SIMULTANEOUS EQUATIONS WITH INCOMPLETE PANELS

2000 ◽  
Vol 16 (2) ◽  
pp. 269-279 ◽  
Author(s):  
Badi H. Baltagi ◽  
Young-Jae Chang
Keyword(s):  
Monte Carlo ◽  
Panel Data ◽  
Variance Components ◽  
Simultaneous Equations ◽  
Error Components ◽  
Individual Effects ◽  
The Individual ◽  
Simultaneous Model ◽  
Unbalanced Panel Data

This paper compares the performance of several single and system estimators of a two equation simultaneous model with unbalanced panel data. The Monte Carlo design varies the degree of unbalancedness in the data and the variance components ratio due to the individual effects. One useful result for applied researchers is that the feasible error components 2SLS and 3SLS procedures based on simple ANOVA type estimators of the variance components perform well with incomplete panels and are recommended in practice.

scholarly journals ESTIMATING A LINEAR SIMULTANEOUS EQUATION MODEL WITH PANEL DATA

2005 ◽  
Vol 50 (spec01) ◽  
pp. 475-494 ◽  
Author(s):  
SOO-BIN PARK
Keyword(s):  
Panel Data ◽  
Single Equation ◽  
Simultaneous Equation ◽  
Equation Model ◽  
Small Sample ◽  
Simultaneous Equation Model ◽  
Individual Effects ◽  
Linear Simultaneous Equation ◽  
The Individual ◽  
Time Invariant

In this paper we consider a class of the GMM estimators for a linear simultaneous equation model with panel data. The model is characterized by the presence of unobserved individual effects as part of structural disturbances and by the correlation of certain time-varying and time-invariant exogenous variables with the individual effects. A class of single-equation and system GMM estimators is proposed on the basis of a class of instruments as a unifying theme. Small-sample performance of this class of estimators is also investigated by a series of sampling experiments.

Fitting partially linear functional-coefficient panel-data models with Stata

2020 ◽  
Vol 20 (4) ◽  
pp. 976-998
Author(s):  
Kerui Du ◽  
Yonghui Zhang ◽  
Qiankun Zhou
Keyword(s):  
Monte Carlo ◽  
Panel Data ◽  
Fixed Effects ◽  
Linear Functional ◽  
Semiparametric Estimation ◽  
Data Models ◽  
Panel Data Models ◽  
Panel Models ◽  
Partially Linear ◽  
Functional Coefficient

In this article, we describe the implementation of fitting partially linear functional-coefficient panel models with fixed effects proposed by An, Hsiao, and Li [2016, Semiparametric estimation of partially linear varying coefficient panel data models in Essays in Honor of Aman Ullah ( Advances in Econometrics, Volume 36)] and Zhang and Zhou (Forthcoming, Econometric Reviews). Three new commands xtplfc, ivxtplfc, and xtdplfc are introduced and illustrated through Monte Carlo simulations to exemplify the effectiveness of these estimators.

Examining sharp restart in a Monte Carlo method for the linearized Poisson–Boltzmann equation

2020 ◽  
Vol 26 (3) ◽  
pp. 223-244
Author(s):  
W. John Thrasher ◽  
Michael Mascagni
Keyword(s):  
Monte Carlo ◽  
Free Energy ◽  
Monte Carlo Algorithm ◽  
Significant Bias ◽  
Poisson Boltzmann ◽  
Electrostatic Free Energy ◽  
Poisson Boltzmann Equation ◽  
The Stability ◽  
Potential Methods ◽  
The Individual

AbstractIt has been shown that when using a Monte Carlo algorithm to estimate the electrostatic free energy of a biomolecule in a solution, individual random walks can become entrapped in the geometry. We examine a proposed solution, using a sharp restart during the Walk-on-Subdomains step, in more detail. We show that the point at which this solution introduces significant bias is related to properties intrinsic to the molecule being examined. We also examine two potential methods of generating a sharp restart point and show that they both cause no significant bias in the examined molecules and increase the stability of the run times of the individual walks.

ALTERNATIVE ROUTES TO INNOVATION — THE EFFECTS OF CULTURAL AND STRUCTURAL FIT

2019 ◽  
Vol 24 (01) ◽  
pp. 2050006
Author(s):  
DAG INGVAR JACOBSEN ◽  
TORE HILLESTAD ◽  
BIRGITTE YTTRI ◽  
JARLE HILDRUM
Keyword(s):  
Response Rate ◽  
Organic Structure ◽  
Configurational Approach ◽  
Culture Strength ◽  
Individual Effects ◽  
Collective Phenomenon ◽  
The Individual ◽  
Alternative Routes ◽  
Innovative Culture ◽  
The Empirical Analysis

A configurational approach to organizations assumes that structural and cultural characteristics must be in “fit” to produce the wanted outcome. With a focus on innovation, this study examines empirically to what extent innovative activities with a large, global telecom company are produced by an innovative culture, an innovative structure, as well as the fit between the two. Based on an extensive survey (N = 21064, response rate = 65) of employees in seven countries in Europe and Asia, data was aggregated to unit level as culture by nature is a collective phenomenon. The empirical analysis detected both the individual effects of culture strength and homogeneity, structure, as well as the fit between the two. The results indicate that an innovative culture and an organic structure indeed fosters innovation, but that, somewhat surprisingly, there are not effects of the fit between the two. Both practical and theoretical implications are discussed.

Factors influencing the individual effects of blood transfusions on oxygen delivery and oxygen consumption*

2001 ◽  
Vol 29 (Supplement) ◽  
pp. S173-S179 ◽  
Author(s):  
Mattias Casutt ◽  
Burkhardt Seifert ◽  
Thomas Pasch ◽  
Edith R. Schmid ◽  
Marko I. Turina ◽  
...  
Keyword(s):  
Oxygen Consumption ◽  
Oxygen Delivery ◽  
Blood Transfusions ◽  
Factors Influencing ◽  
Individual Effects ◽  
The Individual

Incorporating predictions into an annual forest inventory

1996 ◽  
Vol 26 (9) ◽  
pp. 1709-1713 ◽  
Author(s):  
Paul C. Van Deusen
Keyword(s):  
Forest Inventory ◽  
Variance Components ◽  
Growth Modeling ◽  
Estimation Procedure ◽  
Regression Estimator ◽  
Forest Types ◽  
Model Estimate ◽  
Individual Tree ◽  
Time Period ◽  
The Individual

Growth modeling of forests at the individual tree and stand levels is a highly refined procedure for many forest types. A method to incorporate predictions from such models into a forest inventory system is developed. Variance components from the actual measurements and from the predicted measurements are used to estimate the variance of the combined predicted value. The only assumption required to justify this method is that the model estimate has a bias that does not change from one time period to the next. The estimation procedure proposed here can also incorporate remotely sensed information via a regression estimator.

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