ORCID Profile
0000-0003-1125-4009
Current Organisation
UNSW Sydney
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Publisher: The Econometric Society
Date: 2020
DOI: 10.3982/QE1319
Abstract: We study potential impacts of future climate change on U.S. agricultural productivity using county‐level yield and weather data from 1950 to 2015. To account for adaptation of production to different weather conditions, it is crucial to allow for both spatial and temporal variation in the production process mapping weather to crop yields. We present a new panel data estimation technique, called mean observation OLS (MO‐OLS) that allows for spatial and temporal heterogeneity in all regression parameters (intercepts and slopes). Both forms of heterogeneity are important: We find strong evidence that production function parameters adapt to local climate, and also that sensitivity of yield to high temperature declined from 1950–89. We use our estimates to project corn yields to 2100 using 19 climate models and three greenhouse gas emission scenarios. We predict unmitigated climate change will greatly reduce yield. Our mean prediction (over climate models) is that adaptation alone can mitigate 36% of the damage, while emissions reductions consistent with the Paris targets would mitigate 76%.
Publisher: Wiley
Date: 06-02-2013
Publisher: The Econometric Society
Date: 2022
DOI: 10.3982/QE1745
Abstract: We study the impact of child work on cognitive development in four Low‐ and Middle‐Income Countries. We advance the literature by using cognitive test scores collected regardless of school attendance. We also address a key gap in the literature by controlling for children's complete time allocation budget. This allows us to estimate effects of different types of work, like chores and market/farm work, relative to specific alternative time‐uses, like school or study or play/leisure. Our results show child work is more detrimental to child development to the extent that it crowds out school/study time rather than leisure. We also show the adverse effect of time spent on domestic chores is similar to time spent on market and farm work, provided they both crowd out school/study time. Thus, policies to enhance child development should target a shift from all forms of work toward educational activities.
Publisher: Elsevier BV
Date: 08-2023
Publisher: Elsevier BV
Date: 2021
Publisher: Elsevier BV
Date: 05-2021
Publisher: SAGE Publications
Date: 09-2016
DOI: 10.1177/1536867X1601600301
Abstract: In this article, we introduce the new command xtkr, which implements the Keane and Runkle (1992a, Journal of Business and Economic Statistics 10: 1–9) approach for fitting linear panel-data models when the available instruments are predetermined but not strictly exogenous. This is a common case that includes dynamic panel-data models as a leading ex le. Monte Carlo simulations show that, in certain situations, this approach offers an improvement over the popular difference generalized method of moments and system generalized method of moments estimators in terms of bias and root mean squared error. An empirical application to cigarette demand also demonstrates its usefulness for applied researchers.
Publisher: SAGE Publications
Date: 09-2014
DOI: 10.1177/1536867X1401400312
Abstract: In this article, I introduce the new command xtpedroni, which implements the Pedroni (1999, Oxford Bulletin of Economics and Statistics 61: 653–670 2004, Econometric Theory 20: 597–625) panel cointegration test and the Pedroni (2001, Review of Economics and Statistics 83: 727–731) group-mean panel-dynamic ordinary least-squares estimator. For nonstationary heterogeneous panels that are long (large T) and wide (large N), xtpedroni tests for cointegration among one or more regressors by using seven test statistics under the null of no cointegration, and it also estimates the cointegrating equation for each in idual as well as the group mean of the panel. The test can include common time dummies and unbalanced panels.
No related grants have been discovered for Timothy Neal.