Research

Working Papers

JDI

Workshops

QRDC

Research Areas

Conferences

Public Lectures

Prizes and Distinctions

Recent QED Working Papers

Previous Pause Next

#1433.

Christopher Cotton, Brent R. Hickman, Joseph P. Price

Affirmative Action, Shifting Competition, and Human Capital Accumulation: A Comparative Static Analysis of Investment Contests

We develop a model in which many heterogeneous agents invest in human capital as they compete for better college admission slots or employment opportunities. The model provides theoretical predictions about how affirmative action or preferential treatment policies change the distribution of effort, human capital accumulation, and job/college slot allocations across different population groups. Our findings deliver two key insights. First, incentives to invest in human capital depends substantially on the strength of one's competition. Second, we find evidence of a counter-intuitive role…

#1432.

Christopher Ferrall

Object Oriented (Dynamic) Programming: Replication, Innovation and "Structural" Estimation

This paper discusses how to design, solve and estimate dynamic programming models using the open source package niqlow. Reasons are given for why such a package has not appeared earlier and why the object-oriented approach followed by niqlow seems essential. An example is followed that starts with basic coding then expands the model and applies different solution methods to finally estimate parameters from data. Using niqlow to organize the empirical DP literature may support new research better than traditional surveys. Replication of…

#1431.

fabrizio.iacone, Morten Ø. Nielsen, A.M. Robert Taylor

Semiparametric Tests for the Order of Integration in the Possible Presence of Level Breaks

Lobato and Robinson (1998) develop semiparametric tests for the null hypothesis that a series is weakly autocorrelated, or I(0), about a constant level, against fractionally integrated alternatives. These tests have the advantage that the user is not required to specify a parametric model for any weak autocorrelation present in the series. We extend this approach in two distinct ways. First, we show that it can be generalised to allow for testing of the null hypothesis that a series is I(\delta) for any \delta lying in the usual stationary and invertible region of the parameter space.…

#1430.

Ardyn Nordstrom, Christopher Cotton

Impact of a Severe Drought on Education: More Schooling but Less Learning

From October 2015 to April 2016, Southern Africa experienced one of the severest droughts in history. The drought's intensity varied significantly across locations. This provides a natural experiment to estimate the effect of large, negative agricultural shocks. We consider the impact of this shock on children’s educational outcomes using data from rural Zimbabwe. Those who experienced the drought may suffer from decreases in income and food access. This can affect household resource allocation and schooling decisions while exposing individuals to stress and uncertainty. We find the…

#1429.

Samuel Brien, Michael Jansson, Morten Ø. Nielsen

Nearly Efficient Likelihood Ratio Tests of a Unit Root in an Autoregressive Model of Arbitrary Order

We study large-sample properties of likelihood ratio tests of the unit root hypothesis in an autoregressive model of arbitrary, finite order. Earlier research on this testing problem has developed likelihood ratio tests in the autoregressive model of order one, but resorted to a plug-in approach when dealing with higher-order models. In contrast, we consider the full model and derive the relevant large-sample properties of likelihood ratio tests under a local-to-unity asymptotic framework. As in the simpler model, we show that the full likelihood ratio tests are nearly efficient, in the…

#1428.

James G. MacKinnon, Morten Ø. Nielsen, Matthew D. Webb

Testing for the appropriate level of clustering in linear regression models

Reliable inference with clustered data has received a great deal of attention in recent years. The overwhelming majority of this research assumes that the cluster structure is known. This assumption is very strong, because there are often several possible ways in which a dataset could be clustered. We propose two tests for the correct level of clustering. One test focuses on inference about a single coefficient, and the other on inference about two or more coefficients. We also prove the asymptotic validity of a wild bootstrap implementation. The proposed tests work for a null hypothesis…

#1427.

Brant Abbott, Robin Brace

Has Consumption Inequality Mirrored Wealth Inequality in the Survey of Consumer Finances?

A method to impute consumption expenditure inequality between wealth groups in the Survey of Consumer Finances is provided, allowing for measurement error that is correlated with income and wealth. Identification is derived from observing food at home and away, which are relative necessities and luxuries, respectively. The gap in expenditure between top and bottom wealth quintiles increased by 50% between 2004 and 2013, indicating that increases in wealth inequality have passed through to consumption.

#1426.

Christopher Cotton, Ardyn Nordstrom, jordan_naowski_wp, Eric Richert

Community Campaigns to Improve Attitudes towards Girls' Education Can Increase Math Scores and Enrollment

We show that information campaigns intended to improve the attitudes and beliefs about girls’ education throughout communities can increase girls’ mathematics performance and school enrolment. These campaigns also increase caregiver support for girls’ education. Our analysis uses data from a large, randomized UKAid Girls’ Education Challenge (GEC) project in rural Zimbabwe, where the staggered roll out of the project allows for us to isolate the impact of a community-wide information campaign to promote girls’ education by shifting the attitudes and beliefs of girls, parents, teachers and…

#1425.

Morten Ø. Nielsen, Antoine Noël

To infinity and beyond: Efficient computation of ARCH(\infty) models

This paper provides an exact algorithm for efficient computation of the time series of conditional variances, and hence the likelihood function, of models that have an ARCH($\infty$) representation. This class of models includes, e.g., the fractionally integrated generalized autoregressive conditional heteroskedasticity (FIGARCH) model. Our algorithm is a variation of the fast fractional difference algorithm of \cite{JensenNielsen2014}. It takes advantage of the fast Fourier transform (FFT) to achieve an order of magnitude improvement in computational speed. The efficiency of the algorithm…

Events

There are currently no upcoming research events.