Gillingham K, Stock JH.
The Cost of Reducing Greenhouse Gas Emissions. Journal of Economic Perspectives. 2018;32 (4) :53-72.
AbstractThis paper reviews the cost of various interventions that reduce greenhouse gas emissions. As much as possible we focus on actual abatement costs (dollars per ton of carbon dioxide avoided), as measured by 50 economic studies of programs over the past decade, supplemented by our own calculations. We distinguish between static costs, which occur over the lifetime of the project, and dynamic costs, which incorporate spillovers. Interventions or policies that are expensive in a static sense can be inexpensive in a dynamic sense if they induce innovation and learning-by-doing.
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Appendix Lazarus E, Lewis DJ, Stock JH, Watson MW.
HAR Inference: Recommendations for Practice. Journal of Business & Economic Statistics. 2018;36 (4) :541-574.
AbstractThe classic papers by Newey and West (1987) and Andrews (1991) spurred a large body of work on how to improve heteroskedasticity- and autocorrelation-robust (HAR) inference in time series regression. This literature finds that using a larger than usual truncation parameter to estimate the long-run variance, combined with Kiefer-Vogelsang (2002, 2005) fixed-b critical values, can substantially reduce size distortions, at only a modest cost in (size-adjusted) power. Empirical practice, however, has not kept up. This paper therefore draws on the post-Newey West/Andrews literature to make concrete recommendations for HAR inference. We derive truncation parameter rules that choose a point on the size-power tradeoff to minimize a loss function. If Newey-West tests are used, we recommend the truncation parameter rule S = 1.3T1/2 and (nonstandard) fixed-b critical values. For tests of a single restriction, we find advantages to using the equal-weighted cosine (EWC) test, where the long run variance is estimated by projections onto Type II cosines, using ν = 0.4T2/3 cosine terms; for this test, fixed-b critical values are, conveniently, tν or F. We assess these rules using first an ARMA/GARCH Monte Carlo design, then a dynamic factor model design estimated using a 207 quarterly U.S. macroeconomic time series.
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Identification and Estimation of Dynamic Causal Effects in Macroeconomics. Economic Journal. 2018;128 (May) :917-948.
AbstractAn exciting development in empirical macroeconometrics is the increasing use of external sources of as-if randomness to identify the dynamic causal effects of macroeconomic shocks. This approach – the use of external instruments – is the time series counterpart of the highly successful strategy in microeconometrics of using external as-if randomness to provide instruments that identify causal effects. This lecture provides conditions on instruments and control variables under which external instrument methods produce valid inference on dynamic causal effects, that is, structural impulse response function; these conditions can help guide the search for valid instruments in applications. We consider two methods, a one-step instrumental variables regression and a two-step method that entails estimation of a vector autoregression. Under a restrictive instrument validity condition, the one-step method is valid even if the vector autoregression is not invertible, so comparing the two estimates provides a test of invertibility. Under a less restrictive condition, in which multiple lagged endogenous variables are needed as control variables in the one-step method, the conditions for validity of the two methods are the same.
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