A mandate framework is proposed for delegating monetary policy to an instrument-independent, but goal-dependent central bank that emphasizes simplicity in both the objectives entering the welfare criterion and those in the instrument rule. It consists of: (i) a simple quadratic loss function penalizing deviations from target variables; (ii) a welfare-optimized, Taylor-type log-linear nominal interest-rate rule with targets that match those in the loss function; (iii) a zero-lower-bound (ZLB) constraint on the nominal interest rate imposing a low unconditional probability of ZLB episodes; and (iv) a long-run inflation target. In an estimated New Keynesian model with these features, we find that for a quarterly probability of 5%, an optimal annual inflation target is close to 2%, weights for real variables in the loss function are small compared with inflation except for the real wage growth mandate and the optimized rules mimic a price-level rule.

In the benchmark New Keynesian (NK) model, I introduce the real cost channel to study government spending multipliers and provide simple Markov chain closed-form solutions. This model departs fundamentally from most previous interpretations of the nominal cost channel by flattening the NK Phillips Curve in liquidity traps. At the zero lower bound, I show analytically that following positive government spending shocks, the real cost channel can make inflation rise less than in a model without this channel. This then causes a smaller drop in real interest rates, resulting in a lower output gap multiplier. Finally, I confirm the robustness of the real cost channel’s effect on multipliers using extensions of two models.

We compare two welfare programs: the universal basic income (UBI) and negative income tax (NIT). Under a linear income tax system, we show that (i) the NIT can replicate the allocation of the UBI exactly by providing an identical marginal effective tax schedule, and (ii) the budget of the NIT is always smaller than that of the UBI. According to our quantitative model, which is calibrated to approximate the income and wealth distributions in the United States, the optimal UBI is to pay everyone 7.2% of the average income. We also show that the NIT can achieve a similar average welfare with a much smaller budget (2.3% of the GDP) by providing a subsidy that is generous to the very poor and quickly phases out as income increases.

This paper deals with the phenomenon of poverty-trap regimes in Mexico, that is, self-reinforcing mechanisms in which municipalities which start poor remain poor. We develop a coordination game of poverty traps driven by strategic interactions of economic agents: people choose to complete or not their education levels since it might be excessively costly and unprofitable. A one-shot game is constructed and then converted into a system of differential equations in which strategies that perform relatively better become more abundant in the population. Applying evolutionary games and symbolic-regimes dynamics (nonparametric and nonlinear techniques), we show that Mexican regions are in poverty-trap regimes (stable and dynamically evolving low-level equilibria) characterized by incomplete education and low income since initial conditions (education and income per capita) are such (very precarious) that poverty is the stable steady-state situation. We examine scenarios to show that to overcome the high-poverty regime by the year 2030, it is necessary to reduce incomplete education by 10% in the 5-year periods 2020–2025 and 2025–2030 and increase per-capita income by 10% in both periods.

Survey evidence tells us that stock prices reflect the risks investors associate with long-run technological change. However, there is a shortage of models that can rationalize long-run risks. Unlike the previous literature assuming a fixed number of products, our model allows for new product varieties that appear in the form of new firms which face entry costs and delay in the entry process. The fixed variety model has a significant limitation in translating macroeconomic volatility into asset return volatility. Our model with growing varieties induces endogenous low-frequency fluctuations in productivity driving large, persistent variations in consumption growth and asset prices. It also changes the valuation of assets through the increase in the volatility of the pricing kernel (with a positive long-run component) and leads to higher excess returns. Our model is motivated by a simple recursively identified VAR model containing quarterly US data 1992Q3-2018Q4 with the following list of variables: total factor productivity, output, a measure of firm entry, and the excess return on stocks.

This paper compares the impact of setting the money growth rate versus pegging the interest rate on aggregate real variables and their distributions. In either case, the monetary policy, in isolation, requires the price level to jump to ensure intertemporal solvency but has no real dynamic effects. The choice of monetary instrument has consequences for wealth and income inequality, doing so in potentially conflicting ways. Following real shocks, the accompanying monetary policy will influence the ensuing transition. For real variables, this operates entirely through its impact on the speed of convergence and is negligible. For financial variables, the impact also depends upon the initial jump in the price level. These effects vary more substantially between policies and have more significant distributional consequences. Overall, the impact on inequality is dominated by the real shocks themselves, rather than the accompanying monetary policy. Finally, we compare these two policies to inflation targeting, which is shown to be the least favorable for reducing wealth inequality, but the most favorable for reducing income inequality.

Stock market bubbles arise as a joint monetary and financial phenomenon. We assess the potential of monetary policy in mitigating the onset of bubbles by means of a Markov-switching Bayesian Vector Autoregression model estimated on US 1960–2019 data. Bubbles are detected and dated from the regime-specific interplay among asset prices, fundamental values, and monetary policy shocks. We rationalize the empirical evidence with an Overlapping Generations model, able to generate a bubbly scenario with shifts in monetary policy, and where agents form beliefs over transition dynamics. By matching the VAR impulse responses, we find that procyclicality and financial instability align with high equity premia and the presence of asset price bubbles. Monetary policy tightening, by increasing real rates, is ineffective in deflating bubble episodes.

This paper develops an efficient Stein-like shrinkage estimator for estimating slope parameters under structural breaks in seemingly unrelated regression models, which is then used for forecasting. The proposed method is a weighted average of two estimators: a restricted estimator that estimates the parameters under the restriction of no break in the coefficients, and an unrestricted estimator that considers break points and estimates the parameters using the observations within each regime. It is established that the asymptotic risk of the Stein-like shrinkage estimator is smaller than that of the unrestricted estimator, which is the method typically used to estimate the slope coefficients under structural breaks. Furthermore, this paper proposes an averaging minimal mean squared error estimator in which the averaging weight is derived by minimizing its asymptotic risk. Insights from the theoretical analysis are demonstrated in Monte Carlo simulations and through an empirical example of forecasting output growth of G7 countries.

I develop and study a factor-augmented quasi-vector autoregressive (FAQVAR) model for economic policy analysis in tumultuous times. An observation-driven framework that exploits the information from the score of the model allows a maximum likelihood estimation. This multivariate FAQVAR model, which assumes a Student t error distribution, is robust to atypical observations such as the global financial crisis and the recent pandemic. The model outperforms the FAVAR moving average model because of the assumed heavy tails that capture the COVID-19 atypical data and other turbulent episodes. An empirical application to the U.S. economy assessing its monetary policy reveals that estimates and impulse responses are stable when considering the sample before and during COVID-19.

A general wisdom, since at least the work of Schmitt-Grohé and Uribe [(1997) Journal of Political Economy 105, 976–1000.], holds that a government that relies on adjusting factor income tax rates to achieve budget objective may induce aggregate instability driven by self-fulfilling expectations. This paper shows that this conventional wisdom may be overturned if the rate of adjustment of the capital income tax rate is bigger than that of the labor income tax rate. How much bigger depends on country specifics and particularly on the levels of the capital and labor income tax rates. Thus, adjustments in capital and labor income tax rates, if properly designed and implemented, can help achieve the budget objective while at the same time preempting extrinsic volatility.