Showing 1 - 10 of 87
spatially correlated errors in static panel data models, by introducing a spatial lag and a one-period lag of the dependent … variable as additional explanatory variables. Combining the extended Kapoor et al. (2007) approach with the dynamic panel data … time lags, spatial lags, and sets of exogenous variables yields new spatial dynamic panel data estimators. We prove their …
Persistent link: https://www.econbiz.de/10011124438
corrects for spatially correlated errors in static panel data models, by introducing a spatial lag and a one-period lag of the … the dynamic panel data model GMM estimators of Arellano and Bond (1991) and Blundell and Bond (1998) and supplementing the … dynamic panel data estimators. The performance of these spatial dynamic panel data estimators is in- vestigated by means of …
Persistent link: https://www.econbiz.de/10011090432
spatially correlated errors in static panel data models, is extended by introducing fixed effects, a spatial lag, and a one …-period lag of the dependent variable as additional explanatory variables. Combining this approach with the dynamic panel-data GMM …, spatial lags, and sets of exogenous variables yields new spatial dynamic panel data estimators. The proposed spatially …
Persistent link: https://www.econbiz.de/10011144455
Persistent link: https://www.econbiz.de/10011090535
and disclosure and using fixed effects estimation in a panel dataset reduces the endogeneity bias and produces consistent …The purpose of this paper is twofold.First, we provide a discussion of the problems associated with endogeneity in … empirical accounting research.We emphasize problems arising when endogeneity is caused by (1) unobservable firm specific factors …
Persistent link: https://www.econbiz.de/10011092860
A major attraction of panel data is the ability to estimate dynamic models on an individual level. Moffitt (1993) and …
Persistent link: https://www.econbiz.de/10011090312
Abstract: Factor screening searches for the really important inputs (factors) among the many inputs that are changed in a realistic simulation experiment. Sequential bifurcation (SB) is a sequential method that changes groups of inputs simultaneously. SB is the most efficient and effective...
Persistent link: https://www.econbiz.de/10011090433
This article illustrates simulation optimization through an (s, S) inventory management system.In this system, the goal function to be minimized is the expected value of specific inventory costs.Moreover, specific constraints must be satisfied for some random simulation responses, namely the...
Persistent link: https://www.econbiz.de/10011090482
Classic linear regression models and their concomitant statistical designs assume a univariate response and white noise.By definition, white noise is normally, independently, and identically distributed with zero mean.This survey tries to answer the following questions: (i) How realistic are...
Persistent link: https://www.econbiz.de/10011090588
This paper derives a novel procedure for testing the Karush-Kuhn-Tucker (KKT) first-order optimality conditions in models with multiple random responses.Such models arise in simulation-based optimization with multivariate outputs.This paper focuses on expensive simulations, which have small...
Persistent link: https://www.econbiz.de/10011090910