Results 1  10
of
23
Robust Inference with Multiway Clustering
, 2006
"... In this paper we propose a new variance estimator for OLS as well as for nonlinear estimators such as logit, probit and GMM. This variance estimator enables clusterrobust inference when there is twoway or multiway clustering that is nonnested. The variance estimator extends the standard clusterr ..."
Abstract

Cited by 135 (4 self)
 Add to MetaCart
In this paper we propose a new variance estimator for OLS as well as for nonlinear estimators such as logit, probit and GMM. This variance estimator enables clusterrobust inference when there is twoway or multiway clustering that is nonnested. The variance estimator extends the standard clusterrobust variance estimator or sandwich estimator for oneway clustering (e.g. Liang and Zeger (1986), Arellano (1987)) and relies on similar relatively weak distributional assumptions. Our method is easily implemented in statistical packages, such as Stata and SAS, that already offer clusterrobust standard errors when there is oneway clustering. The method is demonstrated by a Monte Carlo analysis for a twoway random effects model; a Monte Carlo analysis of a placebo law that extends the stateyear effects example of Bertrand et al. (2004) to two dimensions; and by application to two studies in the empirical public/labor literature where twoway clustering is present.
Zeros, quality, and space: Trade theory and trade evidence
 American Economic Journal: Microeconomics
, 2011
"... Bilateral, productlevel data exhibit a number of strong patterns that can be used to evaluate international trade theories, notably the spatial incidence of “export zeros ” (correlated with distance and importer size), and of export unit values (positively related to distance). We show that leading ..."
Abstract

Cited by 83 (8 self)
 Add to MetaCart
Bilateral, productlevel data exhibit a number of strong patterns that can be used to evaluate international trade theories, notably the spatial incidence of “export zeros ” (correlated with distance and importer size), and of export unit values (positively related to distance). We show that leading theoretical trade models fail to explain at least some of these facts, and propose a variant of the Melitz model that can account for all the facts. In our model, high quality firms are the most competitive, with heterogeneous quality increasing with firms ’ heterogeneous cost. (JEL F11, F14, F40) The gravity equation relates bilateral trade volumes to distance and country size. Countless gravity equations have been estimated, usually with “good ” results, and trade theorists have proposed various theoretical explanations for gravity’s success. However, the many potential explanations for the success of the gravity equation make it a problematic tool for discriminating among trade models. 1 As a matter of arithmetic, the value of trade depends on the number of goods
Inference with Dependent Data Using Cluster Covariance Estimators
"... This paper presents a novel way to conduct inference using dependent data in time series, spatial, and panel data applications. Our method involves constructing t and Wald statistics utilizing a cluster covariance matrix estimator (CCE). We then use an approximation that takes the number of cluster ..."
Abstract

Cited by 18 (0 self)
 Add to MetaCart
This paper presents a novel way to conduct inference using dependent data in time series, spatial, and panel data applications. Our method involves constructing t and Wald statistics utilizing a cluster covariance matrix estimator (CCE). We then use an approximation that takes the number of clusters/groups as fixed and the number of observations per group to be large and calculate limiting distributions of the t and Wald statistics. This approximation is analogous to ‘fixedb’ asymptotics of Kiefer and Vogelsang (2002, 2005) (KV) for heteroskedasticity and autocorrelation consistent inference, but in our case yields standard t and F distributions where the number of groups essentially plays the role of sample size. We provide simulation evidence that demonstrates our procedure outperforms conventional inference procedures and performs well comparably to KV.
t−statistic based correlation and heterogeneity Robust Inference
, 2008
"... We develop a general approach to robust inference about a scalar parameter when the data is potentially heterogeneous and correlated in a largely unknown way. The key ingredient is the following result of Bakirov and Székely (2005) concerning the small sample properties of the standard t−test: For a ..."
Abstract

Cited by 10 (0 self)
 Add to MetaCart
We develop a general approach to robust inference about a scalar parameter when the data is potentially heterogeneous and correlated in a largely unknown way. The key ingredient is the following result of Bakirov and Székely (2005) concerning the small sample properties of the standard t−test: For a significance level of 5 % or lower, the t−test remains conservative for underlying observations that are independent and Gaussian with heterogenous variances. One might thus conduct robust large sample inference as follows: partition the data into q ≥ 2 groups, estimate the model for each group and conduct a standard t−test with the resulting q parameter estimators. This results in valid and in some sense efficient inference when the groups are chosen in a way that ensures the parameter estimators to be asymptotically independent, unbiased and Gaussian of possibly different variances. We provide examples of how to apply this approach to time series, panel, clustered and spatially correlated data.
Fixedb Asymptotics for Spatially Dependent Robust Nonparametric Covariance Matrix Estimators
, 2008
"... This paper develops a method for performing inference using spatially dependent data. We consider test statistics formed using nonparametric covariance matrix estimators that account for heteroskedasticity and spatial correlation (spatial HAC). We provide distributions of commonly used test statist ..."
Abstract

Cited by 6 (1 self)
 Add to MetaCart
This paper develops a method for performing inference using spatially dependent data. We consider test statistics formed using nonparametric covariance matrix estimators that account for heteroskedasticity and spatial correlation (spatial HAC). We provide distributions of commonly used test statistics under “fixedb” asymptotics, in which HAC smoothing parameters are proportional to the sample size. Under this sequence, spatial HAC estimators are not consistent but converge to nondegenerate limiting random variables that depend on the HAC smoothing parameters and kernel. We show that the limit distributions of commonly used test statistics are pivotal but nonstandard, so critical values must be obtained by simulation. We provide a simple and general simulation procedure based on the i.i.d. bootstrap that can be used to obtain critical values. We illustrate the potential gains of the new approximation through simulations and an empirical example that examines the effect of unjust dismissal doctrine on temporary help services employment.
Robust Inference with Clustered Data
, 2010
"... In this paper we survey methods to control for regression model error that is correlated within groups or clusters, but is uncorrelated across groups or clusters. Then failure to control for the clustering can lead to understatement of standard errors and overstatement of statistical significance, a ..."
Abstract

Cited by 6 (1 self)
 Add to MetaCart
In this paper we survey methods to control for regression model error that is correlated within groups or clusters, but is uncorrelated across groups or clusters. Then failure to control for the clustering can lead to understatement of standard errors and overstatement of statistical significance, as emphasized most notably in empirical studies by Moulton (1990) and Bertrand, Duflo and Mullainathan (2004). We emphasize OLS estimation with statistical inference based on minimal assumptions regarding the error correlation process. Complications we consider include clusterspecific fixed effects, few clusters, multiway clustering, more efficient feasible GLS estimation, and adaptation to nonlinear and instrumental variables estimators.
XDifferencing and Dynamic Panel Model Estimation
, 2010
"... This paper introduces a new estimation method for dynamic panel models with xed effects and AR(p) idiosyncratic errors. The proposed estimator uses a novel form of systematic differencing, called Xdifferencing, that eliminates xed effects and retains information and signal strength in cases where t ..."
Abstract

Cited by 4 (3 self)
 Add to MetaCart
This paper introduces a new estimation method for dynamic panel models with xed effects and AR(p) idiosyncratic errors. The proposed estimator uses a novel form of systematic differencing, called Xdifferencing, that eliminates xed effects and retains information and signal strength in cases where there is a root at or near unity. The resulting “panel fully aggregated” estimator (PFAE) is obtained by pooled least squares on the system of Xdifferenced equations. The method is simple to implement, free from bias for all parameter values, including unit root cases, and has strong asymptotic and nite sample performance characteristics that dominate other procedures, such as bias corrected least squares, GMM and system GMM methods. The asymptotic theory holds as long as the cross section (n) or time series (T) sample size is large, regardless of the n=T ratio, which makes the approach appealing for practical work. In the time series AR(1) case (n = 1), the FAE estimator has a limit distribution with smaller bias and variance than the maximum likelihood estimator (MLE) when the autoregressive coef cient is at or near unity and the same limit distribution as the MLE in the stationary case, so the advantages of the approach continue to hold for xed and even small n. For panel
Heteroskedasticity, Autocorrelation, and Spatial Correlation Robust Inference in Linear Panel Models with FixedEffects. Working paper
, 2008
"... This paper develops an asymptotic theory for test statistics in linear panel models that are robust to heteroskedasticity, autocorrelation and/or spatial correlation. Two classes of standard errors are analyzed. Both are based on nonparametric heteroskedasticity autocorrelation (HAC) covariance matr ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
This paper develops an asymptotic theory for test statistics in linear panel models that are robust to heteroskedasticity, autocorrelation and/or spatial correlation. Two classes of standard errors are analyzed. Both are based on nonparametric heteroskedasticity autocorrelation (HAC) covariance matrix estimators. The …rst class is based on averages of HAC estimates across individuals in the crosssection, i.e. "averages of HACs". This class includes the well known cluster standard errors analyzed by Arellano (1987) as a special case. The second class is based on the HAC of crosssection averages and was proposed by Driscoll and Kraay (1998). The "HAC of averages " standard errors are robust to heteroskedasticity, serial correlation and spatial correlation but stationarity in the time dimension is required. The "averages of HACs " standard errors are robust to heteroskedasticity and serial correlation including the nonstationary case but they are not valid in the presence of spatial correlation. The main contribution of the paper is to develop a …xedb asymptotic theory for statistics based on both classes of standard errors in models with individual and possibly time …xede¤ects dummy variables. The asymptotics is carried out for large time sample sizes for both …xed and large crosssection sample sizes.
Heteroskedasticity and Spatiotemporal Dependence Robust Inference for Linear Panel Models with Fixed Effects
, 2010
"... This paper studies robust inference for linear panel models with fixed effects in the presence of heteroskedasticity and spatiotemporal dependence of unknown forms. We propose a bivariate kernel covariance estimator, which is flexible to nest existing estimators as special cases with certain choices ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
This paper studies robust inference for linear panel models with fixed effects in the presence of heteroskedasticity and spatiotemporal dependence of unknown forms. We propose a bivariate kernel covariance estimator, which is flexible to nest existing estimators as special cases with certain choices of bandwidths. For distributional approximations, we consider two different types of asymptotics. When the level of smoothing is assumed to increase with the sample size, the proposed estimator is consistent and the associated Wald statistic converges to a χ2 distribution. We show that our covariance estimator improves upon existing estimators in terms of robustness and efficiency. When we assume the level of smoothing to be held fixed, the covariance estimator has a random limit and we show by asymptotic expansion that the limiting distribution of the test statistic depends on the bandwidth parameters, the kernel function, and the number of restrictions being tested. As this distribution is nonstandard, we establish the validity of an Fapproximation to this distribution, which greatly facilitates the test. For optimal bandwidth selection, we propose a procedure based on the upper bound of asymptotic mean square error criterion. The flexibility of our estimator and proposed bandwidth selection procedure make our estimator adaptive to the dependence structure in data. This adaptiveness automates the selection of covariance estimator. That is, our estimator reduces to the existing estimators which are designed to cope with the particular dependence structures. Simulation results show that the Fapproximation and the adaptiveness work reasonably well.