A Monte Carlo test for the identifying assumptions of the Blanchard and Quah (1989) model

Hyeon Seung Huh

doi:10.1080/13504851.2012.725923

A Monte Carlo test for the identifying assumptions of the Blanchard and Quah (1989) model

Hyeon Seung Huh

Department of Economics

Research output: Contribution to journal › Article › peer-review

Abstract

In their VAR model, Blanchard and Quah (BQ, 1989) employed uncorrelatedness between Aggregate Supply (AS) and Aggregate Demand (AD) shocks and the long-run output neutrality condition as identifying assumptions. This article conducts a simple Monte Carlo experiment to gauge how well the BQ procedure can approximate the true structure if the underlying assumptions of uncorrelatedness and long-run output neutrality are not supported by data.

Original language	English
Pages (from-to)	601-605
Number of pages	5
Journal	Applied Economics Letters
Volume	20
Issue number	6
DOIs	https://doi.org/10.1080/13504851.2012.725923
Publication status	Published - 2013 Apr

Bibliographical note

Funding Information:
I thank the referee for the helpful input and the Department of Economics at the University of Washington for their generous hospitality. This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2010-013-B00007).

All Science Journal Classification (ASJC) codes

Economics and Econometrics

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10.1080/13504851.2012.725923

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abstract = "In their VAR model, Blanchard and Quah (BQ, 1989) employed uncorrelatedness between Aggregate Supply (AS) and Aggregate Demand (AD) shocks and the long-run output neutrality condition as identifying assumptions. This article conducts a simple Monte Carlo experiment to gauge how well the BQ procedure can approximate the true structure if the underlying assumptions of uncorrelatedness and long-run output neutrality are not supported by data.",

author = "Huh, {Hyeon Seung}",

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A Monte Carlo test for the identifying assumptions of the Blanchard and Quah (1989) model

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

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