Banque Nationale De Belgique Sa · EURONEXT:BNB

Banque Nationale De Belgique Sa — Technical Presentation (March 2007)

74 slides from Banque Nationale De Belgique Sa's technical presentation, Mar 1, 2007

1. Slide 1: Aggregating Phillips Curves

   Introduction Aggregating Sectoral Phillips Curves The Biases Econometric Methods Results Aggregating Phillips Curves Jean Imbs, Eric Jondeau, and Florian Pelgrin HEC–University of Lausanne Bruxelles, March 1st, 2007 Jean Imbs, Eric Jondeau, and Florian Pelgrin Aggregating Phillips Curves

2. Slide 2: Objectives

   Introduction Aggregating Sectoral Phillips Curves The Biases Econometric Methods Results Objectives I Show that heterogeneity in the pricing behavior explains the apparent discrepancy in the estimated micro and macro duration of nominal rigidities I Detail the conditions under which imposing homogen

3. Slide 3: Road map

   Introduction Aggregating Sectoral Phillips Curves The Biases Econometric Methods Results Road map 1. Introduction 2. Aggregating Sectoral Phillips Curves 3. Biases 4. Econometric methods 5. Results 6. Robustness 7. Concluding remarks Jean Imbs, Eric Jondeau, and Florian Pelgrin Aggregating Phillips

4. Slide 4: Big picture

   Introduction Aggregating Sectoral Phillips Curves The Biases Econometric Methods Results Big picture On the one hand, I Disaggregated price data seem to have heterogenous dynamics I Discrepancies between aggregate and disaggregate dynamics, especially as regards nominal rigidities I Bils and Klenow

5. Slide 5: On the other hand

   Introduction Aggregating Sectoral Phillips Curves The Biases Econometric Methods Results On the other hand, I Raging debate on the New Keynesian Phillips Curve at the aggregate level 1. Backward-looking component of inflation (and limited rationality): How can purely forward looking pricing account

6. Slide 6: The Two May Be Related

   Introduction Aggregating Sectoral Phillips Curves The Biases Econometric Methods Results The Two May Be Related I Plenty of agreement that price dynamics are heterogenous across sectors/firms I Plenty of disagreement about true coefficients in aggregate Phillips Curve This paper discusses how former

7. Slide 7: Related literature

   Introduction Aggregating Sectoral Phillips Curves The Biases Econometric Methods Results Related literature I Plenty of work on heterogeneity in monetary models - Aoki (2001), Erceg and Levin (2002), Benigno (2004), Huang and Liu (2004): optimal policy under sectoral heterogeneity - Dixon and Kara (

8. Slide 8: On the empirical side

   Introduction Aggregating Sectoral Phillips Curves The Biases Econometric Methods Results On the empirical side I Well-known literature on the effects of aggregation under heterogeneity: Granger (1976, 1980), Pesaran, Pierse and Kumar (1989), Stoker (1993), Pesaran and Smith (1995), Zaffaroni (2004),

9. Slide 9: Our approach

   Introduction Aggregating Sectoral Phillips Curves The Biases Econometric Methods Results Our approach I Aggregate Phillips curves allowing for heterogenous pricing in Christiano, Eichenbaum and Evans (2005), and Galı́ and Gertler (1999). I Characterize heterogeneity biases of ML estimator 1. analyti

10. Slide 10: Inform whether heterogeneity

    Introduction Aggregating Sectoral Phillips Curves The Biases Econometric Methods Results I Inform whether heterogeneity 1. originates from nominal rigidities (extrinsic inflation) or from limited rationality (intrinsic inflation) 2. explains the apparent discrepancy in the estimated duration at macr

11. Slide 11: Overview of results

    Introduction Aggregating Sectoral Phillips Curves The Biases Econometric Methods Results Overview of results I Ignoring heterogeneity leads to overestimate the backward-looking component of inflation and to underestimate the contribution of the real marginal cost with the ML estimator I Heterogeneit

12. Slide 12: A Sectoral Phillips Curve

    Introduction Aggregating Sectoral Phillips Curves A Sectoral Phillips Curve The Biases Aggregation Econometric Methods A two-sector example Results I Under monopolistic competition, the demand faced by firm i −η Pij,t Yij,t = Yj,t Pj,t where η > 1 is the elasticity of substitution across varieties.

13. Slide 13: A Sectoral Phillips Curve: price resetting

    Introduction Aggregating Sectoral Phillips Curves A Sectoral Phillips Curve The Biases Aggregation Econometric Methods A two-sector example Results I Each firm j resets its price with probability 1 − αj each period, independently of the time elapsed since the last adjustment. While a fraction αj of

14. Slide 14: A Sectoral Phillips Curve: marginal cost

    Introduction Aggregating Sectoral Phillips Curves A Sectoral Phillips Curve The Biases Aggregation Econometric Methods A two-sector example Results I Sij,t,t+k is the real marginal cost of production at date t + k for the firms that changed their price at t. I it is related to the average real margi

15. Slide 15: A Sectoral Phillips Curve: price index

    Introduction Aggregating Sectoral Phillips Curves A Sectoral Phillips Curve The Biases Aggregation Econometric Methods A two-sector example Results I Staggered price setting under partial indexation implies the price index in sector j is given by h i1−η 1 ξj 1−ξj ∗ 1−η 1−η Pj,t = αj πj,t−1 π̄j Pj,t−

16. Slide 16: A Sectoral Phillips Curve: log-linearization

    Introduction Aggregating Sectoral Phillips Curves A Sectoral Phillips Curve The Biases Aggregation Econometric Methods A two-sector example Results I Log-linearizing, ξj β πj,t = πj,t−1 + Et πj,t+1 1 + βξj 1 + βξj 1 − βαj 1 − αj 1 avg + sj,t + pt − pj,t 1 + βξj αj 1 + hj where hj = η 1 − aj /aj corr

17. Slide 17: The sectoral Phillips curve

    Introduction Aggregating Sectoral Phillips Curves A Sectoral Phillips Curve The Biases Aggregation Econometric Methods A two-sector example Results The sectoral Phillips curve πj,t = λbj πj,t−1 + λfj Et πj,t+1 + θj sj,t + εj,t where λbj = ξj /(1 + βξj ) λfj = β/(1 + βξj ) 1 − βαj 1 − αj θj = 1 + βξj

18. Slide 18: Heterogeneity and aggregate inflation

    Introduction Aggregating Sectoral Phillips Curves A Sectoral Phillips Curve The Biases Aggregation Econometric Methods A two-sector example Results I Heterogeneity enters as sector-specific deviations from a common mean αj = α + α̃j ξj = ξ + ξ˜j where α̃j and ξ˜j have zero means and constant varianc

19. Slide 19: Linear heterogeneity assumption

    Introduction Aggregating Sectoral Phillips Curves A Sectoral Phillips Curve The Biases Aggregation Econometric Methods A two-sector example Results Assumption: Linear heterogeneity at the level of structural parameters ξj and αj translates into linear heterogeneity in the quasi-reduced form Phillips

20. Slide 20: The Aggregate Phillips curve

    Introduction Aggregating Sectoral Phillips Curves A Sectoral Phillips Curve The Biases Aggregation Econometric Methods A two-sector example Results The Aggregate Phillips curve πt = λb πt−1 + λf Et πt+1 + θ st + ε̄t with λb = ξ/ (1 + βξ) λf = β (1 + βξ) = β 1 − βλb (1 − βα) (1 − α) θ = (1 + βξ) α an

21. Slide 21: Simulations

    Introduction Aggregating Sectoral Phillips Curves A Sectoral Phillips Curve The Biases Aggregation Econometric Methods A two-sector example Results Simulations I Evaluate the relative impact of dispersion in the sectoral values of ξj and αj I Assume πj,t = λbj πj,t−1 + λfj Et πj,t+1 + θj sj,t + εj,t

22. Slide 22: Figure 1: Two-Sector Simulations

    Introduction Aggregating Sectoral Phillips Curves A Sectoral Phillips Curve The Biases Aggregation Econometric Methods A two-sector example Results Figure 1: Two-Sector Simulations Aggregated ξ Aggregated α ML ML 1 1 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0 0 0.8 0.8 0.8 0.8 0.6 0.6 0.6 0.6 0.4 0.4 0.4 0.4

23. Slide 23: Heterogeneity in nominal rigidities affects estimates

    Introduction Aggregating Sectoral Phillips Curves A Sectoral Phillips Curve The Biases Aggregation Econometric Methods A two-sector example Results I The heterogeneity in nominal rigidities affects both structural estimates in the Phillips curve, α and ξ I A given dispersion in the extent of nominal

24. Slide 24: Simulations

    Introduction Aggregating Sectoral Phillips Curves Analytics The Biases Simulations Econometric Methods Results I Assume πj,t = λbj πj,t−1 + λfj Et πj,t+1 + θj sj,t + εj,t sj,t = ρj sj,t−1 + uj,t I The sectoral reduced-form NKPC πj,t = ξj πj,t−1 + ψj sj,t + ηj,t (1) sj,t = ρj sj,t−1 + uj,t (2) with 1

25. Slide 25: The aggregate NKPC

    Introduction Aggregating Sectoral Phillips Curves Analytics The Biases Simulations Econometric Methods Results I The aggregate NKPC (imposing heterogeneity) πt = ξ πt−1 + ψ st + η̄t st = ρ st−1 + ūt where J X J X J X η̄t = wj ηj,t + wj ξ˜j πj,t−1 + wj ψ̃j sj,t j=1 j=1 j=1 J X J X ūt = wj uj,t + wj

26. Slide 26: Orthogonality conditions

    Introduction Aggregating Sectoral Phillips Curves Analytics The Biases Simulations Econometric Methods Results I Orthogonality conditions E [(πt − ξML πt−1 − ψML st ) πt−1 ] = 0 E [(πt − ξML πt−1 − ψML st ) st ] = 0 I In probability limits as T becomes large, e plim (ξML − ξ) = E s2 E (πt−1 η̄t ) −

27. Slide 27: Assumptions

    Introduction Aggregating Sectoral Phillips Curves Analytics The Biases Simulations Econometric Methods Results Assumptions I (H1) Heterogeneity is deterministic. I (H2) The weights of all sectors in the economy are exogenous and uncorrelated with the magnitude of sector-specific estimates of the Phi

28. Slide 28: Proposition 1

    Introduction Aggregating Sectoral Phillips Curves Analytics The Biases Simulations Econometric Methods Results Proposition 1 Under H1-H4, I The asymptotic bias of (ξML − ξ) can be decomposed into the sum of two terms: “ ” st2 X J ´ J E ` “ ” E st πt−1 X “ ” C1 = wj ξ̃j E πt−1 πj,t−1 − wj ξ̃j E st πj

29. Slide 29: Proposition 1 (cont'd)

    Introduction Aggregating Sectoral Phillips Curves Analytics The Biases Simulations Econometric Methods Results Proposition 1 (cont’d) I The asymptotic bias of (ψML − ψ) can be decomposed into the sum of two terms: “ ” 2 J ´ J E πt−1 ` X “ ” E st πt−1 X “ ” D1 = wj ξ̃j E st πj,t−1 − wj ξ̃j E πt−1 πj,

30. Slide 30: Biases decompose into correlation terms

    Introduction Aggregating Sectoral Phillips Curves Analytics The Biases Simulations Econometric Methods Results I Biases decompose into correlation terms involving ξ˜j and ψ̃j I C2 is the main source of bias of ξML : 1. Industries with high extrinsic persistence display high correlations with margina

31. Slide 31: Table 1: Simulations with 16 Sectors

    Introduction Aggregating Sectoral Phillips Curves Analytics The Biases Simulations Econometric Methods Results Table 1: Simulations with 16 Sectors True Value ML Decomposition Reduced Form - Equation (9) C1 C2 ξ 0.449 0.815 0.034 0.311 D1 D2 ψ 1.062 0.664 −0.045 −0.346 ρ 0.935 0.905 Reduced Form - E

32. Slide 32: Table 1b: Alternative Sources of Heterogeneity

    Introduction Aggregating Sectoral Phillips Curves Analytics The Biases Simulations Econometric Methods Results Table 1b: Simulations with 16 Sectors - Alternative Sources of Heterogeneity 2 2 2 2 σξ = 0, σα = 0.2 σξ = 0.2, σα =0 True Value ML Decomposition ML Decomposition Reduced Form - Equation (9

33. Slide 33: Mean Group Estimator

    Introduction Aggregating Sectoral Phillips Curves Without Common Factor The Biases Common Factors Econometric Methods Results Mean Group Estimator I Let Υj denote the vector of sector-specific parameters. The MG estimator Υ̂MG is given by J 1X Υ̂MG = Υ̂j . J j=1 A consistent estimator of the covaria

34. Slide 34: Random Coefficient

    Introduction Aggregating Sectoral Phillips Curves Without Common Factor The Biases Common Factors Econometric Methods Results Random Coefficient I The best linear unbiased estimator of the mean coefficient vector is given by J X Υ̂RC = Wj Υ̂j j=1 where  −1 XJ Wj =  (∆ + ΣΥ̂j )−1  (∆ + ΣΥ̂j )−1 j

35. Slide 35: Estimation method

    Introduction Aggregating Sectoral Phillips Curves Without Common Factor The Biases Common Factors Econometric Methods Results Estimation method 1. Estimation of sectoral Phillips curve based on the reduced form coefficients: ξj , ψ1j , ψ2j , ρ1j , and ρ2j . 2. MG or RCM yields consistent estimate fo

36. Slide 36: SURE estimation

    Introduction Aggregating Sectoral Phillips Curves Without Common Factor The Biases Common Factors Econometric Methods Results SURE estimation I Allow for nonzero cross-sectoral correlations in the residuals I Assume E[εi,t εj,t ] = σε ε , E[ui,t uj,t ] = σu u , and i j i j E[εi,t uj,t ] = σεi uj , f

37. Slide 37: Stacking all sectors

    Introduction Aggregating Sectoral Phillips Curves Without Common Factor The Biases Common Factors Econometric Methods Results I Stacking all sectors,        Y1 X1 0 ··· 0 φ1 v1  Y2   0 X2 ··· 0   φ2   v2   ..  =  ..   ..  +  ..         .. .. ..  .   . . . .  . 

38. Slide 38: Common Correlated Effects

    Introduction Aggregating Sectoral Phillips Curves Without Common Factor The Biases Common Factors Econometric Methods Results Common Correlated Effects I CCE estimators of sectoral parameters are consistent as J, T → ∞. Resulting MG and RC estimators are asymptotically unbiased as J → ∞, for both T

39. Slide 39: Data

    Introduction Data Aggregating Sectoral Phillips Curves Industry estimates The Biases Heterogeneity Bias Econometric Methods Cross-Industry Linkages Results Robustness Data I Production, prices and wages, and employment in 16 French sectors (source INSEE). I Coverage includes agriculture, manufacturi

40. Slide 40: Figure 2: Sectoral Inflation Rates (unfiltered)

    Introduction Data Aggregating Sectoral Phillips Curves Industry estimates The Biases Heterogeneity Bias Econometric Methods Cross-Industry Linkages Results Robustness Figure 2: Sectoral Inflation Rates (unfiltered) Agriculture Food manufacturing 20 20 10 10 0 0 -10 -10 -20 -20 -30 1980 1990 2000 201

41. Slide 41: Figure 2: Sectoral Inflation Rates - Continued

    Introduction Data Aggregating Sectoral Phillips Curves Industry estimates The Biases Heterogeneity Bias Econometric Methods Cross-Industry Linkages Results Robustness Figure 2: Sectoral Inflation Rates (unfiltered) - Continued Equipment goods Intermediary goods 20 10 15 0 10 -10 5 -20 0 -30 -5 -40 -

42. Slide 42: Figure 2: Sectoral Inflation Rates - Continued

    Introduction Data Aggregating Sectoral Phillips Curves Industry estimates The Biases Heterogeneity Bias Econometric Methods Cross-Industry Linkages Results Robustness Figure 2: Sectoral Inflation Rates (unfiltered) - Continued Trade Transportation 15 20 10 15 10 5 5 0 0 -5 -5 -10 1980 1990 2000 2010

43. Slide 43: Figure 2: Sectoral Inflation Rates - Continued

    Introduction Data Aggregating Sectoral Phillips Curves Industry estimates The Biases Heterogeneity Bias Econometric Methods Cross-Industry Linkages Results Robustness Figure 2: Sectoral Inflation Rates (unfiltered) - Continued Business services Personal services 30 10 20 10 5 0 0 -10 -20 -5 1980 199

44. Slide 44: Figure 3: Sectoral Marginal Costs (unfiltered)

    Introduction Data Aggregating Sectoral Phillips Curves Industry estimates The Biases Heterogeneity Bias Econometric Methods Cross-Industry Linkages Results Robustness Figure 3: Sectoral Marginal Costs (unfiltered) Agriculture Food manufacturing 0.3 2 0.2 0.1 1 0 -0.1 0 -0.2 -1 -0.3 1980 1990 2000 20

45. Slide 45: Figure 3: Sectoral Marginal Costs - Continued

    Introduction Data Aggregating Sectoral Phillips Curves Industry estimates The Biases Heterogeneity Bias Econometric Methods Cross-Industry Linkages Results Robustness Figure 3: Sectoral Marginal Costs (unfiltered) - Continued Equipment goods Intermediary goods 8 4 6 2 4 0 2 -2 0 -4 -2 -6 -4 1980 199

46. Slide 46: Figure 3: Sectoral Marginal Costs - Continued

    Introduction Data Aggregating Sectoral Phillips Curves Industry estimates The Biases Heterogeneity Bias Econometric Methods Cross-Industry Linkages Results Robustness Figure 3: Sectoral Marginal Costs (unfiltered) - Continued Trade Transportation 1 2 0.5 1 0 0 -0.5 -1 -1 1980 1990 2000 2010 1980 199

47. Slide 47: Figure 3: Sectoral Marginal Costs - Continued

    Introduction Data Aggregating Sectoral Phillips Curves Industry estimates The Biases Heterogeneity Bias Econometric Methods Cross-Industry Linkages Results Robustness Figure 3: Sectoral Marginal Costs (unfiltered) - Continued Business services Personal services 1 1 0.5 0.5 0 0 -0.5 -1 -0.5 -1.5 -1 -

48. Slide 48: Table 2: Summary statistics

    Introduction Data Aggregating Sectoral Phillips Curves Industry estimates The Biases Heterogeneity Bias Econometric Methods Cross-Industry Linkages Results Robustness Table 2: Summary statistics Industry Weights π̄ s̄ corr (πt−1 , πt ) corr (st−1 , st ) corr (πt , st ) Aggregate 100.00 3.996 −0.095

49. Slide 49: Figure 4: Aggregate Inflation and Marginal Costs

    Introduction Data Aggregating Sectoral Phillips Curves Industry estimates The Biases Heterogeneity Bias Econometric Methods Cross-Industry Linkages Results Robustness Figure 4: Aggregate Inflation and Marginal Costs (unfiltered) 12 10 8 6 4 2 0 -2 -4 -6 1975 1980 1985 1990 1995 2000 2005 2010 Jean I

50. Slide 50: Table 3: Sectoral Phillips Curves

    Introduction Data Aggregating Sectoral Phillips Curves Industry estimates The Biases Heterogeneity Bias Econometric Methods Cross-Industry Linkages Results Robustness Table 3: Sectoral Phillips Curves Industry Agriculture Food Cons. Car Equip. Interm. Energy Reduced Form - Equation (7) ξ 0.776∗∗∗ 0.

51. Slide 51: Table 3: Sectoral Phillips Curves (cont.)

    Introduction Data Aggregating Sectoral Phillips Curves Industry estimates The Biases Heterogeneity Bias Econometric Methods Cross-Industry Linkages Results Robustness Industry Trade Transp. Finance Real Estate Business Pers. Serv. Educ. Health Reduced Form - Equation (7) ξ 0.531∗∗∗ 0.049 0.577∗∗∗ 0.

52. Slide 52: Figure 5: Industry Phillips Curves

    Introduction Data Aggregating Sectoral Phillips Curves Industry estimates The Biases Heterogeneity Bias Econometric Methods Cross-Industry Linkages Results Robustness Figure 5: Industry Phillips Curves Agriculture Food manufacturing 20 15 20 10 10 5 0 0 -5 -10 -10 -15 -20 -20 -30 1975 1980 1985 1990

53. Slide 53: Figure 5: Industry Phillips Curves - Continued

    Introduction Data Aggregating Sectoral Phillips Curves Industry estimates The Biases Heterogeneity Bias Econometric Methods Cross-Industry Linkages Results Robustness Figure 5: Industry Phillips Curves - Continued Equipment goods Intermediary goods 20 10 15 0 10 -10 5 -20 0 -30 -5 -40 -10 1975 1980

54. Slide 54: Figure 5: Industry Phillips Curves - Continued

    Introduction Data Aggregating Sectoral Phillips Curves Industry estimates The Biases Heterogeneity Bias Econometric Methods Cross-Industry Linkages Results Robustness Figure 5: Industry Phillips Curves - Continued Trade Transportation 15 20 10 15 10 5 5 0 0 -5 -5 -10 1975 1980 1985 1990 1995 2000 20

55. Slide 55: Figure 5: Industry Phillips Curves - Continued

    Introduction Data Aggregating Sectoral Phillips Curves Industry estimates The Biases Heterogeneity Bias Econometric Methods Cross-Industry Linkages Results Robustness Figure 5: Industry Phillips Curves - Continued Business services Personal services 30 12 25 10 20 8 15 6 10 4 5 2 0 -5 0 -10 -2 -15 -

56. Slide 56: Summary of Results

    Introduction Data Aggregating Sectoral Phillips Curves Industry estimates The Biases Heterogeneity Bias Econometric Methods Cross-Industry Linkages Results Robustness Summary of Results I There is heterogeneity!!! I Our data are supportive of inflation dynamics at the industry level: the real margin

57. Slide 57: Table 4: Aggregate Phillips Curves

    Introduction Data Aggregating Sectoral Phillips Curves Industry estimates The Biases Heterogeneity Bias Econometric Methods Cross-Industry Linkages Results Robustness Table 4: Aggregate Phillips Curves ML MG MG∗ RC RC∗ RC2 RC∗ 2 Reduced Form - Equation (10) ξ 0.666∗∗∗ 0.530∗∗∗ 0.489∗∗∗ 0.488∗∗∗ 0.48

58. Slide 58: Table 4.1: Aggregate Estimates with Heterogeneity (subsectors)

    Introduction Data Aggregating Sectoral Phillips Curves Industry estimates The Biases Heterogeneity Bias Econometric Methods Cross-Industry Linkages Results Robustness Table 4.1: Aggregate Estimates with Heterogeneity (subsectors) Manufacturing Services Method ξ α Duration ξ α Duration MG 0.384 0.437

59. Slide 59: Results

    Introduction Data Aggregating Sectoral Phillips Curves Industry estimates The Biases Heterogeneity Bias Econometric Methods Cross-Industry Linkages Results Robustness Results I Reduced-form coefficients 1. Large and positive heterogeneity bias on ξ. I Quasi-reduced parameters I Structural parameters

60. Slide 60: Results

    Introduction Data Aggregating Sectoral Phillips Curves Industry estimates The Biases Heterogeneity Bias Econometric Methods Cross-Industry Linkages Results Robustness Results I Reduced-form coefficients 1. Large and positive heterogeneity bias on ξ. 2. Large and negative heterogeneity bias on ψ1 . I

61. Slide 61: Results

    Introduction Data Aggregating Sectoral Phillips Curves Industry estimates The Biases Heterogeneity Bias Econometric Methods Cross-Industry Linkages Results Robustness Results I Reduced-form coefficients 1. Large and positive heterogeneity bias on ξ. 2. Large and negative heterogeneity bias on ψ1 . I

62. Slide 62: Results

    Introduction Data Aggregating Sectoral Phillips Curves Industry estimates The Biases Heterogeneity Bias Econometric Methods Cross-Industry Linkages Results Robustness Results I Reduced-form coefficients 1. Large and positive heterogeneity bias on ξ. 2. Large and negative heterogeneity bias on ψ1 . I

63. Slide 63: Results

    Introduction Data Aggregating Sectoral Phillips Curves Industry estimates The Biases Heterogeneity Bias Econometric Methods Cross-Industry Linkages Results Robustness Results I Reduced-form coefficients 1. Large and positive heterogeneity bias on ξ. 2. Large and negative heterogeneity bias on ψ1 . I

64. Slide 64: Table 5: Aggregate Phillips Curves - Correlated Effects

    Introduction Data Aggregating Sectoral Phillips Curves Industry estimates The Biases Heterogeneity Bias Econometric Methods Cross-Industry Linkages Results Robustness Table 5: Aggregate Phillips Curves - Correlated Effects SURE CCE GMM ML MG RC MG RC Reduced Form - Equation (10) ξ 0.467∗∗∗ 0.666∗∗∗

65. Slide 65: Sectoral Phillips curve (Galí and Gertler)

    Introduction Data Aggregating Sectoral Phillips Curves Industry estimates The Biases Heterogeneity Bias Econometric Methods Cross-Industry Linkages Results Robustness I Sectoral Phillips curve πj,t = λbj πj,t−1 + λfj Et πj,t+1 + θj sj,t + εj,t (3) with λbj = ωj / φ λfj= β αj / φ 1 − ωj 1 − αj 1 − βα

66. Slide 66: Procedure

    Introduction Data Aggregating Sectoral Phillips Curves Industry estimates The Biases Heterogeneity Bias Econometric Methods Cross-Industry Linkages Results Robustness Procedure 1. Estimation of the structural parameters from the non-linear reduced form πj,t = δ 1j πj,t−1 + ψ 1j sj,t + ψ 2j sj,t−1 +

67. Slide 67: Table 6: Aggregate Phillips Curves - Galí and Gertler (1999)

    Introduction Data Aggregating Sectoral Phillips Curves Industry estimates The Biases Heterogeneity Bias Econometric Methods Cross-Industry Linkages Results Robustness Table 6: Aggregate Phillips Curves - Galı́ and Gertler (1999) ML MG MG∗ RC RC∗ RC2 RC∗ 2 Reduced Form - Equation (12) δ1 0.666∗∗∗ 0.5

68. Slide 68: Table 7: Correlated Effects - Galí and Gertler (1999)

    Introduction Data Aggregating Sectoral Phillips Curves Industry estimates The Biases Heterogeneity Bias Econometric Methods Cross-Industry Linkages Results Robustness Table 7: Correlated Effects – Galı́ and Gertler (1999) SURE CCE GMM ML MG RC MG RC Reduced Form - Equation (12) δ1 0.538∗∗∗ 0.666∗∗∗

69. Slide 69: Conclusion

    Introduction Data Aggregating Sectoral Phillips Curves Industry estimates The Biases Heterogeneity Bias Econometric Methods Cross-Industry Linkages Results Robustness Conclusion I Estimates of the aggregate Phillips curve are biased in the presence of heterogeneity, whatever the archetypical model u

70. Slide 70: Conclusion

    Introduction Data Aggregating Sectoral Phillips Curves Industry estimates The Biases Heterogeneity Bias Econometric Methods Cross-Industry Linkages Results Robustness Conclusion I Estimates of the aggregate Phillips curve are biased in the presence of heterogeneity, whatever the archetypical model u

71. Slide 71: Aggregate duration of nominal rigidities

    Introduction Data Aggregating Sectoral Phillips Curves Industry estimates The Biases Heterogeneity Bias Econometric Methods Cross-Industry Linkages Results Robustness I Aggregate duration of nominal rigidities around 2 quarters, as compared to four to five when homogenous approaches are used. I Our

72. Slide 72: Extensions

    Introduction Data Aggregating Sectoral Phillips Curves Industry estimates The Biases Heterogeneity Bias Econometric Methods Cross-Industry Linkages Results Robustness Extensions 1. Other data sets? Jean Imbs, Eric Jondeau, and Florian Pelgrin Aggregating Phillips Curves

73. Slide 73: Extensions

    Introduction Data Aggregating Sectoral Phillips Curves Industry estimates The Biases Heterogeneity Bias Econometric Methods Cross-Industry Linkages Results Robustness Extensions 1. Other data sets? 2. Theoretical model? Jean Imbs, Eric Jondeau, and Florian Pelgrin Aggregating Phillips Curves

74. Slide 74: Extensions

    Introduction Data Aggregating Sectoral Phillips Curves Industry estimates The Biases Heterogeneity Bias Econometric Methods Cross-Industry Linkages Results Robustness Extensions 1. Other data sets? 2. Theoretical model? 3. From aggregate to micro data: Lewbel (1994), Pesaran (2004), Jondeau and Pelg

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