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287
How much should we trust differencesindifferences estimates? Quarterly Journal of Economics 119:249–75
, 2004
"... Most papers that employ DifferencesinDifferences estimation (DD) use many years of data and focus on serially correlated outcomes but ignore that the resulting standard errors are inconsistent. To illustrate the severity of this issue, we randomly generate placebo laws in statelevel data on fema ..."
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Cited by 775 (1 self)
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Most papers that employ DifferencesinDifferences estimation (DD) use many years of data and focus on serially correlated outcomes but ignore that the resulting standard errors are inconsistent. To illustrate the severity of this issue, we randomly generate placebo laws in statelevel data on female wages from the Current Population Survey. For each law, we use OLS to compute the DD estimate of its “effect ” as well as the standard error of this estimate. These conventional DD standard errors severely understate the standard deviation of the estimators: we find an “effect ” significant at the 5 percent level for up to 45 percent of the placebo interventions. We use Monte Carlo simulations to investigate how well existing methods help solve this problem. Econometric corrections that place a specific parametric form on the timeseries process do not perform well. Bootstrap (taking into account the autocorrelation of the data) works well when the number of states is large enough. Two corrections based on asymptotic approximation of the variancecovariance matrix work well for moderate numbers of states and one correction that collapses the time series information into a “pre ” and “post ” period and explicitly takes into account the effective sample size works well even for small numbers of states.
Stock Market Prices Do Not Follow Random Walks: Evidence from a Simple Specification Test
 REVIEW OF FINANCIAL STUDIES
, 1988
"... In this article we test the random walk hypothesis for weekly stock market returns by comparing variance estimators derived from data sampled at different frequencies. The random walk model is strongly rejected for the entire sample period (19621985) and for all subperiod for a variety of aggrega ..."
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Cited by 492 (18 self)
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In this article we test the random walk hypothesis for weekly stock market returns by comparing variance estimators derived from data sampled at different frequencies. The random walk model is strongly rejected for the entire sample period (19621985) and for all subperiod for a variety of aggregate returns indexes and sizesorted portofolios. Although the rejections are due largely to the behavior of small stocks, they cannot be attributed completely to the effects of infrequent trading or timevarying volatilities. Moreover, the rejection of the random walk for weekly returns does not support a meanreverting model of asset prices.
Time series regression with a unit root
 Econometrica
, 1987
"... Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at ..."
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Cited by 400 (35 self)
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Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at
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 ..."
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Cited by 348 (4 self)
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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.
Stock prices, earnings, and expected dividends
 Journal of Finance
, 1988
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Cited by 331 (23 self)
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you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, noncommercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at
BootstrapBased Improvements for Inference with Clustered Errors
, 2006
"... Microeconometrics researchers have increasingly realized the essential need to account for any withingroup dependence in estimating standard errors of regression parameter estimates. The typical preferred solution is to calculate clusterrobust or sandwich standard errors that permit quite general ..."
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Cited by 278 (12 self)
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Microeconometrics researchers have increasingly realized the essential need to account for any withingroup dependence in estimating standard errors of regression parameter estimates. The typical preferred solution is to calculate clusterrobust or sandwich standard errors that permit quite general heteroskedasticity and withincluster error correlation, but presume that the number of clusters is large. In applications with few (530) clusters, standard asymptotic tests can overreject considerably. We investigate more accurate inference using cluster bootstrapt procedures that provide asymptotic refinement. These procedures are evaluated using Monte Carlos, including the muchcited differencesindifferences example of Bertrand, Mullainathan and Duflo (2004). In situations where standard methods lead to rejection rates in excess of ten percent (or more) for tests of nominal size 0.05, our methods can reduce this to five percent. In principle a pairs cluster bootstrap should work well, but in practice a wild cluster bootstrap performs better.
Large Sample Sieve Estimation of SemiNonparametric Models
 Handbook of Econometrics
, 2007
"... Often researchers find parametric models restrictive and sensitive to deviations from the parametric specifications; seminonparametric models are more flexible and robust, but lead to other complications such as introducing infinite dimensional parameter spaces that may not be compact. The method o ..."
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Cited by 181 (19 self)
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Often researchers find parametric models restrictive and sensitive to deviations from the parametric specifications; seminonparametric models are more flexible and robust, but lead to other complications such as introducing infinite dimensional parameter spaces that may not be compact. The method of sieves provides one way to tackle such complexities by optimizing an empirical criterion function over a sequence of approximating parameter spaces, called sieves, which are significantly less complex than the original parameter space. With different choices of criteria and sieves, the method of sieves is very flexible in estimating complicated econometric models. For example, it can simultaneously estimate the parametric and nonparametric components in seminonparametric models with or without constraints. It can easily incorporate prior information, often derived from economic theory, such as monotonicity, convexity, additivity, multiplicity, exclusion and nonnegativity. This chapter describes estimation of seminonparametric econometric models via the method of sieves. We present some general results on the large sample properties of the sieve estimates, including consistency of the sieve extremum estimates, convergence rates of the sieve Mestimates, pointwise normality of series estimates of regression functions, rootn asymptotic normality and efficiency of sieve estimates of smooth functionals of infinite dimensional parameters. Examples are used to illustrate the general results.
Understanding Instrumental Variables in Models with Essential Heterogeneity
 The Review of Economics and Statistics
, 2006
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R.(1993): '' A Test for the Number of Factors in an Approximate Factor Model
 Journal of Finance
"... An important issue in applications of multifactor models of asset returns is the appropriate number of factors. Most extant tests for the number of factors are valid only for strict factor models, in which diversifiable returns are uncorrelated across assets. In this paper we develop a test statisti ..."
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Cited by 134 (10 self)
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An important issue in applications of multifactor models of asset returns is the appropriate number of factors. Most extant tests for the number of factors are valid only for strict factor models, in which diversifiable returns are uncorrelated across assets. In this paper we develop a test statistic to determine the number of factors in an approximate factor model of asset returns, which does not require that diversifiable components of returns be uncorrelated across assets. We find evidence for one to six pervasive factors in the crosssection of New York Stock Exchange and American Stock Exchange stock returns. THE ARBITRAGE PRICING THEORY (APT) of Ross (1976) has generated an increased interest in the application of linear factor models in the study of capital asset pricing. The APT has the attractive feature that it makes a minimal number of assumptions about the nature of the economy (a factor structure for the returns generating process, a large number of assets, and frictionless trading). The costs of these minimalist assumptions include certain ambiguities such as an approximate pricing relation and an unknown number of pervasive factors. In order to estimate and test the APT, one must specify the number of pervasive factors in asset returns. The issue of the appropriate number of factors has been the subject of some controversy (see, for example, Roll and