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Abstract We introduce adaptive confidence intervals on a parameter of interest in the presence of nuisance parameters, such as coefficients on control variables, with known signs. Our confidence intervals are trivial to compute and can provide significant length reductions relative to standard ones when the nuisance parameters are small. At the same time, they entail minimal length increases at any parameter values. We apply our confidence intervals to the linear regression…

估计断点之后的推断AI 解读

In an important class of econometric problems, researchers select a target parameter by maximizing the Euclidean norm of a data-dependent vector. Examples that can be cast into this frame include threshold regression models with estimated thresholds and structural break models with estimated break dates. Estimation and inference procedures that ignore the randomness of the target parameter can be severely biased and misleading when this randomness is non-negligible. This…

线性回归中一致模型选择后的渐近均匀有效检验AI 解读

This article specializes the critical value (CV) methods that are based upon (refinements of) Bonferroni bounds, introduced by McCloskey to a problem of inference after consistent model selection in a general linear regression model. The post-selection problem is formulated to mimic common empirical practice and is applicable to both cross-sectional and time series contexts. We provide algorithms for constructing the CVs in this setting and establish uniform asymptotic size…

雅可比矩阵近乎奇异时的估计与推断AI 解读

This paper develops extremum estimation and inference results for nonlinear models with very general forms of potential identification failure when the source of this identification failure is known. We examine models that may have a general deficient rank Jacobian in certain parts of the parameter space. When identification fails in one of these models, it becomes underidentified and the identification status of individual parameters is not generally straightforward to…

对低频污染稳健的参数估计AI 解读

We provide methods to robustly estimate the parameters of stationary ergodic short-memory time series models in the potential presence of additive low-frequency contamination. The types of contamination covered include level shifts (changes in mean) and monotone or smooth time trends, both of which have been shown to bias parameter estimates toward regions of persistence in a variety of contexts. The estimators presented here minimize trimmed frequency domain quasi-maximum…

非标准检验问题中基于Bonferroni界的尺寸修正AI 解读

We develop a set of powerful and flexible size-correction procedures for general nonstandard testing environments in which the asymptotic distribution of a test statistic is discontinuous in a nuisance parameter under the null hypothesis. Examples of this form of testing problem are pervasive in econometrics and complicate inference by making the size difficult to control. The test constructions introduced here simultaneously control the asymptotic size of the test uniformly…

存在水平变化与确定性趋势时的记忆参数估计AI 解读

We propose estimators of the memory parameter of a time series that are robust to a wide variety of random level shift processes, deterministic level shifts, and deterministic time trends. The estimators are simple trimmed versions of the popular log-periodogram regression estimator that employ certain sample-size-dependent and, in some cases, data-dependent trimmings that discard low-frequency components. We also show that a previously developed trimmed local Whittle…