Decline of interest rates under inflation targeting and previous regimes: Evidence from Latin America and developed countries

Chión-Chacón, Sergio Julio; Álvarez García, Kevin Antonio Article Decline of interest rates under inflation targeting and previous regimes: Evidence from Latin America and developed countries Ekonomika Provided in Cooperation with: Vilnius University Press Suggested Citation: Chión-Chacón, Sergio Julio; Álvarez García, Kevin Antonio (2025) : Decline of interest rates under inflation targeting and previous regimes: Evidence from Latin America and developed countries, Ekonomika, ISSN 2424-6166, Vilnius University Press, Vilnius, Vol. 104, Iss. 1, pp. 6-29, https://doi.org/10.15388/Ekon.2025.104.1.1 This Version is available at: https://hdl.handle.net/10419/323169 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ 6 Ekonomika ISSN 1392-1258 eISSN 2424-6166 2025, vol. 104(1), pp. 6–29 DOI: https://doi.org/10.15388/Ekon.2025.104.1.1 Decline of Interest Rates under Inflation Targeting and Previous Regimes: Evidence from Latin America and Developed Countries Sergio Julio Chión-Chacón CENTRUM Católica Graduate Business School, Lima, Perú Pontificia Universidad Católica del Perú, Lima, Perú Email: [email protected] ORCID: https://orcid.org/0000-0002-7955-3163 Kevin Antonio Álvarez García CENTRUM Católica Graduate Business School, Lima, Perú Pontificia Universidad Católica del Perú, Lima, Perú Email: [email protected] ORCID: https://orcid.org/0000-0003-0037-4865 Abstract. This study empirically investigates the impact of Inflation Targeting (IT) on nominal interest rates over the past 40 years, focusing on 10 advanced and emerging economies. By using a Binary Regime Model embedded within a Backward-Looking Taylor, our findings confirm that IT adoption has significantly contributed to reducing interest rates, with the strongest effects observed in Latin American countries. To reinforce these results, we incorporate Smooth Transition Regression (STR) models, with and without instrumental variables, allowing for a more suitable representation of gradual policy transitions. The STR estimates consistently support our main findings, validating the robustness of the observed impacts. Furthermore, we show that, both before and after IT implementation, central banks display a stronger emphasis on responding to inflation than to the output gap, with this focus intensifying under IT regimes. Keywords: Monetary policy, inflation targeting, interest rates, Taylor Rule, Smooth Transition Regression. 1. Introduction In the last 40 years, interest rates have exhibited a clear decreasing trend in both developed and emerging economies1 (Li, 2012; Bernanke, 2022). Despite the global economy going through various expansionary and contractionary phases, even with significant fluctuations in short-term interest rates, the long-term trend remains intact. Many arguments have been put forth regarding the factors behind this reduction. Bernanke (2022) emphasizes that the decline in inflation could have been a decisive factor, as lenders tend to demand lower premiums (interest rates) when inflation is re1 See Figure 1. Received: 15/09/2024. Revised: 20/12/2024. Accepted: 05/01/2025 Copyright © 2025 Sergio Julio Chión-Chacón, Kevin Antonio Álvarez García. Published by Vilnius University Press This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Contents lists available at Vilnius University Press Sergio Julio Chión-Chacón, Kevin Antonio Álvarez García. Decline of Interest Rates under Inflation Targeting and Previous Regimes:... 7 duced. This observation underscores the importance of understanding the mechanisms that have led to a decrease in inflation as a relevant contributing factor. Moreover, the literature shows that the determinants of interest rates have an important relationship with the monetary policy framework (Bambe, 2023). Empirical studies have shown that inflation targeting regimes have been successful in maintaining low, stable, and less volatile inflation levels (Mishkin and Schmidt-Hebbel, 2007; Vega and Winkelried, 2005; Visokaviciene, 2010; Stojanovikj and Petrevski, 2020; Arsić et al., 2022; Bhalla et al., 2023). Therefore, while there is substantial literature on the effectiveness of inflation targeting in stabilizing inflation, there are no studies that directly explore its impact on nominal interest rates. Our study seeks to fill this gap by examining the role of inflation targeting in reducing interest rates, focusing on the preand post-IT periods in both emerging and advanced economies. This comparative analysis of the periods before and after the adoption of IT is a key innovation of our research, offering new insights into the mechanisms behind interest rate dynamics. The transmission mechanism is quite intuitive. If inflation targeting (IT) generates a climate of trust and credibility, it will anchor inflation expectations and result in lower long-term inflation, thereby leading to a reduction in interest rates. Additionally, the adoption of IT may encourage a greater fiscal discipline (Apeti et al., 2024), which could, in turn, contribute to lowering both inflation and interest rates. Understanding whether IT has been an important factor in the falling rates becomes fundamentally important because it would demonstrate the effectiveness of inflation targeting as a monetary policy tool to promote not only price stability but also more favorable financial conditions. Practically, this could support the credibility and confidence in the monetary policies implemented by the central banks that adopt these targets. 0 5 10 15 20 25 30 Interbank Interest Rate (Average %) Previous IT IT regime -2 0 2 4 6 8 10 12 14 16 18 1980 1986 1992 1998 2004 2010 2016 2022 Percent 10-year Treasury Yields for Developed Countries US UK Canada Euro Area Japan Figure 1. Interbank interest rate and Treasury Yields Note: Figure 1 (a) shows the average of the interbank interest rate in IT the regime and before. The total sample covers from 1962Q2 to 2022Q4 (the start date varies among countries), whereas the periods in IT start from 1992 to 2022 (see Table 1). For Brazil, periods with atypical interest rates are omitted, and the sample from 1995Q3–1999Q2 is considered as pre-IT. The rate for Japan is 0.36 before IT and 0.07 in IT; for graphical purposes, we multiply these values by 10. Source: Federal Reserve Economic Data (FRED). (b)(a) ISSN 1392-1258 eISSN 2424-6166 Ekonomika. 2024, vol. 104(1) 8 The objective of this study is to explore whether Inflation Targeting (IT) has played a role in the historical decline of interest rates observed across a sample of representative economies from both emerging and advanced countries. Also, we study if inflation and the output gap play any role in the process of pursuing the inflation target by central banks. To address this issue, we estimate a binary switching regression embedded within a backward-looking Taylor Rule while using the ordinary least squares (OLS) regression model. Table 1. Inflation Targeting Adoption Country Date I.T. adopted Current Target Canada February 1991 1% – 3% Chile September 1999 1% – 3% Colombia October 1999 2% – 4% Mexico 2001 1% – 3% Peru January 2002 1% – 3% United Kingdom October 1992 2% United States January 2012 2% Japan January 2013 2% Brazil June 1999 1.5% – 4.5% Euro Area 1999 2% Note: The length of the inflation target varies among nations. In the cases of Peru and the United Kingdom, the target is established indefinitely, encompassing all periods. Chile’s inflation target spans approximately two years. Meanwhile, Colombia and Mexico employ a medium-term target, while Brazil opts for a yearly target. Lastly, Canada’s inflation target extends over a period of six to eight quarters. The inflation targets of 2022 are considered as current targets. Sources: Bank of England and Reuters. The use of a backward-looking Taylor Rule is justified by several considerations. First, forward-looking rules rely on expectations of inflation and output gaps, which are often subject to measurement errors and unreliable real-time data (Mankiew et al., 2004; Reid and Siklos, 2021). Backward-looking rules, by focusing on observed historical data, minimize these issues and reduce the potential endogeneity concerns. Additionally, such rules help ensure determinacy in structural models, as highlighted in the literature (Carlstrom and Fuerst, 2000), where backward-looking rules contribute to stable and unique equilibria. While an extension of this analysis could involve the consideration of a forward-looking rule, the backward-looking approach remains robust for the purposes of this study and aligns with empirical evidence in historical contexts. We utilize a simple regression model estimated under OLS since there is recent evidence that this estimation method performs better in estimating Taylor-type rules (Carvalho et al., 2021). The analysis is conducted for a set composed of Advanced economies and of Latin American economies (US, UK, Canada, Japan, Euro Area, Peru, Chile, Colombia, Mexico, and Brazil) that have adopted Inflation Targeting (see Table 1). Additionally, to analyze and compare the variations of the rates in both periods, elasticities are calculated in both regimes. Sergio Julio Chión-Chacón, Kevin Antonio Álvarez García. Decline of Interest Rates under Inflation Targeting and Previous Regimes:... 9 Our article contributes to the literature by expanding empirical evidence on the effect of Inflation Targeting (IT) in reducing the interest rates, particularly in emerging Latin American economies. It further contrasts these experiences with those observed in the developed countries, thus providing a comparative analysis which is bound to highlight differences and similarities in the impact of IT across varying economic contexts. The empirical results show that the adoption of IT has played a substantial role in lowering the interest rates in recent years, particularly with a stronger impact being observed in Latin American economies. Also, our analysis reveals that central banks have exhibited a more pronounced response to inflation compared to the output gap, both prior to and following the implementation of inflation targeting (IT), and the response of central banks to inflation has shown an upward trend after the adoption of IT. The following sections proceed as follows. Section 2 provides a brief literature review. Section 3 demonstrates the methodology applied in this study. Section 4 presents the obtained results and discusses the relevant considerations and facts. Finally, in Section 5, conclusions are presented. 2. Literature Review The relationship between inflation targeting (IT) and interest rates has been the subject of extensive research, yet the evidence remains mixed. For instance, Fouejieu and Roger (2013) explore how IT influences cross-country interest rate spreads in both emerging and advanced economies. By using a dynamic panel data approach and system GMM to address endogeneity, they find that IT leads to a decline in country risk premium spreads, particularly under conditions of a reduced political uncertainty. Similarly, De Mendoça and Souza (2009) examine the relationship between the monetary policy credibility and interest rates. By constructing a novel credibility index based on expert surveys, they demonstrate through OLS regression that, during the IT period, interest rates exhibited less variability due to an enhanced monetary policy credibility. Alternatively, Gehringer and Mayer (2019) investigate the factors driving nominal long-term interest rates. By using a VAR model with a DOLS procedure, they conclude that, in major industrialized economies, central banks’ monetary policies have significantly contributed to maintaining low interest rates. They argue that the close connection between short-term rates (controlled by central banks) and long-term rates is more reflective of central bank perceptions than those of the market participants. The studies supporting a positive effect of IT on interest rates generally argue that IT fosters a climate of confidence and expectation management, leading to consistent reductions in interest rates. Additionally, other research highlights a strong link between the inflation control and lower interest rates. For example, Fazlollahi and Ebrahimijan (2022) provide econometric evidence of a bidirectional causality between interest rates and inflation rates in Canada, thereby supporting Bernanke’s (2022) premise that inflation rates significantly influence historical interest rates. ISSN 1392-1258 eISSN 2424-6166 Ekonomika. 2024, vol. 104(1) 10 However, not all research supports the hypothesis that IT has a substantial impact on interest rates. Lin and Ye (2007), by using Propensity Score Matching, find that IT does not significantly affect inflation or inflation variability in industrialized countries. Similarly, Ball and Sheridan (2005), by employing a difference-in-differences model, find that IT does not exert significant effects on long-term interest rates in advanced economies. They attribute the observed inflation decline to a mean-reversion phenomenon rather than to IT itself. Beyond IT, other factors have been identified as influencing the decline in real interest rates. For instance, research by Dell’Erba and Sola (2016), Barnejee et al. (2022) and Kregzde and Murauskas (2015) points to a strong relationship between fiscal policy management and interest rates. The above-listed authors argue that reducing spreads can lower other interest rates in the economy. However, Dautovic (2017) claims that the relationship between fiscal policy variables – such as government spending or budget deficits – and long-term interest rates tends to weaken or even disappear once the persistent nature of interest rates has been accounted for in econometric models. Another crucial factor is the ‘global saving glut’, a concept introduced by Bernanke (2005a), which refers to a worldwide increase in savings, leading to reduced interest rates. Bernanke identifies demographic changes and income growth as the key drivers of this global savings increase. While supporting this view, Barsky and Easton (2021) argue that the global saving glut hypothesis explains the decline in long-term real interest rates from 2002 to 2006 but may not fully account for the further decrease observed after the Great Recession. Upon reviewing the literature, several considerations emerge. First, studies relying on expectation data may be flawed. In this context, Reid and Siklos (2021) highlight that measuring inflation expectations is challenging due to their unobservable nature and the respondents’ misunderstanding of economic concepts, leading to biased and inconsistent data. Second, much of the empirical literature focuses on advanced industrialized countries, thereby limiting the generalizability of the findings. These countries had a low inflation and more efficient institutions even before adopting IT. Finally, studies using treatment effects may be biased. For example, difference-in-differences models that do not account for time-varying treatment effects can introduce bias (Goodman-Bacon, 2021). Moreover, if countries in the sample are not genuine inflation targeters but behave as such, the estimated treatment effects may be misleading. This research seeks to fill these gaps by providing evidence for emerging Latin American countries. Instead of relying on expectation data, we adopt a more straightforward and methodologically robust approach to address these issues. 3. Method 3.1. Research Model 3.1.1. Binary Regime Model We estimate regressions of the Backward-Looking type Taylor Rule for ten different countries as follows: Sergio Julio Chión-Chacón, Kevin Antonio Álvarez García. Decline of Interest Rates under Inflation Targeting and Previous Regimes:... 11 𝑟𝑟𝑡𝑡= 𝜃𝜃0+𝜃𝜃1𝜋𝜋𝑡𝑡−1 +𝜃𝜃2𝑦𝑦𝑡𝑡−1 +𝜃𝜃3𝐷𝐷𝑡𝑡+𝜀𝜀𝑡𝑡 𝐸𝐸(𝑟𝑟𝑡𝑡|𝜋𝜋𝑡𝑡−1,𝑦𝑦𝑡𝑡−1,𝐷𝐷𝑡𝑡= 1)= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋𝑡𝑡−1 +𝜃𝜃 2𝑦𝑦𝑡𝑡−1 +𝜃𝜃 3; I.T. regime 𝐸𝐸(𝑟𝑟𝑡𝑡|𝜋𝜋𝑡𝑡−1,𝑦𝑦𝑡𝑡−1,𝐷𝐷𝑡𝑡= 0)= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋𝑡𝑡−1 +𝜃𝜃 2𝑦𝑦𝑡𝑡−1 ; previous regimes 𝑟𝑟𝑡𝑡= 𝛽𝛽0+𝛽𝛽1𝜋𝜋𝑡𝑡+ 𝛽𝛽2𝑦𝑦𝑡𝑡+𝛾𝛾𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)+𝜖𝜖𝑡𝑡 (2) 𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)=1 1+𝑒𝑒−𝑘𝑘𝜏𝜏𝑡𝑡 Exponential function: 𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)= 𝑒𝑒^(−𝑘𝑘|𝜏𝜏𝑡𝑡|) 𝜋𝜋 = (𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−4 𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−4 )∗100 . 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗+𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(1) (3) 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗+𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(0) (4) Elasticity of inflation = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗ (5) Elasticity of output gap = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗; 𝜕𝜕=𝜕𝜕  (6) 𝐴𝐴𝐴𝐴𝑒𝑒𝑟𝑟𝐴𝐴𝐴𝐴𝑒𝑒 𝑜𝑜𝑜𝑜 𝜋𝜋∗=∑ ∑ 𝜋𝜋𝑖𝑖 ∗𝑤𝑤𝑗𝑗 𝑁𝑁 𝑖𝑖=1 𝐾𝐾 𝑗𝑗=1 (7) After IT: 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(1)= 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3 (8) Before IT: 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(0)= 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗ (9) Elasticity of inflation = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗= 𝜃𝜃 1×𝜕𝜕∗ 𝜕𝜕𝑡𝑡 (10) Elasticity of output gap = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗; 𝜕𝜕=𝜕𝜕 =𝜃𝜃 2 𝜕𝜕×𝜕𝜕 𝜕𝜕𝑡𝑡=𝜃𝜃 2 𝜕𝜕𝑡𝑡 (11) (1) where rt is the nominal interbank interest rate, π is the inflation rate gap, while 𝑟𝑟𝑡𝑡= 𝜃𝜃0+𝜃𝜃1𝜋𝜋𝑡𝑡−1 +𝜃𝜃2𝑦𝑦𝑡𝑡−1 +𝜃𝜃3𝐷𝐷𝑡𝑡+𝜀𝜀𝑡𝑡 𝐸𝐸(𝑟𝑟𝑡𝑡|𝜋𝜋𝑡𝑡−1,𝑦𝑦𝑡𝑡−1,𝐷𝐷𝑡𝑡= 1)= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋𝑡𝑡−1 +𝜃𝜃 2𝑦𝑦𝑡𝑡−1 +𝜃𝜃 3; I.T. regime 𝐸𝐸(𝑟𝑟𝑡𝑡|𝜋𝜋𝑡𝑡−1,𝑦𝑦𝑡𝑡−1,𝐷𝐷𝑡𝑡= 0)= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋𝑡𝑡−1 +𝜃𝜃 2𝑦𝑦𝑡𝑡−1 ; previous regimes 𝑟𝑟𝑡𝑡= 𝛽𝛽0+𝛽𝛽1𝜋𝜋𝑡𝑡+ 𝛽𝛽2𝑦𝑦𝑡𝑡+𝛾𝛾𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)+𝜖𝜖𝑡𝑡 (2) 𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)=1 1+𝑒𝑒−𝑘𝑘𝜏𝜏𝑡𝑡 Exponential function: 𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)= 𝑒𝑒^(−𝑘𝑘|𝜏𝜏𝑡𝑡|) 𝜋𝜋 = (𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−4 𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−4 )∗100 . 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗+𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(1) (3) 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗+𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(0) (4) Elasticity of inflation = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗ (5) Elasticity of output gap = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗; 𝜕𝜕=𝜕𝜕  (6) 𝐴𝐴𝐴𝐴𝑒𝑒𝑟𝑟𝐴𝐴𝐴𝐴𝑒𝑒 𝑜𝑜𝑜𝑜 𝜋𝜋∗=∑ ∑ 𝜋𝜋𝑖𝑖 ∗𝑤𝑤𝑗𝑗 𝑁𝑁 𝑖𝑖=1 𝐾𝐾 𝑗𝑗=1 (7) After IT: 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(1)= 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3 (8) Before IT: 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(0)= 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗ (9) Elasticity of inflation = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗= 𝜃𝜃 1×𝜕𝜕∗ 𝜕𝜕𝑡𝑡 (10) Elasticity of output gap = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗; 𝜕𝜕=𝜕𝜕 =𝜃𝜃 2 𝜕𝜕×𝜕𝜕 𝜕𝜕𝑡𝑡=𝜃𝜃 2 𝜕𝜕𝑡𝑡 (11) is the output gap (defined as the percentage difference between the output and its long-term trend level). Central to our analysis is the introduction of a dummy variable, Dt, which takes the value of ‘1’ during the inflation targeting period and ‘0’ otherwise. This allows us to capture the effects of both the inflation targeting period and the preceding monetary policy regimes. We use the Ordinary Least Squared (OLS) method to estimate the coefficient of the regressions for both Latin American and developed countries. We capture the estimator for the two monetary policy regimes as follows: 𝑟𝑟𝑡𝑡= 𝜃𝜃0+𝜃𝜃1𝜋𝜋𝑡𝑡−1 +𝜃𝜃2𝑦𝑦𝑡𝑡−1 +𝜃𝜃3𝐷𝐷𝑡𝑡+𝜀𝜀𝑡𝑡 𝐸𝐸(𝑟𝑟𝑡𝑡|𝜋𝜋𝑡𝑡−1,𝑦𝑦𝑡𝑡−1,𝐷𝐷𝑡𝑡= 1)= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋𝑡𝑡−1 +𝜃𝜃 2𝑦𝑦𝑡𝑡−1 +𝜃𝜃 3; I.T. regime 𝐸𝐸(𝑟𝑟𝑡𝑡|𝜋𝜋𝑡𝑡−1,𝑦𝑦𝑡𝑡−1,𝐷𝐷𝑡𝑡= 0)= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋𝑡𝑡−1 +𝜃𝜃 2𝑦𝑦𝑡𝑡−1 ; previous regimes 𝑟𝑟𝑡𝑡= 𝛽𝛽0+𝛽𝛽1𝜋𝜋𝑡𝑡+ 𝛽𝛽2𝑦𝑦𝑡𝑡+𝛾𝛾𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)+𝜖𝜖𝑡𝑡 (2) 𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)=1 1+𝑒𝑒−𝑘𝑘𝜏𝜏𝑡𝑡 Exponential function: 𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)= 𝑒𝑒^(−𝑘𝑘|𝜏𝜏𝑡𝑡|) 𝜋𝜋 = (𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−4 𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−4 )∗100 . 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗+𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(1) (3) 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗+𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(0) (4) Elasticity of inflation = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗ (5) Elasticity of output gap = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗; 𝜕𝜕=𝜕𝜕  (6) 𝐴𝐴𝐴𝐴𝑒𝑒𝑟𝑟𝐴𝐴𝐴𝐴𝑒𝑒 𝑜𝑜𝑜𝑜 𝜋𝜋∗=∑ ∑ 𝜋𝜋𝑖𝑖 ∗𝑤𝑤𝑗𝑗 𝑁𝑁 𝑖𝑖=1 𝐾𝐾 𝑗𝑗=1 (7) After IT: 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(1)= 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3 (8) Before IT: 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(0)= 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗ (9) Elasticity of inflation = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗= 𝜃𝜃 1×𝜕𝜕∗ 𝜕𝜕𝑡𝑡 (10) Elasticity of output gap = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗; 𝜕𝜕=𝜕𝜕 =𝜃𝜃 2 𝜕𝜕×𝜕𝜕 𝜕𝜕𝑡𝑡=𝜃𝜃 2 𝜕𝜕𝑡𝑡 (11) ; I.T. regime 𝑟𝑟𝑡𝑡= 𝜃𝜃0+𝜃𝜃1𝜋𝜋𝑡𝑡−1 +𝜃𝜃2𝑦𝑦𝑡𝑡−1 +𝜃𝜃3𝐷𝐷𝑡𝑡+𝜀𝜀𝑡𝑡 𝐸𝐸(𝑟𝑟𝑡𝑡|𝜋𝜋𝑡𝑡−1,𝑦𝑦𝑡𝑡−1,𝐷𝐷𝑡𝑡= 1)= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋𝑡𝑡−1 +𝜃𝜃 2𝑦𝑦𝑡𝑡−1 +𝜃𝜃 3; I.T. regime 𝐸𝐸(𝑟𝑟𝑡𝑡|𝜋𝜋𝑡𝑡−1,𝑦𝑦𝑡𝑡−1,𝐷𝐷𝑡𝑡= 0)= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋𝑡𝑡−1 +𝜃𝜃 2𝑦𝑦𝑡𝑡−1 ; previous regimes 𝑟𝑟𝑡𝑡= 𝛽𝛽0+𝛽𝛽1𝜋𝜋𝑡𝑡+ 𝛽𝛽2𝑦𝑦𝑡𝑡+𝛾𝛾𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)+𝜖𝜖𝑡𝑡 (2) 𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)=1 1+𝑒𝑒−𝑘𝑘𝜏𝜏𝑡𝑡 Exponential function: 𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)= 𝑒𝑒^(−𝑘𝑘|𝜏𝜏𝑡𝑡|) 𝜋𝜋 = (𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−4 𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−4 )∗100 . 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗+𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(1) (3) 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗+𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(0) (4) Elasticity of inflation = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗ (5) Elasticity of output gap = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗; 𝜕𝜕=𝜕𝜕  (6) 𝐴𝐴𝐴𝐴𝑒𝑒𝑟𝑟𝐴𝐴𝐴𝐴𝑒𝑒 𝑜𝑜𝑜𝑜 𝜋𝜋∗=∑ ∑ 𝜋𝜋𝑖𝑖 ∗𝑤𝑤𝑗𝑗 𝑁𝑁 𝑖𝑖=1 𝐾𝐾 𝑗𝑗=1 (7) After IT: 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(1)= 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3 (8) Before IT: 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(0)= 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗ (9) Elasticity of inflation = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗= 𝜃𝜃 1×𝜕𝜕∗ 𝜕𝜕𝑡𝑡 (10) Elasticity of output gap = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗; 𝜕𝜕=𝜕𝜕 =𝜃𝜃 2 𝜕𝜕×𝜕𝜕 𝜕𝜕𝑡𝑡=𝜃𝜃 2 𝜕𝜕𝑡𝑡 (11) ; previous regimes Endogeneity in structural models like the Taylor Rule can lead to biased estimators. The common approach in the literature is to use Instrumental Variables (IV) or GMM (Maher et al., 2022; Horvath et al., 2022). However, ensuring the exogeneity and validity of instruments, especially in time series, is challenging. Empirical evidence suggests that Ordinary Least Squares (OLS) provides a better performance as well as a smaller endogeneity bias in reasonably sized samples (Carvalho et al., 2021). Following Miles and Schreyer (2012) and Carvalho et al. (2021), we opt for OLS instead of 2SLS2. To address serial correlation, heteroskedasticity, and autocorrelation, we use HAC standard errors and bootstrap techniques. 3.1.2. Smooth Transition Regression (STR) The smooth transition model captures gradual changes in the structural parameters of an equation, as opposed to abrupt shifts, thus making it particularly useful for analyzing policies with potential regime changes. In this context, the model incorporates a transition dynamic which depends on a threshold variable (such as the temporal distance from the implementation date of a regime). We estimate the model both without and with instrumental variable (IV) estimation to address potential endogeneity issues. The general form of the model is: 𝑟𝑟𝑡𝑡= 𝜃𝜃0+𝜃𝜃1𝜋𝜋𝑡𝑡−1 +𝜃𝜃2𝑦𝑦𝑡𝑡−1 +𝜃𝜃3𝐷𝐷𝑡𝑡+𝜀𝜀𝑡𝑡 𝐸𝐸(𝑟𝑟𝑡𝑡|𝜋𝜋𝑡𝑡−1,𝑦𝑦𝑡𝑡−1,𝐷𝐷𝑡𝑡= 1)= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋𝑡𝑡−1 +𝜃𝜃 2𝑦𝑦𝑡𝑡−1 +𝜃𝜃 3; I.T. regime 𝐸𝐸(𝑟𝑟𝑡𝑡|𝜋𝜋𝑡𝑡−1,𝑦𝑦𝑡𝑡−1,𝐷𝐷𝑡𝑡= 0)= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋𝑡𝑡−1 +𝜃𝜃 2𝑦𝑦𝑡𝑡−1 ; previous regimes 𝑟𝑟𝑡𝑡= 𝛽𝛽0+𝛽𝛽1𝜋𝜋𝑡𝑡+ 𝛽𝛽2𝑦𝑦𝑡𝑡+𝛾𝛾𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)+𝜖𝜖𝑡𝑡 (2) 𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)=1 1+𝑒𝑒−𝑘𝑘𝜏𝜏𝑡𝑡 Exponential function: 𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)= 𝑒𝑒^(−𝑘𝑘|𝜏𝜏𝑡𝑡|) 𝜋𝜋 = (𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−4 𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−4 )∗100 . 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗+𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(1) (3) 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗+𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(0) (4) Elasticity of inflation = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗ (5) Elasticity of output gap = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗; 𝜕𝜕=𝜕𝜕  (6) 𝐴𝐴𝐴𝐴𝑒𝑒𝑟𝑟𝐴𝐴𝐴𝐴𝑒𝑒 𝑜𝑜𝑜𝑜 𝜋𝜋∗=∑ ∑ 𝜋𝜋𝑖𝑖 ∗𝑤𝑤𝑗𝑗 𝑁𝑁 𝑖𝑖=1 𝐾𝐾 𝑗𝑗=1 (7) After IT: 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(1)= 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3 (8) Before IT: 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(0)= 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗ (9) Elasticity of inflation = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗= 𝜃𝜃 1×𝜕𝜕∗ 𝜕𝜕𝑡𝑡 (10) Elasticity of output gap = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗; 𝜕𝜕=𝜕𝜕 =𝜃𝜃 2 𝜕𝜕×𝜕𝜕 𝜕𝜕𝑡𝑡=𝜃𝜃 2 𝜕𝜕𝑡𝑡 (11) (2) In this model,rt represents the interbank interest rate, 𝑟𝑟𝑡𝑡= 𝜃𝜃0+𝜃𝜃1𝜋𝜋𝑡𝑡−1 +𝜃𝜃2𝑦𝑦𝑡𝑡−1 +𝜃𝜃3𝐷𝐷𝑡𝑡+𝜀𝜀𝑡𝑡 𝐸𝐸(𝑟𝑟𝑡𝑡|𝜋𝜋𝑡𝑡−1,𝑦𝑦𝑡𝑡−1,𝐷𝐷𝑡𝑡= 1)= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋𝑡𝑡−1 +𝜃𝜃 2𝑦𝑦𝑡𝑡−1 +𝜃𝜃 3; I.T. regime 𝐸𝐸(𝑟𝑟𝑡𝑡|𝜋𝜋𝑡𝑡−1,𝑦𝑦𝑡𝑡−1,𝐷𝐷𝑡𝑡= 0)= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋𝑡𝑡−1 +𝜃𝜃 2𝑦𝑦𝑡𝑡−1 ; previous regimes 𝑟𝑟𝑡𝑡= 𝛽𝛽0+𝛽𝛽1𝜋𝜋𝑡𝑡+ 𝛽𝛽2𝑦𝑦𝑡𝑡+𝛾𝛾𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)+𝜖𝜖𝑡𝑡 (2) 𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)=1 1+𝑒𝑒−𝑘𝑘𝜏𝜏𝑡𝑡 Exponential function: 𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)= 𝑒𝑒^(−𝑘𝑘|𝜏𝜏𝑡𝑡|) 𝜋𝜋 = (𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−4 𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−4 )∗100 . 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗+𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(1) (3) 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗+𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(0) (4) Elasticity of inflation = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗ (5) Elasticity of output gap = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗; 𝜕𝜕=𝜕𝜕  (6) 𝐴𝐴𝐴𝐴𝑒𝑒𝑟𝑟𝐴𝐴𝐴𝐴𝑒𝑒 𝑜𝑜𝑜𝑜 𝜋𝜋∗=∑ ∑ 𝜋𝜋𝑖𝑖 ∗𝑤𝑤𝑗𝑗 𝑁𝑁 𝑖𝑖=1 𝐾𝐾 𝑗𝑗=1 (7) After IT: 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(1)= 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3 (8) Before IT: 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(0)= 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗ (9) Elasticity of inflation = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗= 𝜃𝜃 1×𝜕𝜕∗ 𝜕𝜕𝑡𝑡 (10) Elasticity of output gap = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗; 𝜕𝜕=𝜕𝜕 =𝜃𝜃 2 𝜕𝜕×𝜕𝜕 𝜕𝜕𝑡𝑡=𝜃𝜃 2 𝜕𝜕𝑡𝑡 (11) is the inflation rate relative to its target, and 𝑟𝑟𝑡𝑡= 𝜃𝜃0+𝜃𝜃1𝜋𝜋𝑡𝑡−1 +𝜃𝜃2𝑦𝑦𝑡𝑡−1 +𝜃𝜃3𝐷𝐷𝑡𝑡+𝜀𝜀𝑡𝑡 𝐸𝐸(𝑟𝑟𝑡𝑡|𝜋𝜋𝑡𝑡−1,𝑦𝑦𝑡𝑡−1,𝐷𝐷𝑡𝑡= 1)= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋𝑡𝑡−1 +𝜃𝜃 2𝑦𝑦𝑡𝑡−1 +𝜃𝜃 3; I.T. regime 𝐸𝐸(𝑟𝑟𝑡𝑡|𝜋𝜋𝑡𝑡−1,𝑦𝑦𝑡𝑡−1,𝐷𝐷𝑡𝑡= 0)= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋𝑡𝑡−1 +𝜃𝜃 2𝑦𝑦𝑡𝑡−1 ; previous regimes 𝑟𝑟𝑡𝑡= 𝛽𝛽0+𝛽𝛽1𝜋𝜋𝑡𝑡+ 𝛽𝛽2𝑦𝑦𝑡𝑡+𝛾𝛾𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)+𝜖𝜖𝑡𝑡 (2) 𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)=1 1+𝑒𝑒−𝑘𝑘𝜏𝜏𝑡𝑡 Exponential function: 𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)= 𝑒𝑒^(−𝑘𝑘|𝜏𝜏𝑡𝑡|) 𝜋𝜋 = (𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−4 𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−4 )∗100 . 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗+𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(1) (3) 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗+𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(0) (4) Elasticity of inflation = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗ (5) Elasticity of output gap = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗; 𝜕𝜕=𝜕𝜕  (6) 𝐴𝐴𝐴𝐴𝑒𝑒𝑟𝑟𝐴𝐴𝐴𝐴𝑒𝑒 𝑜𝑜𝑜𝑜 𝜋𝜋∗=∑ ∑ 𝜋𝜋𝑖𝑖 ∗𝑤𝑤𝑗𝑗 𝑁𝑁 𝑖𝑖=1 𝐾𝐾 𝑗𝑗=1 (7) After IT: 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(1)= 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3 (8) Before IT: 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(0)= 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗ (9) Elasticity of inflation = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗= 𝜃𝜃 1×𝜕𝜕∗ 𝜕𝜕𝑡𝑡 (10) Elasticity of output gap = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗; 𝜕𝜕=𝜕𝜕 =𝜃𝜃 2 𝜕𝜕×𝜕𝜕 𝜕𝜕𝑡𝑡=𝜃𝜃 2 𝜕𝜕𝑡𝑡 (11) is the output gap. G(τt; κ) is the transition function, which depends on the threshold variable τt (in this case, representing the temporal distance from the date of IT adoption), and κ is the smoothness parameter. In this framework, two types of transition functions are feasible, as follows: 2 To enhance the robustness of our results, we also estimate Equation (1) by using Instrumental Variables (IV). ISSN 1392-1258 eISSN 2424-6166 Ekonomika. 2024, vol. 104(1) 12 Logistic function 𝑟𝑟𝑡𝑡= 𝜃𝜃0+𝜃𝜃1𝜋𝜋𝑡𝑡−1 +𝜃𝜃2𝑦𝑦𝑡𝑡−1 +𝜃𝜃3𝐷𝐷𝑡𝑡+𝜀𝜀𝑡𝑡 𝐸𝐸(𝑟𝑟𝑡𝑡|𝜋𝜋𝑡𝑡−1,𝑦𝑦𝑡𝑡−1,𝐷𝐷𝑡𝑡= 1)= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋𝑡𝑡−1 +𝜃𝜃 2𝑦𝑦𝑡𝑡−1 +𝜃𝜃 3; I.T. regime 𝐸𝐸(𝑟𝑟𝑡𝑡|𝜋𝜋𝑡𝑡−1,𝑦𝑦𝑡𝑡−1,𝐷𝐷𝑡𝑡= 0)= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋𝑡𝑡−1 +𝜃𝜃 2𝑦𝑦𝑡𝑡−1 ; previous regimes 𝑟𝑟𝑡𝑡= 𝛽𝛽0+𝛽𝛽1𝜋𝜋𝑡𝑡+ 𝛽𝛽2𝑦𝑦𝑡𝑡+𝛾𝛾𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)+𝜖𝜖𝑡𝑡 (2) 𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)=1 1+𝑒𝑒−𝑘𝑘𝜏𝜏𝑡𝑡 Exponential function: 𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)= 𝑒𝑒^(−𝑘𝑘|𝜏𝜏𝑡𝑡|) 𝜋𝜋 = (𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−4 𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−4 )∗100 . 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗+𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(1) (3) 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗+𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(0) (4) Elasticity of inflation = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗ (5) Elasticity of output gap = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗; 𝜕𝜕=𝜕𝜕  (6) 𝐴𝐴𝐴𝐴𝑒𝑒𝑟𝑟𝐴𝐴𝐴𝐴𝑒𝑒 𝑜𝑜𝑜𝑜 𝜋𝜋∗=∑ ∑ 𝜋𝜋𝑖𝑖 ∗𝑤𝑤𝑗𝑗 𝑁𝑁 𝑖𝑖=1 𝐾𝐾 𝑗𝑗=1 (7) After IT: 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(1)= 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3 (8) Before IT: 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(0)= 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗ (9) Elasticity of inflation = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗= 𝜃𝜃 1×𝜕𝜕∗ 𝜕𝜕𝑡𝑡 (10) Elasticity of output gap = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗; 𝜕𝜕=𝜕𝜕 =𝜃𝜃 2 𝜕𝜕×𝜕𝜕 𝜕𝜕𝑡𝑡=𝜃𝜃 2 𝜕𝜕𝑡𝑡 (11) Exponential function: 𝑟𝑟𝑡𝑡= 𝜃𝜃0+𝜃𝜃1𝜋𝜋𝑡𝑡−1 +𝜃𝜃2𝑦𝑦𝑡𝑡−1 +𝜃𝜃3𝐷𝐷𝑡𝑡+𝜀𝜀𝑡𝑡 𝐸𝐸(𝑟𝑟𝑡𝑡|𝜋𝜋𝑡𝑡−1,𝑦𝑦𝑡𝑡−1,𝐷𝐷𝑡𝑡= 1)= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋𝑡𝑡−1 +𝜃𝜃 2𝑦𝑦𝑡𝑡−1 +𝜃𝜃 3; I.T. regime 𝐸𝐸(𝑟𝑟𝑡𝑡|𝜋𝜋𝑡𝑡−1,𝑦𝑦𝑡𝑡−1,𝐷𝐷𝑡𝑡= 0)= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋𝑡𝑡−1 +𝜃𝜃 2𝑦𝑦𝑡𝑡−1 ; previous regimes 𝑟𝑟𝑡𝑡= 𝛽𝛽0+𝛽𝛽1𝜋𝜋𝑡𝑡+ 𝛽𝛽2𝑦𝑦𝑡𝑡+𝛾𝛾𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)+𝜖𝜖𝑡𝑡 (2) 𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)=1 1+𝑒𝑒−𝑘𝑘𝜏𝜏𝑡𝑡 Exponential function: 𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)= 𝑒𝑒^(−𝑘𝑘|𝜏𝜏𝑡𝑡|) 𝜋𝜋 = (𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−4 𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−4 )∗100 . 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗+𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(1) (3) 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗+𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(0) (4) Elasticity of inflation = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗ (5) Elasticity of output gap = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗; 𝜕𝜕=𝜕𝜕  (6) 𝐴𝐴𝐴𝐴𝑒𝑒𝑟𝑟𝐴𝐴𝐴𝐴𝑒𝑒 𝑜𝑜𝑜𝑜 𝜋𝜋∗=∑ ∑ 𝜋𝜋𝑖𝑖 ∗𝑤𝑤𝑗𝑗 𝑁𝑁 𝑖𝑖=1 𝐾𝐾 𝑗𝑗=1 (7) After IT: 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(1)= 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3 (8) Before IT: 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(0)= 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗ (9) Elasticity of inflation = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗= 𝜃𝜃 1×𝜕𝜕∗ 𝜕𝜕𝑡𝑡 (10) Elasticity of output gap = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗; 𝜕𝜕=𝜕𝜕 =𝜃𝜃 2 𝜕𝜕×𝜕𝜕 𝜕𝜕𝑡𝑡=𝜃𝜃 2 𝜕𝜕𝑡𝑡 (11) The selection between the two functions is made by minimizing the sum of squared residuals (SSR) for different values of the smoothness parameter 𝜅3. To address potential inference issues caused by severe autocorrelation, Bootstrap was used to estimate the standard errors of the model4. Therefore, under this specification, the aim is to address (i) the gradual nature of the effects of IT adoption, (ii) potential endogeneity issues, and (iii) possible autocorrelation problems by providing a more robust estimation of the standard errors, thereby enhancing the reliability of the results. 3.2. Data and Variables We collected quarterly data on the interbank interest rate, inflation and output for the ten countries included in our sample. The specific variables employed were dictated by the best data available. We follow Miles and Schreyer (2012), who used a measure of short term interest rates5. For the developed economies and Brazil, we use the 3-month interbank rate6. For Mexico, we use the ‘28 days interbank rate’, while for Peru, Chile and Colombia, we refer to the 1-day interbank rate. The interbank rate, the inflation, and the real output were taken from the Federal Reserve Economic Data (FRED), Economic Commission for Latin America (CEPAL) the International Monetary Fund (IMF) and the central banks websites of each country to be analyzed. We consider the inflation rate as the year-to-year variation of the Consumer Price Index (CPI). This measure includes the interannual variation of the quarterly average7 of CPI 𝑟𝑟𝑡𝑡= 𝜃𝜃0+𝜃𝜃1𝜋𝜋𝑡𝑡−1 +𝜃𝜃2𝑦𝑦𝑡𝑡−1 +𝜃𝜃3𝐷𝐷𝑡𝑡+𝜀𝜀𝑡𝑡 𝐸𝐸(𝑟𝑟𝑡𝑡|𝜋𝜋𝑡𝑡−1,𝑦𝑦𝑡𝑡−1,𝐷𝐷𝑡𝑡= 1)= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋𝑡𝑡−1 +𝜃𝜃 2𝑦𝑦𝑡𝑡−1 +𝜃𝜃 3; I.T. regime 𝐸𝐸(𝑟𝑟𝑡𝑡|𝜋𝜋𝑡𝑡−1,𝑦𝑦𝑡𝑡−1,𝐷𝐷𝑡𝑡= 0)= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋𝑡𝑡−1 +𝜃𝜃 2𝑦𝑦𝑡𝑡−1 ; previous regimes 𝑟𝑟𝑡𝑡= 𝛽𝛽0+𝛽𝛽1𝜋𝜋𝑡𝑡+ 𝛽𝛽2𝑦𝑦𝑡𝑡+𝛾𝛾𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)+𝜖𝜖𝑡𝑡 (2) 𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)=1 1+𝑒𝑒−𝑘𝑘𝜏𝜏𝑡𝑡 Exponential function: 𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)= 𝑒𝑒^(−𝑘𝑘|𝜏𝜏𝑡𝑡|) 𝜋𝜋 = (𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−4 𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−4 )∗100 . 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗+𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(1) (3) 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗+𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(0) (4) Elasticity of inflation = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗ (5) Elasticity of output gap = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗; 𝜕𝜕=𝜕𝜕  (6) 𝐴𝐴𝐴𝐴𝑒𝑒𝑟𝑟𝐴𝐴𝐴𝐴𝑒𝑒 𝑜𝑜𝑜𝑜 𝜋𝜋∗=∑ ∑ 𝜋𝜋𝑖𝑖 ∗𝑤𝑤𝑗𝑗 𝑁𝑁 𝑖𝑖=1 𝐾𝐾 𝑗𝑗=1 (7) After IT: 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(1)= 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3 (8) Before IT: 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(0)= 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗ (9) Elasticity of inflation = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗= 𝜃𝜃 1×𝜕𝜕∗ 𝜕𝜕𝑡𝑡 (10) Elasticity of output gap = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗; 𝜕𝜕=𝜕𝜕 =𝜃𝜃 2 𝜕𝜕×𝜕𝜕 𝜕𝜕𝑡𝑡=𝜃𝜃 2 𝜕𝜕𝑡𝑡 (11) . The output gap was obtained by the Hodrick-Prescott filter8. According to the sample, Canada has the largest data series, running from 1962Q1 to 2022Q4. It is followed by the United States, with the data spanning the period from 1964Q3 to 2022Q2. The United Kingdom’s data run from 1986Q1 to 2022Q2. The Euro Area provides data from 1995Q1 to 2022Q4, while Japan’s data run from 2002Q2 to 2022Q4. 3 Values from 0.1 to 10 were used with an increase rate of 0.20. 4 2000 simulations were considered. 5 In our analysis, we employ the same dependent variable, namely, the interbank rate, across all countries. However, we vary the terms associated with the interbank rate, specifically considering intervals of 1, 28 and 30 days. 6 Quarterly data are obtained from the 3-month average of monthly data. 7 The CPI is the monthly average index, while the QACPI is the quarterly average of CPI. 8 The value of λ considered in this analysis was 1600. However, the Hodrick-Prescott filter (1997) is known to have weaknesses, including sensitivity to the choice of λ, end-point bias that affects estimates near the sample edges (Cogley and Nason, 1995; Ravn and Uhlig, 2002), and its inability to account for structural breaks or economic shocks, which may lead to misleading results in volatile contexts (Hamilton, 2018). Sergio Julio Chión-Chacón, Kevin Antonio Álvarez García. Decline of Interest Rates under Inflation Targeting and Previous Regimes:... 13 Table 2. Summary statistics on inflation and interbank interest rate (% annual rates) Perú Chile Colombia Mexico Brazil U.S. U.K. E.A. Canada Japan Inflation Before Mean 8.3 5.9 18.3 21.2 587.9 4.3 5.4 1.5 5.5 -0.2 Median 8.2 5.7 19.5 17.6 14.0 3.4 5.0 1.5 4.6 -0.2 S.D. 2.7 1.4 3.6 12.1 1273.8 2.9 1.8 0.3 3.4 0.8 Inflation Targeting Mean 2.9 3.6 5.1 4.4 6.4 2.4 2.3 2.0 2.0 0.6 Median 2.8 3.0 4.8 4.1 6.1 1.8 2.0 1.9 1.7 0.5 S.D. 1.7 2.6 2.4 1.2 2.7 2.1 1.3 1.7 1.6 1.0 Full period Mean 3.7 3.9 7.2 8.2 111.7 3.9 2.8 1.9 3.7 0.2 Median 3.1 3.4 5.3 4.7 6.2 3.2 2.3 1.8 2.5 0.0 S.D. 2.7 2.6 5.5 9.1 576.9 1.4 1.9 1.6 3.2 0.9 Interbank Rate Before Mean 13.4 14.3 26.7 27.2 131.3 6.0 11.7 5.0 8.7 0.4 Median 12.8 14.5 25.6 22.4 23.8 5.6 11.2 4.5 8.5 0.3 S.D. 4.6 3.7 5.7 12.0 242.6 3.4 2.0 1.2 3.5 0.3 Inflation Targeting Mean 3.5 4.1 6.0 6.4 10.5 0.9 3.3 1.5 3.1 0.1 Median 3.7 3.5 5.5 6.6 10.8 0.3 3.9 1.0 2.7 0.0 S.D. 1.4 2.5 2.6 2.0 4.5 0.9 2.6 1.8 2.3 0.1 Full period Mean 5.7 5.4 9.4 11.0 32.3 5.0 4.8 2.0 5.8 0.2 Median 4.2 4.3 6.3 7.6 11.8 5.5 4.9 2.0 5.1 0.1 S.D. 4.9 4.4 8.3 10.5 111.5 3.7 4.1 2.1 4.0 0.2 Note: The data in this table are presented in quarterly frequency. The sample prior to inflation targeting was chosen based on the availability of data. The data sample for each country corresponds exactly to the one specified in the data section. Sources: Federal Reserve Database (FRED) and Central Banks of each country. ISSN 1392-1258 eISSN 2424-6166 Ekonomika. 2024, vol. 104(1) 20 Table 5. Smooth transition regression model estimated using instrumental variables (IV) Peru Chile Colombia Mexico Brazil U.S. U.K. Canada E.A. Japan Intercept 12.62*** (2.42) 23.00*** (6.30) 11.02 (8.89) 6.66 (12.9) 44.99** (20.59) 6.47*** (0.54) 17.76*** (4.03) 12.21*** (2.42) 20.27*** (5.17) 0.89*** (0.17) Output gap 0.17 (0.33) 0.56** (0.26) 0.24 (0.98) 1.00* (0.58) 0.61 (0.74) 0.40 (0.27) 0.52** (0.24) 0.74*** (0.27) 0.31 (0.24) -0.02 (0.04) Inflation gap 0.61** (0.24) 0.52*** (0.19) 1.03 (0.64) 0.96 (0.65) 0.43 (0.58) 0.54*** (0.16) -0.14 (0.35) -0.12 (0.37) 0.21 (0.17) 0.15 (0.09) Transition -10.93*** (5.13) -22.38*** (8.64) -6.73 (11.21) 1.34 (14.09) -39.99 (25.93) -18.58 (11.66) -14.38*** (5.33) -14.25** (6.17) -21.8*** (6.33) -0.89*** (0.16) Intercept – pre-IT 12.624*** (2.42) 23.00*** (6.30) 11.02 (8.89) 6.66 (12.87) 44.985** (20.59) 6.47*** (0.54) 17.76*** (4.04) 12.21*** (2.42) 20.27*** (5.17) 0.89*** (0.17) Intercept – post-IT 1.72 (2.89) 0.63 (2.43) 4.29** (2.38) 7.99*** (1.49) 4.99 (5.48) -12.12 (11.45) 3.39** (1.36) -2.03 (3.80) -1.54 (1.27) -0.00 (0.24) Notes: The smooth transition regression model used a continuous logistic function in all cases, after evaluation of the nonlinear test based on Teräsvirta (1994). The model uses estimates under instrumental variables (IV), where the instruments were the lags (2) of the independent variables. It is worth noting that the results are subject to the possible weakness of the instruments. The standard error in parentheses was estimated by using the bootstrap method with 2000 iterations. Table 6. Smooth transition regression model estimated by OLS Peru Chile Colombia Mexico Brazil U.S. U.K. Canada E.A. Japan Intercept 10.10*** (0.94) 16.45*** (1.20) 8.04*** (1.06) 17.85*** (2.11) 29.20*** (0.95) 4.61*** (0.17) 13.99*** (0.80) 7.43*** (0.26) 7.61*** (0.45) 0.53*** (0.07) Output gap 0.10 (0.09) 0.28*** (0.09) 0.06 (0.13) 0.11 (0.18) -0.16 (0.26) 0.20* (0.12) 0.15* (0.08) 0.36*** (0.10) 0.17 (0.10) 0.00 (0.02) Inflation gap 0.62*** (0.13) 0.52*** (0.08) 1.19*** (0.11) 0.72*** (0.12) -0.05 (0.11) 0.73*** (0.06) 0.22 (0.13) 0.54*** (0.08) 0.16 (0.15) 0.07*** (0.02) Transition -7.54*** (0.94) -13.67*** (1.26) -4.60*** (1.20) -13.18*** (2.11) -20.20*** (1.24) -5.08*** (0.39) -11.17*** (0.88) -4.60*** (0.33) -6.66*** (0.54) -0.38*** (0.05) Intercept – pre-IT 10.10*** (0.94) 16.45*** (1.20) 8.04*** (1.06) 17.85*** (2.11) 29.20*** (0.95) 4.61*** (0.17) 13.99*** (0.80) 7.43*** (0.26) 7.61*** (0.45) 0.53*** (0.08) Intercept – post-IT 2.57* (1.33) 2.79 (1.73) 3.43** (1.60) 4.67 (3.00) 8.98*** (1.56) -0.46 (0.43) 2.83** (1.20) 2.83*** (0.41) 0.95 (0.71) 0.15 (0.09) Notes: The smooth transition regression model used a continuous logistic function in all cases, after evaluation of the nonlinear test based on Teräsvirta (1994). The standard error in parentheses was estimated by using the bootstrap method with 2000 iterations. Sergio Julio Chión-Chacón, Kevin Antonio Álvarez García. Decline of Interest Rates under Inflation Targeting and Previous Regimes:... 21 indicating a structural shift consistent with a regime change. Additionally, comparing the baseline model to the one without the dummy reveals whether the inclusion of the dummy improves the explanatory power. A higher R2 and significant coefficients in the baseline model suggest that the dummy effectively captures regime-specific dynamics. Table 10 shows substantial changes in parameters across subsamples for the 10 countries, thus indicating a potential regime shift. All regressions, except for the Euro Area, display a higher R2 value in the baseline model, thus suggesting that the model with the dummy is robust and captures regime-specific dynamics better in most cases. 6. Conclusions This study aims to answer the question whether the inflation targeting (IT) regime and its variation have contributed to the historic reduction in interest rates experienced by both developed and emerging economies. Furthermore, it examines whether the output gap and the inflation level have played a significant role in this process of rate reduction. A Binary Regime Model embedded within a Backward-Looking Taylor and STR was estimated to capture the monetary policy regime, and it has been found that the inflation targeting regime has played a crucial role in the historic reduction of interest rates in the emerging economies of Latin America and the developed economies of our sample. The empirical results show that this effect has had a stronger impact in Latin American economies. These results are consistent with the assertions made by Bernanke (2022) and Fazlollahi and Ebrahimijan (2022). However, while the evidence indicates that the adoption of IT has been an important factor in explaining the decline in interest rates, in Brazil, the United Kingdom, and the Eurozone, inflation and the output gap would not have played a significant role, and other mechanisms would be behind this reduction (e.g., the real exchange rate, terms of trade, etc.). Regarding the output gap, the evidence shows that it has only been a significant factor in Peru, Chile, Canada, and Colombia. Additionally, two key conclusions emerge from our findings. First, the elasticity of inflation has notably increased over time. Second, in both preand post-IT periods, the magnitude of inflation elasticities exceeds that of the output gap. These results suggest that central banks, adhering to a backward-looking Taylor Rule, have consistently responded more strongly to inflation than to the output gap, with this response intensifying after the adoption of IT. The reduction in nominal interest rates under the IT regime suggests better inflation expectation anchoring, leading to lower inflation and borrowing costs. However, as rates approach their lower bound, the traditional monetary policy becomes less effective, thereby forcing central banks to rely on unconventional measures with uncertain long-term impacts. Given the use of post-pandemic data, the results may be subject to structural breaks due to significant changes in the global economy. An extension of this study could involve addressing these breaks, possibly through techniques like Markov-switching models, so that to better capture the impact of the pandemic on monetary policy. ISSN 1392-1258 eISSN 2424-6166 Ekonomika. 2024, vol. 104(1) 22 References Apeti, A. E., Combes, J., & Minea, A. (2024). Inflation targeting and fiscal policy volatility: Evidence from developing countries. Journal Of International Money And Finance, 141, 102996. https://doi.org/10.1016/j. jimonfin.2023.102996 Arsić, M., Mladenović, Z., & Nojković, A. (2022). Macroeconomic performance of inflation targeting in European and Asian emerging economies. Journal Of Policy Modeling, 44(3), 675–700. https://doi. org/10.1016/j.jpolmod.2022.06.002 Bhalla, S., Bhasin, K., & Loungani, P. (2023). Macro Effects of Formal Adoption of Inflation Targeting. IMFWorking Paper, https://doi.org/10.5089/9798400229169.001 Ball, L.M. and Sheridan, N. (2003). Does inflation targeting matter? IMF Working Papers, 2003(129), 1018–5941. https://doi.org/10.5089/9781451855135.001 Bambe, B. (2023). Inflation targeting and private domestic investment in developing countries. Economic Modelling, 125, 106353. https://doi.org/10.1016/j.econmod.2023.106353 Banerjee, R. N., Boctor, V., Mehrotra, A., & Zampolli, F. (2022). Fiscal deficits and inflation risks: the role of fiscal and monetary policy regimes. Working paper, Bank for International Settlements, available at: 1028-BIS Barsky, R., & Easton, M. (2021). The global saving glut and the fall in US real interest rates: A 15-year retrospective. Economic Perspectives, 1. https://www.chicagofed.org/publications/economic-perspectives/2021/1 Bernanke, B. S. (2005a). The global savings glut and the U.S. current account deficit. Remarks by Governor Ben S. Bernanke at the Homer Jones Lecture, St. Luois, Missouri. The Federal Reserve Board of Governors, available at: 200503102 Bernanke, B. S. (2022). 21st Century Monetary Policy: The Federal Reserve from the Great Inflation to COVID-19. WW Norton & Company. Carlstrom, C. T., & Fuerst, T. S. (2000). Forward-Looking versus Backward-Looking Taylor rules. En Working Paper. https://doi.org/10.26509/frbc-wp-200009 Carvalho, C., Nechio, F., & Tristao, T. (2021). Taylor rule estimation by OLS. Journal of Monetary Economics, 124, 140–154. https://doi.org/10.1016/j.jmoneco.2021.10.010 Cogley, T., & Nason, J. M. (1995). Effects of the Hodrick-Prescott filter on trend and difference stationary time series Implications for business cycle research. Journal Of Economic Dynamics And Control, 19(1-2), 253–278. https://doi.org/10.1016/0165-1889(93)00781-x Dautovic, E. (2017). The effect of real-time fiscal policy on sovereign interest rates in OECD countries. International Economics and Economic Policy, 14(1), 167–185. https://doi.org/10.1007/s10368-015-0334-y De Mendonça, H. F., & e Souza, G. J. D. G. (2009). Inflation targeting credibility and reputation: the consequences for the interest rate. Economic Modelling, 26(6), 1228–1238. https://doi.org/10.1016/j.econmod.2009.05.010 Dell’Erba, S., & Sola, S. (2016). Does fiscal policy affect interest rates? Evidence from a factor-augmented panel. The BE Journal of Macroeconomics, 16(2), 395–437. https://doi.org/10.1515/bejm-2015-0119 Fazlollahi, N., Ebrahimijam, S. (2022). The Relationship Between Interest Rates and Inflation: Time Series Evidence from Canada. In: Özataç, N., Gökmenoğlu, K.K., Rustamov, B. (eds) New Dynamics in Banking and Finance. Springer Proceedings in Business and Economics. Springer, Cham. https://doi. org/10.1007/978-3-030-93725-6_11 Fouejieu, A. and Roger, S. (2013). Inflation targeting and country risk: an empirical investigation. IMF Working Paper WP/13/21. https://doi.org/10.5089/9781475554717.001 Gehringer, A., & Mayer, T. (2019). Understanding low interest rates: evidence from Japan, Euro Area, United States and United Kingdom. Scottish Journal of Political Economy, 66(1), 28–53. https://doi.org/10.1111/ sjpe.12176 Sergio Julio Chión-Chacón, Kevin Antonio Álvarez García. Decline of Interest Rates under Inflation Targeting and Previous Regimes:... 23 Goodman-Bacon, A. (2021). Difference-in-differences with variation in treatment timing. Journal Of Econometrics, 225(2), 254–277. https://doi.org/10.1016/j.jeconom.2021.03.014 Hamilton, J. D. (2018). Why You Should Never Use the Hodrick-Prescott Filter. The Review Of Economics And Statistics, 100(5), 831–843. https://doi.org/10.1162/rest_a_00706 Hodrick, R. J., & Prescott, E. C. (1997). Postwar US business cycles: an empirical investigation. Journal of Money, credit, and Banking, 26(1), 1–16. https://doi.org/10.2307/2953682 Horvath, R., Kaszab, L., & Marsal, A. (2022). Interest rate rules and inflation risks in a macro‐finance model. Scottish Journal Of Political Economy, 69(4), 416–440. https://doi.org/10.1111/sjpe.12307 Kregždė, A., & Murauskas, G. (2015). Impact of Sovereign Credit Risk on the Lithuanian Interest Rate on Loans. Ekonomika, 94(2), 113–128. https://doi.org/10.15388/ekon.2015.2.8236 Li, K.-W. (2012). Is There a “Low Interest Rate Trap? Ekonomika, 91(1), 7–23. https://doi.org/10.15388/ Ekon.2012.0.910. Lin, S. and Ye, H. (2007). Does inflation targeting really make a difference? Evaluating the treatment effect of inflation targeting in seven industrial countries. Journal of Monetary Economics, 54, 2521–33. https:// doi.org/10.1016/j.jmoneco.2007.06.017 Maher, M., Zhao, Y., & Tang, C. (2022). The Taylor Rule in Egypt: Is it Optimal? Is there Equilibrium Determinacy? Journal Of Economic Integration, 37(3), 484-522. https://doi.org/10.11130/jei.2022.37.3.484 Mankiw, N. G., Reis, R., & Wolfers, J. (2003). Disagreement about Inflation Expectations. NBER Macroeconomics Annual, 18, 209–248. https://doi.org/10.1086/ma.18.3585256 Miles, W., & Schreyer, S. (2012). Is monetary policy non‐linear in Indonesia, Korea, Malaysia, and Thailand? A quantile regression analysis. Asian‐Pacific Economic Literature, 26(2), 155–166. https://doi. org/10.1111/j.1467-8411.2012.01344.x Mishkin, F. and Schmidt-Hebbel, K. (2007). Does inflation targeting make a difference? Working Paper Series, NBER, 12876. Retrieved from https://www.nber.org/system/files/working_papers/w12876/w12876.pdf Ravn, M. O., & Uhlig, H. (2002). On Adjusting the Hodrick-Prescott Filter for the Frequency of Observations. The Review Of Economics And Statistics, 84(2), 371–376. https://doi.org/10.1162/003465302317411604 Reid, M., & Siklos, P. (2021). Inflation expectations surveys: a review of some survey design choices and their implications. Studies in Economics and Econometrics, 45(4), 283–303. https://doi.org/10.1080/03 796205.2022.2060299 Rossetti, N., Nagano, M. S., & Meirelles, J. L. F. (2017). A behavioral analysis of the volatility of interbank interest rates in developed and emerging countries. Journal of Economics, Finance and Administrative Science, 22(42), 99–128. Retrieved from https://www.emerald.com/insight/content/doi/10.1108/ jefas-02-2017-0033/full/html Stojanovikj, M., & Petrevski, G. (2020). Macroeconomic effects of inflation targeting in emerging market economies. Empirical Economics, 61(5), 2539-2585. https://doi.org/10.1007/s00181-020-01987-0 Vega, M. and Winkelried, D. (2005). Inflation Targeting and inflation behaviour: a successful story? International Journal of Central Banking, 1, 153–75. Retrieved from https://www.ijcb.org/journal/ijcb05q4a5.htm Visokaviciene, B. (2010). Monetary Policy Creates Macroeconomic Stability. Ekonomika, 89(3), 55–68. https:// doi.org/10.15388/ekon.2010.0.975 ISSN 1392-1258 eISSN 2424-6166 Ekonomika. 2024, vol. 104(1) 24 Appendices Appendix A. Econometric Methods and Estimations 1. Growth rate of Natural Output Estimation Table 7. Potential GDP growth rate Country Sample Average potential GDP growth rate (%) Peru 1997Q1 – 2022Q4 4.0 Chile 1997Q1 – 2022Q4 3.5 Colombia 1996Q2 – 2022Q4 3.0 Mexico 1996Q2 – 2022Q4 2.1 Brazil 1997Q1 – 2022Q4 2.1 US 1965Q3 – 2022Q4 2.8 UK 1987Q1 – 2022Q4 1.8 Canada 1963Q1 – 2022Q4 2.9 Euro Area 1996Q1 – 2022Q4 1.4 Japan 1995Q1 – 2022Q4 0.6 Note. The trend of output gap obtained by the HP filter is considered as the potential GDP. Source: Own elaboration. 2. Weight Average of Inflation Target In order to compute the inflation elasticities presented in Table 5, we employ a weighted average of the inflation target, where the weighting is determined by the duration for which the target was maintained. The Formula used for this calculation is as follows: Average of π* 𝑟𝑟𝑡𝑡= 𝜃𝜃0+𝜃𝜃1𝜋𝜋𝑡𝑡−1 +𝜃𝜃2𝑦𝑦𝑡𝑡−1 +𝜃𝜃3𝐷𝐷𝑡𝑡+𝜀𝜀𝑡𝑡 𝐸𝐸(𝑟𝑟𝑡𝑡|𝜋𝜋𝑡𝑡−1,𝑦𝑦𝑡𝑡−1,𝐷𝐷𝑡𝑡= 1)= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋𝑡𝑡−1 +𝜃𝜃 2𝑦𝑦𝑡𝑡−1 +𝜃𝜃 3; I.T. regime 𝐸𝐸(𝑟𝑟𝑡𝑡|𝜋𝜋𝑡𝑡−1,𝑦𝑦𝑡𝑡−1,𝐷𝐷𝑡𝑡= 0)= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋𝑡𝑡−1 +𝜃𝜃 2𝑦𝑦𝑡𝑡−1 ; previous regimes 𝑟𝑟𝑡𝑡= 𝛽𝛽0+𝛽𝛽1𝜋𝜋𝑡𝑡+ 𝛽𝛽2𝑦𝑦𝑡𝑡+𝛾𝛾𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)+𝜖𝜖𝑡𝑡 (2) 𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)=1 1+𝑒𝑒−𝑘𝑘𝜏𝜏𝑡𝑡 Exponential function: 𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)= 𝑒𝑒^(−𝑘𝑘|𝜏𝜏𝑡𝑡|) 𝜋𝜋 = (𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−4 𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−4 )∗100 . 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗+𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(1) (3) 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗+𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(0) (4) Elasticity of inflation = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗ (5) Elasticity of output gap = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗; 𝜕𝜕=𝜕𝜕  (6) 𝐴𝐴𝐴𝐴𝑒𝑒𝑟𝑟𝐴𝐴𝐴𝐴𝑒𝑒 𝑜𝑜𝑜𝑜 𝜋𝜋∗=∑ ∑ 𝜋𝜋𝑖𝑖 ∗𝑤𝑤𝑗𝑗 𝑁𝑁 𝑖𝑖=1 𝐾𝐾 𝑗𝑗=1 (7) After IT: 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(1)= 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3 (8) Before IT: 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(0)= 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗ (9) Elasticity of inflation = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗= 𝜃𝜃 1×𝜕𝜕∗ 𝜕𝜕𝑡𝑡 (10) Elasticity of output gap = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗; 𝜕𝜕=𝜕𝜕 =𝜃𝜃 2 𝜕𝜕×𝜕𝜕 𝜕𝜕𝑡𝑡=𝜃𝜃 2 𝜕𝜕𝑡𝑡 (11) (7) where πi* is the inflation target, and i = 1, 2, 3, … N indicates the number of objectives that each country has had. wj indicates the period of time that said objective has been maintained, thus j = 1, 2, 3, … K indicates the quarters. Details are given in Table 6. Table 8. Average target inflation Peru Chile Colombia Mexico Brazil U.K. Canada Target (%) (time frame in quarters) 2.5 (13Q) 3.5 (4Q) 5.5 (4Q) 4 (7QQ) 8 (3Q) 2.5 (47Q) 3 (5Q) 2 (64Q) 3 (56Q) 4.5 (3) 3 (69Q) 6 (4Q) 2 (77Q) 2.5 (7Q) -2.5 (24Q) 4 (16) 3.75 (3Q) 4.5 (53Q) -2 (108Q) -2.4 (10) 3.5 (8) 3.5 (5Q) 4 (12Q) - - - - 3 (41) -3.75 (5Q) - - ----3.5 (8Q) - - ----3.25 (4Q) - - ----3 (6Q) - - Average 2.1 2.8 1.8 3.1 4.3 2.2 2.1 Notes: US, Japan and EA have had an inflation target of 2% since the adoption of IT. ‘Q’ represents quarters. Source: Own elaboration. Sergio Julio Chión-Chacón, Kevin Antonio Álvarez García. Decline of Interest Rates under Inflation Targeting and Previous Regimes:... 25 3. Interbank Interest Rate Estimation We estimate the regression by assuming that inflation is at its targeted level and the output gap is zero. The rationale behind this estimation approach stems from our objective of comparing periods during which the economy operated at its natural level. After IT: 𝑟𝑟𝑡𝑡= 𝜃𝜃0+𝜃𝜃1𝜋𝜋𝑡𝑡−1 +𝜃𝜃2𝑦𝑦𝑡𝑡−1 +𝜃𝜃3𝐷𝐷𝑡𝑡+𝜀𝜀𝑡𝑡 𝐸𝐸(𝑟𝑟𝑡𝑡|𝜋𝜋𝑡𝑡−1,𝑦𝑦𝑡𝑡−1,𝐷𝐷𝑡𝑡= 1)= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋𝑡𝑡−1 +𝜃𝜃 2𝑦𝑦𝑡𝑡−1 +𝜃𝜃 3; I.T. regime 𝐸𝐸(𝑟𝑟𝑡𝑡|𝜋𝜋𝑡𝑡−1,𝑦𝑦𝑡𝑡−1,𝐷𝐷𝑡𝑡= 0)= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋𝑡𝑡−1 +𝜃𝜃 2𝑦𝑦𝑡𝑡−1 ; previous regimes 𝑟𝑟𝑡𝑡= 𝛽𝛽0+𝛽𝛽1𝜋𝜋𝑡𝑡+ 𝛽𝛽2𝑦𝑦𝑡𝑡+𝛾𝛾𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)+𝜖𝜖𝑡𝑡 (2) 𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)=1 1+𝑒𝑒−𝑘𝑘𝜏𝜏𝑡𝑡 Exponential function: 𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)= 𝑒𝑒^(−𝑘𝑘|𝜏𝜏𝑡𝑡|) 𝜋𝜋 = (𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−4 𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−4 )∗100 . 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗+𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(1) (3) 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗+𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(0) (4) Elasticity of inflation = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗ (5) Elasticity of output gap = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗; 𝜕𝜕=𝜕𝜕  (6) 𝐴𝐴𝐴𝐴𝑒𝑒𝑟𝑟𝐴𝐴𝐴𝐴𝑒𝑒 𝑜𝑜𝑜𝑜 𝜋𝜋∗=∑ ∑ 𝜋𝜋𝑖𝑖 ∗𝑤𝑤𝑗𝑗 𝑁𝑁 𝑖𝑖=1 𝐾𝐾 𝑗𝑗=1 (7) After IT: 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(1)= 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3 (8) Before IT: 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(0)= 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗ (9) Elasticity of inflation = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗= 𝜃𝜃 1×𝜕𝜕∗ 𝜕𝜕𝑡𝑡 (10) Elasticity of output gap = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗; 𝜕𝜕=𝜕𝜕 =𝜃𝜃 2 𝜕𝜕×𝜕𝜕 𝜕𝜕𝑡𝑡=𝜃𝜃 2 𝜕𝜕𝑡𝑡 (11) (8) Before IT: 𝑟𝑟𝑡𝑡= 𝜃𝜃0+𝜃𝜃1𝜋𝜋𝑡𝑡−1 +𝜃𝜃2𝑦𝑦𝑡𝑡−1 +𝜃𝜃3𝐷𝐷𝑡𝑡+𝜀𝜀𝑡𝑡 𝐸𝐸(𝑟𝑟𝑡𝑡|𝜋𝜋𝑡𝑡−1,𝑦𝑦𝑡𝑡−1,𝐷𝐷𝑡𝑡= 1)= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋𝑡𝑡−1 +𝜃𝜃 2𝑦𝑦𝑡𝑡−1 +𝜃𝜃 3; I.T. regime 𝐸𝐸(𝑟𝑟𝑡𝑡|𝜋𝜋𝑡𝑡−1,𝑦𝑦𝑡𝑡−1,𝐷𝐷𝑡𝑡= 0)= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋𝑡𝑡−1 +𝜃𝜃 2𝑦𝑦𝑡𝑡−1 ; previous regimes 𝑟𝑟𝑡𝑡= 𝛽𝛽0+𝛽𝛽1𝜋𝜋𝑡𝑡+ 𝛽𝛽2𝑦𝑦𝑡𝑡+𝛾𝛾𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)+𝜖𝜖𝑡𝑡 (2) 𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)=1 1+𝑒𝑒−𝑘𝑘𝜏𝜏𝑡𝑡 Exponential function: 𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)= 𝑒𝑒^(−𝑘𝑘|𝜏𝜏𝑡𝑡|) 𝜋𝜋 = (𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−4 𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−4 )∗100 . 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗+𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(1) (3) 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗+𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(0) (4) Elasticity of inflation = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗ (5) Elasticity of output gap = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗; 𝜕𝜕=𝜕𝜕  (6) 𝐴𝐴𝐴𝐴𝑒𝑒𝑟𝑟𝐴𝐴𝐴𝐴𝑒𝑒 𝑜𝑜𝑜𝑜 𝜋𝜋∗=∑ ∑ 𝜋𝜋𝑖𝑖 ∗𝑤𝑤𝑗𝑗 𝑁𝑁 𝑖𝑖=1 𝐾𝐾 𝑗𝑗=1 (7) After IT: 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(1)= 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3 (8) Before IT: 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(0)= 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗ (9) Elasticity of inflation = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗= 𝜃𝜃 1×𝜕𝜕∗ 𝜕𝜕𝑡𝑡 (10) Elasticity of output gap = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗; 𝜕𝜕=𝜕𝜕 =𝜃𝜃 2 𝜕𝜕×𝜕𝜕 𝜕𝜕𝑡𝑡=𝜃𝜃 2 𝜕𝜕𝑡𝑡 (11) (9) where the parameters are those that we obtain from the model, whereas the average target inflation level parameters (π*) are the ones we estimate in Figure 2. 4. Elasticities for Inflation and Output For reference purposes, we estimate the elasticities of inflation and the output gap in both periods (preand post-IT). The estimate is as follows (Table 4): Elasticity of inflation = 𝑟𝑟𝑡𝑡= 𝜃𝜃0+𝜃𝜃1𝜋𝜋𝑡𝑡−1 +𝜃𝜃2𝑦𝑦𝑡𝑡−1 +𝜃𝜃3𝐷𝐷𝑡𝑡+𝜀𝜀𝑡𝑡 𝐸𝐸(𝑟𝑟𝑡𝑡|𝜋𝜋𝑡𝑡−1,𝑦𝑦𝑡𝑡−1,𝐷𝐷𝑡𝑡= 1)= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋𝑡𝑡−1 +𝜃𝜃 2𝑦𝑦𝑡𝑡−1 +𝜃𝜃 3; I.T. regime 𝐸𝐸(𝑟𝑟𝑡𝑡|𝜋𝜋𝑡𝑡−1,𝑦𝑦𝑡𝑡−1,𝐷𝐷𝑡𝑡= 0)= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋𝑡𝑡−1 +𝜃𝜃 2𝑦𝑦𝑡𝑡−1 ; previous regimes 𝑟𝑟𝑡𝑡= 𝛽𝛽0+𝛽𝛽1𝜋𝜋𝑡𝑡+ 𝛽𝛽2𝑦𝑦𝑡𝑡+𝛾𝛾𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)+𝜖𝜖𝑡𝑡 (2) 𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)=1 1+𝑒𝑒−𝑘𝑘𝜏𝜏𝑡𝑡 Exponential function: 𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)= 𝑒𝑒^(−𝑘𝑘|𝜏𝜏𝑡𝑡|) 𝜋𝜋 = (𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−4 𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−4 )∗100 . 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗+𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(1) (3) 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗+𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(0) (4) Elasticity of inflation = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗ (5) Elasticity of output gap = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗; 𝜕𝜕=𝜕𝜕  (6) 𝐴𝐴𝐴𝐴𝑒𝑒𝑟𝑟𝐴𝐴𝐴𝐴𝑒𝑒 𝑜𝑜𝑜𝑜 𝜋𝜋∗=∑ ∑ 𝜋𝜋𝑖𝑖 ∗𝑤𝑤𝑗𝑗 𝑁𝑁 𝑖𝑖=1 𝐾𝐾 𝑗𝑗=1 (7) After IT: 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(1)= 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3 (8) Before IT: 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(0)= 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗ (9) Elasticity of inflation = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗= 𝜃𝜃 1×𝜕𝜕∗ 𝜕𝜕𝑡𝑡 (10) Elasticity of output gap = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗; 𝜕𝜕=𝜕𝜕 =𝜃𝜃 2 𝜕𝜕×𝜕𝜕 𝜕𝜕𝑡𝑡=𝜃𝜃 2 𝜕𝜕𝑡𝑡 (11) (10) Elasticity of output gap = 𝑟𝑟𝑡𝑡= 𝜃𝜃0+𝜃𝜃1𝜋𝜋𝑡𝑡−1 +𝜃𝜃2𝑦𝑦𝑡𝑡−1 +𝜃𝜃3𝐷𝐷𝑡𝑡+𝜀𝜀𝑡𝑡 𝐸𝐸(𝑟𝑟𝑡𝑡|𝜋𝜋𝑡𝑡−1,𝑦𝑦𝑡𝑡−1,𝐷𝐷𝑡𝑡= 1)= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋𝑡𝑡−1 +𝜃𝜃 2𝑦𝑦𝑡𝑡−1 +𝜃𝜃 3; I.T. regime 𝐸𝐸(𝑟𝑟𝑡𝑡|𝜋𝜋𝑡𝑡−1,𝑦𝑦𝑡𝑡−1,𝐷𝐷𝑡𝑡= 0)= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋𝑡𝑡−1 +𝜃𝜃 2𝑦𝑦𝑡𝑡−1 ; previous regimes 𝑟𝑟𝑡𝑡= 𝛽𝛽0+𝛽𝛽1𝜋𝜋𝑡𝑡+ 𝛽𝛽2𝑦𝑦𝑡𝑡+𝛾𝛾𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)+𝜖𝜖𝑡𝑡 (2) 𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)=1 1+𝑒𝑒−𝑘𝑘𝜏𝜏𝑡𝑡 Exponential function: 𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)= 𝑒𝑒^(−𝑘𝑘|𝜏𝜏𝑡𝑡|) 𝜋𝜋 = (𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−4 𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−4 )∗100 . 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗+𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(1) (3) 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗+𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(0) (4) Elasticity of inflation = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗ (5) Elasticity of output gap = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗; 𝜕𝜕=𝜕𝜕  (6) 𝐴𝐴𝐴𝐴𝑒𝑒𝑟𝑟𝐴𝐴𝐴𝐴𝑒𝑒 𝑜𝑜𝑜𝑜 𝜋𝜋∗=∑ ∑ 𝜋𝜋𝑖𝑖 ∗𝑤𝑤𝑗𝑗 𝑁𝑁 𝑖𝑖=1 𝐾𝐾 𝑗𝑗=1 (7) After IT: 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(1)= 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3 (8) Before IT: 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(0)= 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗ (9) Elasticity of inflation = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗= 𝜃𝜃 1×𝜕𝜕∗ 𝜕𝜕𝑡𝑡 (10) Elasticity of output gap = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗; 𝜕𝜕=𝜕𝜕 =𝜃𝜃 2 𝜕𝜕×𝜕𝜕 𝜕𝜕𝑡𝑡=𝜃𝜃 2 𝜕𝜕𝑡𝑡 (11) (11) where the only thing that changes is 𝑟𝑟𝑡𝑡= 𝜃𝜃0+𝜃𝜃1𝜋𝜋𝑡𝑡−1 +𝜃𝜃2𝑦𝑦𝑡𝑡−1 +𝜃𝜃3𝐷𝐷𝑡𝑡+𝜀𝜀𝑡𝑡 𝐸𝐸(𝑟𝑟𝑡𝑡|𝜋𝜋𝑡𝑡−1,𝑦𝑦𝑡𝑡−1,𝐷𝐷𝑡𝑡= 1)= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋𝑡𝑡−1 +𝜃𝜃 2𝑦𝑦𝑡𝑡−1 +𝜃𝜃 3; I.T. regime 𝐸𝐸(𝑟𝑟𝑡𝑡|𝜋𝜋𝑡𝑡−1,𝑦𝑦𝑡𝑡−1,𝐷𝐷𝑡𝑡= 0)= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋𝑡𝑡−1 +𝜃𝜃 2𝑦𝑦𝑡𝑡−1 ; previous regimes 𝑟𝑟𝑡𝑡= 𝛽𝛽0+𝛽𝛽1𝜋𝜋𝑡𝑡+ 𝛽𝛽2𝑦𝑦𝑡𝑡+𝛾𝛾𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)+𝜖𝜖𝑡𝑡 (2) 𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)=1 1+𝑒𝑒−𝑘𝑘𝜏𝜏𝑡𝑡 Exponential function: 𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)= 𝑒𝑒^(−𝑘𝑘|𝜏𝜏𝑡𝑡|) 𝜋𝜋 = (𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−4 𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−4 )∗100 . 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗+𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(1) (3) 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗+𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(0) (4) Elasticity of inflation = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗ (5) Elasticity of output gap = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗; 𝜕𝜕=𝜕𝜕  (6) 𝐴𝐴𝐴𝐴𝑒𝑒𝑟𝑟𝐴𝐴𝐴𝐴𝑒𝑒 𝑜𝑜𝑜𝑜 𝜋𝜋∗=∑ ∑ 𝜋𝜋𝑖𝑖 ∗𝑤𝑤𝑗𝑗 𝑁𝑁 𝑖𝑖=1 𝐾𝐾 𝑗𝑗=1 (7) After IT: 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(1)= 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3 (8) Before IT: 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(0)= 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗ (9) Elasticity of inflation = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗= 𝜃𝜃 1×𝜕𝜕∗ 𝜕𝜕𝑡𝑡 (10) Elasticity of output gap = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗; 𝜕𝜕=𝜕𝜕 =𝜃𝜃 2 𝜕𝜕×𝜕𝜕 𝜕𝜕𝑡𝑡=𝜃𝜃 2 𝜕𝜕𝑡𝑡 (11) for both periods (estimate is obtained from Equations (11) and (12), 𝑟𝑟𝑡𝑡= 𝜃𝜃0+𝜃𝜃1𝜋𝜋𝑡𝑡−1 +𝜃𝜃2𝑦𝑦𝑡𝑡−1 +𝜃𝜃3𝐷𝐷𝑡𝑡+𝜀𝜀𝑡𝑡 𝐸𝐸(𝑟𝑟𝑡𝑡|𝜋𝜋𝑡𝑡−1,𝑦𝑦𝑡𝑡−1,𝐷𝐷𝑡𝑡= 1)= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋𝑡𝑡−1 +𝜃𝜃 2𝑦𝑦𝑡𝑡−1 +𝜃𝜃 3; I.T. regime 𝐸𝐸(𝑟𝑟𝑡𝑡|𝜋𝜋𝑡𝑡−1,𝑦𝑦𝑡𝑡−1,𝐷𝐷𝑡𝑡= 0)= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋𝑡𝑡−1 +𝜃𝜃 2𝑦𝑦𝑡𝑡−1 ; previous regimes 𝑟𝑟𝑡𝑡= 𝛽𝛽0+𝛽𝛽1𝜋𝜋𝑡𝑡+ 𝛽𝛽2𝑦𝑦𝑡𝑡+𝛾𝛾𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)+𝜖𝜖𝑡𝑡 (2) 𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)=1 1+𝑒𝑒−𝑘𝑘𝜏𝜏𝑡𝑡 Exponential function: 𝛾𝛾(𝜏𝜏𝑡𝑡;𝜅𝜅)= 𝑒𝑒^(−𝑘𝑘|𝜏𝜏𝑡𝑡|) 𝜋𝜋 = (𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−4 𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑡𝑡−4 )∗100 . 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗+𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(1) (3) 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗+𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(0) (4) Elasticity of inflation = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗ (5) Elasticity of output gap = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗; 𝜕𝜕=𝜕𝜕  (6) 𝐴𝐴𝐴𝐴𝑒𝑒𝑟𝑟𝐴𝐴𝐴𝐴𝑒𝑒 𝑜𝑜𝑜𝑜 𝜋𝜋∗=∑ ∑ 𝜋𝜋𝑖𝑖 ∗𝑤𝑤𝑗𝑗 𝑁𝑁 𝑖𝑖=1 𝐾𝐾 𝑗𝑗=1 (7) After IT: 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(1)= 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3 (8) Before IT: 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗−𝜃𝜃 3𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑦𝑦(0)= 𝑟𝑟𝑡𝑡= 𝜃𝜃 0+𝜃𝜃 1𝜋𝜋∗ (9) Elasticity of inflation = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗= 𝜃𝜃 1×𝜕𝜕∗ 𝜕𝜕𝑡𝑡 (10) Elasticity of output gap = 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 ×𝜕𝜕 𝜕𝜕|𝜕𝜕=𝜕𝜕∗; 𝜕𝜕=𝜕𝜕 =𝜃𝜃 2 𝜕𝜕×𝜕𝜕 𝜕𝜕𝑡𝑡=𝜃𝜃 2 𝜕𝜕𝑡𝑡 (11) is the growth rate of natural output for each country (Table 5). ISSN 1392-1258 eISSN 2424-6166 Ekonomika. 2024, vol. 104(1) 26 Appendix B. Tables and Figures Table 9. Results with IV estimation (Equation (1)) Intercept Inflation Output Gap Dummy R2 (pseudo) Peru (1996Q1-2022Q4) 13.43*** (0.97) 1.18*** (0.20) -0.03 (0.22) -10.67*** (1.02) 0.47 Chile (1996Q2-2022Q4) 20.17*** (2.12) 0.58*** (0.11) 0.34** (0.16) -16.9*** (2.12) 0.42 Colombia (1995Q3-2021Q4) 2.75* (1.66) 2.09*** (0.19) -0.74** (0.25) -1.01 (1.58) 0.70 Mexico (1995Q3-2022Q4) 12.15*** (1.71) 1.00*** (0.21) 0.71*** (0.24) -8.57*** (1.58) 0.68 Brazil (1996Q2-2022Q4) 22.15*** (4.20) 0.09 (0.17) -0.16 (0.38) -12.13** (4.44) 0.41 United States (1964Q4-2022Q4) 4.05 (7.84) 0.69*** (0.15) 0.02 (0.19) 1.42 (7.59) 0.25 United Kingdom (1986Q2-2022Q4) 22.90*** (3.53) -1.07** (0.44) 0.13 (0.22) -19.00*** (3.50) 0.14 Euro Area (1997Q2-2022Q4) -92.80 (87.83) -0.42 (1.15) 0.42 (1.39) 94.21 (87.82) 0.03 Canada (1962Q2-2022Q4) 12.49*** (1.25) -0.16 (0.19) 0.80*** (0.20) -9.73*** (1.33) 0.26 Japan (2002Q2-2022Q4) 0.35*** (0.10) 0.00 (0.04) 0.04* (0.02) -0.29*** (0.06) 0.37 Notes: Standard errors are reported in parentheses. Asterisks denote statistical significance at the 1% (*), 5% (**), and 10% (***) levels. Standard errors were calculated by using the HAC robust estimator. The instruments used were the own lags of each variable. The Akaike Information Criterion (AIC) was used to determine the optimal number of lags, with a maximum of 12 lags (6 for the Euro Area to avoid rank issues). For individual countries: for Peru, we used 10 lags for inflation and 1 lag for the output gap; for Chile, we used 4 lags for the output gap and 10 lags for inflation; for Colombia, we used 10 lags for inflation and 5 lags for the output gap; for Mexico, we used 12 lags for inflation and 1 lag for the output gap; for Brazil, we used 2 lags for inflation and 3 lags for the output gap; for Canada, we used 9 lags for inflation and 1 lag for the output gap; for the U.K., we used 9 lags for inflation and 4 lags for the output gap; for the U.S., we used 10 lags for inflation and 4 lags for the output gap; for Japan, we used 5 lags for inflation and 1 lag for the output gap; and, for the Euro Area, we used 6 lags for inflation and 1 lag for the output gap. Sergio Julio Chión-Chacón, Kevin Antonio Álvarez García. Decline of Interest Rates under Inflation Targeting and Previous Regimes:... 27 28 Figure 3. Transition functions Notes: Dynamic of logistic transition functions for each country are presented. These results are derived from the estimation of Equation (2). 28 Figure 3. Transition functions Notes: Dynamic of logistic transition functions for each country are presented. These results are derived from the estimation of Equation (2). 28 Figure 3. Transition functions Notes: Dynamic of logistic transition functions for each country are presented. These results are derived from the estimation of Equation (2). 28 Figure 3. Transition functions Notes: Dynamic of logistic transition functions for each country are presented. These results are derived from the estimation of Equation (2). Figure 3. Transition functions Notes: Dynamic of logistic transition functions for each country are presented. These results are derived from the estimation of Equation (2). ISSN 1392-1258 eISSN 2424-6166 Ekonomika. 2024, vol. 104(1) 28 Table 10. Robustness check Country Model Intercept Inflation gap Output gap Dummy R2 Peru Complete 10.88*** (1.45) 0.41*** (0.15) 0.11*** (0.04) -8.57*** (1.26) 0.78 No dummy 4.23*** (0.46) 1.00*** (0.15) 0.05 (0.12) - 0.30 Before IT 9.07*** (1.10) 0.81*** (0.24) 0.31 (0.49) - 0.30 After IT 3.04*** (0.16) 0.42*** (0.08) 0.09** (0.04) - 0.34 Chile Complete 11.34*** (0.85) 0.50*** (0.08) 0.35*** (0.09) -8.92*** (0.85) 0.75 No dummy 4.60*** (0.39) 0.80*** (0.15) 0.21 (0.15) - 0.27 Before IT 12.38*** (2.37) 0.59 (0.75) 0.60 (0.41) - 0.19 After IT 3.76*** (0.23) 0.48*** (0.09) 0.19** (0.09) - 0.32 Colombia Complete 9.95*** (2.09) 0.89*** (0.10) 0.30* (0.18) -8.41*** (1.86) 0.92 No dummy 4.19*** (0.38) 1.44*** (0.06) -0.04 (0.12) - 0.85 Before IT 4.76*** (1.27) 1.45*** (0.12) 0.53 (0.42) - 0.84 After IT 4.73*** (0.23) 0.55*** (0.11) 0.10 (0.08) - 0.36 Mexico Complete 12.05*** (2.62) 0.62*** (0.10) 0.15 (0.09) -8.36*** (2.30) 0.89 No dummy 5.89 (0.49) 1.08 (0.05) 0.24 (0.17) - 0.83 Before IT 23.65 (4.70) 0.35 (0.21) -3.67 (1.42) - 0.70 After IT 5.33 (0.38) 1.05 (0.24) 0.16 (0.09) - 0.22 Brazil Complete 20.13*** (2.02) 0.22 (0.22) -0.08 (0.35) -11.10*** (1.43) 0.45 No dummy 12.12*** (0.62) -0.15 (0.16) -0.13 (0.30) - 0.59 Before IT 21.80*** (0.48) -0.05 (0.08) 0.64 (0.39) - 0.28 After IT 10.24*** (0.56) 0.10 (0.16) -0.27 (0.24) - 0.45 Sergio Julio Chión-Chacón, Kevin Antonio Álvarez García. Decline of Interest Rates under Inflation Targeting and Previous Regimes:... 29 Country Model Intercept Inflation gap Output gap Dummy R2 U.S. Complete 2.81*** (0.54) 0.75*** (0.12) 0.33 (0.20) -3.71*** (0.58) 0.67 No dummy 3.34*** (0.20) 0.90*** (0.06) 0.16 (0.11) - 0.50 Before IT 4.13*** (0.21) 0.84*** (0.06) 0.26** (0.11) - 0.55 After IT 0.83*** (0.14) 0.14** (0.07) 0.15* (0.09) - 0.18 U.K. Complete 10.40*** (1.51) 0.23 (0.25) 0.17 (0.10) -7.56*** (1.16) 0.64 No dummy 4.22*** (0.31) 1.08*** (0.16) 0.04 (0.12) - 0.24 Before IT 9.67*** (0.59) 0.69*** (0.17) 0.34* (0.18) - 0.54 After IT 3.52*** (0.25) -0.00 (0.17) 0.08 (0.09) -0.00 (0.73) Canada Complete 5.24*** (0.74) 0.62*** (0.13) 0.41** (0.17) -3.29*** (0.67) 0.68 No dummy 4.77*** (0.20) 0.89*** (0.07) 0.32*** (0.12) - 0.45 Before IT 6.97*** (0.26) 0.68*** (0.06) 0.52*** (0.14) - 0.53 After IT 2.73*** (0.17) -0.30** (0.12) 0.42*** (0.11) - 0.13 Japan Complete 0.38*** (0.07) 0.07** (0.03) 0.02 (0.03) -0.34*** (0.09) 0.44 No dummy 0.20*** (0.06) -0.01 (0.03) 0.02 (0.02) - 0.01 Before IT 0.66*** (0.14) 0.14** (0.06) -0.00 (0.02) - 0.14 After IT 0.12*** (0.02) 0.04*** (0.01) 0.01 (0.00) - 0.19 Euro Area Complete 3.81*** (0.49) 0.25 (0.29) 0.18 (0.16) -2.78*** (0.36) 0.23 No dummy 1.71*** (0.18) 0.11 (0.12) 0.17* (0.10) - 0.05 Before IT 4.68*** (0.11) 0.80*** (0.15) 0.21* (0.12) - 0.85 After IT 1.50*** (0.18) 0.14 (0.11) 0.19** (0.09) - 0.08 Notes: The dates before and after IT correspond to a specific date for each country. Standard errors are reported in parentheses. Asterisks denote statistical significance at the 1% (*), 5% (**), and 10% (***) levels. Standard errors were calculated by using the HAC robust estimator.