## INFLATION IN THE CZECH REPUBLIC: ANALYSIS USING AN ADAPTIVE LEARNING STRUCTURAL MODEL ## Source details **Canonical URL:** [INFLATION IN THE CZECH REPUBLIC: ANALYSIS USING AN ADAPTIVE LEARNING STRUCTURAL MODEL](https://www.imf.org/-/media/files/publications/cr/2024/english/1czeea2024002.pdf) ## Other formats - [Markdown version](/-/media/files/publications/cr/2024/english/1czeea2024002.pdf.md) - [Structured JSON version](/-/media/files/publications/cr/2024/english/1czeea2024002.pdf.json) --- ### Inflation developments and central bank response - Headline inflation peaked at 18.0 percent (year-on-year) in September 2022; core inflation peaked at 14.7 percent (year-on-year) in October 2022. - The central bank raised interest rates from 0.25 percent in June 2021 to 7 percent in June 2022 and maintained that level thereafter. - Since the start of 2023, both headline and core inflation rates have experienced a rapid decline. ### Forecast approaches and baseline projection - Multiple forecasting methods were used, including simulations from estimated structural models and bottom-up autoregression, due to uncertainties in modeling headline and core inflation. - Staff’s baseline assumptions: projections from different methodologies suggest headline inflation rates are expected to reach the inflation tolerance band in early 2024 and the inflation target by early 2025. ### Structural model with adaptive learning: setup and rationale - Motivation: Inflation expectations in Czechia are notably shaped by past inflation outcomes, creating feedbacks where rapidly decreasing inflation contributes to shorter duration of elevated inflation and vice-versa. - Expectations formation: Agents form expectations by updating beliefs using a simple AR(2) model with the most recent data; this departs from rational expectations. - Expectation updating mechanism: Agents’ expectations follow an AR(2) process with time-varying coefficients, updated via a Kalman filter mechanism following Slobodyan and Wouters (2012a; 2012b). - Central bank channels: - Direct: tightening policy reduces demand, lowers the output gap, and decreases inflation. - Expectation channels (two): tightening policy reduces current inflation and shapes future expectations because lower-than-expected current inflation leads agents to adjust how past inflation is perceived, influencing future inflation. - Workhorse model: semi-structural variation of a small open economy New Keynesian framework with wage and price Phillips curves; adaptation of models by Galí, Smets, and Wouters (2012), Berg and others (2006), Dizioli and Wang (2022), Dizioli (2022). - Estimation: Bayesian methods with quarterly macroeconomic data from 2000: Q1 to 2023: Q3 for Czechia. Variables: output gap (estimated by IMF team), real wage gap, annualized quarterly price inflation deviation from target, and policy rate. ### Core model equations (as specified) - IS Curve: y_t = α_y^L y_{t−1} + α_{yy} y_{t+1} + γ(π_{t+1} − r_t) + φ_Z Z_t + S_{y_t} - Demand shock: sY_t = ρ_ε sY_{t−1} + ε_{y_t} - Price Phillips Curve: π_t = α(π^L) π_{t−1} + α(π^F) π_{t+1} + k π_w_t + λ z_t + ε_{π_t} - Nominal wage definition: π_{w,t} = w_t − w_{t−1} + π_t - Wage Phillips Curve: π_{w,t} = −α_w^L w_{t−1} + α_{wy} π_{w,t+1} + K_w y_t + ε_{w,t} - Policy reaction function: r_t = ρ r_{t−1} + (1−ρ)[ρ_π π_{t+1} + ρ_y y_t] + ε_{r,t} - UIP condition: z_t = E_t[z_{t+1}] − (r_t − π_t − (r_t^y − π_t^y)) + ε_{z,t} - Definitions: y_t = output gap; π = quarter-on-quarter annualized inflation rate; r_t = nominal monetary policy interest rate; w_t = constant composition real wage deviation from labor productivity growth; π_{w,t} = real wage inflation; z_t = koruna bilateral real exchange rate with respect to the US dollar. ### Model implications: policy stance and inflation dynamics - The speed to reach the inflation target depends on the strength of the monetary policy stance. - Baseline scenario: year-on-year inflation falls within the tolerance band in early 2024 and reaches the target in early 2025. This scenario requires a moderately tight policy stance during most of 2024, followed by a significant relaxation during 2025. - Alternative simulation (earlier and sharper policy relaxation starting in 2023: Q4): year-on-year inflation falls more slowly, entering the tolerance band later in 2024 and approaching the target by the end of 2025. - Simulation caveat: simulations assume future policy is guided by the policy reaction function in the model (policy deviation is temporary). Prolonged policy deviations that undermine time consistency would have a more pronounced effect on inflation. ### Scenario outcomes: Policy Stance and CPI Inflation (Early Rate Drop vs Baseline) - 2023Q4: 3.1 / 8.7 vs 3.3 / 7.4 - 2024Q1: 2.7 / 5.3 vs 3.4 / 2.6 - 2024Q2: 2.5 / 3.9 vs 3.2 / 3.0 - 2024Q3: 2.2 / 3.2 vs 2.9 / 2.7 - 2024Q4: 2.0 / 2.7 vs 2.7 / 2.9 - 2025Q1: 1.7 / 2.4 vs 2.4 / 2.1 - 2025Q2: 1.5 / 2.5 vs 2.1 / 2.0 - 2025Q3: 1.2 / 2.2 vs 1.9 / 2.0 - 2025Q4: 1.0 / 2.2 vs 1.7 / 2.0 ### Financial conditions: summary of findings and drivers - Financial conditions continue to tighten, albeit at a decreasing pace, based on the IMF European Department financial conditions index (FCI) covering prices and quantities for households and nonfinancial corporations. - The overall economy FCI shows significant tightening starting in early 2022 and continuing through 2024, with the pace of tightening slowing. - Tightening is occurring across European countries but at different paces; within the euro area, dispersion of tightening in the first half of 2023 is more pronounced than in previous contraction episodes, suggesting stronger idiosyncratic factors across countries. - Sectoral detail: FCIs for all major sectors in Czechia demonstrate tightening; the pace of tightening was stronger in the first half of 2023 for households but started to decrease in the second half of 2023 relative to nonfinancial corporations. - Main drivers of tight financial conditions: tight monetary policy stance, availability of credit, and higher price of risk. - Timeline of influences: - Since H2 2021: loosening from pandemic-related measures began to slow; risk pricing deteriorated due to worsening sentiment linked to the energy crisis and rising inflation from mid-2021. - 2022: financial conditions reversed and tightened as price of risk, policy tightening, and funding constraints became important drivers amid high inflation and deteriorating sentiment. - From Q1 2023: pace of tightening has slowed due to weakening impact of price of risk and policy stance, improved consumer sentiment, stabilizing real wages, fading of the energy crisis, and continuing disinflation. ### Enhancing the green transition: progress, targets, and investment needs - Progress and gaps: - GHG emissions reduced by about 43 percent in 2021 relative to 1990, driven by coal phase-out progress. - To meet EU Fit-for-55 agenda goals by 2030 and longer-term net zero targets, stricter climate policies and accelerated transition are needed. - Czechia remains highly energy dependent and reliant on fossil fuels, with GHG-emission intensities among the highest in the EU (OECD, 2023). Energy intensity per unit of GDP is considerably higher than in many other OECD countries. - Barriers: energy-inefficient residential building stock, coal-fueled heating systems, and a large industrial sector. - Energy mix and renewables: - In 2021, solid fossil fuels accounted for 30 percent of Czechia’s energy mix (EC, 2023). - Renewable share has increased slowly; scaling up renewables (solar, wind, geothermal, renewable hydrogen, sustainable bio-methane) is critical to reduce coal dependence and cover natural gas shortfalls. - Decarbonization targets: - Phase out coal by 2033. - Raise renewable energy share to 30 percent by 2030 (from close to 18 percent in 2021). - Raise nuclear power to 33 percent by 2040 (up from 15 percent in 2016). - Investment needs: - Estimated investment needs by 2050: about CZK 3.5 trillion, of which about CZK 1–1.2 trillion are identified from public support including EU funds. - Public support sources include: Cohesion Funds, Recovery and Resilience Plan, ETS1 and ETS2 revenues, Modernization Fund, Social Climate Fund, and Loan from EIB-SFDI. - Authorities’ modeled scenario outcomes: - In ETS-covered sectors, expected emissions reduction by 2030 is 63 percent vs 1990 level. - In non-ETS sectors (Effort Sharing Regulation), expected emissions reduction by 2030 is 32 percent vs 1990 level (compared to target of 26 percent). ### Supporting and complementary policies for the green transition - Existing and envisaged policies: - Carbon prices under EU’s Emissions Trading System ETS1 and ETS2 (commencing in 2027) to shift relative prices toward greener energy and encourage efficiency. - Regulatory directives: prohibiting registration of fossil-fuel vehicles within the next decade and establishing energy efficiency standards for buildings to incentivize private green investment. - Complementary measures recommended by staff: feebates in agriculture and other non-covered sectors, targeted subsidies to bolster R&D investments, accelerated depreciation rates for machinery and equipment reliant on fossil fuels provided they are replaced by cleaner alternatives. - Investment Incentive Act (amended by Act No. 89/2023 Coll.) incentives to private sector (as described by authorities): - Corporate income tax relief for 10 years. - Cash grant CZK 200,000 per newly created job. - Cash grant for training employees of 50 percent to 70 percent of training costs. - Cash grant on capital investment up to 20 percent of eligible costs (max. CZK 0.5 billion). - Authorities should assess effectiveness and flexibility of these measures. ### Socio-economic and sectoral transition measures - Coal regions (e.g., Ústí nad Labem and Karlovy Vary) face higher unemployment and poverty; phase-out risks exacerbating local impacts. - Recommended support via Operational Program Just Transition: investment in business start-ups and SMEs, up-skilling and retraining of workers, and swift implementation of measures to support workers at risk of redundancy in coal regions. - Energy efficiency priorities: - Scale up investment in building and transport energy efficiency, prioritizing renovation of buildings connected to coal-based district heating. - REPowerEU-funded measures, such as subsidies for replacement of gas heating in low-income households, are positive steps. - Promote low- and zero-emission vehicles and modal shifts in transport (rail, public transport) to reduce CO2 emissions in the transport sector. ### Electric vehicle adoption and transport decarbonization (Section 2) ### Current status and constraints - Czechia has comparatively lower electric vehicle adoption and one of the oldest car fleets in the European Union. - Contributing factors: a lack of infrastructure for electric vehicles, ineffectual incentives, and comparatively weak public support for hybrid or electric vehicles. ### Policies, strategic documents, and existing measures - The Transport Policy of the Czech Republic (Ministry of Transport, 2021) is a key strategic document aiming to raise the CO2-efficiency of the vehicle fleet. - Government aid to purchase electric vehicles (announced in June 2022) funded by the National Recovery Plan targets public entities, educational institutions, public institutions, and NGOs. - The RRF supports decarbonization of transport through EUR 1.2 billion of investments in low-emission vehicles for the business sector, railway infrastructure, and to promote electric charging stations and cycling pathways. ### Findings and policy recommendations for transport - To achieve faster replacement of the vehicle fleet, the government could consider offering specific incentives to scrap old vehicles and replace them with newer models. - Measures to facilitate the use of electric vehicles, particularly charging infrastructure, and more public support for hybrid or electric vehicles would help improve adoption. - Continued support through targeted investments (charging stations, low-emission vehicle purchases, railway infrastructure, cycling pathways) is important for decarbonizing transport. *Source: IMF Country Report No. 24/29 — Section 1 (prepared December 15, 2023); 1czeea2024002 - Section 2* ### Section 1 ### INFLATION IN THE CZECH REPUBLIC: ANALYSIS USING AN ADAPTIVE LEARNING STRUCTURAL MODEL ### Inflation developments and central bank response - Headline inflation peaked at 18.0 percent (year-on-year) in September 2022; core inflation peaked at 14.7 percent (year-on-year) in October 2022. - The central bank raised interest rates from 0.25 percent in June 2021 to 7 percent in June 2022 and maintained that level thereafter. - Since the start of 2023, both headline and core inflation rates have experienced a rapid decline. ### Forecast approaches and baseline projection - Multiple forecasting methods were used, including simulations from estimated structural models and bottom-up autoregression, due to uncertainties in modeling headline and core inflation. - Staff’s baseline assumptions: projections from different methodologies suggest headline inflation rates are expected to reach the inflation tolerance band in early 2024 and the inflation target by early 2025. ### Structural model with adaptive learning: setup and rationale - Motivation: In Czechia, inflation expectations are notably shaped by past inflation outcomes, creating feedbacks where rapidly decreasing inflation contributes to shorter duration of elevated inflation and vice-versa. - Departing from rational expectations, agents form expectations by updating beliefs using a simple AR(2) model with the most recent data. - Central bank influences inflation through three channels: - Direct: tightening policy reduces demand, lowers the output gap, and decreases inflation. - Two expectation channels: tightening policy reduces current inflation and shapes future expectations because lower-than-expected current inflation leads agents to adjust how past inflation is perceived, influencing future inflation. - The workhorse model: semi-structural variation of a small open economy New Keynesian framework with wage and price Phillips curves; adaptation of models by Galí, Smets, and Wouters (2012), Berg and others (2006), Dizioli and Wang (2022), Dizioli (2022). - Agents’ expectations follow an AR(2) process with time-varying coefficients, updated via a Kalman filter mechanism following Slobodyan and Wouters (2012a; 2012b). - Estimation: Bayesian methods with quarterly macroeconomic data from 2000: Q1 to 2023: Q3 for Czechia. Variables: output gap (estimated by IMF team), real wage gap, annualized quarterly price inflation deviation from target, and policy rate. ### Core model equations (as specified) - IS Curve: y_t = α_y^L y_{t−1} + α_{yy} y_{t+1} + γ(π_{t+1} − r_t) + φ_Z Z_t + S_{y_t} - Demand shock: sY_t = ρ_ε sY_{t−1} + ε_{y_t} - Price Phillips Curve: π_t = α(π^L) π_{t−1} + α(π^F) π_{t+1} + k π_w_t + λ z_t + ε_{π_t} - Nominal wage definition: π_{w,t} = w_t − w_{t−1} + π_t - Wage Phillips Curve: π_{w,t} = −α_w^L w_{t−1} + α_{wy} π_{w,t+1} + K_w y_t + ε_{w,t} - Policy reaction function: r_t = ρ r_{t−1} + (1−ρ)[ρ_π π_{t+1} + ρ_y y_t] + ε_{r,t} - UIP condition: z_t = E_t[z_{t+1}] − (r_t − π_t − (r_t^y − π_t^y)) + ε_{z,t} - Definitions: y_t = output gap; π = quarter-on-quarter annualized inflation rate; r_t = nominal monetary policy interest rate; w_t = constant composition real wage deviation from labor productivity growth; π_{w,t} = real wage inflation; z_t = koruna bilateral real exchange rate with respect to the US dollar. ### Model implications: policy stance and inflation dynamics - The speed to reach the inflation target depends on the strength of the monetary policy stance. - Baseline scenario: year-on-year inflation falls within the tolerance band in early 2024 and reaches the target in early 2025. This scenario requires a moderately tight policy stance during most of 2024, followed by a significant relaxation during 2025. - Alternative simulation (earlier and sharper policy relaxation starting in 2023: Q4): year-on-year inflation falls more slowly, entering the tolerance band later in 2024 and approaching the target by the end of 2025. - Note: simulations assume future policy is guided by the policy reaction function in the model (policy deviation is temporary). Prolonged policy deviations that undermine time consistency would have a more pronounced effect on inflation. ### Scenario table (Policy Stance and CPI Inflation) - Early Rate Drop (Policy Stance / CPI Inflation) vs Baseline (Policy Stance / CPI Inflation): - 2023Q4: 3.1 / 8.7 vs 3.3 / 7.4 - 2024Q1: 2.7 / 5.3 vs 3.4 / 2.6 - 2024Q2: 2.5 / 3.9 vs 3.2 / 3.0 - 2024Q3: 2.2 / 3.2 vs 2.9 / 2.7 - 2024Q4: 2.0 / 2.7 vs 2.7 / 2.9 - 2025Q1: 1.7 / 2.4 vs 2.4 / 2.1 - 2025Q2: 1.5 / 2.5 vs 2.1 / 2.0 - 2025Q3: 1.2 / 2.2 vs 1.9 / 2.0 - 2025Q4: 1.0 / 2.2 vs 1.7 / 2.0 *Source: IMF Staff Calculation* --- ### Financial conditions in Czech Republic: summary of findings - Financial conditions continue to tighten, albeit at a decreasing pace, based on the IMF European Department financial conditions index (FCI) covering prices and quantities for households and nonfinancial corporations. - The overall economy FCI shows significant tightening starting in early 2022 and continuing through 2024, with the pace of tightening slowing. - Tightening is occurring across European countries but at different paces; within the euro area, dispersion of tightening in the first half of 2023 is more pronounced than in previous contraction episodes, suggesting stronger idiosyncratic factors across countries. - Sectoral detail: FCIs for all major sectors in Czechia demonstrate tightening; the pace of tightening was stronger in the first half of 2023 for households but started to decrease in the second half of 2023 relative to nonfinancial corporations. ### Drivers and recent dynamics - Main drivers of tight financial conditions: tight monetary policy stance, availability of credit, and higher price of risk. - Timeline of influences: - Since H2 2021: loosening from pandemic-related measures began to slow; risk pricing deteriorated due to worsening sentiment linked to the energy crisis and rising inflation from mid-2021. - 2022: financial conditions reversed and tightened as price of risk, policy tightening, and funding constraints became important drivers amid high inflation and deteriorating sentiment. - From Q1 2023: pace of tightening has slowed due to weakening impact of price of risk and policy stance, improved consumer sentiment, stabilizing real wages, fading of the energy crisis, and continuing disinflation. --- ### Enhancing the green transition in Czech Republic: overview and policy recommendations - Progress and remaining gaps: - GHG emissions reduced by about 43 percent in 2021 relative to 1990, driven by coal phase-out progress. - To meet EU Fit-for-55 agenda goals by 2030 and longer-term net zero targets, stricter climate policies and accelerated transition are needed. - Czechia remains highly energy dependent and reliant on fossil fuels, with GHG-emission intensities among the highest in the EU (OECD, 2023). Energy intensity per unit of GDP is considerably higher than in many other OECD countries. - Barriers: energy-inefficient residential building stock, coal-fueled heating systems, and a large industrial sector. - Energy mix and renewables: - In 2021, solid fossil fuels accounted for 30 percent of Czechia’s energy mix (EC, 2023). - Renewable share has increased slowly; scaling up renewables (solar, wind, geothermal, renewable hydrogen, sustainable bio-methane) is critical to reduce coal dependence and cover natural gas shortfalls. ### Czechia’s decarbonization strategy and investment needs - Strategy objectives: - Phase out coal by 2033. - Raise renewable energy share to 30 percent by 2030 (from close to 18 percent in 2021). - Raise nuclear power to 33 percent by 2040 (up from 15 percent in 2016). - These targets are intended to help meet 2030 European climate and energy goals. - Investment needs: - Estimated investment needs by 2050: about CZK 3.5 trillion, of which about CZK 1–1.2 trillion are identified from public support including EU funds. - Public support sources include: Cohesion Funds, Recovery and Resilience Plan, ETS1 and ETS2 revenues, Modernization Fund, Social Climate Fund, and Loan from EIB-SFDI. - Authorities’ modeled scenario: - In ETS-covered sectors, expected emissions reduction by 2030 is 63 percent vs 1990 level. - In non-ETS sectors (Effort Sharing Regulation), expected emissions reduction by 2030 is 32 percent vs 1990 level (compared to target of 26 percent). ### Supporting policies to facilitate the green transition - Policies envisaged: - Carbon prices under EU’s Emissions Trading System ETS1 and ETS2 (commencing in 2027) to shift relative prices toward greener energy and encourage efficiency. - Regulatory directives: prohibiting registration of fossil-fuel vehicles within the next decade and establishing energy efficiency standards for buildings to incentivize private green investment. - Complementary measures recommended by staff: feebates in agriculture and other non-covered sectors, targeted subsidies to bolster R&D investments, accelerated depreciation rates for machinery and equipment reliant on fossil fuels provided they are replaced by cleaner alternatives. - Investment Incentive Act (amended by Act No. 89/2023 Coll.) incentives to private sector (as described by authorities): (i) Corporate income tax relief for 10 years, (ii) Cash grant CZK 200,000 per newly created job, (iii) Cash grant for training employees of 50 percent to 70 percent of training costs, (iv) Cash grant on capital investment up to 20 percent of eligible costs (max. CZK 0.5 billion). Authorities should assess effectiveness and flexibility. ### Socio-economic and sectoral transition measures - Coal regions (e.g., Ústí nad Labem and Karlovy Vary) face higher unemployment and poverty; phase-out risks exacerbating local impacts. - Recommended support via Operational Program Just Transition: investment in business start-ups and SMEs, up-skilling and retraining of workers, and swift implementation of measures to support workers at risk of redundancy in coal regions. - Energy efficiency priorities: - Scale up investment in building and transport energy efficiency, prioritizing renovation of buildings connected to coal-based district heating. - REPowerEU-funded measures, such as subsidies for replacement of gas heating in low-income households, are positive steps. - Promote low- and zero-emission vehicles and modal shifts in transport (rail, public transport) to reduce CO2 emissions in the transport sector. *Source: IMF Country Report No. 24/29 — Section 1 (prepared December 15, 2023).* ### Section 2 ### 1czeea2024002 - Section 2 ### Electric vehicle adoption and infrastructure - Czechia has comparatively lower electric vehicle adoption and one of the oldest car fleets in the European Union. - Contributing factors: a lack of infrastructure for electric vehicles, ineffectual incentives, and comparatively weak public support for hybrid or electric vehicles. ### Policies, strategic documents, and existing measures - The Transport Policy of the Czech Republic (Ministry of Transport, 2021) is a key strategic document aiming to raise the CO2-efficiency of the vehicle fleet. - Government aid to purchase electric vehicles (announced in June 2022) funded by the National Recovery Plan targets public entities, educational institutions, public institutions, and NGOs. - The RRF supports decarbonization of transport through EUR 1.2 billion of investments in low-emission vehicles for the business sector, railway infrastructure, and to promote electric charging stations and cycling pathways. ### Findings and policy recommendations - To achieve faster replacement of the vehicle fleet, the government could consider offering specific incentives to scrap old vehicles and replace them with newer models. - Measures to facilitate the use of electric vehicles, particularly charging infrastructure, and more public support for hybrid or electric vehicles would help improve adoption. - Continued support through targeted investments (charging stations, low-emission vehicle purchases, railway infrastructure, cycling pathways) is important for decarbonizing transport. *Source: 1czeea2024002 - Section 2* --- _Source: https://www.imf.org/-/media/files/publications/cr/2024/english/1czeea2024002.pdf_