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The transient climate response (TCR) is the change in global mean temperature at the time of an exogenous doubling in atmospheric CO2 concentration increasing at a rate of 1% per year. A problem with estimating the TCR using observational data is that observed CO2 concentrations depend in turn on temperature. Therefore, the observed concentration data are endogenous, potentially leading to simultaneous causation bias of regression estimates of the TCR. We address this problem by employing instrumental variables regression, which uses changes in radiative forcing external to earth systems to provide quasi-experiments that can be used to estimate the TCR. Because the modern instrumental record is short, we focus on decadal fluctuations (up to 30-year changes), which also mitigate some statistical issues associated with highly persistent temperature and concentration data. Our estimates of the TCR for these shorter horizons, normalized to be comparable to the traditional 70-year TCR, fall within the range in the IPCC-AR5 and provide new observational confirmation of model-based estimates. [ABSTRACT FROM AUTHOR]

Concerns about climate change have led to calls for reforming or eliminating the extensive US federal fossil fuel leasing program. One proposed reform is adding a climate surcharge to the existing royalty rate. We consider determining this surcharge by maximizing social welfare, including the climate damages from combusting federal fossil fuels and the value of raising revenue when the marginal value of public funds exceeds one. We estimate that the resulting climate royalty surcharge would lead to meaningful declines in global emissions, would significantly increase royalty receipts, and would result in royalty rates substantially greater than those currently in place. We also evaluate the change in onshore royalty rates made by the Inflation Reduction Act of 2022, finding the law's modest rate increases leave substantial welfare gains, emissions reductions, and royalty revenues on the table. [ABSTRACT FROM AUTHOR]

Abstract We examine the pass-through of wholesale prices to retail prices in the market for E85, which contains 51%–83% ethanol, and in the much larger market for E10, which contains 10% ethanol. We use a panel dataset consisting of monthly observations from 2007 to March 2015 on wholesale and retail prices for 274 Minnesota gas stations that sell both E10 and E85. Consistent with prior research, the cumulative pass-through coefficient for E10 is 1.00 after one month. In contrast, the E85 market is sparse, and although pass-through increased over time, we estimate it to be only 0.53 statewide from 2012 to 2015. Pass-through is higher at stations with more local E85 competitors. In the Twin Cities, which has a high density of E85 stations, pass-through is nearly complete, but outside the Twin Cities slightly less than half the wholesale discount of E85, relative to E10, is passed on to the consumer. [ABSTRACT FROM AUTHOR]

Examines the range of leading economic indicators during the recession in the U.S. that began in March 2001. Assessment of the quantitative review of the 2001 recession; Forecasts on the recession based on individual leading indicators; Performance during the 2001 recession of combination forecasts.

This article considers forecasting a single time series when there are many predictors (N) and time series observations (T). When the data follow an approximate factor model, the predictors can be summarized by a small number of indexes, which we estimate using principal components. Feasible forecasts are shown to be asymptotically efficient in the sense that the difference between the feasible forecasts and the infeasible forecasts constructed using the actual values of the factors converges in probability to 0 as both N and T grow large. The estimated factors are shown to be consistent, even in the presence of time variation in the factor model. [ABSTRACT FROM AUTHOR]

Deals with the inference about the variance of coefficients in time-varying parameter models with stationary regressors. Asymptotically median unbiased estimators and asymptotically valid confidence intervals developed by the study; Theoretical results; Numerical results for the special cases of the univariate local-level model; Application to the estimation of stochastic trend for the growth rate of postwar gross domestic product in the United States.

This article considers the problem of predicting the mean effect of a change in the distribution of certain policy-related variables on a dependent variable (Y). This is conventionally done using a parametric model. If, however, the conditional expectation of Y, given policy and nonpolicy variables X, is unaltered by the policy intervention, and if the support of X after the policy intervention lies within the support of X before the intervention, then this analysis can be performed nonparametrically. The proposed nonparametric estimator is developed for the model Y[sub j] = g(X[sub j]) + A'd[sub j] + u[sub j], where g(.) is a continuous unknown function of the continuous variables X, d[sub j] is an m-vector of dummy variables, A is an m-vector of unknown parameters representing fixed cell-specific effects, and u[sub j] is an error term with E(u[sub j]|x[sub j], d[sub j) = 0. The estimand is B = Eg(X[sup *, sub j]) - Eg(X[sub j]), where X and X[sup *] (respectively) denote the values of X before and after the policy intervention. A nonparametric estimator B[sub n] is proposed. The estimator is the sample average of the difference between the kernel regression estimates of E(Y|X[sup *, sub j], d[sub j]) and E(Y|X[sub j], d[sub j]). To estimate these conditional expectations, A is first estimated using the residuals from nonparametric regressions of Y and d on X. The consistency and asymptotic normality of B[sub n] are studied. The estimator, along with two estimators of its variance, is examined in a Monte Carlo experiment. In this experiment, the cost of using the nonparametric estimator, relative to the efficient parametric estimator, is found to be modest in terms of increased root mean squared error. When the dimension of X is large and the sample size is small, however, the nonparametric estimator can exhibit substantial bias. [ABSTRACT FROM AUTHOR]

Cointegrated multiple time series share at least one common trend. Two tests are developed for the number of common stochastic trends (i.e., for the order of cointegration) in a multiple time series with and without drift. Both tests revolve the roots of the ordinary least squares coefficient matrix obtained by regressing the series onto its first lag. Critical values for the tests are tabulated, and their power is examined in a Monte Carlo study. Economic time series are often modeled as having a unit root in their autoregressive representation, or (equivalently) as containing a stochastic trend. But both casual observation and economic theory suggest that many series might contain the same stochastic trends so that they are cointegrated. If each of n series is integrated of order 1 but can be jointly characterized by k < n stochastic trends, then the vector representation of these series has k unit roots and n - k distinct stationary linear combinations. Our proposed tests can be viewed alternatively as tests of the number of common trends, linearly independent cointegrating vectors, or autoregressive unit roots of the vector process. Both of the proposed tests are asymptotically similar. The first test (q[sub f]) is developed under the assumption that certain components of the process have a finite-order vector autoregressive (VAR) representation, and the nuisance parameters are handled by estimating this VAR. The second test (q[sub c]) entails computing the eigenvalues of a corrected sample first-order autocorrelation matrix, where the correction is essentially a sum of the autocovariance matrices. Previous researchers have found that U.S. postwar interest rates, taken individually, appear to be integrated of order 1. In addition, the theory of the term structure implies that yields on similar assets of different maturities will be cointegrated. Applying these tests to postwar... [ABSTRACT FROM AUTHOR]

A class of time series models is presented in which variables evolve on a data-based rather than calendar time scale. The discrete calendar-time model thus obtained exhibits time-varying parameters and conditional heteroscedasticity. Using a procedure based on the Kalman filter, univariate models are estimated for postwar U.S. real gross national product (GNP) and short- and long-term interest rates. The results indicate significant time scale nonlinearities. [ABSTRACT FROM AUTHOR]

The Rainy River Watershed on the Superior National Forest is home to the Boundary Waters Canoe Area Wilderness (BWCAW). It also contains deposits of copper, nickel, and trace metals, and copper-nickel mining has been proposed adjacent to and upstream of the BWCAW. In 2017, the US Department of Agriculture proposed withdrawing land in the Rainy River Watershed within the Superior National Forest from mineral leasing, a position it reversed in 2018. These developments highlight the potential tradeoff between economic benefits from mining and concerns about its negative economic consequences for the local recreational and amenity-based economy. Previous studies of mining in the Superior National Forest focus on static effects on a single industry (e.g., mining) at some unspecified point over a medium-run horizon. We draw on these studies and the economics literature to provide a unified analysis of the effect of the proposed mining development on income and employment over time. Our results suggest that the proposed mining would lead to a boom-bust cycle that is typical of resource extraction economies, exacerbated by the likely negative effect on the recreation industry. [ABSTRACT FROM AUTHOR]

We decompose the decline in coal production from 2008 to 2016 into the contributions of several sources. In particular, we estimate the effects of declining natural gas prices and the introduction of new environmental regulations along with several other factors, using both monthly state-level data and annual information on coal plant closings. We estimate that the declining price of natural gas relative to coal is responsible for 92 percent of the total decline in coal production over this period and that environmental regulations account for an additional six percent, with other factors making small and offsetting contributions. [ABSTRACT FROM AUTHOR]

We evaluate the claim by Gay et al. (Clim Change 94:333–349, ) that “surface temperature can be better described as a trend stationary process with a one-time permanent shock” than efforts by Kaufmann et al. (Clim Change 77:249–278, ) to model surface temperature as a time series that contains a stochastic trend that is imparted by the time series for radiative forcing. We test this claim by comparing the in-sample forecast generated by the trend stationary model with a one-time permanent shock to the in-sample forecast generated by a cointegration/error correction model that is assumed to be stable over the 1870–2000 sample period. Results indicate that the in-sample forecast generated by the cointegration/error correction model is more accurate than the in-sample forecast generated by the trend stationary model with a one-time permanent shock. Furthermore, Monte Carlo simulations of the cointegration/error correction model generate time series for temperature that are consistent with the trend-stationary-with-a-break result generated by Gay et al. (Clim Change 94:333–349, ), while the time series for radiative forcing cannot be modeled as trend stationary with a one-time shock. Based on these results, we argue that modeling surface temperature as a time series that shares a stochastic trend with radiative forcing offers the possibility of greater insights regarding the potential causes of climate change and efforts to slow its progression. [ABSTRACT FROM AUTHOR]

Comparing statistical estimates for the long-run temperature effect of doubled CO2 with those generated by climate models begs the question, is the long-run temperature effect of doubled CO2 that is estimated from the instrumental temperature record using statistical techniques consistent with the transient climate response, the equilibrium climate sensitivity, or the effective climate sensitivity. Here, we attempt to answer the question, what do statistical analyses of the observational record measure, by using these same statistical techniques to estimate the temperature effect of a doubling in the atmospheric concentration of carbon dioxide from seventeen simulations run for the Coupled Model Intercomparison Project 2 (CMIP2). The results indicate that the temperature effect estimated by the statistical methodology is consistent with the transient climate response and that this consistency is relatively unaffected by sample size or the increase in radiative forcing in the sample. [ABSTRACT FROM AUTHOR]

We use recent advances in time series econometrics to estimate the relation among emissions of CO2 and CH4, the concentration of these gases, and global surface temperature. These models are estimated and specified to answer two questions; (1) does human activity affect global surface temperature and; (2) does global surface temperature affect the atmospheric concentration of carbon dioxide and/or methane. Regression results provide direct evidence for a statistically meaningful relation between radiative forcing and global surface temperature. A simple model based on these results indicates that greenhouse gases and anthropogenic sulfur emissions are largely responsible for the change in temperature over the last 130 years. The regression results also indicate that increases in surface temperature since 1870 have changed the flow of carbon dioxide to and from the atmosphere in a way that increases its atmospheric concentration. Finally, the regression results for methane hint that higher temperatures may increase its atmospheric concentration, but this effect is not estimated precisely. [ABSTRACT FROM AUTHOR]

The article presents the discussion on standard measure of the benefits of a climate policy being the social cost of carbon (SCC). Topics include costs of a climate policy being the abatement costs of reducing emissions of carbon dioxide (CO2); impacts on agricultural production, reductions in labor productivity, and property damage; and informing the design of carbon-pricing climate policy instruments using price signals.

Given the widely noted increase in the warming effects of rising greenhouse gas concentrations, it has been unclear why global surface temperatures did not rise between 1998 and 2008. We find that this hiatus in warming coincides with a period of little increase in the sum of anthropogenic and natural forcings. Declining solar insolation as part of a normal eleven-year cycle, and a cyclical change from an El Nino to a La Nina dominate our measure of anthropogenic effects because rapid growth in short-lived sulfur emissions partially offsets rising greenhouse gas concentrations. As such, we find that recent global temperature records are consistent with the existing understanding of the relationship among global surface temperature, internal variability, and radiative forcing, which includes anthropogenic factors with well known warming and cooling effects. [ABSTRACT FROM AUTHOR]