Results 1  10
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31
A Practitioner’s Guide to ClusterRobust Inference
, 2013
"... We consider statistical inference for regression when data are grouped into clusters, with regression model errors independent across clusters but correlated within clusters. Examples include data on individuals with clustering on village or region or other category such as industry, and stateyear ..."
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Cited by 31 (0 self)
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We consider statistical inference for regression when data are grouped into clusters, with regression model errors independent across clusters but correlated within clusters. Examples include data on individuals with clustering on village or region or other category such as industry, and stateyear differencesindifferences studies with clustering on state. In such settings default standard errors can greatly overstate estimator precision. Instead, if the number of clusters is large, statistical inference after OLS should be based on clusterrobust standard errors. We outline the basic method as well as many complications that can arise in practice. These include clusterspecific fixed efects, few clusters, multiway clustering, and estimators other than OLS.
Does Management Matter? Evidence from India
, 2011
"... A longstanding question in social science is to what extent differences in management cause differences in firm performance. To investigate this we ran a management field experiment on large Indian textile firms. We provided free consulting on modern management practices to a randomly chosen set of ..."
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Cited by 12 (0 self)
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A longstanding question in social science is to what extent differences in management cause differences in firm performance. To investigate this we ran a management field experiment on large Indian textile firms. We provided free consulting on modern management practices to a randomly chosen set of treatment plants and compared their performance to the control plants. We find that adopting these management practices had three main effects. First, it raised average productivity by 11 % through improved quality and efficiency and reduced inventory. Second, it increased decentralization of decision making, as better information flow enabled owners to delegate more decisions to middle managers. Third, it increased the use of computers, necessitated by the data collection and analysis involved in modern management. Since these practices were profitable this raises the question of why firms had not adopted these before. Our results suggest that informational barriers were a primary factor in explaining this lack of adoption. Modern management is a technology that diffuses slowly between firms, with many Indian firms initially unaware of its existence or impact. Since competition was
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 ..."
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Cited by 6 (0 self)
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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.
The moving blocks bootstrap for panel linear regression models with individual fixed effects
, 2008
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Necessity is the Mother of Invention: Input Supplies and Directed Technical Change ∗
, 2013
"... The leading theory of directed technical change, developed by Acemoglu (2002), offers two main predictions. First, when inputs are sufficiently substitutable, a change in relative input supplies will generate technical change that augments inputs which become relatively more abundant. Second, if thi ..."
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Cited by 4 (1 self)
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The leading theory of directed technical change, developed by Acemoglu (2002), offers two main predictions. First, when inputs are sufficiently substitutable, a change in relative input supplies will generate technical change that augments inputs which become relatively more abundant. Second, if this effect is sufficiently strong, the relative price of the relatively more abundant inputs will increase – the strong inducedbias hypothesis. This paper provides the first empirical test of these predictions using the shock to the British cotton textile industry caused by the U.S. Civil War (18611865). Using detailed new patent data, I show that the shock increased innovation in Britain directed towards taking advantage of Indian cotton, which had became relatively more abundant. The relative price of Indian cotton first declined and then rebounded, consistent with strong inducedbias. Given my elasticity of substitution estimates, these findings are consistent with the predictions of the theory.
HeavyTail and PlugIn Robust Consistent Conditional Moment Tests of Functional Form
, 2012
"... We present asymptotic powerone tests of regression model functional form for heavy tailed time series. Under the null hypothesis of correct specification the model errors must have a …nite mean, and otherwise only need to have a fractional moment. If the errors have an infinite variance then in p ..."
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Cited by 3 (3 self)
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We present asymptotic powerone tests of regression model functional form for heavy tailed time series. Under the null hypothesis of correct specification the model errors must have a …nite mean, and otherwise only need to have a fractional moment. If the errors have an infinite variance then in principle any consistent plugin is allowed, depending on the model, including those with nonGaussian limits and/or a subp convergence rate. One test statistic exploits an orthogonalized test equation that promotes plugin robustness irrespective of tails. We derive chisquared weak limits of the statistics, we characterize an empirical process method for smoothing over a trimming parameter, and we study the finite sample properties of the test statistics.
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 ..."
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Cited by 3 (2 self)
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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.
Randomization Tests under an Approximate Symmetry Assumption∗
, 2014
"... This paper develops a theory of randomization tests under an approximate symmetry assumption. Randomization tests provide a general means of constructing tests that control size in finite samples whenever the distribution of the observed data exhibits symmetry under the null hypothesis. Here, by ex ..."
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Cited by 2 (1 self)
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This paper develops a theory of randomization tests under an approximate symmetry assumption. Randomization tests provide a general means of constructing tests that control size in finite samples whenever the distribution of the observed data exhibits symmetry under the null hypothesis. Here, by exhibits symmetry we mean that the distribution remains invariant under a group of transformations. In this paper, we provide conditions under which the same construction can be used to construct tests that asymptotically control the probability of a false rejection whenever the distribution of the observed data exhibits approximate symmetry in the sense that the limiting distribution of a function of the data exhibits symmetry under the null hypothesis. An important application of this idea is in settings where the data may be grouped into a fixed number of “clusters ” with a large number of observations within each cluster. In such settings, we show that the distribution of the observed data satisfies our approximate symmetry requirement under weak assumptions. In particular, our results allow for the clusters to be heterogeneous and also have dependence not only within each cluster, but also across clusters. This approach enjoys several advantages over other approaches in these settings. Among other things, it leads to a test that is asymptotically similar, which, as shown in a simulation study, translates into improved power at many alternatives. Finally, we use our results to revisit the analysis of Angrist and Lavy (2009), who examine the impact of a cash award on exam performance for lowachievement students in Israel.