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Regression discontinuity designs have become popular in empirical studies due to their attractive properties for estimating causal effects under transparent assumptions. Nonetheless, most popular procedures assume i.i.d. data, which is unreasonable in many common applications. To fill this gap, we derive the properties of traditional local polynomial estimators in a fixed- G setting that allows for cluster dependence in the error term. Simulation results demonstrate that accounting for clustering in the data while selecting bandwidths may lead to lower MSE while maintaining proper coverage. We then apply our cluster-robust procedure to an application examining the impact of Low-Income Housing Tax Credits on neighborhood characteristics and low-income housing supply.

We analyze Lagrange Multiplier (LM) tests for a shift in trend of a univariate time series at an unknown date. We focus on the class of LM statistics based on nonparametric kernel estimates of the long run variance. Extending earlier work for models with nontrending data, we develop a fixed-b asymptotic theory for the statistics. The fixed-b theory suggests that, for a given statistic, kernel, and significance level, there usually exists a bandwidth such that the fixed-b asymptotic critical value is the same for both I(0) and I(1) errors. These “robust” bandwidths are calculated using simulation methods for a selection of well-known kernels. We find when the robust bandwidth is used, the supremum statistic configured with either the Bartlett or Daniell kernel gives LM tests with good power. When testing for a slope change, we obtain the surprising finding that less trimming of potential shift dates leads to higher power, which contrasts the usual relationship between trimming and power. Finite sample simulations indicate that the robust LM statistics have stable size with good power.

A regression kink design (RKD or RK design) can be used to identify casual effects in settings where the regressor of interest is a kinked function of an assignment variable. In this chapter, we apply an RKD approach to study the effect of unemployment benefits on the duration of joblessness in Austria, and discuss implementation issues that may arise in similar settings, including the use of bandwidth selection algorithms and bias-correction procedures. Although recent developments in nonparametric estimation (Calonico, Cattaneo, & Farrell, 2014; Imbens & Kalyanaraman, 2012) are sometimes interpreted by practitioners as pointing to a default estimation procedure, we show that in any given application different procedures may perform better or worse. In particular, Monte Carlo simulations based on data-generating processes that closely resemble the data from our application show that some asymptotically dominant procedures may actually perform worse than “sub-optimal” alternatives in a given empirical application.

This chapter develops a novel bootstrap procedure to obtain robust bias-corrected confidence intervals in regression discontinuity (RD) designs. The procedure uses a wild bootstrap from a second-order local polynomial to estimate the bias of the local linear RD estimator; the bias is then subtracted from the original estimator. The bias-corrected estimator is then bootstrapped itself to generate valid confidence intervals (CIs). The CIs generated by this procedure are valid under conditions similar to Calonico, Cattaneo, and Titiunik’s (2014) analytical correction – that is, when the bias of the naive RD estimator would otherwise prevent valid inference. This chapter also provides simulation evidence that our method is as accurate as the analytical corrections and we demonstrate its use through a reanalysis of Ludwig and Miller’s (2007) Head Start dataset.

Delivering real‐time services (Internet telephony, video conferencing, and streaming media as well as business‐critical data applications) across the Internet requires end‐to‐end quality of service (QoS) guarantees, which requires a hierarchy of contracts. These standardized contracts may be referred to as service level agreements (SLAs). SLAs provide a mechanism for service providers and customers to flexibly specify the service to be delivered. The emergence of bandwidth and service agents, traders, brokers, exchanges and contracts can provide an institutional and business framework to support effective competition. This article identifies issues that must be addressed by SLAs for consumer applications. We introduce a simple taxonomy for classifying SLAs based on the identity of the contracting parties. We conclude by discussing implications for public policy, Internet architecture, and competition.

The R environment for statistical computing and graphics (R Development Core Team, 2008) offers practitioners a rich set of statistical methods ranging from random number generation and optimization methods through regression, panel data, and time series methods, by way of illustration. The standard R distribution (base R) comes preloaded with a rich variety of functionality useful for applied econometricians. This functionality is enhanced by user-supplied packages made available via R servers that are mirrored around the world. Of interest in this chapter are methods for estimating nonparametric and semiparametric models. We summarize many of the facilities in R and consider some tools that might be of interest to those wishing to work with nonparametric methods who want to avoid resorting to programming in C or Fortran but need the speed of compiled code as opposed to interpreted code such as Gauss or Matlab by way of example. We encourage those working in the field to strongly consider implementing their methods in the R environment thereby making their work accessible to the widest possible audience via an open collaborative forum.

Recently, there has been a call for replication research to validate empirical findings, especially findings that are important for development policies. Thus, the purpose of this paper is to replicate the estimation results from Mu and van de Walle (2011).

The author used raw data sets provided by Mu Ren and Dominique van de Walle and the same methods of Mu and van de Walle (2011). In addition to the pure replication, the author conducted the two extensions: sensitivity analysis of covariates and bandwidth selection and analysis of the effect of the road project on additional outcome variables.

Overall, the author ables to replicate most estimates from Mu and van de Walle (2011). The author find a positive effect of rural roads on local market development. The impact estimates of the road project are not sensitive to the selection of the bandwidth in kernel propensity score (PS) matching. There are no significant effects of road projects on additional outcomes, including access to credit and migration.

The study confirms a positive effect of rural roads on local market development. Thus, the government can provide investment in rural roads to improve the local market and its welfare.

This study tried to replicate and verify an important study on the impact of the rural road in Vietnam.

This paper proposes a new approach to testing in the generalized method of moments (GMM) framework. The new tests are constructed using heteroskedasticity autocorrelation (HAC) robust standard errors computed using nonparametric spectral density estimators without truncation. While such standard errors are not consistent, a new asymptotic theory shows that they lead to valid tests nonetheless. In an over-identified linear instrumental variables model, simulations suggest that the new tests and the associated limiting distribution theory provide a more accurate first order asymptotic null approximation than both standard nonparametric HAC robust tests and VAR based parametric HAC robust tests. Finite sample power of the new tests is shown to be comparable to standard tests.