Most papers studying the impacts of technology adoption on income trajectories assume that firms adopt frontier technologies when available. If these technologies are skill intensive, less-developed economies may fail in successfully implementing them and may become trapped in a low-growth equilibrium. Within a Schumpeterian growth model, we show that differences in adoption barriers and incentives to the accumulation of skills produce differences in the technology level that is optimal to adopt. If the economy is not overly distorted, copying nonfrontier technologies helps compensating for the scarcity of skills and increases the likelihood of copying frontier technologies in the long run. If distortions are significant, it may be optimal to copy less-advanced technologies even in the long run. If adoption is not a skill-intensive activity, then copying frontier technologies is always optimal; all economies achieve a high-growth equilibrium and only income differences persist in the long run.

This paper uses a global vector autoregressive (GVAR) model to analyze the relationship between foreign direct investment (FDI) inflows and output dynamics in a multicountry context. The GVAR model enables us to make two important contributions: First, to model international linkages among a large number of countries, which is a key asset given the diversity of countries involved, and second, to model foreign direct investment and output dynamics jointly. The country-specific small-dimensional vector autoregressive submodels are estimated utilizing a Bayesian version of the model coupled with stochastic search variable selection priors to account for model uncertainty. Using a sample of 15 emerging and advanced economies over the period 1998:Q1–2012:Q4, we find that US outbound FDI exerts a positive long-term effect on output. Asian and Latin American economies tend to react faster and also stronger than Western European countries. Forecast error variance decompositions indicate that FDI plays a prominent role in explaining gross domestic product fluctuations, especially in emerging market economies.

This paper tests the ability of New Keynesian models to match the data regarding a key channel for monetary transmission: the dynamic interactions between macroeconomic variables and their corresponding expectations. We exploit survey expectations data and adopt a dynamic stochastic general equilibrium (DSGE)-VAR approach to assess the extent and sources of model misspecification. The results point to serious misspecification in the expectations-formation side of the DSGE model. The rational expectations hypothesis is primarily responsible for the model's failure to capture the co-movements between observed macroeconomic expectations and realizations. Alternative models of expectations formation help partially reconcile the New Keynesian model with the data.

Under adaptive learning, recursive algorithms are proposed to represent how agents update their beliefs over time. For applied purposes, these algorithms require initial estimates of agents perceived law of motion. Obtaining appropriate initial estimates can become prohibitive within the usual data availability restrictions of macroeconomics. To circumvent this issue, we propose a new smoothing-based initialization routine that optimizes the use of a training sample of data to obtain initials consistent with the statistical properties of the learning algorithm. Our method is generically formulated to cover different specifications of the learning mechanism, such as the least-squares and the stochastic gradient algorithms. Using simulations, we show that our method is able to speed up the convergence of initial estimates in exchange for a higher computational cost.

In this paper, I analyze the ability of monetary policy to stabilize both the macroeconomy and financial markets under two different scenarios: fixed- and variable-rate mortgages. I develop and solve a new Keynesian dynamic stochastic general equilibrium model (DSGE) that features a housing market and a group of constrained individuals who need housing collateral to obtain loans. A given share of constrained households borrows at a variable rate, whereas the rest borrow at a fixed rate. I consider two alternative ways of introducing a macroprudential approach to enhancing financial stability: one in which monetary policy, using the interest rate as an instrument, responds to credit growth; and a second one in which the macroprudential instrument is instead the loan-to-value ratio (LTV). The results show that when rates are variable, a countercyclical LTV rule performs better in stabilizing financial markets than monetary policy. However, when rates are fixed, even though monetary policy is less effective in stabilizing the macroeconomy, it does a good job in promoting financial stability.

In an incomplete markets economy with sunspots, the Pareto-criterion cannot rank sunspot equilibria of different levels of excess price-level volatility. Therefore, I propose a measure of excess volatility cost in terms of a period-0 endowment good. Ex-ante endowment subsidies are provided, in theory, to each consumer, so that the resulting equilibrium allocation of the higher volatility is Pareto-equivalent to the original benchmark equilibrium with a lower volatility level. The aggregate volatility cost is computed as the sum of all consumers' subsidies. Focusing on local analysis that considers small variations around a given volatility level, I show that the aggregate cost strictly increases in volatility even though each individual cost does not necessarily have this property.

We present a stylized model that illustrates how interbank trading can reduce the sensitivity of lending to entrepreneurs' net worth, thus affecting the transmission mechanism of monetary policy through the credit channel. We build a model-consistent measure of interconnectedness and document that, in the United States, this measure has increased substantially during the period 1952–2016. Finally, interacting the measure of interconnectedness in a structural vector autoregression and a factor-augmented vector autoregression for the US economy, we find that the impulse responses of several real and financial variables to monetary policy shocks are dampened as interconnectedness increases. We confirm the same result using data from 10 Euro area countries for the period 1999–2016.

This paper examines the relationship between environmental policy and growth when green preferences are endogenously determined by education and pollution. We consider an environmental policy in which the government implements a tax on pollution and recycles the revenue to fund pollution abatement activities and/or an education subsidy (influencing green behaviors). When the sensitivity of agents' environmental preferences to pollution and human capital is high, the economy can converge to a balanced growth path equilibrium with damped oscillations. We show that this environmental policy can both remove the oscillations, associated with intergenerational inequalities, and enhance the long-term growth rate. However, this solution requires that the revenue from the tax rate must be allocated to education and direct environmental protection simultaneously. We demonstrate that this type of mixed-instrument environment policy is an effective way to address environmental and economic issues in both the short and the long run.

This paper investigates the effect of financial shocks using a general equilibrium model that links the firm's flows of financing with labor market variables. The results show that financial shocks have sizeable effects on debt, dividend payout, and wages. Shocks to the job destruction rate are important in explaining fluctuations in unemployment. The analysis also investigates the underlying driving forces of some key comovements in the data and finds shocks to the job destructions rate important.

The initial government debt-to-gross domestic product (GDP) ratio and the government's commitment play a pivotal role in determining the welfare-optimal speed of fiscal consolidation in the management of a debt crisis. Under commitment, for low or moderate initial government debt-to-GDP ratios, the optimal consolidation is very slow. A faster pace is optimal when the economy starts from a high level of public debt implying high sovereign risk premia, unless these are suppressed via a bailout by official creditors. Under discretion, the cost of not being able to commit is reflected into a quick consolidation of government debt. Simple monetary–fiscal rules with passive fiscal policy, designed for an environment with “normal shocks,” perform reasonably well in mimicking the Ramsey-optimal response to one-off government debt shocks. When the government can issue also long-term bonds—under commitment—the optimal debt consolidation pace is slower than in the case of short-term bonds only, and entails an increase in the ratio between long- and short-term bonds.

This paper explores how different credit market and banking regulations affect business fluctuations. Capital adequacy- and reserve requirements are analyzed for their effect on the risk of severe downturns. We develop an agent-based macroeconomic model in which financial contagion is transmitted through balance sheets in an endogenous firm-bank network, which incorporates firm bankruptcy and heterogeneity among banks to capture the fact that contagion effects are bank specific. Using concepts from the empirical literature to identify amplitude and duration of recessions and expansions, we show that more stringent liquidity regulations are best to dampen output fluctuations and prevent severe downturns. Under such regulations, both leverage along expansions and amplitude of recessions become smaller. More stringent capital requirements induce larger output fluctuations and lead to deeper, more fragile recessions. This indicates that the capital adequacy requirement is procyclical and therefore not advisable as a measure to prevent financial contagion.

This paper incorporates an education signaling mechanism into a dynamic model of production and asks if “higher education as a signal” helps explain the simultaneous increase in the supply and price of skilled relative to unskilled labor in the United States since 1980. The key mechanism is that if college degrees serve as a signal of unobservable talent and talent is productive at the workplace, then improved access to college will enable a higher fraction of the population to signal talent by completing college, resulting in degrees being a better signal about talent and a widening skill premium. When I assess the contribution of signaling in the model calibrated to the US economy from 1980 to 2003, I find that about 10% of the increase in the skill premium can be attributed to the signaling mechanism, after adjusting for the potential decline in the quality of college graduates.

In a recent book, Roger Farmer offers a quick but exhaustive rundown of the research agenda that he drove forward over the last 10 years with the aim to offer a novel microfoundation for Keynesian macroeconomics and—as a by-product—providing practical remedies to prevent financial crises, reduce unemployment, and ensure prosperity for all. In that work, the Farmerian arguments question the conventional visions underlying the Neo-Classical and New Keynesian paradigms and the addressed topics cover relevant theoretical, empirical, and policy issues that have been widely debated after the Great Recession of 2007–2009.

We introduce automation into a standard model of capital accumulation and show that (i) there is the possibility of perpetual growth, even in the absence of technological progress; (ii) the long-run economic growth rate declines with population growth, which is consistent with the available empirical evidence; (iii) there is a unique share of savings diverted to automation that maximizes long-run growth; and (iv) automation explains around 14% of the observed decline of the labor share over the last decades in the United States.

We use the longest span data that have ever been studied before (from 1870 to 2014) to investigate the relationship between the price of oil and the level of economic activity in the United States. In the context of a bivariate (identified) structural generalized autoregressive conditional heteroscedasticity (GARCH)-in-Mean VAR in real output growth and the change in the real price of oil, we find that uncertainty about oil prices has had a negative and significant effect on real output. We also find that the responses of real output growth to positive and negative shocks are not very informative of whether they are symmetric or asymmetric, and that accounting for oil price uncertainty tends to amplify the negative dynamic response of real output growth to unfavorable (positive) oil price shocks.