1932

Abstract

The dynamic causal effect of an intervention on an outcome is of paramount interest to applied macro- and microeconomics research. However, this question has been generally approached differently by the two literatures. In making the transition from traditional time series methods to applied microeconometrics, local projections can serve as a natural bridge. Local projections can translate the familiar language of vector autoregressions and impulse responses into the language of potential outcomes and treatment effects. There are gains to be made by both literatures from greater integration of well-established methods in each. This review shows how to make these connections and points to potential areas of further research.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-economics-082222-065846
2023-09-13
2024-04-30
Loading full text...

Full text loading...

/deliver/fulltext/economics/15/1/annurev-economics-082222-065846.html?itemId=/content/journals/10.1146/annurev-economics-082222-065846&mimeType=html&fmt=ahah

Literature Cited

  1. Abadie A, Athey S, Imbens GW, Wooldridge J. 2023. When should you adjust standard errors for clustering?. Q. J. Econ. 138:11–35
    [Google Scholar]
  2. Álvarez J, Arellano M. 2003. The time series and cross-section asymptotics of dynamic panel data estimators. Econometrica 71:41121–59
    [Google Scholar]
  3. Angrist JD, Jordà Ò, Kuersteiner GM. 2018. Semiparametric estimates of monetary policy effects: string theory revisited. J. Bus. Econ. Stat. 36:3371–87
    [Google Scholar]
  4. Angrist JD, Kuersteiner GM. 2011. Causal effects of monetary shocks: semiparametric conditional independence tests with a multinomial propensity score. Rev. Econ. Stat. 93:3725–47
    [Google Scholar]
  5. Arellano M, Bond S. 1991. Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Rev. Econ. Stud. 58:2277–97
    [Google Scholar]
  6. Arellano M, Bover O. 1995. Another look at the instrumental variable estimation of error-components models. J. Econometr. 68:129–51
    [Google Scholar]
  7. Auerbach AJ, Gorodnichenko Y 2012. Fiscal multipliers in recession and expansion. Fiscal Policy After the Financial Crisis A Alesina, F Giavazzi 63–98. Chicago: Univ. Chicago Press
    [Google Scholar]
  8. Barnichon R, Brownlees C. 2019. Impulse response estimation by smooth local projections. Rev. Econ. Stat. 101:3522–30
    [Google Scholar]
  9. Barnichon R, Matthes C. 2018. Functional approximations of impulse responses. J. Monet. Econ. 99:41–55
    [Google Scholar]
  10. Blinder AS. 1973. Wage discrimination: reduced form and structural estimates. J. Hum. Resourc. 8:4436–55
    [Google Scholar]
  11. Blundell R, Bond S. 1998. Initial conditions and moment restrictions in dynamic panel data models. J. Econometr. 87:1115–43
    [Google Scholar]
  12. Callaway B, Sant'Anna PH. 2021. Difference-in-differences with multiple time periods. J. Econometr. 225:2200–30
    [Google Scholar]
  13. Cameron AC, Gelbach JB, Miller DL. 2008. Bootstrap-based improvements for inference with clustered errors. Rev. Econ. Stat. 90:3414–27
    [Google Scholar]
  14. Cameron AC, Gelbach JB, Miller DL. 2011. Robust inference with multiway clustering. J. Bus. Econ. Stat. 29:2238–49
    [Google Scholar]
  15. Cameron AC, Miller DL. 2015. A practitioner's guide to cluster-robust inference. J. Hum. Resourc. 50:2317–72
    [Google Scholar]
  16. Cengiz D, Dube A, Lindner A, Zipperer B. 2019. The effect of minimum wages on low-wage jobs. Q. J. Econ. 134:31405–54
    [Google Scholar]
  17. Chong Y, Jordà Ò, Taylor AM. 2012. The Harrod–Balassa–Samuelson hypothesis: real exchange rates and their long-run equilibrium. Int. Econ. Rev. 53:2609–34
    [Google Scholar]
  18. Cloyne JS, Jordà O, Taylor AM. 2023. State-dependent local projections: understanding impulse response heterogeneity NBER Work. Pap.30971
  19. Cox DR. 1961. Prediction by exponentially weighted moving averages and related methods. J. R. Stat. Soc. B 23:2414–22
    [Google Scholar]
  20. De Chaisemartin C, D'Haultfoeuille X. 2020. Two-way fixed effects estimators with heterogeneous treatment effects. Am. Econ. Rev. 110:92964–96
    [Google Scholar]
  21. De Chaisemartin C, D'Haultfoeuille X. 2022. Difference-in-differences estimators of intertemporal treatment effects. NBER Work. Pap.29873
  22. Dolado JJ, Lütkepohl H. 1996. Making Wald tests work for cointegrated VAR systems. Econometr. Rev. 15:4369–86
    [Google Scholar]
  23. Driscoll JC, Kraay AC. 1998. Consistent covariance matrix estimation with spatially dependent panel data. Rev. Econ. Stat. 80:4549–60
    [Google Scholar]
  24. Dube A, Girardi D, Jordà O, Taylor AM. 2023. A local projections approach to difference-in-differences event studies NBER Work. Pap.31184
  25. Ferrari Minesso M, Lebastard L, Le Mezo H 2023. Text-based recession probabilities. IMF Econ. Rev. 71:415–38
    [Google Scholar]
  26. Fortin N, Lemieux T, Firpo S 2011. Decomposition methods in economics. Handbook of Labor Economics, Vol. 4A O Ashenfelter, D Card 1–102. Amsterdam: Elsevier
    [Google Scholar]
  27. Frisch R. 1933. Propagation problems and impulse problems in dynamic economics. Publikasjon, Vol. 3171–206. Oslo, Nor.: Univ. Økonomiske Inst.
    [Google Scholar]
  28. Gonçalves S, Herrera AM, Kilian L, Pesavento E. 2022. When do state-dependent local projections work? Res. Dep. Work. Pap. 2205, Fed. Reserve Bank Dallas Dallas, TX:
  29. Goodman-Bacon A. 2021. Difference-in-differences with variation in treatment timing. J. Econometr. 225:2254–77
    [Google Scholar]
  30. Hansen LP, Heaton J, Yaron A. 1996. Finite-sample properties of some alternative GMM estimators. J. Bus. Econ. Stat. 14:3262–80
    [Google Scholar]
  31. Hirano K, Imbens GW, Ridder G. 2003. Efficient estimation of average treatment effects using the estimated propensity score. Econometrica 71:41161–89
    [Google Scholar]
  32. Horvitz DG, Thompson DJ. 1952. A generalization of sampling without replacement from a finite universe. J. Am. Stat. Assoc. 47:260663–85
    [Google Scholar]
  33. Inoue A, Kilian L. 2020. The uniform validity of impulse response inference in autoregressions. J. Econometr. 215:2450–72
    [Google Scholar]
  34. Jordà Ò. 2005. Estimation and inference of impulse responses by local projections. Am. Econ. Rev. 95:1161–82
    [Google Scholar]
  35. Jordà Ò. 2009. Simultaneous confidence regions for impulse responses. Rev. Econ. Stat. 91:3629–47
    [Google Scholar]
  36. Jordà Ò, Kornejew M, Schularick M, Taylor AM. 2022. Zombies at large? Corporate debt overhang and the macroeconomy. Rev. Financ. Stud. 35:104561–86
    [Google Scholar]
  37. Jordà Ò, Singh SR, Taylor AM. 2020. The long-run effects of monetary policy NBER Work. Pap. 26666
  38. Jordà Ò, Taylor AM. 2016. The time for austerity: estimating the average treatment effect of fiscal policy. Econ. J. 126:590219–55
    [Google Scholar]
  39. Keynes JM. 1936. The marginal propensity to consume and the multiplier. The General Theory of Employment, Interest, and Money101–16. London: Macmillan
    [Google Scholar]
  40. Kitagawa EM. 1955. Components of a difference between two rates. J. Am. Stat. Assoc. 50:2721168–94
    [Google Scholar]
  41. Klein LR. 1968. Essay on the Theory of Economic Prediction Helsinki, Finl: Yrjo Jahnsson Lect.
  42. Kline P, Santos A. 2012. A score based approach to wild bootstrap inference. J. Econometr. Methods 1:123–41
    [Google Scholar]
  43. Kuersteiner GM. 2005. Automatic inference for infinite order vector autoregressions. Econometr. Theory 21:185–115
    [Google Scholar]
  44. Li D, Plagborg-Møller M, Wolf CK. 2022. Local projections vs. VARs: lessons from thousands of DGPs NBER Work. Pap. 30207
  45. Lusompa AB. 2021. Local projections, autocorrelation, and efficiency Unpublished manuscript Fed. Reserve Bank Kansas City Kansas City, MO:
  46. MacKinnon JG, Nielsen , Webb MD. 2022. Cluster-robust inference: a guide to empirical practice. J. Econometr. 232:2272–99
    [Google Scholar]
  47. Mahalonobis P. 1936. On the generalized distance in statistics. Proc. Natl. Inst. Sci. India 2:49–55
    [Google Scholar]
  48. Mertens K, Montiel-Olea JL. 2018. Marginal tax rates and income: new time series evidence. Q. J. Econ. 133:41803–84
    [Google Scholar]
  49. Mertens K, Ravn MO. 2013. The dynamic effects of personal and corporate income tax changes in the United States. Am. Econ. Rev. 103:41212–47
    [Google Scholar]
  50. Miranda-Agrippino S, Ricco G. 2021. Bayesian local projections Work. Pap., Univ. Warwick Coventry, UK:
  51. Montiel Olea JL, Plagborg-Møller M 2019. Simultaneous confidence bands: theory, implementation, and an application to SVARs. J. Appl. Econometr. 34:11–17
    [Google Scholar]
  52. Montiel Olea JL, Plagborg-Møller M 2021. Local projection inference is simpler and more robust than you think. Econometrica 89:41789–823
    [Google Scholar]
  53. Mountford A, Uhlig H. 2009. What are the effects of fiscal policy shocks?. J. Appl. Econometr. 24:6960–92
    [Google Scholar]
  54. Oaxaca R. 1973. Male-female wage differentials in urban labor markets. Int. Econ. Rev. 14:3693–709
    [Google Scholar]
  55. Petersen MA. 2009. Estimating standard errors in finance panel data sets: comparing approaches. Rev. Financ. Stud. 22:1435–80
    [Google Scholar]
  56. Plagborg-Møller M, Wolf CK. 2021. Local projections and VARs estimate the same impulse responses. Econometrica 89:2955–80
    [Google Scholar]
  57. Ramey V 2016. Chapter 2 - Macroeconomic shocks and their propagation. Handbook of Macroeconomics JB Taylor, H Uhlig 71–162. Amsterdam: Elsevier
    [Google Scholar]
  58. Ramey VA, Zubairy S. 2018. Government spending multipliers in good times and in bad: evidence from US historical data. J. Political Econ. 126:2850–901
    [Google Scholar]
  59. Romer CD, Romer DH. 2004. A new measure of monetary shocks: derivation and implications. Am. Econ. Rev. 94:41055–84
    [Google Scholar]
  60. Roth J, Sant'Anna PH, Bilinski A, Poe J. 2022. What's trending in difference-in-differences? A synthesis of the recent econometrics literature. arXiv:2201.01194[econ.EM]
  61. Rubin DB. 1974. Estimating causal effects of treatments in randomized and nonrandomized studies. J. Educ. Psychol. 66:5688–701
    [Google Scholar]
  62. Scheffé H. 1953. A method for judging all contrasts in the analysis of variance. Biometrika 40:1–287–110
    [Google Scholar]
  63. Sims CA. 1980. Macroeconomics and reality. Econometrica 48:11–48
    [Google Scholar]
  64. Stock JH, Watson MW. 2012. Disentangling the channels of the 2007–09 recession. Brook. Pap. Econ. Act. 43:181–156
    [Google Scholar]
  65. Stock JH, Watson MW. 2018. Identification and estimation of dynamic causal effects in macroeconomics using external instruments. Econ. J. 128:610917–48
    [Google Scholar]
  66. Sun L, Abraham S. 2021. Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. J. Econometr. 225:2175–99
    [Google Scholar]
  67. Tanaka M. 2020. Bayesian inference of local projections with roughness penalty priors. Comput. Econ. 55:2629–51
    [Google Scholar]
  68. Tenreyro S, Thwaites G. 2016. Pushing on a string: US monetary policy is less powerful in recessions. Am. Econ. J. Macroecon. 8:443–74
    [Google Scholar]
  69. Toda HY, Yamamoto T. 1995. Statistical inference in vector autoregressions with possibly integrated processes. J. Econometr. 66:1–2225–50
    [Google Scholar]
  70. Xu KL. 2023. Local projection based inference under general conditions Tech. Rep., Indiana Univ. Bloomington, IN:
/content/journals/10.1146/annurev-economics-082222-065846
Loading
/content/journals/10.1146/annurev-economics-082222-065846
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error