EMSA showed the activity of NF-κB p65 in ACC-M, ACC-M/IκBαM, ACC-2 and ACC-2/IκBαM cell lines

<p><b>Copyright information:</b></p><p>Taken from "In vitro angiogenesis and expression of nuclear factor κB and VEGF in high and low metastasis cell lines of salivary gland Adenoid Cystic Carcinoma"</p><p>http://www.biomedcentral.com/1471-2407/7/95</p><p>BMC Cancer 2007;7():95-95.</p><p>Published online 1 Jun 2007</p><p>PMCID:PMC1903362.</p><p></p> indicate the migration of the induced NF-κB DNA-binding complexes. Migration of the free probe is not shown. The Oct-1 motif was used as a control for quality and quantity of cell extract

RT-PCR showed the mRNA expression of VEGF in ACC-M, ACC-2, ACC-M/IκBαM and ACC-2/IκBαM cell lines

<p><b>Copyright information:</b></p><p>Taken from "In vitro angiogenesis and expression of nuclear factor κB and VEGF in high and low metastasis cell lines of salivary gland Adenoid Cystic Carcinoma"</p><p>http://www.biomedcentral.com/1471-2407/7/95</p><p>BMC Cancer 2007;7():95-95.</p><p>Published online 1 Jun 2007</p><p>PMCID:PMC1903362.</p><p></p

Consistent Estimation in Large Heterogeneous Panels with Multifactor Structure Endogeneity

The set-up considered by Pesaran (Econometrica, 2006) is extended to allow for endogenous explanatory variables. A class of instrumental variables estimators is studied and it is shown that estimators in this class are consistent and asymptotically normally distributed as both the cross-section and time-series dimensions tend to infinity

Immuofluorescence double staining and semi-quantitative confocal laser scanning analysis showed NF-κB p65 and VEGF expressed in ACC-M and ACC-2 cell lines

<p><b>Copyright information:</b></p><p>Taken from "In vitro angiogenesis and expression of nuclear factor κB and VEGF in high and low metastasis cell lines of salivary gland Adenoid Cystic Carcinoma"</p><p>http://www.biomedcentral.com/1471-2407/7/95</p><p>BMC Cancer 2007;7():95-95.</p><p>Published online 1 Jun 2007</p><p>PMCID:PMC1903362.</p><p></p> As figure 3A showed, the rate of NF-κB p65 nuclear localization (a) (white arrow) and VEGF staining intensity (b) in ACC-M was higher than that in ACC-2 (e) and (f). As figure 3B shows, bars represent the mean value of immunofluorescence intensity of VEGF and nuclear staining rate of NF-κB p65 in two cell lines, < 0.01(*)

Common Shocks in panels with Endogenous Regressors

This paper introduces a novel approach to study the effects of common shocks on panel data models with endogenous explanatory variables when the cross section dimension (N) is large and the time series dimension (T) is fixed: this relies on conditional strong laws of large numbers and conditional central limit theorems. These results can act as a useful reference for readers who wish to further investigate the effects of common shocks on panel data. The paper shows that the key assumption in determining consistency of the panel TSLS and LIML estimators is the independence of the factor loadings in the reduced form errors from the factor loadings in the exogenous variables and instruments conditional on the factors. We also show that these estimators have non-standard asymptotic distributions but tests on the coefficients have standard distributions under the null hypothesis provided the estimators are consistent

Estimation, Inference, and Empirical Analysis for Time-Varying VAR Models

Vector autoregressive (VAR) models are widely used in practical studies, for example, forecasting, modeling policy transmission mechanism, and measuring connection of economic agents. To better capture the dynamics, this article introduces a new class of time-varying VAR models in which the coefficients and covariance matrix of the error innovations are allowed to change smoothly over time. Accordingly, we establish a set of asymptotic properties including the impulse response analyses subject to structural VAR identification conditions, an information criterion to select the optimal lag, and a Wald-type test to determine the constant coefficients. Simulation studies are conducted to evaluate the theoretical findings. Finally, we demonstrate the empirical relevance and usefulness of the proposed methods through an application on U.S. government spending multipliers.</p

An Integrated Panel Data Approach to Modelling Economic Growth

Empirical growth analysis has three major problems -- variable selection, parameter heterogeneity and cross-sectional dependence -- which are addressed independently from each other in most studies. The purpose of this study is to propose an integrated framework that extends the conventional linear growth regression model to allow for parameter heterogeneity and cross-sectional error dependence, while simultaneously performing variable selection. We also derive the asymptotic properties of the estimator under both low and high dimensions, and furtherinvestigate the finite sample performance of the estimator through Monte Carlo simulations. We apply the framework to a dataset of 89 countries over the period from 1960 to 2014. Our results reveal some cross-country patterns not found in previous studies (e.g., "middle income trap hypothesis", "natural resources curse hypothesis", "religion works via belief, not practice", etc.)

Semiparametric Single-Index Panel Data Models with Cross-Sectional Dependence

In this paper, we consider a semiparametric single index panel data mode with cross-sectional dependence, high-dimensionality and stationarity. Meanwhile, we allow fixed effects to be correlated with the regressors to capture unobservable heterogeneity. Under a general spatial error dependence structure, we then establish some consistent closed-form estimates for both the unknown parameters and a link function for the case where both N and T go to ∞. Rates of convergence and asymptotic normality consistencies are established for the proposed estimates. Our experience suggests that the proposed estimation method is simple and thus attractive for finite-sample studies and empirical implementations. Moreover, both the finite-sample performance and the empirical applications show that the proposed estimation method works well when the cross-sectional dependence exists in the data set

A Nonparametric Panel Model for Climate Data with Seasonal and Spatial Variation

In this paper, we consider a panel data model which allows for heterogeneous time trends at different locations. We propose a new estimation method for the panel data model before we establish an asymptotic theory for the proposed estimation method. For inferential purposes, we develop a bootstrap method for the case where weak correlation presents in both dimensions of the error terms. We examine the finite-sample properties of the proposed model and estimation method through extensive simulated studies. Finally, we use the newly proposed model and method to investigate rainfall, temperature and sunshine data of U.K. respectively. Overall, we find the weather of winter has changed dramatically over the past fifty years. Changes may vary with respect to locations for the other seasons

Another Look at Single-Index Models Based on Series Estimation

In this paper, a semiparametric single-index model is investigated. The link function is allowed to be unbounded and has unbounded support that answers a pen ding issue in the literature. Meanwhile, the link function is treated as a point in an infinitely many dimensional function space which enables us to derive the estimates for the index parameter and the link function simultaneously. This approach is different from the profile method commonly used in the literature. The estimator is derive d from an optimization with the constraint of identification condition for index parameter, which is a natural way but ignored in the literature