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1. When is Growth at Risk?

   Plagborg-Møller, Mikkel; Reichlin, Lucrezia; Ricco, Giovanni; Hasenzagl, Thomas (April 2020, Brookings papers on economic activity)

   This paper empirically evaluates the potentially non-linear nexus between financial indicators and the distribution of future GDP growth, using a rich set of macroeconomic and financial variables covering 13 advanced economies. We evaluate the out-of-sample forecast performance of financial variables for GDP growth, including a fully real-time exercise based on a flexible non-parametric model. We also use a parametric model to estimate the moments of the time-varying distribution of GDP and evaluate their in-sample estimation uncertainty. Our overall conclusion is pessimistic: Moments other than the conditional mean are poorly estimated, and no predictors we consider provide robust and precise advance warnings of tail risks or indeed about any features of the GDP growth distribution other than the mean. In particular, financial variables contribute little to such distributional forecasts, beyond the information contained in real indicators. 

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2. Informal Modes of Social Support among Residents of the Rural American West during the COVID ‐19 Pandemic ^☆

   https://doi.org/10.1111/ruso.12507  

   McConnell, Kathryn; Mueller, J. Tom; Merdjanoff, Alexis A.; Burow, Paul Berne; Farrell, Justin (July 2023, Rural Sociology)

   Abstract During the first year of the COVID‐19 pandemic, federal spending on government safety net programs in the United States increased dramatically. Despite this unparalleled spending, government safety nets were widely critiqued for failing to fully meet many households' needs. Disaster research suggests thatinformalmodes of social support often emerge during times of disruption, such as the first year of the pandemic. However, use of formal government programs and informal support are rarely examined relative to each other, resulting in an incomplete picture of how households navigate disaster impacts and financial shocks. This study compares estimates of informal social support to formal government program use in the rural U.S. West, drawing on data from a rapid response survey fielded during the summer of 2020 and the 2021 Annual Social and Economic Supplement of the Current Population Survey (CPS‐ASEC). We find that informal social support systems were, on aggregate, used almost as extensively as long‐standing government programs. Our findings highlight the critical role of person‐to‐person assistance, such as sharing financial resources, among rural households during a disruptive disaster period. Routine and standardized data collection on these informal support behaviors could improve future disaster research and policy responses, especially among rural populations. 

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3. How Are SNAP Benefits Spent? Evidence from a Retail Panel

   https://doi.org/10.1257/aer.20170866  

   Hastings, Justine; Shapiro, Jesse M. (December 2018, American Economic Review)

   null (Ed.)

   We use a novel retail panel with detailed transaction records to study the effect of the Supplemental Nutrition Assistance Program (SNAP) on household spending. We use administrative data to motivate three approaches to causal inference. The marginal propensity to consume SNAP-eligible food (MPCF) out of SNAP benefits is 0.5 to 0.6. The MPCF out of cash is much smaller. These patterns obtain even for households for whom SNAP benefits are economically equivalent to cash because their benefits are below their food spending. Using a semiparametric framework, we reject the hypothesis that households respect the fungibility of money. A model with mental accounting can match the facts. (JEL D12, H75, I12, I18, I38) 

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4. Credit Shocks and Equilibrium Dynamics in Consumer Durable Goods Markets

   https://doi.org/10.1093/restud/rdab004  

   Gavazza, Alessandro; Lanteri, Andrea (March 2021, The Review of Economic Studies)

   Abstract This article studies equilibrium dynamics in consumer durable goods markets after aggregate credit shocks. We introduce two novel features into a general-equilibrium model of durable consumption with heterogeneous households facing idiosyncratic income risk and borrowing constraints: (1) indivisible durable goods are vertically differentiated in their quality and (2) trade on secondary markets at market-clearing prices, with households endogenously choosing when to trade or scrap their durables. The model highlights a new transmission mechanism for macroeconomic shocks and successfully matches several empirical patterns that we document using data on U.S. car markets around the Great Recession. After a tightening of the borrowing limit, debt-constrained households postpone the decision to scrap and upgrade their low-quality cars, which depresses mid-quality car prices. In turn, this effect reduces wealthy households’ incentives to replace their mid-quality cars with high-quality ones, thereby decreasing new-car sales. We further use our framework to evaluate targeted fiscal stimulus policies such as the Car Allowance Rebate System in 2009 (“Cash for Clunkers”). 

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5. Efficiency of Uncertainty Propagation Methods for Estimating Output Moments

   https://doi.org/10.1016/B978-0-12-818597-1.50078-3  

   Mohammadi, Samira; Cremaschi, Selen (July 2019, Computeraided Chemical Engineering)

   Uncertainty propagation methods are used to estimate the distribution of model outputs resulting from a set of uncertain model outputs. There are a number of uncertainty propagation methods available in literature. This paper compares six non-intrusive uncertainty propagation methods, Latin Hypercube Sampling, Full Factorial Integration, Univariate Dimension Reduction, Halton series, Sobol series, and Polynomial Chaos Expansion, in terms of their efficiency for estimating the first four moments of the output distribution using computational experiments. The results suggest employing FFNI if there are few uncertain inputs, up to three. Uncertainty propagation methods that utilize Halton and Sobol series are found to be robust for estimating output moments as the number of uncertain inputs increased. In general, higher order polynomial chaos expansion approximations (3rd-5th order) obtained accurate estimates of model outputs with fewer model evaluations. 

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