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偶尔约束生效情形下的协整AI 解读

In the literature on nonlinear cointegration, a long-standing open problem relates to how a (nonlinear) vector autoregression, which provides a unified description of the short- and long-run dynamics of a vector of time series, can generate ‘nonlinear cointegration’ in the profound sense of those series sharing common nonlinear stochastic trends. We consider this problem in the setting of the censored and kinked structural VAR (CKSVAR), which provides a flexible yet…

条件异方差下线性工具变量回归的强功效子向量 Anderson–Rubin 检验AI 解读

We introduce a new test for a two-sided hypothesis involving a subset of the structural parameter vector in the linear instrumental variables (IVs) model. Guggenberger, Kleibergen, and Mavroeidis (2019, Quantitative Economics , 10, 487–526; hereafter GKM19) introduce a subvector Anderson–Rubin (AR) test with data-dependent critical values that has asymptotic size equal to nominal size for a parameter space that allows for arbitrary strength or weakness of the IVs and has…

岭正则化的刀切 Anderson–Rubin 检验AI 解读

We consider hypothesis testing in instrumental variable regression models with few included exogenous covariates but many instruments-possibly more than the number of observations. We show that a ridge-regularized version of the jackknifed Anderson and Rubin (henceforth AR) test controls asymptotic size in the presence of heteroscedasticity, and when the instruments may be arbitrarily weak. Asymptotic size control is established under weaker assumptions than those imposed…

美国投资 Euler 方程的实证证据AI 解读

Summary Is the typical specification of the Euler equation for investment employed in dynamic stochastic general equilibrium (DSGE) models consistent with aggregate macro data? The answer is yes using state‐of‐the‐art econometric methods that are robust to weak instruments and exploit information in possible structural changes. Unfortunately, however, there is very little information about the values of the parameters in aggregate data because investment is unresponsive to…

协方差矩阵 Kronecker 积结构的检验AI 解读

We propose a test for a covariance matrix to have Kronecker Product Structure (KPS). KPS implies a reduced rank restriction on an invertible transformation of the covariance matrix and the new procedure is an adaptation of the Kleibergen-Paap (2006) reduced rank test. The main extension concerns the singularity of the covariance matrix estimator involved in the rank test which complicates the derivation of its limiting distribution. We show this limiting distribution to be…

零利率下限下的识别AI 解读

I show that the zero lower bound (ZLB) on interest rates can be used to identify the causal effects of monetary policy. Identification depends on the extent to which the ZLB limits the efficacy of monetary policy. I propose a simple way to test the efficacy of unconventional policies, modeled via a “shadow rate.” I apply this method to U.S. monetary policy using a three‐equation structural vector autoregressive model of inflation, unemployment, and the Federal Funds rate. I…

长期限制结构向量自回归中的稳健推断AI 解读

Long-run restrictions are a very popular method for identifying structural vector autoregressions, but they suffer from weak identification when the data is very persistent, i.e., when the highest autoregressive roots are near unity. Near unit roots introduce additional nuisance parameters and make standard weak-instrument-robust methods of inference inapplicable. We develop a method of inference that is robust to both weak identification and strong persistence. The method…

线性工具变量回归中更有功效的子向量 Anderson–Rubin 检验AI 解读

We study subvector inference in the linear instrumental variables model assuming homoskedasticity but allowing for weak instruments. The subvector Anderson and Rubin (1949) test that uses chi square critical values with degrees of freedom reduced by the number of parameters not under test, proposed by Guggenberger, Kleibergen, Mavroeidis, and Chen (2012), controls size but is generally conservative. We propose a conditional subvector Anderson and Rubin test that uses…

利用短面板估计异质自回归参数的截面分布AI 解读

This paper presents a maximum likelihood approach to estimation of cross sectional distributions of heterogeneous autoregressive (AR) parameters with short panel data. We construct a panel likelihood by integrating the unknown cross sectional density of heterogeneous AR parameters with respect to a known time-series data generating kernel. The solution to this extremal criterion recovers the unknown density of heterogeneous AR parameters. Applying our method to a model of…

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…

答复AI 解读

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…