Quarterly Projection Model for India: Key Elements and Properties

Beneš, Jaromí­r; Clinton, Kevin; George, Asish; Gupta, Pranav; John, Joice; Kamenik, Ondra; Laxton, Douglas; Mitra, Pratik; Nadhanael, G.V.; Portillo, Rafael; Wang, Hou; Zhang, Fan

doi:10.2139/ssrn.2938332

Cited by 3 publications

(10 citation statements)

References 4 publications

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“…However, calibration helps matching theoretically-consistent propagation mechanisms and achieve robust data fit -making the QPM useful for practical policy analysis and medium-term forecasting. Furthermore, calibration is the preferred approach at other central banks; e.g., Benes et al (2017), Baksa et al (2020), Vlcek et al (2020, Epstein et al (2022).…”

Section: Model Structurementioning

confidence: 99%

“…Modeling these in full force requires a non-linear model, as in Argov et al (2007), Benes et al (2017), Mkhatrishvili et al (2019), or Chansriniyom et al (2020). However, it is also possible to maintain the simplicity of a linear model but still allow for the QPM to incorporate the implications of only partial credibility, up to a first order approximation.…”

Section: Monetary Policy Credibility Channel In the Bog Qpmmentioning

confidence: 99%

“…Various central banks developed modelling tools that have at their core this analytical structure, especially among those adopting inflation targeting or inflation forecast targeting as the operational regime; see Svensson (1997) and Adrian et al (2018). In particular, the canonical model was tailored and extended to capture relevant channels, country characteristics and policy framework particularities; see Benes et al (2017) for India, Baksa et al (2020) for Cambodia, Vlcek et al (2020) for Rwanda, Baksa et al (2021) for Morocco, Karam et al (2021) ©International Monetary Fund. Not for Redistribution INTERNATIONAL MONETARY FUND 7 for the Philippines, Epstein et al (2022) for Vietnam.…”

Section: Introductionmentioning

confidence: 99%

“…Not for Redistribution INTERNATIONAL MONETARY FUND 7 for the Philippines, Epstein et al (2022) for Vietnam. The introduction of the monetary policy credibility channel -whereby past inflation deviations from the target make prices more persistent, create the risk of unanchored inflation expectations, and impair the ability of the central bank to quickly bring inflation back to the target -follows the intuition in Argov et al (2007), Benes et al (2017) and Chansriniyom et al (2020). Practical and real-time implementation of analytical frameworks to study the impact of COVID-19 shocks and the effects of alternative policy packages designed to support the economy are described in Gonzales and Rodriguez (2021).…”

Section: Introductionmentioning

confidence: 99%

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Quarterly Projection Model for the Bank of Ghana

Mkhatrishvili¹,

Nalban²,

Abradu-Otoo³

et al. 2022

IMF Working Papers

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Bank of Ghana staff and the IMF team that has led the Technical Assistance (TA) project on developing a Forecasting and Policy Analysis System (FPAS) at the Bank of Ghana (BOG). The TA project has been conducted by ICD since 2019 and financed by the second African Regional Technical Assistance Centre in West Africa (AFRITAC West 2). The authors greatly appreciate the BOG management for their support to the FPAS TA project. The authors are also grateful to Martin Fukac for his earlier contributions to the TA project; to Paul Cashin and Philippe Karam for their feedback; to the IMF African Department Ghana team for their collaboration and support to the FPAS TA project; and to participants at an ICD seminar for their helpful comments. The views expressed in this paper are those of the authors and do not necessarily represent the views of the Bank of Ghana, National Bank of Georgia, IMF, its Executive Board, or IMF management.

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Section: Model Structurementioning

confidence: 99%

Section: Monetary Policy Credibility Channel In the Bog Qpmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Quarterly Projection Model for the Bank of Ghana

Mkhatrishvili¹,

Nalban²,

Abradu-Otoo³

et al. 2022

IMF Working Papers

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“…The QPM is based on the principles of New-Keynesian open economy models, with equations for output (IS curve), inflation (the Phillips curve), the shot-term interest rate (a policy reaction function), and the exchange rate (an uncovered interest rate parity condition). There are numerous references regarding the use of these models and its implementation in several emerging market economies (for example see Berg and Laxton (2006), Benes et al (2017), and Gonzalez et al (2020) among others).In addition, some of this models have tried to incorporate simple modifications to the food inflation Phillips curves, allowing for example, rain precipitations (Berg and Laxton (2006), Uribe et al (2002), and Benes et al (2017)). In this context, our work contributes to the literature by providing a theoretical framework that evaluates to what extent the central bank should react to these shocks to anchor inflation expectation.…”

Section: Introductionmentioning

confidence: 99%

Weather Shocks and Inflation Expectations in Semi-Structural Models

Romero-Chamorro¹,

Naranjo-Saldarriaga²

2022

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Colombia is particularly affected by the El Niño Southern Oscillation (ENSO) weather fluctuations. In this context, this study explores how the adverse weather events linked to ENSO affect the inflation expectations in Colombia and how to incorporate these second-round effects into a small open economy New Keynesian model. Using BVARx models we provide evidence that the inflation expectations obtained from surveys and break-even inflation measures are affected by weather supply shocks. Later, using this stylised fact, we modify one of the core forecasting models of the Banco de la República by incorporating the mechanisms in which weather-related shocks affect marginal costs and inflation expectations. We find that ENSO shocks had an important role in both inflation and the dynamics of inflation expectations, and that policymakers should consider this fact.

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The Effects of Monetary Policy Shock: Evidence From Systemically Important Economies

Bazhenova,

Banna,

Bazhenov

et al. 2024

ФКДПТП

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In the paper, we explore the effects of monetary policy shock on the economic growth in systemically important countries such as the US, the Euro Area and China and their impact on Ukraine. Thus, the war in Ukraine and the rise of key policy rates by central banks to curb inflation have had a significant negative impact on economic activity. There has been both a significant decrease in trade activity and a slowdown in the services sector growth, which was the main engine of global economic growth at the beginning of 2023. Based on the vector autoregression model results, we demonstrated a slight initial decline of GDP growth with following stabilization in response to the rise of key policy rates in the US. In China, this decline is much bigger and constitutes 11% after the shock. In the Euro Area, we also observe a similar pattern as in the US with an initial decline up to 2% and a further return to equilibrium. Analyzing the results of the forecast error variance decomposition, we should note that GDP fluctuations in systemically significant economies are mainly explained by their own fluctuations. The key policy rate’s contribution ranges from 1% in the US to 11% in China. At the same time, economic growth in China is less vulnerable to inflation fluctuations, in the Eurozone we observe the most sustained one among the considered economies. In addition, the study shows that the key policy rate in the United States has a positive effect on the one in Ukraine.

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Quarterly Projection Model for India: Key Elements and Properties

Cited by 3 publications

References 4 publications

Quarterly Projection Model for the Bank of Ghana

Quarterly Projection Model for the Bank of Ghana

Weather Shocks and Inflation Expectations in Semi-Structural Models

The Effects of Monetary Policy Shock: Evidence From Systemically Important Economies

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