Banque Nationale De Belgique Sa · EURONEXT:BNB
Banque Nationale De Belgique Sa — Technical Presentation (2018)
62 slides from Banque Nationale De Belgique Sa's technical presentation, 2018
Slide 1: Pipeline Pressures and Sectoral Inflation Dynamics
Pipeline Pressures and Sectoral Inflation Dynamics1 Frank Smets1 Joris Tielens2,3 Jan Van Hove3,4 1 European Central Bank, UGent and CEPR 2 National Bank of Belgium 3 KU Leuven 4 KBC Group 1 Disclaimer: The views expressed in this paper are those of the authors and do not necessarily reflect the vie
Slide 2: Outline
Outline Motivation What do we know so far? The model Overview of the model Estimation details Results Test prevalence pipeline pressures Comparison dfm vs. structural model Volatility & Persistence Additional results Concluding remarks
Slide 3: Motivation (i): Pipeline pressures
Motivation (i): Pipeline pressures I Pipeline pressures – “Pipeline pressures have built up in some sectors in early stages of the production and pricing chain.” (ECB, 2017 ) – “Producer price data of the construction sector were also consistent with a moderation of pressures in the inflation pipeli
Slide 4: Motivation (ii): Pipeline pressures
Motivation (ii): Pipeline pressures – Micro–level PPI’s/CPI’s often represent sequential input Freight insurance vs. Truck transp. services 0.8 Rubber vs. Tires 1 Autocorrelation Autocorrelation 0.8 0.7 0.6 0.6 0.5 0.4 0.4 0.2 0.3 0 0.2 −0.2 0.1 0 −0.4 −15 −10 −5 0 5 10 15 −15 −10 −5 0 5 10 15 20 mo
Slide 5: Motivation (iii): Pipeline pressures
Motivation (iii): Pipeline pressures – This paper: study implications of pipeline pressures for inflation dynamics. – Focus on two properties of inflation data: 1. Persistence 2. Volatility
Slide 6: Plan
Plan Motivation What do we know so far? The model Overview of the model Estimation details Results Test prevalence pipeline pressures Comparison dfm vs. structural model Volatility & Persistence Additional results Concluding remarks
Slide 7: What do we know so far?
What do we know so far? I Persistence and volatility have been studied before – Landmark paper: Boivin et al. (AER, 2009) I Dynamic factor model (dfm) Common component Residual z}|{ z}|{ Disaggregate index: πit = λ0i f t + it |{z} |{z} Sector Aggregate specific shocks shock 0 0 Headline index: πt =
Slide 8: What do we know so far?
What do we know so far? Stylized facts; disaggregate inflation (πit = λ0i f t + it ) Consumer prices Producer prices Mean Median Mean Median Persistence ρ(it ) 0.07 0.12 0.14 0.16 ρ(λ0i f t ) 0.57 0.62 0.44 0.51 2 Volatility 100 × σσ2 (π (it ) it ) 63.00 61.69 63.54 65.07 σ 2 (λ0 f ) 100 × σ2 (πiit
Slide 9: What do we know so far?
What do we know so far? Stylized facts; disaggregate inflation (πit = λ0i f t + it ) Consumer prices Producer prices Mean Median Mean Median Persistence ρ(it ) 0.07 0.12 0.14 0.16 ρ(λ0i f t ) 0.57 0.62 0.44 0.51 2 Volatility 100 × σσ2 (π (it ) it ) 63.00 61.69 63.54 65.07 σ 2 (λ0 f ) 100 × σ2 (πiit
Slide 10: What do we know so far?
What do we know so far? Stylized facts; disaggregate inflation (πit = λ0i f t + it ) Consumer prices Producer prices Mean Median Mean Median Persistence ρ(it ) 0.07 0.12 0.14 0.16 ρ(λ0i f t ) 0.57 0.62 0.44 0.51 2 Volatility 100 × σσ2 (π (it ) it ) 63.00 61.69 63.54 65.07 σ 2 (λ0 f ) 100 × σ2 (πiit
Slide 11: What do we know so far?
What do we know so far? Stylized facts; disaggregate inflation (πit = λ0i f t + it ) Consumer prices Producer prices Mean Median Mean Median Persistence ρ(it ) 0.07 0.12 0.14 0.16 ρ(λ0i f t ) 0.57 0.62 0.44 0.51 2 Volatility 100 × σσ2 (π (it ) it ) 63.00 61.69 63.54 65.07 σ 2 (λ0 f ) 100 × σ2 (πiit
Slide 12: What do we know so far?
What do we know so far? Stylized facts; disaggregate inflation (πit = λ0i f t + it ) Consumer prices Producer prices Mean Median Mean Median Persistence ρ(it ) 0.07 0.12 0.14 0.16 ρ(λ0i f t ) 0.57 0.62 0.44 0.51 2 Volatility 100 × σσ2 (π (it ) it ) 63.00 61.69 63.54 65.07 σ 2 (λ0 f ) 100 × σ2 (πiit
Slide 13: What we do
What we do I Traditional: two–way view on the data: πit = λ0i f t + it |{z} |{z} Aggregate Sector i shocks shock I Sector shocks → pipeline pressures → comovement I So λ0i f t confounds two types of shocks (Foerster e.a, 2011): – Aggregate shocks – Sector–specific shocks, through pipeline pressures
Slide 14: What we do
What we do In a nutshell, what we do 1. Infer pipeline pressures from the data I Structural model 2. Verify empirical relevance I Bayes factor 3. Verify difficulties of the dfm to correctly disentangle pipeline pressures from aggregate shocks I Compare model decomposition with dfm decomposition 4. A
Slide 15: Preview main findings
Preview main findings 1. Persistence I Important source of persistence I Pipeline pressures (often) take time to build 2. Volatility I Headline inflation: 21% (ppi) and 28% (pce) I Disaggregate price inflation – 40% for Healthcare (pce index) vs. 0.87% for Agriculture & Forestry (ppi index). – Gener
Slide 16: Literature
Literature Crossroads of multiple literatures 1. Origins inflation persistence: I Basu (AER, 1995); Blanchard (AER, 1982); Huang et al. (AER, 2006); Carvalho (JME, 2006); etc. 2. Origins inflation volatility: I Bouakez (EER, 2014); Schoenle et al. (2017, 2018); etc. 3. Dynamic factor model decomposi
Slide 17: Plan
Plan Motivation What do we know so far? The model Overview of the model Estimation details Results Test prevalence pipeline pressures Comparison dfm vs. structural model Volatility & Persistence Additional results Concluding remarks
Slide 18: Overview of the model (i)
Overview of the model (i) I Shocks I Type: standard set of shocks (Workhorse dsge models) I Level: aggregate and sectoral Type and level of shocks Level of shock Type Aggregate Sectoral Monetary policy shock X − Risk shock X − Aggregate demand shock X − Productivity shock X X Price markup shock X X
Slide 19: Overview of the model (ii)
Overview of the model (ii) Household PCE cat. 1 PCE cat. 2 PCE cat. 3 E.g. Healthcare E.g. Tobacco E.g. Footwear
Slide 20: Overview model (ii): Household
Overview model (ii): Household Maximize utility (household member h in sector j) ∞ X (C (h) − χC t t−1 (h)) 1−σ Ljt (h)1+ϕ Uj,t (h) = β s−t − s=t 1−σ 1+ϕ s.t. budget constraint J Z µ̄j Bt X Pt Ct + = Ljt (h)Wjt (h)dh + Bt−1 + Dt − Pt Tt Rt Zb,t µ̄j−1 j=1 Aggregate consumption bundle Z νc Z 1 1− 1 X
Slide 21: Overview of the model (iii)
Overview of the model (iii) Household PCE cat. 1 PCE cat. 2 PCE cat. 3 E.g. Healthcare E.g. Tobacco E.g. Footwear Sector 1 Sector 2 E.g. Rubber E.g. Chemicals mfg. mfg. Sector 3 Sector 4 E.g. Car tires E.g. Transporta- mfg. tion services
Slide 22: Overview model (iii): Intermediate goods producers
Overview model (iii): Intermediate goods producers Production n m k Yjt (f ) = Zp,t Zp,j,t Njt (f )φj Mjt (f )φj Kjt (f )φj −Φj (f ) | {z } | {z } | {z } our ate s al Lab edi pit rm Ca e Int J νm 1 νm −1 X νm −1 Mjt (f ) = ωjj 0 νm Mjj 0 t (f ) νm j 0 =1 Z 1 1 1+m,j 0 ,t Mjj 0 t (f ) = Mjj 0 t (f, f
Slide 23: Overview of the model (iv)
Overview of the model (iv) Household PCE cat. 1 PCE cat. 2 PCE cat. 3 E.g. Healthcare E.g. Tobacco E.g. Footwear Sector 1 Sector 2 E.g. Rubber E.g. Chemicals mfg. mfg. Sector 3 Sector 4 E.g. Car tires E.g. Transporta- mfg. tion services
Slide 24: Overview model (iv): Capital goods producers
Overview model (iv): Capital goods producers Law of motion capital e jt+1 (g) = 1 − ∆ Ujt (g) K e jt (g) + Zi,t Zi,j,t 1 − S Ijt (g) K Ijt (g) Ijt−1 (g) Investment J νi −1 νi X 1 νi −1 Ijt (g) = ψjj 0 νi Ijj 0 t (g) νi j 0 =1 Z 1 1 1+p,j 0 ,t Ijj 0 t (g) = Ijj 0 t (g, f ) 1+p,j 0 ,t df 0 Investment
Slide 25: Overview of the model (v)
Overview of the model (v) Household PCE cat. 1 PCE cat. 2 PCE cat. 3 E.g. Healthcare E.g. Tobacco E.g. Footwear Sector 1 Sector 2 E.g. Rubber E.g. Chemicals mfg. mfg. Sector 3 Sector 4 E.g. Car tires E.g. Transporta- mfg. tion services
Slide 26: Overview model (v): Final goods producers
Overview of the model (v): Final goods producers Production Yzt (q) = ςMzt (q) − Φz (q) J X 1 νf −1 νf −1 νf Mzt (q) = κzj νf Mzjt (q) νf j=1 Z 1 1 1+p,j,t Mzjt (q) = Mzjt (q, f ) 1+p,j,t df 0 Set consumer prices (pce’s) Calvo price stickiness αzpce Flow structure K creates role for pipeline pressur
Slide 27: Overview of the model (vi)
Overview of the model (vi) Household PCE cat. 1 PCE cat. 2 PCE cat. 3 E.g. Healthcare E.g. Tobacco E.g. Footwear Sector 1 Sector 2 E.g. Rubber E.g. Chemicals mfg. mfg. Sector 3 Sector 4 E.g. Car tires E.g. Transporta- mfg. tion services Monetary authority Government
Slide 28: Overview model (vii): Decomposition
Overview model (vii): Decomposition Disaggregate ppi inflation ppi πjt = αt (πjppi ) + β t (πjppi ) + γ t (πjppi ) | {z } | {z } | {z } Aggregate Sector j P ipeline shocks shocks P ressures Type and level of shocks Level of shock Type Aggregate Micro ppi j ppi −j Monetary policy shock X − − Risk sho
Slide 29: Overview model (vii): Decomposition
Overview model (vii): Decomposition Disaggregate ppi inflation ppi πjt = αt (πjppi ) + β t (πjppi ) + γ t (πjppi ) | {z } | {z } | {z } Aggregate Sector j P ipeline shocks shocks P ressures Type and level of shocks Level of shock Type Aggregate Micro ppi j ppi −j Monetary policy shock X − − Risk sho
Slide 30: Overview model (vii): Decomposition
Overview model (vii): Decomposition Disaggregate ppi inflation ppi πjt = αt (πjppi ) + β t (πjppi ) + γ t (πjppi ) | {z } | {z } | {z } Aggregate Sector j P ipeline shocks shocks P ressures Type and level of shocks Level of shock Type Aggregate Micro ppi j ppi −j Monetary policy shock X − − Risk sho
Slide 31: Overview model (vii): Decomposition
Overview model (vii): Decomposition Disaggregate ppi inflation ppi πjt = αt (πjppi ) + β t (πjppi ) + γ t (πjppi ) | {z } | {z } | {z } Aggregate Sector j P ipeline shocks shocks P ressures Type and level of shocks Level of shock Type Aggregate Micro ppi j ppi −j Monetary policy shock X − − Risk sho
Slide 32: Overview model (vii): Decomposition
Overview model (vii): Decomposition Disaggregate pce inflation pce πzt = αt (πzpce ) + β t (πzpce ) + γ t (πzpce ) | {z } | {z } | {z } Aggregate Sector z P ipeline shocks shocks P ressures Type and level of shocks Level of shock Type Aggregate Micro pce z pce −z Monetary policy shock X − − Risk sho
Slide 33: Estimation details
Estimation details I Dimensions of the model I 7 sectors: Agriculture, Mining, Utilities, Construction, Manufacturing, Services, Public Sector I 4 final consumption goods: Durables, Non–Durables, Services, Public sector goods I Partly calibrated 1. Behavioural parameters {β, σ, ...} 2. Structure of
Slide 34: Plan
Plan Motivation What do we know so far? The model Overview of the model Estimation details Results Test prevalence pipeline pressures Comparison dfm vs. structural model Volatility & Persistence Additional results Concluding remarks
Slide 35: Test empirical relevance of pipeline pressures
Test empirical relevance of pipeline pressures I Empirical relevance of pipeline pressures? I PPI sector j relevant for PPI of sector j 0 ? I PPI sector j relevant for PCE of product z? I Compare 2 models (with/without pipeline pressures) I Bayes factor to test for relevance pipeline pressures ωj 0j
Slide 36: Comparison dfm vs. structural model
Comparison dfm vs. structural model I Decompose U.S. inflation I Factor model: πit = λ0i f t + it I Structural model: π it = αt (πi ) + β t (πi ) + γ t (πi ) Headline PPI: dsge vs dfm 4 3 2 1 0 -1 -2 -3 -4 1970 1975 1980 1985 1990 1995 2000 2005 dfm: 2 0 $ft
Slide 37: Comparison dfm vs. structural model
Comparison dfm vs. structural model I Decompose U.S. inflation I Factor model: πit = λ0i f t + it I Structural model: π it = αt (πi ) + β t (πi ) + γ t (πi ) Headline PPI: dsge vs dfm 4 3 2 1 0 -1 -2 -3 -4 1970 1975 1980 1985 1990 1995 2000 2005 0 dfm: 9 $ft dsge: ,t (: ppi )h=1
Slide 38: Comparison dfm vs. structural model
Comparison dfm vs. structural model I Decompose U.S. inflation I Factor model: πit = λ0i f t + it I Structural model: π it = αt (πi ) + β t (πi ) + γ t (πi ) Headline PPI: dsge vs dfm 4 3 2 1 0 -1 -2 -3 1970 1975 1980 1985 1990 1995 2000 2005 0 dfm: 9 $ft dsge: ,t (: ppi )h=1 dsge: ,t (: ppi )h=1 +
Slide 39: Comparison dfm vs. structural model
Comparison dfm vs. structural model I Decompose U.S. inflation I Factor model: πit = λ0i f t + it I Structural model: π it = αt (πi ) + β t (πi ) + γ t (πi ) Headline PPI: dsge vs dfm 4 3 2 1 0 -1 -2 -3 -4 1970 1975 1980 1985 1990 1995 2000 2005 0 dfm: 9 $ft dsge: ,t (: ppi )h=1 dsge: ,t (: ppi )h=1
Slide 40: Volatility & Persistence (iii): Volatility
Volatility & Persistence (iii): Volatility Forecast Error Variance Decomposition: Producer Prices πjt = αt (πj ) + β t (πj ) + γ t (πj ) Infinite horizon (F EV D(∞)) F EV D(1) Macro Micro Micro Direct Pipeline Pressures Pipeline Pressures (1) (2) (3) (4) Agriculture & Forestry 5.46 93.68 0.86 0.73 O
Slide 41: Volatility & Persistence (iii): Volatility
Volatility & Persistence (iii): Volatility Forecast Error Variance Decomposition: Producer Prices πjt = αt (πj ) + β t (πj ) + γ t (πj ) Infinite horizon (F EV D(∞)) F EV D(1) Macro Micro Micro Direct Pipeline Pressures Pipeline Pressures (1) (2) (3) (4) Agriculture & Forestry 5.46 93.68 0.86 0.73 O
Slide 42: Volatility & Persistence (iii): Volatility
Volatility & Persistence (iii): Volatility Forecast Error Variance Decomposition: Producer Prices πjt = αt (πj ) + β t (πj ) + γ t (πj ) Infinite horizon (F EV D(∞)) F EV D(1) Macro Micro Micro Direct Pipeline Pressures Pipeline Pressures (1) (2) (3) (4) Agriculture & Forestry 5.46 93.68 0.86 0.73 O
Slide 43: Volatility & Persistence (iii): Volatility
Volatility & Persistence (iii): Volatility Forecast Error Variance Decomposition: Producer Prices πjt = αt (πj ) + β t (πj ) + γ t (πj ) Infinite horizon (F EV D(∞)) F EV D(1) Macro Micro Micro Direct Pipeline Pressures Pipeline Pressures (1) (2) (3) (4) Agriculture & Forestry 5.46 93.68 0.86 0.73 O
Slide 44: Volatility & Persistence (iii): Volatility
Volatility & Persistence (iii): Volatility Forecast Error Variance Decomposition: Producer Prices πjt = αt (πj ) + β t (πj ) + γ t (πj ) Infinite horizon (F EV D(∞)) F EV D(1) Macro Micro Micro Direct Pipeline Pressures Pipeline Pressures (1) (2) (3) (4) Agriculture & Forestry 5.46 93.68 0.86 0.73 O
Slide 45: Volatility & Persistence (iii): Volatility
Volatility & Persistence (iii): Volatility Forecast Error Variance Decomposition: Producer Prices πjt = αt (πj ) + β t (πj ) + γ t (πj ) Infinite horizon (F EV D(∞)) F EV D(1) Macro Micro Micro Direct Pipeline Pressures Pipeline Pressures (1) (2) (3) (4) Agriculture & Forestry 5.46 93.68 0.86 0.73 O
Slide 46: Volatility & Persistence (iii): Volatility
Volatility & Persistence (iii): Volatility Forecast Error Variance Decomposition: Producer Prices πjt = αt (πj ) + β t (πj ) + γ t (πj ) Infinite horizon (F EV D(∞)) F EV D(1) Macro Micro Micro Direct Pipeline Pressures Pipeline Pressures (1) (2) (3) (4) Agriculture & Forestry 5.46 93.68 0.86 0.73 O
Slide 47: Volatility & Persistence (iii): Volatility
Volatility & Persistence (iii): Volatility Forecast Error Variance Decomposition: Producer Prices πjt = αt (πj ) + β t (πj ) + γ t (πj ) Infinite horizon (F EV D(∞)) F EV D(1) Macro Micro Micro Direct Pipeline Pressures Pipeline Pressures (1) (2) (3) (4) Agriculture & Forestry 5.46 93.68 0.86 0.73 O
Slide 48: Volatility & Persistence (iii): Volatility
Volatility & Persistence (iii): Volatility Forecast Error Variance Decomposition: Consumer Prices πzt = αt (πz ) + β t (πz ) + γ t (πz ) Infinite horizon (F EV D(∞)) F EV D(1) Macro Micro Micro Direct Pipeline Pressures Pipeline Pressures (1) (2) (3) (4) Motor vehicles and parts 19.76 42.98 37.26 36
Slide 49: Volatility & Persistence (ii): Persistence
Volatility & Persistence (ii): Persistence ppi Persistence decomposition πjt = αt (πjppi ) + β t (πjppi ) + γ t (πjppi ) Macro Micro Direct Pipeline Pressures (1) (2) (3) ρ(αt (π ppi )) ρ(β t (π ppi )) ρ(γ t (π ppi )) πtppi 0.332 0.080 0.793 ρ(αt (πjppi )) ρ(β t (πjppi )) ρ(γ t (πjppi )) ppi πjt Ave
Slide 50: Volatility & Persistence (ii): Persistence
Volatility & Persistence (ii): Persistence ppi Persistence decomposition πjt = αt (πjppi ) + β t (πjppi ) + γ t (πjppi ) Macro Micro Direct Pipeline Pressures (1) (2) (3) ρ(αt (π ppi )) ρ(β t (π ppi )) ρ(γ t (π ppi )) πtppi 0.332 0.080 0.793 ρ(αt (πjppi )) ρ(β t (πjppi )) ρ(γ t (πjppi )) ppi πjt Ave
Slide 51: Volatility & Persistence (ii): Persistence
Volatility & Persistence (ii): Persistence ppi Persistence decomposition πjt = αt (πjppi ) + β t (πjppi ) + γ t (πjppi ) Macro Micro Direct Pipeline Pressures (1) (2) (3) ρ(αt (π ppi )) ρ(β t (π ppi )) ρ(γ t (π ppi )) πtppi 0.332 0.080 0.793 ρ(αt (πjppi )) ρ(β t (πjppi )) ρ(γ t (πjppi )) ppi πjt Ave
Slide 52: Volatility & Persistence (ii): Persistence
Volatility & Persistence (ii): Persistence ppi Persistence decomposition πjt = αt (πjppi ) + β t (πjppi ) + γ t (πjppi ) Macro Micro Direct Pipeline Pressures (1) (2) (3) ρ(αt (π ppi )) ρ(β t (π ppi )) ρ(γ t (π ppi )) πtppi 0.332 0.080 0.793 ρ(αt (πjppi )) ρ(β t (πjppi )) ρ(γ t (πjppi )) ppi πjt Ave
Slide 53: Volatility & Persistence (ii): Persistence
Volatility & Persistence (ii): Persistence pce Persistence decomposition πzt = αt (πzpce ) + β t (πzpce ) + γ t (πzpce ) Macro Micro Direct Pipeline Pressures (1) (2) (3) ρ(αt (π pce )) ρ(β t (π pce )) ρ(γ t (π pce )) πtpce 0.570 0.275 0.901 ρ(αt (πzpce )) ρ(β t (πzpce )) ρ(γ t (πzpce )) pce πzt Ave
Slide 54: Additional results (i): Origins of pipeline pressures
Additional results (i): Origins of pipeline pressures Pipeline pressure decomposition: Infinite Horizon Computer & electronics Agriculture & Forestry Oil & gas extraction Motorized vehicles Wholesale trade Construction Machinery PROF FIRE Producer prices Utilities 02.37 52.82 02.19 02.02 02.47 01.33
Slide 55: Additional results (i): Origins of pipeline pressures
Additional results (i): Origins of pipeline pressures Pipeline pressure decomposition: Infinite Horizon Computer & electronics Agriculture & Forestry Oil & gas extraction Motorized vehicles Wholesale trade Construction Machinery PROF FIRE Producer prices Utilities 02.37 52.82 02.19 02.02 02.47 01.33
Slide 56: Additional results (i): Origins of pipeline pressures
Additional results (i): Origins of pipeline pressures Pipeline pressure decomposition: Infinite Horizon Computer & electronics Agriculture & Forestry Oil & gas extraction Motorized vehicles Wholesale trade Construction Machinery PROF FIRE Producer prices Utilities 02.37 52.82 02.19 02.02 02.47 01.33
Slide 57: Additional results (i): Origins of pipeline pressures
Additional results (i): Origins of pipeline pressures Pipeline pressure decomposition: Infinite Horizon Computer & electronics Agriculture & Forestry Oil & gas extraction Motorized vehicles Wholesale trade Construction Machinery PROF FIRE Producer prices Utilities 02.37 52.82 02.19 02.02 02.47 01.33
Slide 58: Additional results (i): Origins of pipeline pressures
Additional results (i): Origins of pipeline pressures Pipeline pressure decomposition: Infinite Horizon Computer & electronics Agriculture & Forestry Oil & gas extraction Motorized vehicles Wholesale trade Construction Machinery PROF FIRE Producer prices Utilities 02.37 52.82 02.19 02.02 02.47 01.33
Slide 59: Additional results (ii): Historical Mechanism decomposition
Additional results (ii): Historical “Mechanism” decomposition Producer Price Inflation 4 3 2 Aggregate (,t (: ppi )h=1 ) 1 :tppi Micro direct (-t (: ppi )h=1 ) Pipeline Pressures (.t (: ppi )h=1 ) 0 Headline PPI in.ation -1 -2 -3 1970 1975 1980 1985 1990 1995 2000 2005 Time Producer Price Inflation
Slide 60: Additional results (ii): Historical Mechanism decomposition
Additional results (ii): Historical “Mechanism” decomposition Mining 20 15 10 5 Aggregate (,t (:jppi )h=1 ) Micro direct (-t (:jppi )h=1 ) ppi :jt 0 Pipeline Pressures (.t (:jppi )h=1 ) Sectoral PPI in.ation -5 -10 -15 -20 1970 1975 1980 1985 1990 1995 2000 2005 Mining 2.5 2 1.5 Agriculture .t (:jpp
Slide 61: Plan
Plan Motivation What do we know so far? The model Overview of the model Estimation details Results Test prevalence pipeline pressures Comparison dfm vs. structural model Volatility & Persistence Additional results Concluding remarks
Slide 62: Concluding remarks
Concluding remarks This paper 1. Studied the role of pipeline pressures for inflation volatility and persistence. 2. Benchmarked with traditional dfm framework Policy implications 1. Useful framework to study sector–specific policy interventions. I E.g., Healthcare sector regulation (e.g. Affordable
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