majority of Central Banks apply instruments with variables such as “real exchange rate, equity returns, and housing prices” (Siklos & Bohl, 2009) to establish any idea of the way markets will react to inflation and output rates. Roskelley (2016) findings show that the addition of nowcasting components to the Taylor rule (augmented) displays a clearer view “of linear and non-linear Taylor rules, both in and out-of-sample.” Rudebusch (2002) provides support to this claim that ignoring key information that is available to policymakers will result in inconsistent findings.
A trend associated with emerging countries and their monetary policies in global markets, is the discrete choice model (Nojkovic & Petrovic, 2015). The paper adopts the same discrete choice methodology of Hu and Philips (2004). Evidence suggests that the Central banks of emerging economies will adjust their policies in accordance with how the actual rate differs from the estimated rate in a discrete manner. Frömmel, Garabedian, & Schobert (2011) states that there was a shift from the central banks in these emerging economies from the Taylor rule (focus on interest rates) to a concentration on inflation levels. This shift in strategy provided these economies with a clearer view of the expected rates, than the Taylor Rule approach. Azienman, Hutchison & Noy (2011) analyses the effect of “inflation targeting (IT) in emerging markets”. There is evidence to support IT in emerging markets. The IMF (2005) carried out a study, referencing Ball & Sheridan (2005) that concluded with results showing an average reduction in inflation rates associated with countries engaging in IT. Likewise Gonçalves and Salles (2008), Lin and Ye (2009) came up with corresponding results using different methods. Central banks that focus their attention on IT have witnessed lower averages in inflation rates compared to those that do not. However, Brito & Bystedt (2010) contradict these findings. Too much capital is required to push disinflation. There is no congruent evidence that lowering inflation increases economic growth. Other studies such as Bernanke & Woodford (2005) support these findings, claiming that IT does not contribute to better results.
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