the normal science of heterogeneous agents macroeconomics
play

The Normal Science of Heterogeneous Agents Macroeconomics - PowerPoint PPT Presentation

Jan 27, 2024 •167 likes •1.88k views

The Normal Science of Heterogeneous Agents Macroeconomics Christopher Carroll 1 1 Johns Hopkins University and NBER ccarroll@jhu.edu RBNZ Conference on Heterogeneous Agents And Housing December 11, 2017 Is Economics A Science? Frank

  1. Microfoundations of Macro: “Serious” and “Unserious” Broad agreement by 1970s (from all camps): Macroeconomics needed better “microfoundations” Lucas (1970s-vintage) Macro theories should be tightly constrained to be consistent with all the relevant micro evidence Call this “Serious” microfoundations Fatal Step in 1980s: Accepting “unserious” microfoundations “If model has has only one agent, it is microfounded” Where Did That Leave Us? Carroll Behavioral-Macro

  2. RA DSGE Is Not And Can Never Be “Normal” Science By RA DSGE I mean a hard-core version: Micro evidence inadmissible Example: Call “habit” γ in ∆ C t +1 = γ ∆ C t + ǫ t +1 EER metadata analysis of 597 estimates NIPA data: Average γ ≈ 0 . 6, always highly significant Micro data: Average γ ≈ 0 . 1, almost never significant Response? Ignore micro evidence Partial Equilibrium is for wussies RA DSPE (e.g., Mian and Sufi; Steinsson and Nakamura)? Useless because not GE Only criterion of success: How well does RA DSGE model fit existing NIPA data Nothing else in heaven and earth, Horatio . . . Carroll Behavioral-Macro

  3. RA DSGE Is Not And Can Never Be “Normal” Science By RA DSGE I mean a hard-core version: Micro evidence inadmissible Example: Call “habit” γ in ∆ C t +1 = γ ∆ C t + ǫ t +1 EER metadata analysis of 597 estimates NIPA data: Average γ ≈ 0 . 6, always highly significant Micro data: Average γ ≈ 0 . 1, almost never significant Response? Ignore micro evidence Partial Equilibrium is for wussies RA DSPE (e.g., Mian and Sufi; Steinsson and Nakamura)? Useless because not GE Only criterion of success: How well does RA DSGE model fit existing NIPA data Nothing else in heaven and earth, Horatio . . . Carroll Behavioral-Macro

  4. RA DSGE Is Not And Can Never Be “Normal” Science By RA DSGE I mean a hard-core version: Micro evidence inadmissible Example: Call “habit” γ in ∆ C t +1 = γ ∆ C t + ǫ t +1 EER metadata analysis of 597 estimates NIPA data: Average γ ≈ 0 . 6, always highly significant Micro data: Average γ ≈ 0 . 1, almost never significant Response? Ignore micro evidence Partial Equilibrium is for wussies RA DSPE (e.g., Mian and Sufi; Steinsson and Nakamura)? Useless because not GE Only criterion of success: How well does RA DSGE model fit existing NIPA data Nothing else in heaven and earth, Horatio . . . Carroll Behavioral-Macro

  5. RA DSGE Is Not And Can Never Be “Normal” Science By RA DSGE I mean a hard-core version: Micro evidence inadmissible Example: Call “habit” γ in ∆ C t +1 = γ ∆ C t + ǫ t +1 EER metadata analysis of 597 estimates NIPA data: Average γ ≈ 0 . 6, always highly significant Micro data: Average γ ≈ 0 . 1, almost never significant Response? Ignore micro evidence Partial Equilibrium is for wussies RA DSPE (e.g., Mian and Sufi; Steinsson and Nakamura)? Useless because not GE Only criterion of success: How well does RA DSGE model fit existing NIPA data Nothing else in heaven and earth, Horatio . . . Carroll Behavioral-Macro

  6. RA DSGE Is Not And Can Never Be “Normal” Science By RA DSGE I mean a hard-core version: Micro evidence inadmissible Example: Call “habit” γ in ∆ C t +1 = γ ∆ C t + ǫ t +1 EER metadata analysis of 597 estimates NIPA data: Average γ ≈ 0 . 6, always highly significant Micro data: Average γ ≈ 0 . 1, almost never significant Response? Ignore micro evidence Partial Equilibrium is for wussies RA DSPE (e.g., Mian and Sufi; Steinsson and Nakamura)? Useless because not GE Only criterion of success: How well does RA DSGE model fit existing NIPA data Nothing else in heaven and earth, Horatio . . . Carroll Behavioral-Macro

  7. RA DSGE Is Not And Can Never Be “Normal” Science By RA DSGE I mean a hard-core version: Micro evidence inadmissible Example: Call “habit” γ in ∆ C t +1 = γ ∆ C t + ǫ t +1 EER metadata analysis of 597 estimates NIPA data: Average γ ≈ 0 . 6, always highly significant Micro data: Average γ ≈ 0 . 1, almost never significant Response? Ignore micro evidence Partial Equilibrium is for wussies RA DSPE (e.g., Mian and Sufi; Steinsson and Nakamura)? Useless because not GE Only criterion of success: How well does RA DSGE model fit existing NIPA data Nothing else in heaven and earth, Horatio . . . Carroll Behavioral-Macro

  8. RA DSGE Is Not And Can Never Be “Normal” Science By RA DSGE I mean a hard-core version: Micro evidence inadmissible Example: Call “habit” γ in ∆ C t +1 = γ ∆ C t + ǫ t +1 EER metadata analysis of 597 estimates NIPA data: Average γ ≈ 0 . 6, always highly significant Micro data: Average γ ≈ 0 . 1, almost never significant Response? Ignore micro evidence Partial Equilibrium is for wussies RA DSPE (e.g., Mian and Sufi; Steinsson and Nakamura)? Useless because not GE Only criterion of success: How well does RA DSGE model fit existing NIPA data Nothing else in heaven and earth, Horatio . . . Carroll Behavioral-Macro

  9. RA DSGE Is Not And Can Never Be “Normal” Science By RA DSGE I mean a hard-core version: Micro evidence inadmissible Example: Call “habit” γ in ∆ C t +1 = γ ∆ C t + ǫ t +1 EER metadata analysis of 597 estimates NIPA data: Average γ ≈ 0 . 6, always highly significant Micro data: Average γ ≈ 0 . 1, almost never significant Response? Ignore micro evidence Partial Equilibrium is for wussies RA DSPE (e.g., Mian and Sufi; Steinsson and Nakamura)? Useless because not GE Only criterion of success: How well does RA DSGE model fit existing NIPA data Nothing else in heaven and earth, Horatio . . . Carroll Behavioral-Macro

  10. RA DSGE Is Not And Can Never Be “Normal” Science By RA DSGE I mean a hard-core version: Micro evidence inadmissible Example: Call “habit” γ in ∆ C t +1 = γ ∆ C t + ǫ t +1 EER metadata analysis of 597 estimates NIPA data: Average γ ≈ 0 . 6, always highly significant Micro data: Average γ ≈ 0 . 1, almost never significant Response? Ignore micro evidence Partial Equilibrium is for wussies RA DSPE (e.g., Mian and Sufi; Steinsson and Nakamura)? Useless because not GE Only criterion of success: How well does RA DSGE model fit existing NIPA data Nothing else in heaven and earth, Horatio . . . Carroll Behavioral-Macro

  11. RA DSGE Is Not And Can Never Be “Normal” Science By RA DSGE I mean a hard-core version: Micro evidence inadmissible Example: Call “habit” γ in ∆ C t +1 = γ ∆ C t + ǫ t +1 EER metadata analysis of 597 estimates NIPA data: Average γ ≈ 0 . 6, always highly significant Micro data: Average γ ≈ 0 . 1, almost never significant Response? Ignore micro evidence Partial Equilibrium is for wussies RA DSPE (e.g., Mian and Sufi; Steinsson and Nakamura)? Useless because not GE Only criterion of success: How well does RA DSGE model fit existing NIPA data Nothing else in heaven and earth, Horatio . . . Carroll Behavioral-Macro

  12. RA DSGE Is Not And Can Never Be “Normal” Science By RA DSGE I mean a hard-core version: Micro evidence inadmissible Example: Call “habit” γ in ∆ C t +1 = γ ∆ C t + ǫ t +1 EER metadata analysis of 597 estimates NIPA data: Average γ ≈ 0 . 6, always highly significant Micro data: Average γ ≈ 0 . 1, almost never significant Response? Ignore micro evidence Partial Equilibrium is for wussies RA DSPE (e.g., Mian and Sufi; Steinsson and Nakamura)? Useless because not GE Only criterion of success: How well does RA DSGE model fit existing NIPA data Nothing else in heaven and earth, Horatio . . . Carroll Behavioral-Macro

  13. RA DSGE Is Not And Can Never Be “Normal” Science By RA DSGE I mean a hard-core version: Micro evidence inadmissible Example: Call “habit” γ in ∆ C t +1 = γ ∆ C t + ǫ t +1 EER metadata analysis of 597 estimates NIPA data: Average γ ≈ 0 . 6, always highly significant Micro data: Average γ ≈ 0 . 1, almost never significant Response? Ignore micro evidence Partial Equilibrium is for wussies RA DSPE (e.g., Mian and Sufi; Steinsson and Nakamura)? Useless because not GE Only criterion of success: How well does RA DSGE model fit existing NIPA data Nothing else in heaven and earth, Horatio . . . Carroll Behavioral-Macro

  14. RA DSGE Is Not And Can Never Be “Normal” Science By RA DSGE I mean a hard-core version: Micro evidence inadmissible Example: Call “habit” γ in ∆ C t +1 = γ ∆ C t + ǫ t +1 EER metadata analysis of 597 estimates NIPA data: Average γ ≈ 0 . 6, always highly significant Micro data: Average γ ≈ 0 . 1, almost never significant Response? Ignore micro evidence Partial Equilibrium is for wussies RA DSPE (e.g., Mian and Sufi; Steinsson and Nakamura)? Useless because not GE Only criterion of success: How well does RA DSGE model fit existing NIPA data Nothing else in heaven and earth, Horatio . . . Carroll Behavioral-Macro

  15. RA DSGE Is Not And Can Never Be “Normal” Science By RA DSGE I mean a hard-core version: Micro evidence inadmissible Example: Call “habit” γ in ∆ C t +1 = γ ∆ C t + ǫ t +1 EER metadata analysis of 597 estimates NIPA data: Average γ ≈ 0 . 6, always highly significant Micro data: Average γ ≈ 0 . 1, almost never significant Response? Ignore micro evidence Partial Equilibrium is for wussies RA DSPE (e.g., Mian and Sufi; Steinsson and Nakamura)? Useless because not GE Only criterion of success: How well does RA DSGE model fit existing NIPA data Nothing else in heaven and earth, Horatio . . . Carroll Behavioral-Macro

  16. RA DSGE Is Not And Can Never Be “Normal” Science By RA DSGE I mean a hard-core version: Micro evidence inadmissible Example: Call “habit” γ in ∆ C t +1 = γ ∆ C t + ǫ t +1 EER metadata analysis of 597 estimates NIPA data: Average γ ≈ 0 . 6, always highly significant Micro data: Average γ ≈ 0 . 1, almost never significant Response? Ignore micro evidence Partial Equilibrium is for wussies RA DSPE (e.g., Mian and Sufi; Steinsson and Nakamura)? Useless because not GE Only criterion of success: How well does RA DSGE model fit existing NIPA data Nothing else in heaven and earth, Horatio . . . Carroll Behavioral-Macro

  17. RA DSGE Is Not And Can Never Be “Normal” Science By RA DSGE I mean a hard-core version: Micro evidence inadmissible Example: Call “habit” γ in ∆ C t +1 = γ ∆ C t + ǫ t +1 EER metadata analysis of 597 estimates NIPA data: Average γ ≈ 0 . 6, always highly significant Micro data: Average γ ≈ 0 . 1, almost never significant Response? Ignore micro evidence Partial Equilibrium is for wussies RA DSPE (e.g., Mian and Sufi; Steinsson and Nakamura)? Useless because not GE Only criterion of success: How well does RA DSGE model fit existing NIPA data Nothing else in heaven and earth, Horatio . . . Carroll Behavioral-Macro

  18. RA DSGE Is Not And Can Never Be “Normal” Science By RA DSGE I mean a hard-core version: Micro evidence inadmissible Example: Call “habit” γ in ∆ C t +1 = γ ∆ C t + ǫ t +1 EER metadata analysis of 597 estimates NIPA data: Average γ ≈ 0 . 6, always highly significant Micro data: Average γ ≈ 0 . 1, almost never significant Response? Ignore micro evidence Partial Equilibrium is for wussies RA DSPE (e.g., Mian and Sufi; Steinsson and Nakamura)? Useless because not GE Only criterion of success: How well does RA DSGE model fit existing NIPA data Nothing else in heaven and earth, Horatio . . . Carroll Behavioral-Macro

  19. RA DSGE Is Not And Can Never Be “Normal” Science By RA DSGE I mean a hard-core version: Micro evidence inadmissible Example: Call “habit” γ in ∆ C t +1 = γ ∆ C t + ǫ t +1 EER metadata analysis of 597 estimates NIPA data: Average γ ≈ 0 . 6, always highly significant Micro data: Average γ ≈ 0 . 1, almost never significant Response? Ignore micro evidence Partial Equilibrium is for wussies RA DSPE (e.g., Mian and Sufi; Steinsson and Nakamura)? Useless because not GE Only criterion of success: How well does RA DSGE model fit existing NIPA data Nothing else in heaven and earth, Horatio . . . Carroll Behavioral-Macro

  20. RA DSGE Is Ptolemaic, Not Galilean Astronomy Ptolemaic: Reverse Engineer Theory to Match All the Past Data New Data Are A Trickle (almost no out-of-sample testing) Resolve “Puzzles” By Adding “Epicycles” Galilean: Collect New Data OMG - Jupiter has Moons! When Data Reject Theory, Consider New Theory Not just epicycles on old one Carroll Behavioral-Macro

  21. RA DSGE Is Ptolemaic, Not Galilean Astronomy Ptolemaic: Reverse Engineer Theory to Match All the Past Data New Data Are A Trickle (almost no out-of-sample testing) Resolve “Puzzles” By Adding “Epicycles” Galilean: Collect New Data OMG - Jupiter has Moons! When Data Reject Theory, Consider New Theory Not just epicycles on old one Carroll Behavioral-Macro

  22. RA DSGE Is Ptolemaic, Not Galilean Astronomy Ptolemaic: Reverse Engineer Theory to Match All the Past Data New Data Are A Trickle (almost no out-of-sample testing) Resolve “Puzzles” By Adding “Epicycles” Galilean: Collect New Data OMG - Jupiter has Moons! When Data Reject Theory, Consider New Theory Not just epicycles on old one Carroll Behavioral-Macro

  23. RA DSGE Is Ptolemaic, Not Galilean Astronomy Ptolemaic: Reverse Engineer Theory to Match All the Past Data New Data Are A Trickle (almost no out-of-sample testing) Resolve “Puzzles” By Adding “Epicycles” Galilean: Collect New Data OMG - Jupiter has Moons! When Data Reject Theory, Consider New Theory Not just epicycles on old one Carroll Behavioral-Macro

  24. RA DSGE Is Ptolemaic, Not Galilean Astronomy Ptolemaic: Reverse Engineer Theory to Match All the Past Data New Data Are A Trickle (almost no out-of-sample testing) Resolve “Puzzles” By Adding “Epicycles” Galilean: Collect New Data OMG - Jupiter has Moons! When Data Reject Theory, Consider New Theory Not just epicycles on old one Carroll Behavioral-Macro

  25. RA DSGE Is Ptolemaic, Not Galilean Astronomy Ptolemaic: Reverse Engineer Theory to Match All the Past Data New Data Are A Trickle (almost no out-of-sample testing) Resolve “Puzzles” By Adding “Epicycles” Galilean: Collect New Data OMG - Jupiter has Moons! When Data Reject Theory, Consider New Theory Not just epicycles on old one Carroll Behavioral-Macro

  26. RA DSGE Is Ptolemaic, Not Galilean Astronomy Ptolemaic: Reverse Engineer Theory to Match All the Past Data New Data Are A Trickle (almost no out-of-sample testing) Resolve “Puzzles” By Adding “Epicycles” Galilean: Collect New Data OMG - Jupiter has Moons! When Data Reject Theory, Consider New Theory Not just epicycles on old one Carroll Behavioral-Macro

  27. RA DSGE Is Ptolemaic, Not Galilean Astronomy Ptolemaic: Reverse Engineer Theory to Match All the Past Data New Data Are A Trickle (almost no out-of-sample testing) Resolve “Puzzles” By Adding “Epicycles” Galilean: Collect New Data OMG - Jupiter has Moons! When Data Reject Theory, Consider New Theory Not just epicycles on old one Carroll Behavioral-Macro

  28. Culmination of Ptolemaic Astronomy Figure: Armillary Sphere, 1593 Carroll Behavioral-Macro

  29. Last Time Anybody Tried This For Economics . . . Bill Phillips (a Kiwi!): Figure: MONIAC Hydraulic Model of the Economy Source: Reserve Bank of New Zealand Carroll Behavioral-Macro

  30. RA DSGE “Puzzles” get “Solved” By Adding Epicycles “Epicycles” 0. Add “Frictions” of various kinds; 1. Change dynamics of shocks; 2. Change production function; 3. Change Utility Function: Habits Epstein-Weil Time Varying: Risk Aversion Labor/Leisure Preferences 4. Unobservable “shocks” to marginal utility; 5. Unobservable “intermediate” sectors; 6. . . . Carroll Behavioral-Macro

  31. RA DSGE “Puzzles” get “Solved” By Adding Epicycles “Epicycles” 0. Add “Frictions” of various kinds; 1. Change dynamics of shocks; 2. Change production function; 3. Change Utility Function: Habits Epstein-Weil Time Varying: Risk Aversion Labor/Leisure Preferences 4. Unobservable “shocks” to marginal utility; 5. Unobservable “intermediate” sectors; 6. . . . Carroll Behavioral-Macro

  32. RA DSGE “Puzzles” get “Solved” By Adding Epicycles “Epicycles” 0. Add “Frictions” of various kinds; 1. Change dynamics of shocks; 2. Change production function; 3. Change Utility Function: Habits Epstein-Weil Time Varying: Risk Aversion Labor/Leisure Preferences 4. Unobservable “shocks” to marginal utility; 5. Unobservable “intermediate” sectors; 6. . . . Carroll Behavioral-Macro

  33. RA DSGE “Puzzles” get “Solved” By Adding Epicycles “Epicycles” 0. Add “Frictions” of various kinds; 1. Change dynamics of shocks; 2. Change production function; 3. Change Utility Function: Habits Epstein-Weil Time Varying: Risk Aversion Labor/Leisure Preferences 4. Unobservable “shocks” to marginal utility; 5. Unobservable “intermediate” sectors; 6. . . . Carroll Behavioral-Macro

  34. RA DSGE “Puzzles” get “Solved” By Adding Epicycles “Epicycles” 0. Add “Frictions” of various kinds; 1. Change dynamics of shocks; 2. Change production function; 3. Change Utility Function: Habits Epstein-Weil Time Varying: Risk Aversion Labor/Leisure Preferences 4. Unobservable “shocks” to marginal utility; 5. Unobservable “intermediate” sectors; 6. . . . Carroll Behavioral-Macro

  35. RA DSGE “Puzzles” get “Solved” By Adding Epicycles “Epicycles” 0. Add “Frictions” of various kinds; 1. Change dynamics of shocks; 2. Change production function; 3. Change Utility Function: Habits Epstein-Weil Time Varying: Risk Aversion Labor/Leisure Preferences 4. Unobservable “shocks” to marginal utility; 5. Unobservable “intermediate” sectors; 6. . . . Carroll Behavioral-Macro

  36. RA DSGE “Puzzles” get “Solved” By Adding Epicycles “Epicycles” 0. Add “Frictions” of various kinds; 1. Change dynamics of shocks; 2. Change production function; 3. Change Utility Function: Habits Epstein-Weil Time Varying: Risk Aversion Labor/Leisure Preferences 4. Unobservable “shocks” to marginal utility; 5. Unobservable “intermediate” sectors; 6. . . . Carroll Behavioral-Macro

  37. RA DSGE “Puzzles” get “Solved” By Adding Epicycles “Epicycles” 0. Add “Frictions” of various kinds; 1. Change dynamics of shocks; 2. Change production function; 3. Change Utility Function: Habits Epstein-Weil Time Varying: Risk Aversion Labor/Leisure Preferences 4. Unobservable “shocks” to marginal utility; 5. Unobservable “intermediate” sectors; 6. . . . Carroll Behavioral-Macro

  38. RA DSGE “Puzzles” get “Solved” By Adding Epicycles “Epicycles” 0. Add “Frictions” of various kinds; 1. Change dynamics of shocks; 2. Change production function; 3. Change Utility Function: Habits Epstein-Weil Time Varying: Risk Aversion Labor/Leisure Preferences 4. Unobservable “shocks” to marginal utility; 5. Unobservable “intermediate” sectors; 6. . . . Carroll Behavioral-Macro

  39. RA DSGE “Puzzles” get “Solved” By Adding Epicycles “Epicycles” 0. Add “Frictions” of various kinds; 1. Change dynamics of shocks; 2. Change production function; 3. Change Utility Function: Habits Epstein-Weil Time Varying: Risk Aversion Labor/Leisure Preferences 4. Unobservable “shocks” to marginal utility; 5. Unobservable “intermediate” sectors; 6. . . . Carroll Behavioral-Macro

  40. RA DSGE “Puzzles” get “Solved” By Adding Epicycles “Epicycles” 0. Add “Frictions” of various kinds; 1. Change dynamics of shocks; 2. Change production function; 3. Change Utility Function: Habits Epstein-Weil Time Varying: Risk Aversion Labor/Leisure Preferences 4. Unobservable “shocks” to marginal utility; 5. Unobservable “intermediate” sectors; 6. . . . Carroll Behavioral-Macro

  41. RA DSGE “Puzzles” get “Solved” By Adding Epicycles “Epicycles” 0. Add “Frictions” of various kinds; 1. Change dynamics of shocks; 2. Change production function; 3. Change Utility Function: Habits Epstein-Weil Time Varying: Risk Aversion Labor/Leisure Preferences 4. Unobservable “shocks” to marginal utility; 5. Unobservable “intermediate” sectors; 6. . . . Carroll Behavioral-Macro

  42. After 30 years of reverse engineering of Epicycles: From practitioners, not uncommon to see claims like: RA DSGE Models Match The Data Remarkably Well This A Bug Not A Feature Not that there’s anything wrong . . . . . . with (most of) Epicycles per se Many might even be right Problems: 0. No way to test the epicycles 1. Complexity Carroll Behavioral-Macro

  43. After 30 years of reverse engineering of Epicycles: From practitioners, not uncommon to see claims like: RA DSGE Models Match The Data Remarkably Well This A Bug Not A Feature Not that there’s anything wrong . . . . . . with (most of) Epicycles per se Many might even be right Problems: 0. No way to test the epicycles 1. Complexity Carroll Behavioral-Macro

  44. After 30 years of reverse engineering of Epicycles: From practitioners, not uncommon to see claims like: RA DSGE Models Match The Data Remarkably Well This A Bug Not A Feature Not that there’s anything wrong . . . . . . with (most of) Epicycles per se Many might even be right Problems: 0. No way to test the epicycles 1. Complexity Carroll Behavioral-Macro

  45. After 30 years of reverse engineering of Epicycles: From practitioners, not uncommon to see claims like: RA DSGE Models Match The Data Remarkably Well This A Bug Not A Feature Not that there’s anything wrong . . . . . . with (most of) Epicycles per se Many might even be right Problems: 0. No way to test the epicycles 1. Complexity Carroll Behavioral-Macro

  46. After 30 years of reverse engineering of Epicycles: From practitioners, not uncommon to see claims like: RA DSGE Models Match The Data Remarkably Well This A Bug Not A Feature Not that there’s anything wrong . . . . . . with (most of) Epicycles per se Many might even be right Problems: 0. No way to test the epicycles 1. Complexity Carroll Behavioral-Macro

  47. After 30 years of reverse engineering of Epicycles: From practitioners, not uncommon to see claims like: RA DSGE Models Match The Data Remarkably Well This A Bug Not A Feature Not that there’s anything wrong . . . . . . with (most of) Epicycles per se Many might even be right Problems: 0. No way to test the epicycles 1. Complexity Carroll Behavioral-Macro

  48. For Ptolemic and and For RA DSGE No way to test whether whether epicycle is right or not within the rules of the game: If adding the epicycle fixes the problem . . . In principle, nothing more can be done What if ∃ 5 equally good fixes for existing NIPA data? If NIPA-indistinguishable in principle, we’ll never know If NIPA-distinguishable, wait a long time Carroll Behavioral-Macro

  49. For Ptolemic and and For RA DSGE No way to test whether whether epicycle is right or not within the rules of the game: If adding the epicycle fixes the problem . . . In principle, nothing more can be done What if ∃ 5 equally good fixes for existing NIPA data? If NIPA-indistinguishable in principle, we’ll never know If NIPA-distinguishable, wait a long time Carroll Behavioral-Macro

  50. For Ptolemic and and For RA DSGE No way to test whether whether epicycle is right or not within the rules of the game: If adding the epicycle fixes the problem . . . In principle, nothing more can be done What if ∃ 5 equally good fixes for existing NIPA data? If NIPA-indistinguishable in principle, we’ll never know If NIPA-distinguishable, wait a long time Carroll Behavioral-Macro

  51. For Ptolemic and and For RA DSGE No way to test whether whether epicycle is right or not within the rules of the game: If adding the epicycle fixes the problem . . . In principle, nothing more can be done What if ∃ 5 equally good fixes for existing NIPA data? If NIPA-indistinguishable in principle, we’ll never know If NIPA-distinguishable, wait a long time Carroll Behavioral-Macro

  52. For Ptolemic and and For RA DSGE No way to test whether whether epicycle is right or not within the rules of the game: If adding the epicycle fixes the problem . . . In principle, nothing more can be done What if ∃ 5 equally good fixes for existing NIPA data? If NIPA-indistinguishable in principle, we’ll never know If NIPA-distinguishable, wait a long time Carroll Behavioral-Macro

  53. Complexity With all the “standard” epicycles Benchmark RA DSGE Models Now have ≈ 50 parameters Estimated with 60 years of data Key original selling point of simplicty has been lost With All the Epicycles, RA DSGE is to the MONIAC . . . . . . as the Death Star is to the Armillary Sphere Carroll Behavioral-Macro

  54. Complexity With all the “standard” epicycles Benchmark RA DSGE Models Now have ≈ 50 parameters Estimated with 60 years of data Key original selling point of simplicty has been lost With All the Epicycles, RA DSGE is to the MONIAC . . . . . . as the Death Star is to the Armillary Sphere Carroll Behavioral-Macro

  55. Complexity With all the “standard” epicycles Benchmark RA DSGE Models Now have ≈ 50 parameters Estimated with 60 years of data Key original selling point of simplicty has been lost With All the Epicycles, RA DSGE is to the MONIAC . . . . . . as the Death Star is to the Armillary Sphere Carroll Behavioral-Macro

  56. Complexity With all the “standard” epicycles Benchmark RA DSGE Models Now have ≈ 50 parameters Estimated with 60 years of data Key original selling point of simplicty has been lost With All the Epicycles, RA DSGE is to the MONIAC . . . . . . as the Death Star is to the Armillary Sphere Carroll Behavioral-Macro

  57. Complexity With all the “standard” epicycles Benchmark RA DSGE Models Now have ≈ 50 parameters Estimated with 60 years of data Key original selling point of simplicty has been lost With All the Epicycles, RA DSGE is to the MONIAC . . . . . . as the Death Star is to the Armillary Sphere Carroll Behavioral-Macro

  58. Complexity With all the “standard” epicycles Benchmark RA DSGE Models Now have ≈ 50 parameters Estimated with 60 years of data Key original selling point of simplicty has been lost With All the Epicycles, RA DSGE is to the MONIAC . . . . . . as the Death Star is to the Armillary Sphere Carroll Behavioral-Macro

  59. “The Normal Science of HA Macro” HA Macro is like Galilean (that is, scientific) Astronomy What Does Normal Science Do? Seek New and Better Data To Measure Key Predictions Better and Better Telescopes Test New Propositions (Not Thought Of Before): Parallax seasonal shift of nearby stars’ apparent positions Use Theory To Address Previous Non-Questions Orbits of Comets Halley New Explanations Of Old Phenomena Tides Reflect Moon’s Gravity Not, e.g., Moon Blowing On Ocean Carroll Behavioral-Macro

  60. “The Normal Science of HA Macro” HA Macro is like Galilean (that is, scientific) Astronomy What Does Normal Science Do? Seek New and Better Data To Measure Key Predictions Better and Better Telescopes Test New Propositions (Not Thought Of Before): Parallax seasonal shift of nearby stars’ apparent positions Use Theory To Address Previous Non-Questions Orbits of Comets Halley New Explanations Of Old Phenomena Tides Reflect Moon’s Gravity Not, e.g., Moon Blowing On Ocean Carroll Behavioral-Macro

  61. “The Normal Science of HA Macro” HA Macro is like Galilean (that is, scientific) Astronomy What Does Normal Science Do? Seek New and Better Data To Measure Key Predictions Better and Better Telescopes Test New Propositions (Not Thought Of Before): Parallax seasonal shift of nearby stars’ apparent positions Use Theory To Address Previous Non-Questions Orbits of Comets Halley New Explanations Of Old Phenomena Tides Reflect Moon’s Gravity Not, e.g., Moon Blowing On Ocean Carroll Behavioral-Macro

  62. “The Normal Science of HA Macro” HA Macro is like Galilean (that is, scientific) Astronomy What Does Normal Science Do? Seek New and Better Data To Measure Key Predictions Better and Better Telescopes Test New Propositions (Not Thought Of Before): Parallax seasonal shift of nearby stars’ apparent positions Use Theory To Address Previous Non-Questions Orbits of Comets Halley New Explanations Of Old Phenomena Tides Reflect Moon’s Gravity Not, e.g., Moon Blowing On Ocean Carroll Behavioral-Macro

  63. “The Normal Science of HA Macro” HA Macro is like Galilean (that is, scientific) Astronomy What Does Normal Science Do? Seek New and Better Data To Measure Key Predictions Better and Better Telescopes Test New Propositions (Not Thought Of Before): Parallax seasonal shift of nearby stars’ apparent positions Use Theory To Address Previous Non-Questions Orbits of Comets Halley New Explanations Of Old Phenomena Tides Reflect Moon’s Gravity Not, e.g., Moon Blowing On Ocean Carroll Behavioral-Macro

  64. “The Normal Science of HA Macro” HA Macro is like Galilean (that is, scientific) Astronomy What Does Normal Science Do? Seek New and Better Data To Measure Key Predictions Better and Better Telescopes Test New Propositions (Not Thought Of Before): Parallax seasonal shift of nearby stars’ apparent positions Use Theory To Address Previous Non-Questions Orbits of Comets Halley New Explanations Of Old Phenomena Tides Reflect Moon’s Gravity Not, e.g., Moon Blowing On Ocean Carroll Behavioral-Macro

  65. “The Normal Science of HA Macro” HA Macro is like Galilean (that is, scientific) Astronomy What Does Normal Science Do? Seek New and Better Data To Measure Key Predictions Better and Better Telescopes Test New Propositions (Not Thought Of Before): Parallax seasonal shift of nearby stars’ apparent positions Use Theory To Address Previous Non-Questions Orbits of Comets Halley New Explanations Of Old Phenomena Tides Reflect Moon’s Gravity Not, e.g., Moon Blowing On Ocean Carroll Behavioral-Macro

  66. “The Normal Science of HA Macro” HA Macro is like Galilean (that is, scientific) Astronomy What Does Normal Science Do? Seek New and Better Data To Measure Key Predictions Better and Better Telescopes Test New Propositions (Not Thought Of Before): Parallax seasonal shift of nearby stars’ apparent positions Use Theory To Address Previous Non-Questions Orbits of Comets Halley New Explanations Of Old Phenomena Tides Reflect Moon’s Gravity Not, e.g., Moon Blowing On Ocean Carroll Behavioral-Macro

  67. “The Normal Science of HA Macro” HA Macro is like Galilean (that is, scientific) Astronomy What Does Normal Science Do? Seek New and Better Data To Measure Key Predictions Better and Better Telescopes Test New Propositions (Not Thought Of Before): Parallax seasonal shift of nearby stars’ apparent positions Use Theory To Address Previous Non-Questions Orbits of Comets Halley New Explanations Of Old Phenomena Tides Reflect Moon’s Gravity Not, e.g., Moon Blowing On Ocean Carroll Behavioral-Macro

  68. “The Normal Science of HA Macro” HA Macro is like Galilean (that is, scientific) Astronomy What Does Normal Science Do? Seek New and Better Data To Measure Key Predictions Better and Better Telescopes Test New Propositions (Not Thought Of Before): Parallax seasonal shift of nearby stars’ apparent positions Use Theory To Address Previous Non-Questions Orbits of Comets Halley New Explanations Of Old Phenomena Tides Reflect Moon’s Gravity Not, e.g., Moon Blowing On Ocean Carroll Behavioral-Macro

  69. “The Normal Science of HA Macro” HA Macro is like Galilean (that is, scientific) Astronomy What Does Normal Science Do? Seek New and Better Data To Measure Key Predictions Better and Better Telescopes Test New Propositions (Not Thought Of Before): Parallax seasonal shift of nearby stars’ apparent positions Use Theory To Address Previous Non-Questions Orbits of Comets Halley New Explanations Of Old Phenomena Tides Reflect Moon’s Gravity Not, e.g., Moon Blowing On Ocean Carroll Behavioral-Macro

  70. “The Normal Science of HA Macro” HA Macro is like Galilean (that is, scientific) Astronomy What Does Normal Science Do? Seek New and Better Data To Measure Key Predictions Better and Better Telescopes Test New Propositions (Not Thought Of Before): Parallax seasonal shift of nearby stars’ apparent positions Use Theory To Address Previous Non-Questions Orbits of Comets Halley New Explanations Of Old Phenomena Tides Reflect Moon’s Gravity Not, e.g., Moon Blowing On Ocean Carroll Behavioral-Macro

  71. “The Normal Science of HA Macro” HA Macro is like Galilean (that is, scientific) Astronomy What Does Normal Science Do? Seek New and Better Data To Measure Key Predictions Better and Better Telescopes Test New Propositions (Not Thought Of Before): Parallax seasonal shift of nearby stars’ apparent positions Use Theory To Address Previous Non-Questions Orbits of Comets Halley New Explanations Of Old Phenomena Tides Reflect Moon’s Gravity Not, e.g., Moon Blowing On Ocean Carroll Behavioral-Macro

  72. “The Normal Science of HA Macro” HA Macro is like Galilean (that is, scientific) Astronomy What Does Normal Science Do? Seek New and Better Data To Measure Key Predictions Better and Better Telescopes Test New Propositions (Not Thought Of Before): Parallax seasonal shift of nearby stars’ apparent positions Use Theory To Address Previous Non-Questions Orbits of Comets Halley New Explanations Of Old Phenomena Tides Reflect Moon’s Gravity Not, e.g., Moon Blowing On Ocean Carroll Behavioral-Macro

  73. “The Normal Science of HA Macro” HA Macro is like Galilean (that is, scientific) Astronomy What Does Normal Science Do? Seek New and Better Data To Measure Key Predictions Better and Better Telescopes Test New Propositions (Not Thought Of Before): Parallax seasonal shift of nearby stars’ apparent positions Use Theory To Address Previous Non-Questions Orbits of Comets Halley New Explanations Of Old Phenomena Tides Reflect Moon’s Gravity Not, e.g., Moon Blowing On Ocean Carroll Behavioral-Macro

  74. “The Normal Science of HA Macro” HA Macro is like Galilean (that is, scientific) Astronomy What Does Normal Science Do? Seek New and Better Data To Measure Key Predictions Better and Better Telescopes Test New Propositions (Not Thought Of Before): Parallax seasonal shift of nearby stars’ apparent positions Use Theory To Address Previous Non-Questions Orbits of Comets Halley New Explanations Of Old Phenomena Tides Reflect Moon’s Gravity Not, e.g., Moon Blowing On Ocean Carroll Behavioral-Macro

  75. Recent ‘Scientific’ Triumphs of HA Macro Characteristics: 1. About Questions Central to Core Macroeconomic Questions Fiscal policy, monetary policy, aggregate shocks, dynamics 2. Impossible To Do Using RA DSGE methodology Carroll Behavioral-Macro

Recommend

Macroeconomic models with Heterogeneous Agents Nets Hawk Katz, joint work with Karsten Chipeniuk

Macroeconomic models with Heterogeneous Agents Nets Hawk Katz, joint work with Karsten Chipeniuk

Macroeconomic models with Heterogeneous Agents Nets Hawk Katz, joint work with Karsten Chipeniuk and Todd Walker February 17,2015 Outline of the talk Prehistory of macroeconomics Outline of the talk Prehistory of macroeconomics

1.17k views • 79 slides
Heterogeneous Agent Macroeconomics: An Example and an Agenda Christopher Carroll 1 1 Johns Hopkins

Heterogeneous Agent Macroeconomics: An Example and an Agenda Christopher Carroll 1 1 Johns Hopkins

Heterogeneous Agent Macroeconomics: An Example and an Agenda Christopher Carroll 1 1 Johns Hopkins University and Consumer Financial Protection Bureau (CFPB) ccarroll@jhu.edu Presentation at IMF Workshop on Computational Macroeconomics

1.42k views • 115 slides
Energy-efficient Delivery by Heterogeneous Mobile Agents Andreas B artschi J er emie

Energy-efficient Delivery by Heterogeneous Mobile Agents Andreas B artschi J er emie

Energy-efficient Delivery by Heterogeneous Mobile Agents Andreas B artschi J er emie Chalopin, Shantanu Das, Yann Disser, Daniel Graf, Jan Hackfeld, Paolo Penna Department of Computer Science Motivation / Toy model 7 /100 km 6

535 views • 50 slides
Engineering Agreement: The Naming Game wit ith Asymmetric and Heterogeneous Agents Jie Gao, Bo

Engineering Agreement: The Naming Game wit ith Asymmetric and Heterogeneous Agents Jie Gao, Bo

Engineering Agreement: The Naming Game wit ith Asymmetric and Heterogeneous Agents Jie Gao, Bo Li, Grant Schoenebeck, Fan ang-Yi i Yu Social Convention Conventions are universally adopted from two or more alternatives. Language,

658 views • 50 slides
Interactions and Protocols Distributed systems of autonomous, heterogeneous agents Enable

Interactions and Protocols Distributed systems of autonomous, heterogeneous agents Enable

BSPL, the Blindingly Simple Protocol Language Interactions and Protocols Distributed systems of autonomous, heterogeneous agents Enable interoperation Maintain independence from internal reasoning (policies) Support composition of

538 views • 43 slides
Normal Form Games 2-12-16 Game Representations Extensive Form Game Normal Form Game

Normal Form Games 2-12-16 Game Representations Extensive Form Game Normal Form Game

Normal Form Games 2-12-16 Game Representations Extensive Form Game Normal Form Game Agents take turns Agents move simultaneously. Represented as a tree. Represented as a matrix. Internal nodes are agent decisions.

563 views • 12 slides
The Macroeconomics of The Macroeconomics of (time-varying) Uncertainty (t e a y g) U ce ta

The Macroeconomics of The Macroeconomics of (time-varying) Uncertainty (t e a y g) U ce ta

The Macroeconomics of The Macroeconomics of (time-varying) Uncertainty (t e a y g) U ce ta ty Nick Bloom (Stanford & NBER) ( ) CREI, December 2014 In these lectures I want to argue uncertainty is another potential driver of growth

1.5k views • 147 slides
A preliminary result on synchronization of heterogeneous agents via funnel control Stephan Trenn

A preliminary result on synchronization of heterogeneous agents via funnel control Stephan Trenn

A preliminary result on synchronization of heterogeneous agents via funnel control Stephan Trenn Technomathematics group, University of Kaiserslautern, Germany joint work with Hyungbo Shim (Seoul National University, Korea) 54th IEEE Conference

381 views • 21 slides
Identity and Macroeconomics ISEO SUMMER SCHOOL June 19, 2013 I. Revolution in Macroeconomics

Identity and Macroeconomics ISEO SUMMER SCHOOL June 19, 2013 I. Revolution in Macroeconomics

Identity and Macroeconomics ISEO SUMMER SCHOOL June 19, 2013 I. Revolution in Macroeconomics Macroeconomics changed . Objection to casual ways. Derivation from economic principles. Five Neutralities Independence of consumption and

652 views • 37 slides
Reasoning Agents Jos e M Vidal Department of Computer Science and Engineering University of

Reasoning Agents Jos e M Vidal Department of Computer Science and Engineering University of

Utility Maximizing Agents Deductive Reasoning Agents Agent-Oriented Programming Practical Reasoning Agents Reasoning Agents Jos e M Vidal Department of Computer Science and Engineering University of South Carolina August 30, 2005

741 views • 49 slides
Is Everything You Hear About Macroeconomics True? Professor Wouter Den Haan Co-director of the

Is Everything You Hear About Macroeconomics True? Professor Wouter Den Haan Co-director of the

Department of Economics and Centre For Macroeconomics public lecture Is Everything You Hear About Macroeconomics True? Professor Wouter Den Haan Co-director of the Centre for Macroeconomics, LSE Stephen Millard Chair, Bank of England Suggested

693 views • 43 slides
Decentralized Dynamic Scheduling across Heterogeneous Multi core across Heterogeneous Multi

Decentralized Dynamic Scheduling across Heterogeneous Multi core across Heterogeneous Multi

Decentralized Dynamic Scheduling across Heterogeneous Multi core across Heterogeneous Multi core Desktop Grids J Jaehwan Lee , Pete Keleher, Alan Sussman h L P t K l h Al S Department of Computer Science University of Maryland Multi

357 views • 25 slides
The American University at Cairo The Computer Science Department Csci485-Intelligent Agents

The American University at Cairo The Computer Science Department Csci485-Intelligent Agents

The American University at Cairo The Computer Science Department Csci485-Intelligent Agents Spring 2006 Dr Rafea JADE Tutorial 1. Introduction Intelligent Agents have been extensively used in different aspects of IT world. Intelligent agents

782 views • 36 slides
Lecture 13: Kuhn and Progress 1 1. Pre-paradigm science... 2. Normal science... 3. Accumulation

Lecture 13: Kuhn and Progress 1 1. Pre-paradigm science... 2. Normal science... 3. Accumulation

HPSC 1001/1901/2101/2901 WHAT IS THIS THING CALLED SCIENCE? Semester 2, 2020 Lecture 13: Kuhn and Progress 1 1. Pre-paradigm science... 2. Normal science... 3. Accumulation of Anomalies... 4. Crisis... 5. Scientific Revolution. A new paradigm

605 views • 16 slides
Reactive and Hybrid Agents Jos e M Vidal Department of Computer Science and Engineering

Reactive and Hybrid Agents Jos e M Vidal Department of Computer Science and Engineering

Prelude Subsumption Architecture Hybrid Agents Reactive and Hybrid Agents Jos e M Vidal Department of Computer Science and Engineering University of South Carolina September 2, 2005 Abstract We summarize [Wooldridge, 2002, Chapter 5].

321 views • 29 slides
Chapter 2 Intelligent Agents CS4811 - Artificial Intelligence Nilufer Onder Department of

Chapter 2 Intelligent Agents CS4811 - Artificial Intelligence Nilufer Onder Department of

Chapter 2 Intelligent Agents CS4811 - Artificial Intelligence Nilufer Onder Department of Computer Science Michigan Technological University Outline Agents and environments Agent types Agents and environments Agents include humans,

559 views • 11 slides
More Examples Relative Prices (RP)? Consumers make decisions based on relative prices rather

More Examples Relative Prices (RP)? Consumers make decisions based on relative prices rather

Microeconomics and Macroeconomics What is microeconomics? Microeconomics deals with the behavior of individual consumers, households, and businesses. What is macroeconomics? Macroeconomics deals with national economic policy and

259 views • 5 slides
Logical Agents Marco Chiarandini Department of Mathematics & Computer Science University of

Logical Agents Marco Chiarandini Department of Mathematics & Computer Science University of

Lecture 20 Logical Agents Marco Chiarandini Department of Mathematics & Computer Science University of Southern Denmark Slides by Stuart Russell and Peter Norvig Knowledge-based Agents Course Overview Logic in General Introduction

1.28k views • 75 slides
Simplification of CFG and Normal Forms Wen-Guey Tzeng Computer Science Department National

Simplification of CFG and Normal Forms Wen-Guey Tzeng Computer Science Department National

Simplification of CFG and Normal Forms Wen-Guey Tzeng Computer Science Department National Chiao Tung University Normal Forms We want a cfg with either Chomsky or Greibach normal form Chomsky normal form A a, A BC

332 views • 30 slides
Lecture 12: Kuhn and Revolutions 1 From lecture 11: 1. Pre-paradigm science... 2. Normal

Lecture 12: Kuhn and Revolutions 1 From lecture 11: 1. Pre-paradigm science... 2. Normal

HPSC 1001/1901/2101/2901 WHAT IS THIS THING CALLED SCIENCE? Semester 2, 2020 Lecture 12: Kuhn and Revolutions 1 From lecture 11: 1. Pre-paradigm science... 2. Normal science... 3. Accumulation of Anomalies... 4. Crisis... then 5.

536 views • 14 slides
Coverage in Heterogeneous Coverage in Heterogeneous Networks Xiaoli Chu King s College

Coverage in Heterogeneous Coverage in Heterogeneous Networks Xiaoli Chu King s College

Coverage in Heterogeneous Coverage in Heterogeneous Networks Xiaoli Chu King s College London UC4G Beijing Workshop August 2010 Outline Introduction Heterogeneous networks Heterogeneous networks Challenges Coverage in

344 views • 32 slides
Introduction to Agents John Lloyd School of Computer Science College of Engineering and Computer

Introduction to Agents John Lloyd School of Computer Science College of Engineering and Computer

Introduction to Agents John Lloyd School of Computer Science College of Engineering and Computer Science Australian National University Topics Agents and agent architectures Historical issues Philosophical issues Reference:

972 views • 39 slides
A(b)normal Environments Andrea Condoluci Department of Computer Science and Engineering

A(b)normal Environments Andrea Condoluci Department of Computer Science and Engineering

A(b)normal Environments Andrea Condoluci Department of Computer Science and Engineering University of Bologna April 24 th , 2019 Joint work with B. Accattoli, G. Guerrieri & C. Sacerdoti Coen 1/30 A(b)normal Environments Crumbling

778 views • 45 slides
Bhandari, Evans, Golosov, Sargent On The Role of Public Debt in Economies with Heterogeneous

Bhandari, Evans, Golosov, Sargent On The Role of Public Debt in Economies with Heterogeneous

Bhandari, Evans, Golosov, Sargent On The Role of Public Debt in Economies with Heterogeneous Agents Dirk Niepelt Study Center Gerzensee; U of Bern; CEPR October 2016 A Nice Paper Two themes Ricardian equivalence Ramsey policy (using

566 views • 12 slides

More recommend