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137
How Much Should We Trust DifferencesinDifferences Estimates?” Quarterly
 Journal of Economics
, 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 femal ..."
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Cited by 264 (0 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 �nd an “effect ” signi�cant 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 speci�c 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. I.
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 226 (13 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.
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 135 (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.
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 96 (5 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 92 (17 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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Tests of conditional predictive ability
 Econometrica
, 2006
"... We argue that the current framework for predictive ability testing (e.g.,West, 1996) is not necessarily useful for realtime forecast selection, i.e., for assessing which of two competing forecasting methods will perform better in the future. We propose an alternative framework for outofsample com ..."
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Cited by 46 (1 self)
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We argue that the current framework for predictive ability testing (e.g.,West, 1996) is not necessarily useful for realtime forecast selection, i.e., for assessing which of two competing forecasting methods will perform better in the future. We propose an alternative framework for outofsample comparison of predictive ability which delivers more practically relevant conclusions. Our approach is based on inference about conditional expectations of forecasts and forecast errors rather than the unconditional expectations that are the focus of the existing literature. We capture important determinants of forecast performance that are neglected in the existing literature by evaluating what we call the forecasting method (the model and the parameter estimation procedure), rather than just the forecasting model. Compared to previous approaches, our tests are valid under more general data assumptions (heterogeneity rather than stationarity) and estimation methods, and they can handle comparison of both nested and nonnested models, which is not currently possible. To illustrate the usefulness of the proposed tests, we compare the forecast performance of three leading parameterreduction methods for macroeconomic forecasting using a large number of predictors: a sequential model selection approach,
Asymptotic distributions of quasimaximum likelihood estimates for spatial autoregressive models. Econometrica
, 2004
"... This paper investigates asymptotic properties of the maximim likelihood estimator and the quasimaximum likelihood estimator for the spatial autoregressive model. The rates of convergence of those estimators may depend on some general features of the spatial weights matrix of the model. It is import ..."
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Cited by 45 (7 self)
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This paper investigates asymptotic properties of the maximim likelihood estimator and the quasimaximum likelihood estimator for the spatial autoregressive model. The rates of convergence of those estimators may depend on some general features of the spatial weights matrix of the model. It is important to make the distinction with different spatial scenarios. Under the scenario that each unit will be influenced by only a few neighboring units, the estimators may have √ nrate of convergence and be asymptotic normal. When each unit can be influenced by many neighbors, irregularity of the information matrix may occur and various components of the estimators may have different rates of convergence.
The timeseries relations among expected return, risk, and booktomarket
 Journal of Financial Economics
, 1999
"... This paper examines the timeseries relations among expected return, risk, and booktomarket (B/M) at the portfolio level. I "nd that B/M predicts economically and statistically signi"cant timevariation in expected stock returns. Further, B/M is strongly associated with changes in risk, as measured ..."
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Cited by 34 (0 self)
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This paper examines the timeseries relations among expected return, risk, and booktomarket (B/M) at the portfolio level. I "nd that B/M predicts economically and statistically signi"cant timevariation in expected stock returns. Further, B/M is strongly associated with changes in risk, as measured by the Fama and French (1993) (Journal of Financial Economics, 33, 3}56) threefactor model. After controlling for risk, B/M provides no incremental information about expected returns. The evidence suggests that the threefactor model explains timevarying expected returns better than a characteristicsbased
An Extended Class of Instrumental Variables for the Estimation of Causal Effects
 UCSD DEPT. OF ECONOMICS DISCUSSION PAPER
, 1996
"... This paper builds on the structural equations, treatment effect, and machine learning literatures to provide a causal framework that permits the identification and estimation of causal effects from observational studies. We begin by providing a causal interpretation for standard exogenous regresso ..."
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Cited by 32 (13 self)
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This paper builds on the structural equations, treatment effect, and machine learning literatures to provide a causal framework that permits the identification and estimation of causal effects from observational studies. We begin by providing a causal interpretation for standard exogenous regressors and standard “valid” and “relevant” instrumental variables. We then build on this interpretation to characterize extended instrumental variables (EIV) methods, that is methods that make use of variables that need not be valid instruments in the standard sense, but that are nevertheless instrumental in the recovery of causal effects of interest. After examining special cases of single and double EIV methods, we provide necessary and sufficient conditions for the identification of causal effects by means of EIV and provide consistent and asymptotically normal estimators for the effects of interest.