61 research outputs found
Honest confidence sets in nonparametric IV regression and other ill-posed models
This paper provides novel methods for inference in a very general class of ill-posed models in econometrics, encompassing the nonparametric instrumental regression, different functional regressions, and the deconvolution. I focus on uniform confidence sets for the parameter of interest estimated with Tikhonov regularization, as in Darolles, Fan, Florens, and Renault (2011). I first show that it is not possible to develop inferential methods directly based on the uniform central limit theorem. To circumvent this difficulty I develop two approaches that lead to valid confidence sets. I characterize expected diameters and coverage properties uniformly over a large class of models (i.e. constructed confidence sets are honest). Finally, I illustrate that introduced confidence sets have reasonable width and coverage properties in samples commonly used in applications with Monte Carlo simulations and considering application to Engel curves
Honest confidence sets in nonparametric IV regression and other ill-posed models
This paper provides novel methods for inference in a very general class of ill-posed models in econometrics, encompassing the nonparametric instrumental regression, different functional regressions, and the deconvolution. I focus on uniform confidence sets for the parameter of interest estimated with Tikhonov regularization, as in Darolles, Fan, Florens, and Renault (2011). I first show that it is not possible to develop inferential methods directly based on the uniform central limit theorem. To circumvent this difficulty I develop two approaches that lead to valid confidence sets. I characterize expected diameters and coverage properties uniformly over a large class of models (i.e. constructed confidence sets are honest). Finally, I illustrate that introduced confidence sets have reasonable width and coverage properties in samples commonly used in applications with Monte Carlo simulations and considering application to Engel curves
Is completeness necessary? Estimation in nonidentified linear models
This paper documents the consequences of the identification failures in a class of linear ill-posed inverse models. The Tikhonov-regularized estimator converges to a well-defined limit equal to the best approximation of the structural parameter in the orthogonal complement to the null space of the operator. We illustrate that in many instances the best approximation may coincide with the structural parameter or at least may reasonably approximate it. We obtain a new nonasymptotic risk bounds in the uniform and the Hilbert space norms for the best approximation. Nonidentification has important implications for the large sample distribution of the Tikhonov-regularized estimator, and we document the transition between the Gaussian and the weighted chi-squared
limits. The theoretical results are illustrated for the nonparametric IV and the functional linear IV regressions and are further supported by the Monte Carlo experiments
Are unobservables separable?
It is common to assume in empirical research that observables and unobservables
are additively separable, especially, when the former are endogenous.
This is done because it is widely recognized that identification and estimation
challenges arise when interactions between the two are allowed for. Starting
from a nonseparable IV model, where the instrumental variable is independent
of unobservables, we develop a novel nonparametric test of separability of unobservables.
The large-sample distribution of the test statistics is nonstandard and relies on a novel Donsker-type central limit theorem for the empirical distribution of nonparametric IV residuals. Using a dataset drawn from the 2015 US Consumer Expenditure Survey, we find that the test rejects the separability in Engel curves for most of the commodities
Tensor Principal Component Analysis
In this paper, we develop new methods for analyzing high-dimensional tensor
datasets. A tensor factor model describes a high-dimensional dataset as a sum
of a low-rank component and an idiosyncratic noise, generalizing traditional
factor models for panel data. We propose an estimation algorithm, called tensor
principal component analysis (PCA), which generalizes the traditional PCA
applicable to panel data. The algorithm involves unfolding the tensor into a
sequence of matrices along different dimensions and applying PCA to the
unfolded matrices. We provide theoretical results on the consistency and
asymptotic distribution for tensor PCA estimator of loadings and factors. The
algorithm demonstrates good performance in Mote Carlo experiments and is
applied to sorted portfolios
Econometrics of Machine Learning Methods in Economic Forecasting
This paper surveys the recent advances in machine learning method for
economic forecasting. The survey covers the following topics: nowcasting,
textual data, panel and tensor data, high-dimensional Granger causality tests,
time series cross-validation, classification with economic losses
Корпус плуга
Корпус плуга, що складається з лемеша, полиці і польової дошки, які приєднані до відливу стояка, який відрізняється тим, що леміш приєднано до відливу стояка через приєднувальні елементи у вигляді плоских пружин з можливістю його переміщення у двох взаємно перпендикулярних площинах, які одним кінцем закріплено на лемеші, а іншим до відливу стояка через виконане в ньому компенсаційне вікно
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