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The Sine Aggregatio Approach to Applied Macro

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Abstract

We develop a method to use disaggregate data to conduct causal inference in macroeconomics. The approach permits one to infer the aggregate effect of a macro treatment using regional outcome data and a valid instrument. We estimate a macro effect without (sine) the aggregation (aggregatio) of the outcome variable. We exploit cross-equation parameter restrictions to increase precision relative to traditional, aggregate series estimates and provide a method to assess robustness to departures from these restrictions. We illustrate our method via estimating the jobs effect of oil price changes using regional manufacturing employment data and an aggregate oil supply shock.

Suggested Citation

  • Timothy G. Conley & Bill Dupor & Mahdi Ebsim, 2022. "The Sine Aggregatio Approach to Applied Macro," Working Papers 2022-014, Federal Reserve Bank of St. Louis, revised 11 Nov 2022.
    • Handle: RePEc:fip:fedlwp:94511
    DOI: 10.20955/wp.2022.014
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    References listed on IDEAS

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    1. Emi Nakamura & J?n Steinsson, 2014. "Fiscal Stimulus in a Monetary Union: Evidence from US Regions," American Economic Review, American Economic Association, vol. 104(3), pages 753-792, March.
    2. Sims, Christopher A, 1972. "Money, Income, and Causality," American Economic Review, American Economic Association, vol. 62(4), pages 540-552, September.
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    More about this item

    Keywords

    aggregation; macroeconomic causal effect;

    JEL classification:

    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles

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