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Project Citation: 

Favero, Carlo, and Giavazzi, Francesco. Replication data for: Measuring Tax Multipliers: The Narrative Method in Fiscal VARs. Nashville, TN: American Economic Association [publisher], 2012. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2019-10-13. https://doi.org/10.3886/E114786V1

Project Description

Summary:  View help for Summary This paper argues in favor of empirical models built by including in fiscal VAR models structural shocks identified via the narrative method. We first show that "narrative" shocks are orthogonal to the relevant information set a fiscal VAR. We then derive impulse responses to these shocks. The use of narrative shocks does not require the inversion of the moving-average representation of a VAR for the identification of the relevant shocks. Therefore, within this framework, fiscal multipliers can be identified and estimated even when, in the presence of "fiscal foresight," the MA representation of the VARs is not invertible. (JEL C32, E62, H20, H62, H63)

Scope of Project

Subject Terms:  View help for Subject Terms macroeconomic time series; narrative fiscal shocks
JEL Classification:  View help for JEL Classification
      C32 Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
      E62 Fiscal Policy
      H20 Taxation, Subsidies, and Revenue: General
      H62 National Deficit; Surplus
      H63 National Debt; Debt Management; Sovereign Debt
Geographic Coverage:  View help for Geographic Coverage United States
Time Period(s):  View help for Time Period(s) 1/1947 – 1/2010
Data Type(s):  View help for Data Type(s) aggregate data
Collection Notes:  View help for Collection Notes the exogenous narrativeli identified fiscal shocks are the romer&romer database

Methodology

Data Source:  View help for Data Source US FRED database, Romer&Romer
Unit(s) of Observation:  View help for Unit(s) of Observation quarterly time series,

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