Effectiveness of Inflation Targeting in Turkey

1 downloads 0 Views 459KB Size Report
ABSTRACT: Inflation rate targeting (IT) has recently become a popular monetary policy tool to fight inflation, in advanced as well as emerging market economies, ...

Effectiveness of Inflation Targeting in Turkey Ismail H. Genc and Mehmet Balcilar ABSTRACT: Inflation rate targeting (IT) has recently become a popular monetary policy tool to fight inflation, in advanced as well as emerging market economies, by curtailing inflationary expectations. The evidence of IT’s role in anchoring expectations is mixed. In this paper, the authors quantitatively examine IT’s effectiveness in reducing Turkish inflation by comparing forecasted inflation to actual inflation. Furthermore, the possibility of a structural change following IT is examined. The authors show that observed levels of inflation would not have been any different from the forecasted ones if IT had not been adopted. They also fail to find a structural break in inflation at the time of the adoption of IT and conjecture that this might be due to the public’s earlier belief that the central bank’s policy would not be inflationary. KEY WORDS: ARMA, emerging markets, inflation stabilization, inflation targeting, structural break.

Inflation rate targeting (IT) has recently become a popular monetary policy tool to fight inflation in advanced as well as emerging market economies. What lies behind the switch to an IT scheme is the belief that expectations play an unquestionably significant role in the public’s attitude toward future inflation, which may restrain the effectiveness of the policies adopted by the monetary authority. The IT framework anchors inflationary expectations through transparency (Bergo 2004; Croce and Khan 2000; IMF 1999; Posen 2003), credibility (Carare and Stone 2003; Faust and Svensson 1998; Minella et al. 2003), and accountability (Walsh 1996, 2003). In a sense, controlling the formation of the public’s expectations should top the agenda of policymakers for successful implementation of the disinflation policies (Bernanke 2003; Huh 1997; Kadioglu et al. 2000; Meyer 2004; Piger and Thornton 2004; Woodford 2004). Therefore, advance announcements of the policy makers’ intentions based on firm commitments would greatly help anchor the public’s attitude by eliminating uncertainty and, thus, the guessing game for the public. Originally developed and adopted by New Zealand, IT’s success in lowering inflation has been fiercely debated. The evidence can be at best described as mixed, if not confusing. While some find IT successful as a policy to reduce inflation (Choi et al. 2003; Corbo et al. 2001; Johnson 2002; Kontonikas 2004; Mishkin and Posen 1997; Sabban et al. 2003), some others claim the opposite (Ammer and Freeman 1995; Ball and Sheridan 2003; Bernanke and Mihov 1998; Cecchetti and Ehrmann 1999; Genc 2009; Genc et al. 2007; Honda 2000; Hu 2003; Lee 1999; Rogoff 2003; Siklos 1999). However, there are inconclusive findings, such as those of Almeida and Goodhart (1998). These studies

Ismail H. Genc ([email protected]) is a professor of economics, American University of Sharjah, Sharjah, United Arab Emirates. Mehmet Balcilar ([email protected]) is a professor of economics at Eastern Mediterranean University, Famagusta, North Cyprus. The authors thank the participants in the seminar/workshop at the American University of Sharjah and the Bogazici University Center for Economics and Econometrics Conference on the Transformation of the Turkish Economy for their many useful comments. Any errors or omissions are the authors’. Emerging Markets Finance & Trade / November–December 2012, Vol. 48, Supplement 5, pp. 35–47. © 2013 M.E. Sharpe, Inc. All rights reserved. Permissions: www.copyright.com ISSN 1540–496X (print)/ISSN 1558–0938 (online) DOI: 10.2753/REE1540-496X4806S503

36  Emerging Markets Finance & Trade

Table 1. Descriptive statistics of inflation

Mean Maximum Minimum Standard deviation Observations




0.036 0.210 –0.004 0.026 144

0.008 0.019 –0.004 0.007 12

0.028 0.210 –0.007 0.026 156

Notes: “Pre-IT” (“Post-IT”) refers to the period before (after) IT was adopted. “Whole” corresponds to the full sample.

Figure 1. Monthly inflation rates in Turkey Note: The date IT was adopted is marked by the vertical line.

consider a large array of countries and methods of analysis, although Levin et al. (2004) specifically concentrate on emerging market economies and report with positive findings.1 This paper also focuses on the case of an emerging country, namely Turkey, which adopted IT as a policy framework after the failure of an orthodox policy in 2001. Turkey experienced long spells of high inflation (see Akin et al. 2008, 2010, 2011). Coupled with depreciation, the Turkish currency, the lira, collected a large number of zeros, which led to changing the denomination of money by dropping six zeros in January 2005.2 The Central Bank of Turkey switched from orthodox policies to IT in January 2006 to fight inflation. As shown in Table 1 and Figure 1, Turkish inflation decreased appreciably after the adoption of IT. Policymakers assigned the “success” to the switch to IT. But before making firm claims, one needs to statistically identify a change in the inflation regime following IT adoption. In this paper, IT’s effectiveness in reducing inflation is quantitatively examined by comparing forecasted inflation levels based on pre-IT levels to the actual inflation levels following the adoption of IT. Furthermore, the possibility of a structural change following the adoption of IT is examined. Thus, we attempt to achieve two goals: to contribute to the quantitative literature on the topic for emerging market economies and to enlighten policymakers in other countries pondering a similar policy shift.

November–December 2012, Volume 48, Supplement 5  37

Table 2. Mean equality tests Method

Degrees of freedom



ANOVA F-test Welch F-test*

(2, 350) (2, 61.813)

8.810 51.837

0.0002 0.0000

* Test allows for unequal variances.

Data and Methods Monthly data on the consumer price index (CPI) are collected from the Turkish Statistics Institute. The base year is 1994. Inflation, π, is calculated as pt = ln(CPIt /CPIt–1). The study covers 1994M1–2006M12. This period is divided into two subperiods. The first subperiod contains the observations prior to the implementation of IT in Turkey: 1994M1– 2005M12. The second subperiod, 2006M1–2006M12, covers the post-IT implementation era. The end of the sample period is determined in order to avoid the likely impact of the global economic crisis since the central bank changed its primary policy aim from fighting inflation to curbing the extent of the recession. As Figure 1 shows, Turkey adopted IT when inflation was low and quite stable. This is a common practice in IT countries. Furthermore, as shown in Table 1, the average inflation in the post-IT subperiod is lower than the pre-IT subperiod. So is the volatility of inflation, as measured by the standard deviation. To support that contention, we do a mean equality test with the results given in Table 2. The results of the mean equality test show that there is strong evidence that pre-IT inflation differs from post-IT inflation, because both the standard analysis of variance (ANOVA) and the Welch-adjusted ANOVA statistics carry probability values near zero. However, neither the visual observation nor the evidence provided by the unequal means is necessarily a reliable tool for making inferences about the statistical behavior of data, as for many well-known statistical reasons, the t‑tests are not very conclusive. Thus, we adopt an approach based on the autoregressive moving average (ARMA) model of inflation for the statistical analysis. In the first subperiod, that is, the pre-IT adoption period, we estimate a statistically acceptable ARMA model of inflation. This estimation is then employed to forecast the post-IT period, which corresponds to the remaining part of the sample. This yields the inflation levels that would have been expected if the IT had not been adopted. Hence, the impact of IT in Turkey is investigated by comparing the actual inflation levels obtained in the post-IT subperiod to the forecasted inflation levels obtained from the model in the pre-IT period. If the forecasted inflation were found to be statistically significantly above the actual inflation, the IT implementation would then be considered successful in lowering inflation. For the sake of the robustness of our findings, we also estimate a form of the regime-switching model, which incorporates pre- and post-IT subperiods in search of a structural break at the IT adoption date. More precisely, to achieve what is outlined above, in the first subperiod, we estimate a “mean equation” for inflation in the form of ARMA(p, q) models, where p and q represent the maximum autoregressive (AR) and moving average (MA) terms, respectively. To choose the appropriate model, we follow Box and Jenkins (1976). We allow up to twelve lags for both p and q, including the possibility of p = q = 0. This generates 169 models

38  Emerging Markets Finance & Trade

in total, and we choose the best model via the Akaike information criterion (AIC). We also do the same analysis using the Schwartz information criterion, but the results do not differ qualitatively. We then run a battery of diagnostics checks to ensure the statistical reliability of the results. After obtaining a robust model, we forecast the remaining data for the post-IT subperiod. The method used here is the one-step-ahead (static) forecast. That is, after forecasting a single period, we use the known values of the forecasted variable to forecast the next period. This is an appropriate approach, assuming that economic agents have rational expectations and market flexibility allows them to readjust their positions concerning inflationary expectations. It is worth noting that we also conduct multi-stepahead (dynamic) forecasts, which do not produce results statistically different from the one-step-ahead forecasts.3 Then, we compare actual to forecasted inflation in terms of levels and statistical significance. The alternative method is a form of the regime-switching model. Specifically speaking, we jointly estimate pre- and post-IT data in the form of πt = α 0 +

p =12

p =12

i =1

j =1

∑ α i π t − i + τ  β0 + ∑ β j π t − j  + ε t ,

where, to delineate pre- and post-IT subperiods, the dummy variable τ equals 0 before IT adoption and 1 after IT adoption. The α coefficients represent the pre-IT data, while the β coefficients are based on the post-IT data. In keeping with the above-mentioned methodology, we use AIC to pick the best model. The statistically significant β coefficients indicate a structural break in the data. In this case, IT is said to have a statistical impact on the inflation. Estimation Results The results of the econometric analysis are presented in Tables 3 and 4 and Figure 2. Specifically, Table 3 shows results for the best ARMA(p, q) model estimated for the preIT subperiod. Initially, AIC selected an ARMA(12, 7) model. However, we eliminated the most insignificant term at a time and continued to reestimate the next best ARM(p, q) model until we find a model with all statistically significant coefficients. The constant term is excluded from this process. Table 3 reports the final model. As shown in Table 3, the best ARMA model identified using the iterative elimination of insignificant terms is free from serial correlation, and has statistically significant coefficients. We employ this model to forecast the inflation in the post-IT subperiod. Figure 2 shows actual and forecasted inflation levels, as well as the lower and upper bounds of two standard error forecast intervals . The forecasted inflation levels are what otherwise might have occurred. Not only are actual and forecasted inflation levels statistically indistinguishable from each other, there is not a clear pattern between the two. Putting it differently, there is no statistical evidence to suggest that the actual inflation levels might have been any different if IT had not been adopted. Expanding the forecast period to 2010M7 does not alter our findings.4 We arrive at the same qualitative conclusion by excluding the constant from the estimation equation.5 To further investigate the robustness of our results, we conduct the regime-switching model estimation as stated above. The best model is shown in Table 4. The terms in the estimation process are selected using stepwise regression with the forward-addition

November–December 2012, Volume 48, Supplement 5  39

Table 3. ARMA(p, q) estimations Coefficient Constant AR(1) AR(9) AR(12) MA(6) Adj. R2 AIC AUTO

0.017 0.588 0.104 0.195 0.225 0.647 –5.871 17.910

Standard error


0.016 0.064 0.059 0.050 0.088

1.051 9.158 1.755 3.863 2.549

p-value of AUTO = 0.118

Notes: “AUTO” is the serial correlation Lagrange multiplier (Breusch–Godfrey) test on residuals with 12 lags. The null hypothesis of the test is that there is no serial correlation in the residuals up to 12 lags. “p‑value of AUTO” shows the probability value of this test.

Table 4. Regime-switching estimations Variable


Standard error



Constant α1 α6 α12

0.001 0.568 0.167 0.178

0.002 0.061 0.062 0.049

0.564 9.319 2.683 3.607

0.574 0.000 0.008 0.000


0.679 –5.924 12.181

p-value of AUTO = 0.431

Notes: “Prob” stands for the p-values of the estimated coefficients in the regime switching models. “α” and “β” refer to the coefficients of the estimated model as specified in the paper. “AUTO” is the serial correlation Lagrange multiplier (Breusch–Godfrey) test on residuals with 12 lags. The null hypothesis of the test is that there is no serial correlation in the residuals up to 12 lags. “p‑value of AUTO” shows the probability value of this test.

method. Table 4 reveals that no post-IT period coefficient appears in the estimation. In other words, all the post-IT coefficients are statistically indistinguishable from zero. Thus, there is no statistical evidence to show that there is a structural break in inflation at the IT adoption date. The experiment with the enlarged data set covering data until 2010M7 does not produce statistically different outcomes.6 To further examine the possibility of structural breaks due to IT’s introduction in January 2006, we reestimate the ARMA model reported in Table 3 over the whole sample 1994M1–2006M12 and test for structural breaks using the parameter stability tests developed by Andrews (1993) and Andrews and Ploberger (1994). All these tests are computed from the sequence of Chow F‑statistics that tests constant parameters against the alternative of a one-time structural change at each possible time in the sample. There are three tests proposed by Andrews (1993) and Andrews and Ploberger (1994), namely, Sup-F, Mean-F, and Exp-F. These tests require trimming at the ends of the sample. We trim 5 percent from both ends and

40  Emerging Markets Finance & Trade

Figure 2. Actual and forecasted inflation Notes: In this figure, “Y” refers to actual inflation, “EQAICF” is the forecasted inflation, and “LLA” and “ULA” stand for the lower and upper bounds of two standard error forecast intervals, respectively.

Table 5. Parameter stability tests

Mean-F Exp-F Sup-F


Bootstrap p-value

5.752 1.462 2.441

0.867 0.791 0.755

Note: p-value through 2000 bootstrap repetitions.

calculate the tests for the fraction of the sample in [0.05, 0.95]. In Table 5, the results of the tests for the inflation equation, along with the associated p‑values, are reported. The p‑values are obtained from a bootstrap approximation of the null distribution of the test statistics, constructed by means of a Monte Carlo simulation using 2,000 samples generated by the estimated model with constant parameters. Of the three tests, the Sup-LR tests parameter constancy against a one-time swift shift in the parameters. If the regime shift is gradual, then Mean-LR and Exp-LR, which assume that parameters follow a martingale process, are appropriate. The results for the sequential Sup-LR, Mean-LR, and Exp-LR tests reported in Table 5 suggest that there is no evidence of parameter nonconstancy or structural change in the estimated ARMA model of inflation. The p‑values are all above 0.70, presenting strong evidence in favor of structural stability. The findings of both methods indicate that there is no statistical evidence to support a significant decrease in inflation levels in Turkey after the adoption of IT at the beginning of 2006. This is the same result obtained by Honda (2000) for Canada, New Zealand, and the United Kingdom. Honda’s method, which is similar to that used in this study, was commended by Agenor (2000) as an empirical route to take to test the effectiveness of inflation targeting.

November–December 2012, Volume 48, Supplement 5  41

Table 6. Chow breakpoint test for 2006M1

F-statistic Log likelihood ratio Wald statistic


Degrees of freedom


0.592 3.145 1.529

5,133 5 5

0.706 0.678 0.910

Sample: 1995M2–2006M12.

Discussion Both the ARMA and regime-switching models discussed above point to a failure to find a structural break in inflation at the time of the adoption of IT in Turkey. As a matter of fact, a Chow breakpoint test for January 2006 points to a lack of a break on the specified date, as shown in Table 6. Furthermore, a series of Chow breakpoint tests run on a period of 2002M1–2006M6 indicate that the closest structural break date was in 2002M4.7 This result is complementary to the previously reported structural stability tests. In other words, if there were a structural break in Turkish inflation, it occurred well before the actual IT adoption date. Because of the known statistical concerns in Chow, we replicate this experiment using a Zivot–Andrews test (Zivot and Andrews 1992). The results of the so-called A and C versions of this test show a structural break in 2003M1.8 Both findings are almost identical. This discovery is consistent with the observation that the central bank started to implement an undeclared inflation-targeting framework as of the beginning of 2002 (Aktas et al. 2010). The aforementioned results suggest that the structural break we are looking for may have taken place prior to the official declaration of the adoption of IT. Therefore, we replicated the said tests assuming that the actual, though unofficial, IT adoption date was (1) 2002.1, (2) 2002.4, and (3) 2003.1, in line with the dates implied above. As shown in Figures 3a, 3b, and 3c, in all of these experiments, we observed that the hypothetical inflation rate, which could have been achieved without the adoption of IT, was above the actual level. Yet we still fail to detect a statistically significant difference between the two inflation rates. Obviously, we cannot identify the causes of the failure of the IT process in this sense. However, as mentioned before, monetary authorities tend to adopt an IT framework only after they have already achieved relatively low levels of inflation. But if people believe that the monetary authority’s inflation policy is credible, then they will probably adjust their inflationary expectations accordingly. So the announcement of an expected inflation level to go along with the IT policy will bear its fruit in lowering inflationary expectations even before the country moves into the IT era. Thus, we are not likely to see a structural break in the inflation levels immediately before and immediately after the IT era because of the behavior of the policy makers and other economic agents. In fact, the annual inflation level in Turkey decreased from about 55 percent in the 1990s to around 10 percent in 2004–6 (see Table 7). Indeed, the decline is even steady. This can definitely be designated as a successful policy. Yet the Turkish monetary authorities announced a 7 percent upper level of inflation for 2006. Nevertheless, actual inflation was 9.22 percent (the central bank itself reports 9.56 percent realized inflation in 2006). This is approximately 31.65 percent off the mark. Needless to say, the error in judgment is much higher than the mean inflation targeted by the central bank. The judgmental

42  Emerging Markets Finance & Trade

Figure 3a. Actual and forecasted inflation according to unofficial IT periods (2002M1)

Figure 3b. Actual and forecasted inflation according to unofficial IT periods (2002M4)

error is substantial. To put it differently, the amazing achievement of the central bank in fighting inflation has been turned into a political blunder. It is our opinion that IT was not the right policy framework for Turkey at the time of adoption. Granted inflation rate targeting is appreciably more difficult than interest rate targeting, since the latter is more easily controlled by the monetary authority than the former. Thus, IT requires more effort to successfully implement it. Conclusions IT is an important monetary policy issue that is debated in many countries, if not outright adopted, as a means to attain lower levels of inflation. In this study, we analyze the case

November–December 2012, Volume 48, Supplement 5  43

Figure 3c. Actual and forecasted inflation according to unofficial IT periods (2003M1)

Table 7. Annual inflation Date


1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

56.56 58.64 68.86 52.90 52.35 32.95 52.20 26.04 16.85 8.91 10.01 9.22

Note: Annual rate is calculated from the monthly rates.

of Turkey, which adopted IT in January 2006 as a policy tool after successful efforts at fighting extremely high levels of inflation. Methodologically speaking, we first divide the study period into estimation and forecasting subperiods. In the estimation subperiod, we model inflation using the ARMA models following the Box–Jenkins (1976) approach. A statistically adequate model of inflation is obtained, with which inflation levels for the forecast period are forecasted as if no IT policy had been adopted. Finally, we compare the actual and forecasted data. To make our conclusions more robust, we also estimate a form of the regime-switching models, which jointly considers pre- and post-IT period data to detect the possibility of a structural break indicating the impact of IT adoption. We find that the observed reductions in inflation levels cannot be attributed to the adoption of IT from the statistical viewpoint

44  Emerging Markets Finance & Trade

since the actual and forecasted inflation levels do not appear to be distinguishable from each other. Nor do we detect a regime switch in the data. Our research does not provide answers as to why this was the case, but our results are complementary to prior studies suggesting this outcome. We speculate that if policy makers and economic agents who take future inflation levels into account believe that targeted inflation levels are credible, then self-fulfilling prophecies will generate low levels of inflation even before an IT policy is formally adopted. Thus, we conclude that it is no surprise that no structural break exists between the pre- and post-IT policy adoption inflation levels. Another crucial issue in this discussion is whether Turkey did the right thing by adopting IT in January 2006. We show that the monetary authorities in Turkey failed to keep their promise regarding the announced inflation levels, as actual inflation surpassed what the central bank had announced. The reason, we believe, is that the central bank tied its hands by promising to adhere to a policy that they obviously failed to maintain. Unfortunately, success has been turned into failure, though we do not mean to advance this finding as a negative judgment for IT as a policy framework. Under different conditions, as shown by Levin et al. (2004), it could be a perfectly credible policy, even for emerging markets. Future studies can use multivariate models to determine the causes of the failure of IT in Turkey, where actual inflation levels exceeded preannounced levels. Notes 1. For studies in relation to the emerging market economies’ readiness to move to IT or other regimes, see generally Goncalves and Salles (2008), Masson et al. (1998), and Mishkin (2000, 2004); regarding Armenia, see Dabla-Norris and Floerkemeier (2006); regarding Armenia and Georgia, see Dabla-Norris et al. (2007); regarding Brazil, see Corbo and Schmidt-Hebbel (2001); regarding Georgia, see Bakradze and Billmeier (2007); regarding Ghana, see Alichi et al. (2009); regarding Indonesia, Philippines, South Korea, and Thailand, see Naqvi and Rizvi (2009); regarding Latin America, see Blanchard (2004) and Favero and Giavazzi (2004); regarding Nigeria, see Aliyu and Englama (2009); regarding Poland, see Gottschalk and Moore (2001); regarding South Africa, see Mitchell-Innes et al. (2007), Van Der Merwe (2004), and Woglom (2000); regarding Turkey, see Aktas et al. (2010); Basci et al. (2007), Culha et al. (2008), Hazirolan (1999), Kadioglu et al. (2000), and Tutar (2002). 2. Genc et al. (2008) find that this change in currency denomination did not cause a structural break in the Turkish inflation, in accordance with the “near rationality” model of Ball (2000) à la Akerlof and Yellen (1985). 3. Results are available from the authors. 4. Results are available from the authors. 5. Results are available from the authors. 6. Results are available from the authors. 7. Note that this is different from the parameter stability tests presented above. 8. Results are available from the authors.

References Agenor, P.R. 2000. “Monetary Policy Under Flexible Exchange Rates: An Introduction to Inflation Targeting.” Discussion Paper no. 2511, World Bank, Washington DC. Akerlof, G.A., and J.L. Yellen. 1985. “Can Small Deviations from Rationality Make Significant Differences to Economic Equilibria?” American Economic Review 75, no. 4: 708–720. Akin, G.G.; A.F. Aysan; and L. Yildiran. 2008. “Transformation of the Turkish Financial Sector in the Aftermath of the 2001 Crisis.” MPRA (Munich Personal RePEc Archive) Paper no. 17766, University Library of Munich, Munich.

November–December 2012, Volume 48, Supplement 5  45 Akin, G.G; A.F. Aysan; G.I. Kara; and L. Yildiran. 2010. “The Failure of Price Competition in the Turkish Credit Card Market.” Emerging Markets Finance & Trade 46, supp. (May–June): 23–35. ———. 2011. “Nonprice Competition in the Turkish Credit Card Market.” Contemporary Economic Policy 29, no. 4: 593–604. Aktas, Z.; N. Kaya; and U. Ozlale. 2010. “Coordination Between Monetary Policy and Fiscal Policy for an Inflation Targeting Emerging Market.” Journal of International Money and Finance 29, no. 1: 123–138. Alichi, A.; K. Clinton; J. Dagher; O. Kamenik; D. Laxton; and M. Mills. 2009. “A Model for Full-Fledged Inflation Targeting and Application to Ghana.” Working Paper no. WP/10/25, International Monetary Fund, Washington, DC. Aliyu, S.U.R., and A. Englama. 2009. “Is Nigeria Ready for Inflation Targeting?” MPRA Paper no. 14870, University Library of Munich, Munich (available at http://mpra.ub.uni-muenchen .de/14870/). Almeida, A., and C. Goodhart. 1998. “Does the Adoption of Inflation Targets Affect Central Bank Behaviour?” Banca Nazionale del Lavoro Quarterly Review 51, no. 204 (supp. March): 19–107. Ammer J., and R.T. Freeman. 1995. “Inflation Targeting in the 1990s: The Experiences of New Zealand, Canada, and the United Kingdom.” Journal of Economics and Business 47, no. 2: 165–192. Andrews, D.W.K. 1993. “Tests for Parameter Instability and Structural Change with Unknown Change Point.” Econometrica 61, no. 4: 821–856. Andrews, D.W.K., and W. Ploberger. 1994. “Optimal Tests When a Nuisance Parameter Is Present Only Under the Alternative.” Econometrica 62, no. 6: 1383–1414. Bakradze, G., and A. Billmeier. 2007. “Inflation Targeting in Georgia: Are We There Yet?” Working Paper no. WP/07/193, International Monetary Fund, Washington, DC. Ball, L. 2000. “Near-Rationality and Inflation in Two Monetary Regimes.” Working Paper no. 7988, National Bureau of Economic Research, Cambridge, MA. Ball, L., and N. Sheridan. 2003. “Does Inflation Targeting Matter.” Working Paper no. 9577, National Bureau of Economic Research, Cambridge, MA. Basci, E.; O. Ozel; and C. Sarikaya. 2007. “The Monetary Transmission Mechanism in Turkey: New Developments.” Research and Monetary Policy Department Working Paper no. 07/04, Central Bank of the Republic of Turkey, Ankara. Bergo, J. 2004. “Flexible Inflation Targeting.” Norges Bank Economic Bulletin 75, no. 2: 48–55. Bernanke, B.S. 2003. “A Perspective on Inflation Targeting.” Business Economics 38, no. 3: 7–15. Bernanke, B.S., and I. Mihov. 1998. “Measuring Monetary Policy.” Quarterly Journal of Economics 113, no. 3: 871–902. Blanchard, O. 2004. “Fiscal Dominance and Inflation Targeting: Lessons from Brazil.” Working Paper no. 10389, National Bureau of Economic Research, Cambridge, MA. Box, G.E.P., and G.M. Jenkins. 1976. Time Series Analysis: Forecasting and Control, rev. ed. Oakland, CA: Holden-Day. Carare, A., and M.R. Stone. 2003. “Inflation Targeting Regimes.” Working Paper no. WP/03/9, International Monetary Fund, Washington, DC. Cecchetti, S.G., and M. Ehrmann. 1999. “Does Inflation Targeting Increase Output Volatility? An International Comparison of Policymakers’ Preferences and Outcomes.” Working Paper no. 7426, National Bureau of Economic Research, Cambridge, MA. Choi, K.; C. Jung; and W. Shambra. 2003. “Macroeconomic Effects of Inflation Targeting Policy in New Zealand.” Economic Bulletin 5, no. 17: 1–6 (available at www.economicsbulletin. com/2003/volume5/EB-03E50003A.pdf). Corbo, V., and K. Schmidt-Hebbel. 2001. “Inflation Targeting in Latin America.” Working Paper no. 105, Central Bank of Chile, Santiago. Corbo, V.; O. Landerretche; and K. Schmidt-Hebbel. 2001. “Assessing Inflation Targeting After a Decade of World Experience.” Working Paper no. 51, Oesterreichische Nationalbank, Vienna. Croce, E., and M.S. Khan. 2000. “Monetary Regimes and Inflation Targeting.” Finance and Development 37, no. 3: 48–51.

46  Emerging Markets Finance & Trade Culha, O.; A. Culha; and R. Gonenc. 2008. “The Challenges of Monetary Policy in Turkey.” Economic Department Working Paper no. 646, Organization for Economic Cooperation and Development, Paris. Dabla-Norris, E., and H. Floerkemeier. 2006. “Transmission Mechanisms of Monetary Policy in Armenia: Evidence from VAR Analysis.” Working Paper no. 06/248, International Monetary Fund, Washington, DC. Dabla-Norris, E.; D. Kim; M. Zermeno; A. Billmeier; and V. Kramarenko. 2007. “Modalities of Moving to Inflation Targeting in Armenia and Georgia.” Working Paper no. 07/133, International Monetary Fund, Washington, DC. Faust, J.W., and L.E.O. Svensson. 1998. “Transparency and Credibility: Monetary Policy with Unobservable Goals.” Working Paper no. 6452, National Bureau of Economic Research, Cambridge, MA. Favero, C., and F. Giavazzi 2004. “Inflation Targeting and Debt: Lessons from Brazil.” Working Paper no. 10390, National Bureau of Economic Research, Cambridge, MA. Genc, I.H. 2009. “A Nonlinear Time Series Analysis of Inflation Targeting in Selected Countries.” International Research Journal of Finance and Economics 24: 237–241. Genc, I.H.; H. Sahin; and T. Erol. 2008. “Near Rationality in Turkish Inflation.” Asian-African Journal of Economics and Econometrics 8, no. 2: 153–164. Genc, I.H.; M. Lee; C.O. Rodriguez; and Z. Lutz. 2007. “Time Series Analysis of Inflation Targeting in Selected Countries.” Journal of Economic Policy Reform 10, no. 1: 15–27. Goncalves, C., and J. Salles. 2008. “Inflation Targeting in Emerging Economies: What Do the Data Say?” Journal of Development Economics 85, nos. 1–2: 312–318. Gottschalk, J., and D. Moore. 2001. “Implementing Inflation Targeting Regimes: The Case of Poland.” Journal of Comparative Economics 29, no. 1: 24–39. Hazirolan, U. 1999. “Inflation Targeting: Japanese Case and Prospects for Turkey.” Report, Undersecretaries of the Treasury, Ankara, Turkey. Honda, Y. 2000. “Some Tests on the Effects of Inflation Targeting in New Zealand, Canada, and the UK.” Economics Letters 66, no. 1: 1–6. Hu, Y. 2003. “Empirical Investigations of Inflation Targeting.” Working Paper no. 03–6, Institute for International Economics, Washington, DC. Huh, C. 1997. “Inflation Targeting.” Federal Reserve Bank of San Francisco Economic Letter no. 97‑04, February 7 (available at www.frbsf.org/econrsrch/wklyltr/el97-04.html). International Monetary Fund (IMF). 1999. Code of Good Practices on Transparency in Monetary and Fiscal Policies: Declaration of Principles. Washington, DC. Johnson, D.R. 2002. “The Effect of Inflation Targeting on the Behavior of Expected Inflation: Evidence from an 11 Country Panel.” Journal of Monetary Economics 49, no. 8: 1521–1538. Kadioglu, F.; N. Ozdemir; and G. Yilmaz. 2000. “Inflation Targeting in Developing Countries.” Discussion Paper, Central Bank of the Republic of Turkey, Ankara (available at www.tcmb .gov.tr/research/discus/dpaper50.pdf). Kontonikas, A. 2004. “Inflation and Inflation Uncertainty in the United Kingdom, Evidence from GARCH Modeling.” Economic Modeling 21, no. 3: 525–543. Lee, J. 1999. “Inflation Targeting in Practice: Further Evidence.” Contemporary Economic Policy 17, no. 3: 332–347. Levin, A.T.; F.M. Natalucci; and J.M. Piger. 2004. “The Macroeconomic Effects of Inflation Targeting.” Federal Reserve Bank of Saint Louis Review 86, no. 4: 51–80. Masson, P.; M. Savastano; and S. Sunil. 1998. “Can Inflation Targeting Be a Framework for Monetary Policy in Developing Countries?” Finance and Development 35, no. 1: 34–37. Meyer, L.H. 2004. “Practical Problems and Obstacles to Inflation Targeting.” Federal Reserve Bank of Saint Louis Review 86, no. 4: 151–160. Minella, A.; P.S. de Freitas; I. Goldfajn; and M.K. Muinhos. 2003. “Inflation Targeting in Brazil: Constructing Credibility Under Exchange Rate Volatility.” Journal of International Money and Finance 22, no. 7: 1015–1040. Mishkin, F.S. 2000. “Inflation Targeting in Emerging-Market Countries.” American Economic Review 90, no. 2: 105–109. ———. 2004. “Can Inflation Targeting Work in Emerging Market Countries?” Working Paper no. 10646, National Bureau of Economic Research, Cambridge, MA, July (available at www .nber.org/papers/w10646/).

November–December 2012, Volume 48, Supplement 5  47 Mishkin, F.S., and A.S. Posen. 1997. “Inflation Targeting: Lessons from Four Countries.” Federal Reserve Bank of New York Economic Policy Review 3, no. 3: 9–110. Mitchell-Innes, H.A.; M.J. Aziakpono; and A.P. Faure. 2007. “Inflation Targeting and the Fisher Effect in South Africa: An Empirical Investigation.” South African Journal of Economics 75, no. 4: 693–707. Naqvi, B., and S.K.A. Rizvi. 2009. “Inflation Targeting Framework: Is the Story Different for Asian Economies?” MPRA Paper no. 19546, University Library of Munich, Munich (available at http://mpra.ub.uni-muenchen.de/19546/). Piger, J.M., and D.L. Thornton. 2004. “Inflation Targeting: Prospects and Problems.” Federal Reserve Bank of St. Louis Review 86, no. 4: 3–13. Posen, A.S. 2003. “Six Practical Views of Central Bank Transparency.” In Essays in Honour of Charles Goodhart, Volume 1: Central Banking, Monetary Theory and Practice, ed. P. Mizen, pp. 153–172. Cheltenham, UK: Edward Elgar. Rogoff, K. 2003. “Globalization and Global Disinflation.” Federal Reserve Bank of Kansas City Economic Review 88, no. 4: 45–78. Sabban, C.V.; M.G. Rozada; and A. Powell. 2003. “A New Test for the Success of Inflation Targeting.” Working Paper no. 04/2003, Torcuato Di Tella University, Buenos Aires. Siklos, P.L. 1999. “Inflation Target Design: Changing Inflation Performance and Persistence in Industrialized Countries.” Paper presented at the Midwest Macroeconomics Conference, Federal Reserve Bank of St. Louis, April 17–19. Tutar, E. 2002. “Inflation Targeting in Developing Countries and Its Applicability to the Turkish Economy.” M.S. thesis, Virginia Polytechnic Institute and State University, Blacksburg. Van Der Merwe, E.J. 2004. “Inflation-Targeting in South Africa.” Occasional Paper no. 19, South African Reserve Bank, Pretoria. Walsh, C.E. 1996. “Accountability in Practice: Recent Monetary Policy in New Zealand.” Federal Reserve Bank of San Francisco Economic Letter no. 96-25, September 9 (available at www.frbsf.org/econrsrch/wklyltr/el96-25.html). ———. 2003. “Accountability, Transparency, and Inflation Targeting.” Journal of Money, Credit, and Banking 35, no. 5: 829–849. Woglom, G. 2000. “Inflation Targeting in South Africa: A VAR Analysis.” Journal for Studies in Economics and Econometrics 24, no. 2 (available at http://www1.amherst.edu/~grwoglom/ Infltargetarticle.doc). Woodford, M. 2004. “Inflation Targeting: A Theoretical Evaluation.” Federal Reserve Bank of St. Louis Review 86, no. 4: 15–41. Zivot, E., and D.W.K. Andrews. 1992. “Further Evidence on the Great Crash, the Oil-Price Shock, and the Unit-Root Hypothesis.” Journal of Business and Economic Statistics 10, no. 3: 251–270.

To order reprints, call 1-800-352-2210; outside the United States, call 717-632-3535.

Suggest Documents