TY - UNPB
TI - Differencing Transformations and Inference in Predictive Regression Models
AU - Camponovo, L.
PY - 2012
PB - SSRN
ER -
TY - UNPB
TI - Robust Predictive Regression and Hypothesis Testing
AU - Camponovo, L.
AU - Scaillet, O.
AU - Trojani, F.
PY - 2012
PB - SSRN
ER -
TY - UNPB
TI - On Bartlett Correction on Empirical Likelihood in Generalized Power Divergence Family
AU - Camponovo, L.
AU - Otsu, T.
PY - 2012
PB - SSRN
ER -
TY - UNPB
TI - Robustness of Bootstrap in Instrumental Variable Regression Model
AU - Camponovo, L.
AU - Otsu, T.
PY - 2012
PB - SSRN
ER -
TY - UNPB
TI - Nonparametric Bootstrap for Quasi-Likelihood Ratio Tests
AU - Camponovo, L.
PY - 2012
PB - SSRN
ER -
TY - JOUR
TI - Breakdown Point Theory for Implied Probability Bootstrap
AU - Camponovo, L.
AU - Otsu, T.
PY - 2012
N2 - This paper studies robustness of bootstrap inference methods under moment conditions. In particular, we compare the uniform weight and implied probability bootstraps by analysing behaviours of the bootstrap quantiles when outliers take arbitrarily large values, and derive the breakdown points for those bootstrap quantiles. The breakdown point properties characterize the situation where the implied probability bootstrap is more robust against outliers than the uniform weight bootstrap. Simulation studies illustrate our theoretical findings.
PB - Wiley-Blackwell
CY - Chichester UK
SN - 1368-4221
JF - The Econometrics Journal
VL - 1
IS - 15
SP - 32
EP - 55
ER -
TY - JOUR
TI - Robust Subsampling
AU - Camponovo, L.
AU - Scaillet, O.
AU - Trojani, F.
PY - 2012
N2 - We characterize the robustness of subsampling procedures by deriving a formula for the breakdown point of subsampling quantiles. This breakdown point can be very low for moderate subsampling block sizes, which implies the fragility of subsampling procedures, even when they are applied to robust statistics. This instability arises also for data driven block size selection procedures minimizing the minimum confidence interval volatility index, but can be mitigated if a more robust calibration method can be applied instead. To overcome these robustness problems, we introduce a consistent robust subsampling procedure for M-estimators and derive explicit subsampling quantile breakdown point characterizations for MM-estimators in the linear regression model. Monte Carlo simulations in two settings where the bootstrap fails show the accuracy and robustness of the robust subsampling relative to the subsampling.
PB - Elsevier
CY - Amsterdam
SN - 0304-4076
JF - Journal of Econometrics
VL - 1
IS - 167
SP - 197
EP - 210
ER -