Results 81 to 90 of about 2,019,213 (203)

Investigating uncertainty in macroeconomic forecasts by stochastic simulation [PDF]

We investigate four sources of uncertainty with CPB’s macroeconomic model SAFFIER: provisional data, exogenous variables, model parameters and residuals of behavioural equations. Uncertainty is an inherent attribute of any forecast.
Debby Lanser, Henk Kranendonk
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Estimating a bivariate linear relationship

, 2012
Solutions of the bivariate, linear errors-in-variables estimation problem with unspecified errors are expected to be invariant under interchange and scaling of the coordinates.
Leonard, David
core   +1 more source

Calibrating Car-Following Model Considering Measurement Errors

Advances in Mechanical Engineering, 2013
Car-following model has important applications in traffic and safety engineering. To enhance the accuracy of model in predicting behavior of individual driver, considerable studies strive to improve the model calibration technologies.
Chang-qiao Shao, Xiao-ming Liu, Zhi-yong Zhang   +2 more
doaj   +1 more source

Improving dynamical climate predictions with machine learning: insights from a twin experiment framework [PDF]

Nonlinear Processes in Geophysics
Systematic errors in dynamical climate models remain a significant challenge to accurate climate predictions, particularly when modeling the nonlinear coupling between the atmosphere and ocean.
Z. He, Z. He, Z. He, J. Brajard, Y. Wang, X. Wang, X. Wang, Z. Shen, Z. Shen   +8 more
doaj   +1 more source

Dynamic Error-in-variables Models

Austrian Journal of Statistics, 2016
Several environmental problems are described by models defined by systems of first order differential equations. Typically, the derivatives of concentrations are expressed as functions of independent variables such as temperature, pressure, pH, etc. However, the independent variables are generally known only through measurements made at discrete times.
openaire   +1 more source

Efficient Estimation of a Dynamic Error-Shock Model [PDF]

This paper is concerned with the estimation of the parameters in a dynamic simultaneous equation model with stationary disturbances under the assumption that the variables are subject to random measurement errors.
Cheng Hsiao, P. M. Robinson
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Instrumental variables quantile regression for panel data with measurement errors [PDF]

, 2009
This paper develops an instrumental variables estimator for quantile regression in panel data with fixed effects. Asymptotic properties of the instrumental variables estimator are studied for large N and T when Na/T ! 0, for some a > 0.
Galvao Jr, A. F., Montes-Rojas, G.
core   +1 more source

Strong convergence for weighted sums of (α, β)-mixing random variables and application to simple linear EV regression model

Open Mathematics
In this article, the complete convergence and the Kolmogorov strong law of large numbers for weighted sums of (α,β)\left(\alpha ,\beta )-mixing random variables are presented.
Hu Wenjing, Wang Wei, Wu Yi
doaj   +1 more source

Linearization of randomly weighted empiricals under long range dependence with application to nonlinear regression quantiles [PDF]

, 2000
This paper discusses some asymptotic uniform linearity results of randomly weighted empirical processes based on long range dependent random variables+ These results are subsequently used to linearize nonlinear regression quantiles in a nonlinear ...
Mukherjee, Kanchan
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Is money informative? Evidence from a large model used for policy analysis [PDF]

In this paper we assess whether monetary variables convey marginal information on the state of the Italian economy, taking as a benchmark the forecasting errors generated by the quarterly model used by the Bank of Italy in the 1990s.
Alberto Locarno, Eugenio Gaiotti, Filippo Altissimo   +2 more
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