Optimal Economic Uncertainty Index

Optimal Economic Uncertainty Index Introduction Economic uncertainty states about the uncertain of future economic events. This means we cannot foresee what will happen to the country’s economic in the future and this is why this research is been investigate. There are many situations which caused the uncertainty to happen such as the changes in economic and financial policies, various views about the growth prospects, the productivity movements, wars and natural disasters (Bloom et al., 2013). The first innovative work which discussed about the effects of uncertainty is done by Knight (1921). Knight formalized a distinction between risk and uncertainty. Based on Knight’s research, risk was applies to the situations where is unpredicted but still can accurately calculate the odds. However, uncertainty applies to the situations where is unpredicted all the information we need with the purpose of set accurate odds. According to a theory of profit and entrepreneurship, Knight suggests that the function of the entreprene ur is to earn profit by undertaken the investments with uncertain outcome (Bewley, 2002). Thereafter, more and more researcher has constructed diverse models to investigate the uncertainty. Mises (1949) states that researchers never gave up in searching the best ways to reduce the problems which caused by the uncertainty. However, the results gain by most researchers which tested the uncertainty could not supply suitable and meaningful outcomes. This is because the researchers’ understanding and the modeling created has supply limited information from the empirical data and too much effort has been applied in explaining and estimating the characteristic of human beings under variety situations (Ellsberg 1961; Epstein 1999). However, an open question remains as to whether or not research in this area could better our knowledge of economic turbulences and other utmost uncertainties and improve the scientific accuracy of economic theory. There are fewer of empirical studies on the economic uncertainty index depend on normative analysis. This type of analysis creates value judgments about the economy or the goals of public policy to be archive. (Caplin and Schotte 2008). Thus, normative analysis cannot be used to refute the precision of the economic uncertainty index (Gan 2014). Monetary conditions indexes (MCI) have become famous in several countries over the past few years as a useful tool and indicator for the stance of monetary policy. Although MCI played as an alternative monetary policy rule sparked intense debate and yet whether a consensus can be reached remained unknown at the beginning however it has been losing its magnet effect on the economy from the past years. The main factor of this change would be exchange rate, which is merely determined by the market. Gan (2014) conducted a paper namely the optimal economic uncertainty index constructed is one of many in the economic uncertainty literatures, which the optimal form may reflect the true economic conditions. This study will apply two monetary policy rules which are MCI and OEUI in selected Asian countries to examine which rules would facilitate achieving the best economic outcomes and lead to rational and wise policy decisions through a comparison. Matters of the study The argument of monetary policy rules in an economic uncertainty A good monetary policy rule is very important to the economy. It can help the economy respond efficiently to economic upheavals by limiting the gap of actual economy activity from its equilibrium, without significantly changing the ultimate goals of monetary policy. As stressed by Svensson (2000), monetary policy rule can either describe a systematic response of policy instrument to events in the economy (e.g., changes in macro variables and changes in policy variables), or it can suggest a specific economic outcome, or it can target the central bank’s goals (e.g. output and/or inflation). This behaviour of the policy rule can be inferred only in the context of a full model that links the policy instrument to the targeting variables included in the rule. Levin, Wieland, and Williams (2001) investigated the performance of forecast-based monetary policy rules by using five macroeconomic models that present a wide range of views on aggregate dynamics. They categorize the characteristics of rules that are robust to model uncertainty. However, the performance of monetary policy rule in an economic uncertainty is still under debate. Dotsey and Plosser (2012) examine the design of monetary policy rules in an environment. The argument is lingering around the issue that people have only an imperfect knowledge of the economy to confront challenges arising from various forms of economic uncertainty. The inadequate of non-optimal monetary policy rules in an economic uncertainty Simple monetary policy rules – non-optimal – such as Taylor (1993) may involve a reaction to variables other than conventional variables such as inflation gap and output gap, which are considered intuitively relevant to the conduct of monetary policy. Generally, these rules are similar to those discussed in Clarida et al. (1998). The great virtue of the simple rules is simplicity, which makes them easy to understand. Simple rules still can be used in central bank communication, although the public would not be able to verify the exact rule that is being followed. The rule can perform well in a wide range of models, which makes it robust to model uncertainty (Taylor, 2000). As stressed by Orphanides (2007: 11), some useful elements of policy design surface from historical analysis of TR: (i) good stabilization performance have a strong relation to the inflation’s reaction; (ii) good performance is associated with policy rules that show considerable inertia; (iii) a strong reaction to incorrectly measured output gaps has historically proven counterproductive; (iv) successful policy could still usefully incorporate information from real economic activity by focusing on the growth rate of the economy. Additionally, financial market analysts, scholars and central banks’ staff have been using monetary policy rules increasingly to forecast interest rates and to evaluate and describe central bank actions. Despite the fact that simple policy rules can often provide a good approximation to fully optimal policy under perfect information and are typically more robust to uncertainty (Cateau, 2007), simple rules have a number of weaknesses. Although their performance is rarely disastrous, they can involve large welfare losses relatively to fully optimal rules (Nikolov, 2002). In the similar vein, Svensson (2003) argued that a commitment to a simple instrument rule might be far from optimal in some circumstances. Crucially, simple rules are rarely optimal. Some studies, for example, Batini, Harrison and Millard (2003) argued that Taylor-type rules are not robust to open economy features. Taylor (2007) recognized that the TR is not supposed to be followed mechanically, but he also argued that monetary policy might deliver better results in terms of low inflation and output variability by staying closer to the rule. The failure of monetary conditions index Generally an MCI is a measurement of demand pressure; demand pressure is often measured by the output gap (output gap is the difference between current output and the estimated output in equilibrium level). The MCI is simple economic uncertainty measure because it involves gap variables in estimation; any estimation of gap variables is subject to considerably uncertainty (Gan, 2014). The equation of MCI[1] is a combination of interest rate gap[2] and exchange rate gap[3]. Thereby, increases in interest rates as well as increases in exchange rates[4] indicate a higher MCI figure and, therefore, tighter monetary conditions rested here. The estimation of the weights of the two variables in the MCI is at the heart of calculating of the MCI. The ratio of these weights would encode whether an appreciation in interest rates can be compensated along with depreciation in the exchange rate. The weights also reflect the relative impacts and changes of the interest rates as well as in the exchan ge rate affected on monetary conditions. Therefore, the MCI is generally viewed as a summative and informative tool for the public; meanwhile it serves as an early indicator for the central banks. However, the benign MCI played as an alternative monetary policy rule still sparked intense debate and yet whether a consensus can be reached remained unknown. Noticeably, the MCI has been losing its magnet effect on the economy from the past years. The most contributive factor of this change would be exchange rate, which is merely determined by the market. Further, the MCI mixes up two variables that are situated on two fundamentally different stages of the transmission process. The evidences can be briefly summarized by the application of the two MCI formerly leading advocates, the Bank of Canada (BOC) and the Reserve Bank of New Zealand (RBNZ). Since the early 1990s, BOC officially used MCI as an operating target (operating target is variable that the central bank influences directly by its monetary policy instruments[5]); however, the BOC did not directly control both the interest rate and the exchange rate simultaneously, but merely focused on the control of the interest rate. This means that central bank adjusted its interest rates to modify monetary conditions directly, which in turn is assumed to affect the exchange rate in various systematic approaches (usually via uncovered interest parity – UIP).[6] Due to its inability to target the exchange rate under MCI’s mechanical way, The RBNZ in 1999 gave up the concept of MCI may. The recurrent depreciation during the crisis and the evocable changes in the MCI were interpreted as signals of upcoming tightening that can cause long-term interest rates to rise and consequently exacerbate recessionary forces (Ito and Hayashi, 2004). To sum up, the concept of the MCI had lost a great part of its initial attractiveness in the end of the 1990s. The RNBZ abandoned published an MCI in March 1999 and concentrated its policy statements on a short-term interest rate. While the BOC continues to publish the MCI, its role in taking monetary policy decisions was reduced to that of many other indicators in recent years. The perform of optimal economic uncertainty In economics, optimal analysis is a normative analysis. It studies what the economic ought to be. However, the study about the optimal economic uncertainty is very limited. Most of the empirical studies are approximate form but not optimal. The optimal analysis is very common in most of the economic study such as the studies of monetary policy rule and others. In the late 1970s, the Federal Reserve stabilized the country’s economy by using the optimal Taylor Rule in the monetary policy. However, the Taylor Rule has misplaced the policy makers to face the real time data (Orphanides, 2003). This is because the optimal Taylor Rule only can perform better in the interest rate function. Giannoni (2000) investigated optimal policy rule in a simple forward-looking model, when the policymaker faces uncertainty about model parameters and shock processes. Other than that, Dieppe et al. (2004) examined the optimal monetary policy rule in the model of the euro area which is known as the ECB’s Area Wide Model, including a high degree of intrinsic perseverance and a restricted role for forward-looking anticipation. The optimal economic uncertainty index constructed by Gan (2014) is one of many in the economic uncertainty literatures, which the optimal form may reflect the true economic conditions. Since the economic uncertainty index is not observable. The rule of optimal economic uncertainty index suggests optimal economic uncertainty index can be computed by using grid search method based on small open economic model; a small open economic model is very close to a true economic model and it is not partial economic model. Motivation of the Study What is the response of optimal economic uncertainty to macro variables and policy variables? Until today, there are no policymakers (hereafter, central banks) have publish the optimal economic uncertainty index, for the reason they might be unwilling to publish such an index, or such an explicit formula simply does not exist. As stressed by Bernanke (2010a), economic engineering to address economic uncertainties needs to be improved. Optimality in this study can be given by particular specifications of the central bank’s loss function. Therefore, an optimal rule is the one that derived by minimizing a loss function. Two models are practically employed here. The first model is an optimal economic uncertainty index, which is proposed by Gan (2014). The second one is MCI concept which is based on the theoretical works by De Wet (2002). Thereby the optimal monetary responses can be derived. What is the welfare gain from taking into account the external variable? In order to answer this question, the study attempts to compare the derived optimal economic uncertainty index to a set that assumes with the exchange rate variable and a set that assumes without exchange rate variable. In line with this measure, one feasible approach is to measure the difference in the loss function values. For this purpose, the study would derive the optimal economic uncertainty index for a small open economic model in two setting, in other words, a model with the exchange rate variable and a model without the exchange rate variable. Objectives The objective is to examine the optimal economic uncertainty index (see Figure 1.1) while including the role of external variables – normative analysis. Specifically, this model is augmented and examined with exchange rates. The first model is the MCI of Bank of Canada is a variant of economic conditions rule, which can serve as a competitive rule; this rule consists exchange rates, interest rates and past output. The second model is the optimal economic uncertainty index based on a small open economy model, which was proposed by Gan (2014). Since the basic idea is to keep the rules at constant, i.e., MCI (De Wet, 2002) and optimal economic uncertainty index (Gan, 2014), these optimal rules would bring the economy back to its long-run equilibrium; the process can be indicated by MCI (i.e. an indicator that encompassed the total effects of interest rates and exchange rates), and by optimal economic uncertainty index (i.e. an indicator that encompassed the total effects of inter est rates, exchange rates, inflation and output at optimal level). This study also examines the dynamic profiles of monetary policy rules through a comparison. Eventually, this would enable us to draw an inference regarding which rules would facilitate achieving of the best economic outcomes and lead to rational and wise policy decisions and aid in assessing the behavior of the economic uncertainty in the future. Figure 1.1 Analytical Frameworks (theoretic framework of optimal economic uncertainty index) Figure 1 (a) 3D macro model and the optimal economic uncertainty index at zero uncertainty level of macroeconomic conditions. (b) 2D field of view of the optimal economic uncertainty index with zero uncertainty level of macroeconomic conditions. (c) 2D field of view of the negative-optimal economic uncertainty index with economic contraction. (d) 2D field of view of the positive-optimal economic uncertainty index with economic expansion. Significance of the Study The attainment of these objectives will certainly benefit the central banks. With the growing international mutual consent, both in academia and among central banks, maintaining the medium to long-term price stability is the overriding goal of monetary policy (Asian Development Bank, 2008). Research provides strong support in maintaining low and stable inflation, ultimately; this is beneficial for overall economic outcome (Mishikin, 2008). In line with this consensus, the proposed economic uncertainty index based on a small open economic model – suggested in this study may be considered by the central banks as an alternative framework with the end view of coming up with an improved and more effective economic policy strategy. This study provides exploration of two types of monetary policy rules in an economic uncertainty, the MCI of Bank of Canada and optimal economic uncertainty index based on a small open economic model of Gan (2014), where a set of parameters inducing different decision rules are determined. This study encompasses two macro variables (i.e., output and inflation) and two policy variables (i.e., interest rates and exchange rates) in the optimal model. Not only does this facilitate a better understanding of current policy actions, but it permits markets to better forecast the central bank’s future actions. The optimal policy in one setting may not be optimal policy in some other setting because there is no agreement on the best model of the economy (Plosser, 2008). The rules derived in this study can be contrasted. The optimal economic uncertainty index may outperform other rules (e.g., the Taylor rule and MCI rule) as it can be useful for the central banks. The proposed rule can serve asa policy of inflation targeting that in line with a welfare maximizing policy aiming at minimizing the output gap and inflation gap. This rule is superior that maintaining public confidence while giving monetary authorities to achieve the best economic outcomes in an optimal way. Conclusion This thesis is divided into five chapters. The current chapter sets the background and motivation for the thesis. Chapter 2 explores the theoretical and empirical literature on monetary policy rules with the aim of highlighting two research gaps that motivate the present study. Chapter 3 examines the MCI rule â€Å"true† policy reaction function through optimal TR, while Chapter 4 furthered the MCI rule by employing optimal economic uncertainty index based on a small open economic model. Lastly, Chapter 5 summarizes the major findings of the thesis and their implications, together with some recommendations for future study. [1] [2]Interest rates in the current period minus interest rates in the base period, i.e., changes in interest rates. [3]The level of the effective exchange rate minus the effective exchange rate in the base period divided by the level of the base period. i.e., changes in exchange rates. [4] A positive value for indicates appreciation of the domestic currency. [5] Cf. Burton and Lombra (2006: 671) and Thomas (2006: 608) for textbook definition. [6] Interest-rate-parity theorem states that the interest rate differential between two countries will be equal to the difference between the forward-exchange rate and the spot-exchange rate (equation), in other words, interest differential between two countries should be equally expected in term of the exchange rate change.