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SUMMARY The likelihood of the parameters in structural macroeconomic models typically has non‐identification regions over which it is constant. When sufficiently diffuse priors are used, the posterior piles up in such non‐identification regions. Use of informative priors can lead to the opposite, so both can generate spurious inference. We propose priors/posteriors on the structural parameters that are implied by priors/posteriors on the parameters of an embedding…

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Journal of Business and Economic Statistics. July 1, 2009, 27(3): 331-339. doi:10.1198/jbes.2009.08350. \nIn our article, we explain that the coefficients of the NKPC\nare not identified when λ = 0. For analytical tractability, our\nidentification analysis was based on the pure forward-looking\nversion of the NKPC but the result also applies to the hybrid\nversion [γb = 0 in Equation (2) in the article]; see, for example, Mavroeidis (2006).

GMM 中的弱工具变量稳健检验与新凯恩斯菲利普斯曲线AI 解读

We discuss weak instrument robust statistics in GMM for testing hypotheses on the full parameter vector or on subsets of the parameters. We use these test procedures to reexamine the evidence on the new Keynesian Phillips curve model. We find that U.S. postwar data are consistent with the view that inflation dynamics are predominantly forward-looking, but we cannot rule out the presence of considerable backward-looking dynamics. Moreover, the Phillips curve has become…

向量误差修正模型面板的似然协整分析AI 解读

panels of a fixed number of vector error-correction (VEC) models. We obtain likelihood ratio statistics to test for a common cointegration rank across the individual VEC models with both heterogeneous and homogeneous cointegrating vectors. Their limiting distributions are a summation of the limiting behavior of Johansen trace statistics. We extend the asymptotic distribution theory to cover the case of an infinite cross-sectional dimension. We apply the framework to a…

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协整模型中似然与后验的形状AI 解读

A vector autoregressive (VAR) model is specified with equation system parameters, which directly reflect the possible cointegrating nature of the analyzed time series. By using a flat/diffuse prior, we show that the marginal posteriors of the parameters of interest (multipliers of the cointegrating vectors) may be nonintegrable and favor difference stationary models in an undesired way. To choose between stationary, cointegrated, and difference stationary models in a…

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