IV estimation without distributional assumptions

2020 ◽  
Vol 62 (3) ◽  
pp. 688-696
Author(s):  
Theis Lange ◽  
Aksel K. G. Jensen
Keyword(s):  
Iv Estimation ◽  
Distributional Assumptions

Distributional Assumptions in Voxel-Based Morphometry

NeuroImage ◽  
2002 ◽  
Vol 17 (2) ◽  
pp. 1027-1030 ◽  
Author(s):  
C.H. Salmond ◽  
J. Ashburner ◽  
F. Vargha-Khadem ◽  
A. Connelly ◽  
D.G. Gadian ◽  
...  
Keyword(s):  
Voxel Based Morphometry ◽  
Distributional Assumptions

XX.—On the Estimation of Variance and Covariance

1952 ◽  
Vol 63 (3) ◽  
pp. 280-289
Author(s):  
E. H. Lloyd
Keyword(s):  
Linear Form ◽  
Expected Values ◽  
Distributional Assumptions ◽  
Estimation Of Variance

SynopsisSuppose we have a number of independent pairs of observations (Xi, Yi) on two correlated variates (X, Y), which have constant variances and covariance, and whose expected values are of known linear form, with unknown coefficients: say respectively. The pij and the qij are known, the aj and the bj are unknown. The paper discusses the estimation of the coefficients, and of the variances and the covariance, and evaluates the sampling variances of the estimates. The argument is entirely free of distributional assumptions.

scholarly journals Soil CO<sub>2</sub> efflux errors are lognormally distributed – implications and guidance

2020 ◽  
Vol 9 (1) ◽  
pp. 239-254
Author(s):  
Thomas Wutzler ◽  
Oscar Perez-Priego ◽  
Kendalynn Morris ◽  
Tarek S. El-Madany ◽  
Mirco Migliavacca
Keyword(s):  
Random Error ◽  
Carbon Sink ◽  
Spatial Scales ◽  
Good Practice ◽  
Terrestrial Ecosystems ◽  
Soil Co2 Efflux ◽  
Co2 Efflux ◽  
Soil Co2 ◽  
Value Differences ◽  
Distributional Assumptions

Abstract. Soil CO2 efflux is the second-largest carbon flux in terrestrial ecosystems. Its feedback to climate determines model predictions of the land carbon sink, which is crucial to understanding the future of the earth system. For understanding and quantification, however, observations by the most widely applied chamber measurement method need to be aggregated to larger temporal and spatial scales. The aggregation is hampered by random error that is characterized by occasionally large fluxes and variance heterogeneity that is not properly accounted for under the typical assumption of normally distributed fluxes. Therefore, we explored the effect of different distributional assumptions on the aggregated fluxes. We tested the alternative assumption of lognormally distributed random error in observed fluxes by aggregating 1 year of data of four neighboring automatic chambers at a Mediterranean savanna-type site. With the lognormal assumption, problems with error structure diminished, and more reasonable prediction intervals were obtained. While the differences between distributional assumptions diminished when aggregating data of single chambers to an annual value, differences were important on short timescales and were especially pronounced when aggregating across chambers to plot level. Hence we recommend as a good practice that researchers report plot-level fluxes with uncertainties based on the lognormal assumption. Model data integration studies should compare predictions and observations of soil CO2 efflux on a log scale. This study provides methodology and guidance that will improve the analysis of soil CO2 efflux observations and hence improve understanding of soil carbon cycling and climate feedbacks.

scholarly journals Risk Model Validation: An Intraday VaR and ES Approach Using the Multiplicative Component GARCH

Risks ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 10
Author(s):  
Ravi Summinga-Sonagadu ◽  
Jason Narsoo
Keyword(s):  
Loss Function ◽  
Value At Risk ◽  
Garch Model ◽  
Innovation Process ◽  
Expected Shortfall ◽  
Error Distribution ◽  
Garch Models ◽  
Intraday Data ◽  
Distributional Assumptions ◽  
T Distribution

In this paper, we employ 99% intraday value-at-risk (VaR) and intraday expected shortfall (ES) as risk metrics to assess the competency of the Multiplicative Component Generalised Autoregressive Heteroskedasticity (MC-GARCH) models based on the 1-min EUR/USD exchange rate returns. Five distributional assumptions for the innovation process are used to analyse their effects on the modelling and forecasting performance. The high-frequency volatility models were validated in terms of in-sample fit based on various statistical and graphical tests. A more rigorous validation procedure involves testing the predictive power of the models. Therefore, three backtesting procedures were used for the VaR, namely, the Kupiec’s test, a duration-based backtest, and an asymmetric VaR loss function. Similarly, three backtests were employed for the ES: a regression-based backtesting procedure, the Exceedance Residual backtest and the V-Tests. The validation results show that non-normal distributions are best suited for both model fitting and forecasting. The MC-GARCH(1,1) model under the Generalised Error Distribution (GED) innovation assumption gave the best fit to the intraday data and gave the best results for the ES forecasts. However, the asymmetric Skewed Student’s-t distribution for the innovation process provided the best results for the VaR forecasts. This paper presents the results of the first empirical study (to the best of the authors’ knowledge) in: (1) forecasting the intraday Expected Shortfall (ES) under different distributional assumptions for the MC-GARCH model; (2) assessing the MC-GARCH model under the Generalised Error Distribution (GED) innovation; (3) evaluating and ranking the VaR predictability of the MC-GARCH models using an asymmetric loss function.

Sign in / Sign up

Export Citation Format

Share Document