General characterizations of valid confidence sets and tests in problems which involve locally almost unidentified (LAU) parameters are provided and applied to several econometric models. Two types of inference problems are studied: (1) inference about parameters which are not identifiable on certain subsets of the parameter space, and (2) inference about parameter transformations with discontinuities. When a LAU parameter or parametric function has an unbounded range, it is shown under general regularity conditions that any valid confidence set with level 1 − α for this parameter must be unbounded with probability close to 1−α in the neighborhood of nonidentification subsets and will have a non-zero probability of being unbounded under any...
This paper considers the problem of inference for partially identified econo-metric models. The clas...
When a sample of data does not fully reveal the "true" data generating structure (or parameter) but ...
We examine challenges to estimation and inference when the objects of interest are nondif-ferentiabl...
It is well known that confidence intervals for weakly identified parameters are unbounded with posit...
Abstract. This paper provides confidence regions for minima of an econometric criterion function Q{d...
Abstract. This paper provides confidence regions for minima of an econometric criterion function Q(9...
This thesis consists of three papers which deal with three different econometric problems but have a...
Important estimation problems in econometrics like estimating the value of a spectral density at fre...
Abstract The problem of constructing confidence set estimates for parameter ratios arises in a varie...
The subject of the paper-upper confidence bounds-originates from applications to auditing. Auditors ...
We propose a methodology for constructing valid confidence regions in incomplete models with latent ...
This paper provides novel methods for inference in a very general class of ill-posed models in econo...
spaces to unbounded sample sets. The motivation is to seek the most general pos-sible framework for ...
AbstractStatistical analyses commonly make use of models that suffer from loss of identifiability. I...
We study inference in structural models with a jump in the conditional density, where location and s...
This paper considers the problem of inference for partially identified econo-metric models. The clas...
When a sample of data does not fully reveal the "true" data generating structure (or parameter) but ...
We examine challenges to estimation and inference when the objects of interest are nondif-ferentiabl...
It is well known that confidence intervals for weakly identified parameters are unbounded with posit...
Abstract. This paper provides confidence regions for minima of an econometric criterion function Q{d...
Abstract. This paper provides confidence regions for minima of an econometric criterion function Q(9...
This thesis consists of three papers which deal with three different econometric problems but have a...
Important estimation problems in econometrics like estimating the value of a spectral density at fre...
Abstract The problem of constructing confidence set estimates for parameter ratios arises in a varie...
The subject of the paper-upper confidence bounds-originates from applications to auditing. Auditors ...
We propose a methodology for constructing valid confidence regions in incomplete models with latent ...
This paper provides novel methods for inference in a very general class of ill-posed models in econo...
spaces to unbounded sample sets. The motivation is to seek the most general pos-sible framework for ...
AbstractStatistical analyses commonly make use of models that suffer from loss of identifiability. I...
We study inference in structural models with a jump in the conditional density, where location and s...
This paper considers the problem of inference for partially identified econo-metric models. The clas...
When a sample of data does not fully reveal the "true" data generating structure (or parameter) but ...
We examine challenges to estimation and inference when the objects of interest are nondif-ferentiabl...