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Found 8 papers, 0 papers with code
Assessing the strength of many instruments with the first-stage F and Cragg-Donald statistics
As for the Cragg-Donald statistic, we obtain an asymptotically valid correction in the case of two endogenous variables.
A specification test for the strength of instrumental variables
This paper develops a new specification test for the instrument weakness when the number of instruments $K_n$ is large with a magnitude comparable to the sample size $n$.
Unified and robust Lagrange multiplier type tests for cross-sectional independence in large panel data models
This paper revisits the Lagrange multiplier type test for the null hypothesis of no cross-sectional dependence in large panel data models.
Multiple Descent in the Multiple Random Feature Model
Recent works have demonstrated a double descent phenomenon in over-parameterized learning.
Impact of classification difficulty on the weight matrices spectra in Deep Learning and application to early-stopping
A main contribution from the paper is that we identify the difficulty of the classification problem as a driving factor for the appearance of heavy tail in weight matrices spectra.
Extension of the Lagrange multiplier test for error cross-section independence to large panels with non normal errors
This paper reexamines the seminal Lagrange multiplier test for cross-section independence in a large panel model where both the number of cross-sectional units n and the number of time series observations T can be large.
Ratio-consistent estimation for long range dependent Toeplitz covariance with application to matrix data whitening
We consider a data matrix $X:=C_N^{1/2}ZR_M^{1/2}$ from a multivariate stationary process with a separable covariance function, where $C_N$ is a $N\times N$ positive semi-definite matrix, $Z$ a $N\times M$ random matrix of uncorrelated standardized white noise, and $R_M$ a $M\times M$ Toeplitz matrix.
Probability Methodology Primary 62M15, Secondary 62H10, 15B52
Improving Value-at-Risk prediction under model uncertainty
Several well-established benchmark predictors exist for Value-at-Risk (VaR), a major instrument for financial risk management.
