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Activity Number: 101 - Time Series Modeling: Mixed Frequency Data, Seasonality, and Model Identification
Type: Topic Contributed
Date/Time: Monday, August 8, 2022 : 8:30 AM to 10:20 AM
Sponsor: Business and Economic Statistics Section
Abstract #322407
Title: Linear Identification of Linear Rational Expectations Models by Exogenous Variables
Author(s): Peter Zadrozny*
Companies: Bureau of Labor Statistics
Keywords: cross equation restrictions of rational expectations; factorization of matrix polynomials; Lucas critique of econometric policy evaluation

A linear rational-expectations model (LREM) is specified in terms of a linear (in variables and disturbances) structural equation that determines how endogenous variables are generated and a separate linear process that determines how exogenous variables are generated. The structural equation is solved for a linear rational expectations solution (RES) equation, which, for an assumed linear exogenous vector autoregressive (VAR) process, reduces to a linear reduced-form (RF) equation. The paper contributes by deriving linear identification 1 of structural coefficients from RES coefficients and linear identification 2 of RES coefficients from RF coefficients. An identification is a unique determination of coefficients (or underlying deep parameters) down from assumed data moments. Identifications 1-2 are linear because their determining equations are linear. An identification becomes a consistent estimation if it's a unique determination down from consistently estimated data moments. Identifications 1-2 require only exogenous variables and generic rank conditions on coefficient matrices and don't require any exact restrictions on coefficients. Therefore, as estimations, combined ident

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