Our growth model explores the complex relationship between income, obesity, and changes in exercise-related behavior. Combining Becker’s theory of time allocation (The Economic Journal 75(299), 493–517, 1965) with Veblen’s theory of conspicuous leisure (The Theory of the Leisure Class, 1st ed. New York: Macmillan, 1899), we determine conditions for dynamic and static obesity Kuznets curves. Considering food consumption and exercise choices, we show that dynamic and static Kuznets curves result from the rising opportunity cost of exercise and peer influence, both increasing with income. Focusing on calorie expenditure, we investigate the rise and slowdown in obesity prevalence in the USA and the correlation between obesity and income per worker. Our numerical simulations indicate that, as the economy grows, exercise choices slow down the rise in obesity prevalence but do not generate a dynamic Kuznets curve in the USA. By contrast, they generate a static Kuznets curve for a population cross section. We discuss policy implications of our findings.

We assess the responses of output and inflation to monetary policy shocks in the context of a Bayesian, monetary structural vector autoregressive model. We allow money supply and leverage measures to enter into the interest rate policy rule and use an identification approach that is by construction devoid of any price puzzles. We provide a comprehensive comparison between monetary policy shocks under a policy regime that follows a standard Taylor rule and those that augment the standard reaction function of the central bank with measures of leverage and the money supply. We find that contractionary monetary policy is more pronounced and persistent when the reaction function of the central bank is augmented with measures of money and leverage than when the reaction function follows a typical Taylor rule. Our results support the use and inclusion of monetary aggregates in monetary policy and business cycle analysis.

This paper proposes the volatility of sovereign credit default swaps (CDS) as a measurement of economic uncertainty. Sovereign CDS provide protection against losses from sovereign defaults and are traded for almost all countries by the world’s largest financial institutions. The premium for protection, the so-called CDS spread, depends on a country’s economic conditions and provides an outside view from global financial institutions. Our empirical results show that the volatility of sovereign CDS spreads contains information about economic uncertainty. For a broad panel of 16 countries, we find that sovereign CDS volatility shares directional information with popular news-based economic policy uncertainty (EPU) indices. Using Bayesian panel vector autoregressions, we find similar responses of output and unemployment to shocks in CDS volatility, equity volatility, and EPU. Our results further suggest that sovereign CDS volatility primarily reflects economic and financial uncertainty rather than political uncertainty.

Over the past decade, the most salient changes in macroeconomic conditions in developed economies have included rising government debt and population aging, which are strongly correlated with each other. This paper investigates fiscal multipliers by disentangling the effects of population aging from those of government debt. Our analysis, which uses heterogeneous panel data from 24 OECD economies, shows that while fiscal policy is ineffective for economies with high-debt levels, it is effective for economies with low-debt levels. Furthermore, the estimation results reveal that fiscal policy is ineffective for aged economies, regardless of the level of government debt. However, for nonaged economies, while fiscal policy leads to negative effects on output in times of high debt, its positive effects are more pronounced in times of low debt. Our results suggest that, for the effective implementation of fiscal stimulus policies, policy-based stimulation of employment in the labor market is essential.

The canonical income process, including autoregressive, transitory, and fixed effect components, is routinely used in macro and labor economics. We provide a guide for its estimation using quasidifferences, cataloging biases in the estimated parameters for various $N$, $T$, initial conditions, and weighting schemes. Using Danish administrative data on male earnings, estimation in quasidifferences yields divergent estimates of the autoregressive parameter for different weighting schemes, which conforms to our simulation results when the variance of transitory shocks is higher than that of persistent shocks, true persistence is high, and the persistent component’s variance in the first sample year is nonzero. We further apply quasidifferences to the data from a calibrated lifecycle model and find significant biases in the persistence of shocks and their insurance. Estimation of the income process using quasidifferences is reliable only when the variance of persistent shocks is higher than that of transitory shocks and the moments are equally weighted.

Homeownership rates differ widely across European countries. We document that part of this variation is driven by differences in the fraction of adults co-residing with their parents. Comparing Germany and Italy, we show that in contrast to homeownership rates per household, homeownership rates per individual are very similar during the first part of the life cycle. To understand these patterns, we build an overlapping-generations model where individuals face uninsurable income risk and make consumption-saving and housing tenure decisions. We embed an explicit intergenerational link between children and parents to capture the three-way trade-off between owning, renting, and co-residing. Calibrating the model to Germany we explore the role of income profiles, housing policies, and the taste for independence and show that a combination of these factors goes a long way in explaining the differential life-cycle patterns of living arrangements between the two countries.

In some classes of macroeconomic models with financial frictions, an adverse financial shock successfully explains a decrease in real activity but simultaneously induces a stock price boom. The latter theoretical result is not consistent with data from actual financial crises. This study aims to provide a theoretical explanation for both prolonged recessions and stock price declines. I develop a simple macroeconomic model featuring a banking sector, financial frictions, and R&D-based endogenous growth. Both the analytical and numerical investigations show that endogenous R&D investment and a shock hindering banks’ financial intermediary function can be key to generating both a prolonged recession and a drop in firms’ stock prices.

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.

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 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.

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.

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.

This study develops a new dynamic general equilibrium model to explore the role of people’s love of novelty as a cultural preference in innovation and innovation-based growth. The model considers (a) an infinitely lived representative consumer who has standard love-of-variety preferences for differentiated products and additional love-of-novelty preferences for new products and (b) technological progress driven by two costly and time-consuming innovation activities, new product development and existing product development. We demonstrate that consumers’ love of novelty is a source of innovation-based growth, wherein economies with a moderate love of novelty can achieve innovation and long-run growth through endogenous cycles between periods in which new product development is active and those in which existing product development is active. However, if love of novelty preference is too weak or too strong, the economy is caught in an underdevelopment trap with less innovation and no long-run growth. We also provide some suggestive empirical evidence that supports our theoretical predictions.

We extract dynamic conditional factor premiums from the Fama-French factor model and find that most anomalies disappear after one accounts for time variation in these premiums. Vector autoregression evidence shows that mutual causation between dynamic conditional alphas and macroeconomic surprises serves as a core qualifying condition for fundamental factor selection. This economic insight is an incremental step toward drawing a distinction between rational risk and behavioral mispricing models. To the extent that dynamic conditional alphas can reveal the marginal investor’s fundamental news and expectations about the cross-section of average asset returns, our economic insight helps enrich macroeconomic asset return prediction.

This study develops a Malthusian model for the evolution of human society from hunting-gathering to agriculture and from agriculture to industrial production. Human society evolves across these stages as the population grows. However, under endogenous population growth, the population may stop growing at any stage. If it fails to reach the first threshold, the population remains as hunter-gatherers. If it reaches the first threshold, an agricultural society emerges. Then, if the population fails to reach the industrial threshold, it remains in an agricultural Malthusian trap without experiencing industrialization. Interestingly, high agricultural productivity triggers not only the Neolithic Revolution but also the subsequent industrialization. Using cross-country data to test this result, we employ an index of prehistoric biogeographic conditions that affect agricultural productivity as an instrument for the timing of transitions to agriculture and find that an earlier transition to agriculture has a positive effect on industrialization in the modern era.

We examine a theoretical model of liquidity with three assets—money, government bonds, and equity—that are used for transaction purposes. Money and bonds complement each other in the payment system. The liquidity of equity is derived as an equilibrium outcome. Liquidity cycles arise from the loss of confidence of the traders in the liquidity of the system. Both open market operations and credit easing play a beneficial role for different purposes.

Economists have long been interested in the effect of business sentiment on economic activity. Using text analysis, I construct a new company-level indicator of sentiment based on the net balance of positive and negative words in Australian company disclosures. Company-level investment is very sensitive to changes in this corporate sentiment indicator, even controlling for fundamentals, such as Tobin’s Q, as well as controlling for measures of company-level uncertainty.

The high sensitivity of investment to sentiment could be due to several mechanisms. It could be because of animal spirits among managers or because of sentiment proxies for private information held by managers about company prospects. Overall, I find mixed evidence of the underlying causal mechanism. The effect of sentiment on investment is relatively persistent, which is consistent with managers having private information about company fundamentals. But the sensitivity of investment to sentiment is not any stronger at opaque companies in which managers are likely to be better informed than investors. Further, investment is sensitive to sentiment even when investors have an information advantage over managers by lagging the sentiment indicator by a year. Overall, the sensitivity of investment to sentiment appears to reflect both animal spirits and fundamentals.

Corporate investment has been weak since the global financial crisis (GFC) and demand-side factors, such as lower sales growth, explain more than half of this weakness. Low sentiment and heightened uncertainty weighed on investment during the GFC but have been less important factors since then.

The retirement of old workers increased during the COVID-19 pandemic, and health concerns are considered to be a critical factor. To understand the effect of pure health concerns during the pandemic, we analyze the impact of the aggregate health shock on retirement decisions using a life cycle model. The aggregate health shock changes the economy from the normal state to the pandemic state, where the probability of adverse idiosyncratic health shock increases, especially if agents are working. Simulation results suggest that the shock accelerates the retirement of agents aged over 60. The increase in retirement is significant even though the shock is expected to be temporary. Also, the effect hinges on the assumption that working poses a greater risk of receiving a negative health shock than retiring. Even accounting for the large income and wealth changes that US households experienced in 2020, a counterfactual experiment suggests that the aggregate health shock plays a prominent role in increasing retirement.

The US Social Security program was created in part as an explicit response to the Great Depression. We evaluate the performance of Social Security as a protective safety net against a rare episode of sudden and significant loss of private wealth such as the Great Depression. We construct a model in which a rare event causes a shock to wealth at an unknown time. How well does Social Security function as a safety net against such risk? The answer depends critically on whether households optimize in the face of this risk. If the household has full information on the distribution of rare event risk and solves a dynamic stochastic problem to hedge this risk, then Social Security is unnecessary along this dimension. Alternatively, if the household does not account for rare event risk in its financial planning, then Social Security can provide very large welfare gains as a safety net.