This paper examines how very low fertility rates in East Asia might affect inflation in the face of fiscal limits. In a calibrated overlapping-generations model, low fertility rates cause the debt-to-GDP ratio to rise, which can push the tax rate to a political ceiling and force either monetary accommodation or reduced transfers to retirees. The fiscal limit creates inflationary pressure relative to a scenario with no fiscal limit, adding to our understanding of possible inflation outcomes in aging economies. Korea faces the strongest demographic headwind and is projected to experience the earliest fiscal limit and highest inflation rates, with inflation projected to peak roughly 10 years later and 2.5pp higher with a fiscal limit than without one. Taiwan’s more favorable initial fiscal conditions help reduce inflationary pressure, and China benefits from a delayed demographic transition that leads to lower inflation, despite worse initial fiscal conditions than Taiwan. In all countries, a higher tax rate ceiling or older retirement age effectively reduce peak inflation.

This paper provides new insights into the business cycle mechanics of search and matching models. We develop a novel identification scheme based on the matching elasticity that allows these models to perfectly match a range of labor market moments. Our estimated linear model also matches several non-targeted moments including the Beveridge curve and the decomposition of inflows and outflows of unemployment. A structural decomposition reveals job separation rate shocks explain 40% of unemployment volatility. The nonlinear version of our model generates state-dependent dynamics that produce empirically consistent fluctuations in output growth uncertainty, 37% of which stem from separation rate shocks.

This paper examines the response of the U.S. labor market to a large and persistent job separation rate shock, motivated by the ongoing economic effects of the COVID-19 pandemic. We use nonlinear methods to analytically and numerically characterize the responses of vacancy creation and unemployment. Vacancies decline in response to the shock when firms expect persistent job destruction and the number of unemployed searching for work is low. Quantitatively, under our baseline forecast the unemployment rate peaks at 19.7%, 2 months after the shock, and takes 1 year to return to 5%. Relative to a scenario without the shock, unemployment uncertainty rises by a factor of 11. Nonlinear methods are crucial. In the linear economy, the unemployment rate “only” rises to 9.2%, vacancies increase, and uncertainty is unaffected. In both cases, the severity of the COVID-19 shock depends on the separation rate persistence.

This paper develops a new way to quantify the effects of aggregate uncertainty that accounts for exogenous and endogenous sources. First, we use Bayesian methods to estimate a nonlinear New Keynesian model with stochastic volatility and a zero lower bound constraint on the nominal interest rate. We discipline the model by matching data on uncertainty, in addition to common macro time series. Second, we use the Euler equation to decompose output into expected output and the expected variance and skewness of output. We then filter a time series for each term. Our method captures the effects of higher-order moments over horizons beyond 1 quarter by recursively decomposing expected output. Over a 1-quarter horizon, output uncertainty reduced output less than 0.01% every quarter, similar to volatility shocks in our model. Over horizons that remove the influence of expected output, output uncertainty on average reduced output 0.06% and the peak effect was 0.15% during the Great Recession, similar to structural VAR estimates. Other higher-order moments had much smaller effects on output.

This paper examines the importance of using nonlinear methods to capture the zero lower bound (ZLB) on the Fed’s policy rate. While it may seem obvious to impose the ZLB, ex-ante it is unclear how large of an effect the ZLB has on parameter estimates, since it was only hit once in recent history. The parameters and marginal likelihoods from a linear and nonlinear model are similar, but the linear model does not fit the data as well and predicts counterfactually large policy shocks when the Fed is constrained. A quasi-linear model performs better than a linear model but it still generates less volatility at the ZLB and is not as conducive to estimation. When we add a banking sector to create an interest rate spread, the ZLB is even more important.

Econometric evidence shows that when higher income inequality and financial liberalization are added to a set of conventional explanatory variables, they predict significantly larger current account deficits in a cross-section of advanced economies. To study this mechanism, we develop a DSGE model where investors’ income share increases at the expense of workers, and where workers respond by obtaining loans from domestic and foreign investors. This supports aggregate demand but generates current account deficits, especially if domestic financial markets are simultaneously liberalized. In emerging markets, because domestic workers cannot borrow, investors deploy their surplus funds abroad, leading to current account surpluses.