Macroeconomists construct impulse responses using many competing time series models and different statistical paradigms (Bayesian or frequentist). We adapt optimal linear prediction pools to efficiently combine impulse response estimators for the effects of the same economic shock from this vast class of possible models. We thus alleviate the need to choose one specific model, obtaining weights that are typically positive for more than one model. Three Monte Carlo simulations and two monetary shock empirical applications illustrate how the weights leverage the strengths of each model by (i) trading off properties of each model depending on variable, horizon, and application and (ii) accounting for the full predictive distribution rather than being restricted to specific moments.

We highlight a reason for the vast range of estimates for the effect of demographics on interest rates: the magnitudes are not well-identified without often omitted data on capital and life-cycle consumption. Using nonparametric prior sensitivity analysis for an overlapping generations model estimated through Bayesian methods, we show small changes in the prior for the discount rate, intertemporal elasticity of substitution, and depreciation rate can shift posterior quantiles for the effects of demographics by up to 1.5 percentage points. Capital-output ratio data substantially tighten estimates of the depreciation rate but not the discount rate. Life-cycle consumption is especially informative about the intertemporal elasticity of substitution.

This paper develops a tool for global prior sensitivity analysis in large Bayesian models. Without imposing parametric restrictions, the methodology provides bounds for posterior means or quantiles given any prior close to the original in relative entropy and reveals features of the prior that are important for the posterior statistics of interest. We develop a sequential Monte Carlo algorithm and use approximations to the likelihood and statistic of interest to implement the calculations. The methodology finds that the prior tightness hyperparameters in the hierarchical vector autoregression model from Giannone et al. (2015) are relatively insensitive to their hyperpriors. However, in the New Keynesian model of Smets and Wouters (2007), the error bands for the impulse response of output to a monetary policy shock depend heavily on the prior. The upper bound is especially sensitive and the prior on wage rigidity plays a particularly important role.

We survey approaches to macroeconomic forecasting during the COVID-19 pandemic.  Due to the unprecedented nature of the episode, there was greater dependence on information outside the econometric model, captured through either adjustments to the model or additional data. The transparency and flexibility of assumptions were especially important for interpreting real-time forecasts and updating forecasts as new data were observed.  We revisit these themes with a time-varying parameter vector autoregression, which attributes the large jumps primarily to increased volatility rather than changes in the the type or propagation of shocks.

We estimate a panel model with endogenously time-varying parameters for COVID-19 cases and deaths in U.S. states. The functional form for infections incorporates important features of epidemiological models but is flexibly parameterized to capture different trajectories of the pandemic. Daily deaths are modeled as a spike-and-slab regression on lagged cases. Our Bayesian estimation reveals that social distancing and testing have significant effects on the parameters. For example, a 10 percentage point increase in the positive test rate is associated with a 2 percentage point increase in the death rate among reported cases. The model forecasts perform well, even relative to models from epidemiology and statistics.

Booming innovation often coincides with intense speculation in financial markets. Using over a million patents, we document two ways the market valuation of innovation and its economic impact become disconnected during bubbles. Specifically, an innovation raises the stock price of its creator by 40% more than is justified by future outcomes. In contrast, competitors’ stock prices move little despite their profits suffering. We develop a theory of investor disagreement about which firms will succeed that reconciles both the facts, unlike existing models of bubbles. Optimal innovation policy during bubbles must account for the disconnect.

We estimate a statistical model for COVID-19 cases and deaths in New Zealand.  New Zealand is an important test case for statistical and theoretical research into the dynamics  of  the  global  pandemic  since  it  went  through  a  full  cycle  of  infections.   We choose functional forms for infections and deaths that incorporate important features of epidemiological models but allow for flexible parameterization to capture different trajectories of the pandemic.  Our Bayesian estimation reveals that the simple statistical framework we employ fits the data well and allows for a transparent characterization of the uncertainty surrounding the trajectories of infections and deaths.