This paper combines horizontal and vertical innovations to generate an endogenous growth model allowing for structural change as an endogenous phenomenon. Older technologies are continuously replaced by newer ones due to creative destruction, and new technologies appear as a result of horizontal innovations and due to the consumers' preference for variety. We assume fixed operational costs for the manufacturing sector and an endogenously defined patent price for every new technology. Every industry is profitable only for a limited period of time, making the effective time of existence of the technology endogenous and finite. We find that in such an economy endogenous structural change is the source of ongoing economic growth. Furthermore, the range of existing sectors as well as growth rates stays constant as long as the technologies are symmetric.

In this paper, we present a nonlinear model where the information and communication technology (ICT) sector is endogenized. In the model, there are two intermediate goods: a traditional good produced by capital and labor and the ICT good produced by innovative capital and skilled labor. The final good is obtained combining the two intermediate goods. The model is specified and estimated as continuous-time general disequilibrium framework. Our main results are the following. We find that the elasticity of substitution of the aggregate sector has a value intermediate between that of the ICT sector and that of the traditional sector, indicating that the input complementarity is tighter in the former than in the latter. Moreover, in all the sectors elasticities are well below 1. As for the traditional sector, whose share is predominant in the production of the final good, the input complementarity helps explain most of the labor share decline of Italian economy as a consequence of the slowdown in the growth of capital intensity. In the ICT sector, technological progress, both in the form of capital augmenting and capital bias, showed a decline over the sample period with an obvious negative consequence on the global evolution of the technical progress. The results about the dynamics of the two intermediate sectors allow to interpret the “Italian paradox” of an industrial structure marked by an increasing weight of the traditional sector and the difficulties encountered by the Italian economy in exiting from its actual worst recession since the 1930s.

I develop an assignment model to quantify, in a unified framework, the causal effects of supply and demand forces on the evolution of the college wage premium in the US economy. Specifically, I quantify the relative contributions of four different forces: (i) a within-sector non-neutral technological change, (ii) the creation of new high-skill services/sectors, (iii) polarizing product demand shifts, and (iv) shifts in the relative supply of skilled labor. The model considers endogenous human capital accumulation. I find that positive supply shifts completely explain the fall of the skill premium during the period 1970–1980. Demand forces play a major role in the post-1980 period, when the skill premium rises. Among the demand forces, the results show an increasing contribution of polarizing product demand shifts over the decades. On the other hand, the effect of the within-sector non-neutral technological change is more important in the earlier decades of the post-1980 period.

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.

The paper revisits the literature on real rigidities in New Keynesian models in the context of an economy at the zero lower bound. It identifies strategic interaction among price- and wage-setting agents in the economy as an important determinant of both optimal policy and economic dynamics in deep recessions. In particular, labor market segmentation is shown to have a significant influence on the length of the forward commitment to keep interest rates at zero, the magnitude of the fiscal policy responses as well as inflation volatility in the economy under optimal policy.

In this paper, I explore in an overlapping generations framework, a mechanism motivating a neurobiological poverty trap. Poverty causes stress and depression in individuals susceptible to depression. Poor and depressed individuals discount the future at a higher rate and invest less in the human capital of their children than mentally healthy or rich individuals. This gene–environment interaction generates a vicious cycle in which poor individuals inherit not only susceptibility to depression, but also stress and poverty. I show that a successful one-time intervention has the power to permanently eliminate the neurobiological poverty trap.

This study presents an overlapping-generations model featuring capital accumulation, collective wage-bargaining, and probabilistic voting over fiscal policy. The study characterizes a Markov-perfect political equilibrium of the voting game within and across generations, and it derives the following results. First, the greater bargaining power of unions lowers the capital growth rate and creates a positive correlation between unemployment and public debt. Second, an increase in the political power of elderly persons lowers the growth rate and shifts government expenditure from unemployed persons to elderly ones. Third, prohibiting debt finance increases the growth rate and benefits future generations; however, it worsens the state of present-day employed and unemployed persons.

Recent papers point to the problem that inflation-targeting models do not as of yet consider financial market stability that can considerably derail inflation-targeting monetary policy, implying significant nonzero crisis probabilities that could come along with large negative output and employment gaps. Credit flows and the instability of credit appear to be at the root of the financial instability problem. On the other hand, some authors recently questioned whether a too early and too strong leaning against the wind policy by central banks might have higher costs than benefits in terms of output and employment losses. In our paper, we include in an inflation targeting model a financial stabilization goal. In contrast to infinite horizon and two-period models, we propose a finite horizon model. The model is solved by using a new global solution algorithm, called Nonlinear Model Predictive Control (NMPC), exploring stabilizing/destabilizing effects of price and nonprice (credit volume) drivers of the output gap, inflation, and credit flows. We substantiate the theoretical part of the paper by approaching the subject empirically, relying to that end on a regime-switching structural vector autoregressive (VAR) for the euro area. The empirical model contains standard macroeconomic variables along with credit flows and loan interest rates, the central bank policy rate, and European Central Bank (ECB) balance sheet variables. The regime-switching feature of the model is meant to capture the state-dependent relationship between the variables, with specific nonlinearities having direct counterparts in the theoretical model. Based on a sign restriction methodology, we explore conventional and unconventional monetary policy shocks, loan supply, and demand shocks, under different regime assumptions to reveal the state-dependent effects of both interest rate and volume-based policies. The empirical results are used as guidance for the calibration of the theoretical model variants.

Real-time supervision of shifts in inflation expectations is an important issue for monetary policy makers, especially in the presence of economic uncertainty. In this paper, we elaborate tools for on-line monitoring of such shifts by extracting valuable information from noisy daily financial market data. For this purpose, first, we suggest a new risk adjustment for observable proxies of medium and long run inflation expectations assuming that the latter are well-anchored. Second, we propose an econometric methodology for sequential monitoring of level changes in the associated proxies at daily frequency. Our empirical evidence shows that the on-line surveillance of risk adjusted US forward breakeven inflation rates by means of the cumulative sum (CUSUM) detector appears to be helpful to extract timely signals on potential shifts. In particular, the obtained signals indicate important turning points in market-based measures of inflation expectations, which also tend to materialize in lower frequency experts' surveys.

This paper studies the growth and welfare effects of macroprudential regulation in an overlapping generations model of endogenous growth with banking and agency costs. Indivisible investment projects combine with informational imperfections to create a double moral hazard problem à la Holmström–Tirole and a role for bank monitoring. When the optimal monitoring intensity is endogenously determined, an increase in the required reserve ratio (motivated by systemic risk considerations) has conflicting effects on investment and growth. On one hand, requiring banks to put away a fraction of the deposits that they receive reduces the supply of loanable funds. On the other, a higher required ratio raises incentives to save and mitigates banks' incentives to monitor, thereby lowering monitoring costs and freeing up resources to increase lending. In addition, it may mitigate the systemic risk externality associated with excessive leverage. This trade-off can be internalized by choosing the required reserve ratio that maximizes growth and welfare. However, the risk of disintermediation means that in practice financial supervision may also need to be strengthened, and the perimeter of regulation broadened, if the optimal ratio is relatively high.

This paper builds a theoretical framework for studying migration and education in developing countries. Migration and education decisions are affected by migrants' wealth constraints. Technology and migration costs determine the pattern of migration through level of education, income, and wealth inequality. The model predicts that in the first stages of technological development, migration rates increase, as does economic inequality over time, for high migration costs. At more advanced stages of development, migration rates and wealth inequality decline. I show that these predictions are in line with the data.

This paper examines China's optimal fiscal policy in a general equilibrium model, in which the government finances its budget through both a special instrument, an implicit tax on the residential land, and a typical conventional instrument, the value-added tax (VAT). By solving a Ramsey problem, we find that (i) the optimal policy suggests a much lower land tax rate than the existing rate in China, and (ii) a substantial part of debt stabilization should come through an adjustment in the VAT rate, instead of relying on land financing. Switching from the existing policy to the Ramsey policy generates significant welfare gains.

First generation immigrants to the United States have higher fertility rates than natives. This paper analyzes to what extent this factor provides political support for immigration, using an overlapping generation model with production and capital accumulation. In this setting, immigration represents a dynamic trade-off for native workers as more immigrants decrease current wages but increase the future return on their savings. We find that immigrant fertility has surprisingly strong effects on voter incentives, especially when there is persistence in the political process. If fertility rates are sufficiently high, native workers support immigration. Persistence, either due to inertia induced by frictions in the legal system or through expectational linkages, significantly magnifies the effects. Entry of immigrants with high fertility has redistributive impacts across generations similar to pay-as-you-go social security: initial generations are net winners, whereas later generations are net losers.

Two essential imperfections determine the degree of the financial sector development in an economy: lending frictions, which constrain the ability to extend loans to borrowers; and, trading frictions, which constrain the trading of these loans in secondary markets. I develop a dynamic general equilibrium model where long-term investment is the engine of growth to study macroeconomic consequences of financial development. In the model, long-term loans are extended to entrepreneurs in a primary market and then traded in a secondary market among financiers. In competitive equilibria, reductions in either lending or trading frictions enlarge the financial sector. Although financial deepening through low-cost lending is always welfare improving, financial deepening stimulated by low-cost trading could be detrimental to the society. I illustrate that a model qualitatively consistent with the U.S. financial development episode of the last 30 years should exhibit disproportionately large reductions in trading frictions relative to lending frictions.

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.

The recent global financial crisis and the subsequent recession have revitalized the discussion on causal interactions between financial and economic sectors. In this study, I apply the financial stress and the national activity indices–respectively developed by Federal Reserve Banks of Kansas City and Chicago–to investigate the impact of financial uncertainty on an overall economic performance. I examine nonlinear dynamics in a vector smooth transition autoregressive framework, and illustrate regime-dependent asymmetries in the financial and economic indices using the generalized impulse-response functions. The results reveal more amplified dynamics during the stressed conditions. I further evaluate benefits of nonlinear modeling in an out-of-sample setting. The forecasting exercise brings out the important advantages that nonlinear modeling provides in the identification of the causal effect of financial instability on overall economic performance.

We study the exchange rate effects of monetary policy in a balanced macroeconometric two-country model for the United States and United Kingdom. In contrast to the empirical literature, which consistently treats the domestic and foreign countries unequally in the modeling process, we consider full model feedback, allowing for a thorough analysis of the system dynamics. The problem of model dimensionality is tackled by invoking the approach by Aoki (1981). Assuming country symmetry in the long run allows to decouple the two-country macrodynamics of country averages and differences such that the cointegration analysis can be applied to smaller systems. Second, the econometric modeling is general-to-specific, a graph-theoretic approach for the contemporaneous effects combined with automatic general-to-specific model selection. We find delayed overshooting of the exchange rate in the case of a Bank of England monetary shock but instantaneous response to a Fed shock. Altogether the response is more pronounced in the former case.

There are suggestions that increased uncertainty makes fiscal policy temporarily less effective. In this paper, I examine the relationship between business uncertainty and fiscal policy effectiveness in Germany. I use measures of business uncertainty that are derived from the firm-level data of the Ifo Business Climate Survey and interact them with the parameters of a structural vector autoregression to produce state-dependent spending multipliers. The impact of increased uncertainty on the spending multiplier is generally small and often statistically not significant in the short run. By contrast, I obtain a significant positive impact on the long-run multiplier. These baseline results are supported by a variety of robustness checks and specifications.

We consider a firm with assets-in-place and a growth option. There is a funding gap for the expansion investment, which is covered by entering into an equity-for-guarantee swap or fee-for-guarantee swap. We explicitly derive all contingent claim prices with the pricing and timing of the growth option taking business cycle and debt maturity into account. For short-term loan, we produce an explanation why Chinese government suggests that guarantee fee rate should be approximately the fraction 50% of the interest rate of bank loans with the same maturity, but for long-term debt, we show this fraction is too small.

Empirical studies on the trade-environment nexus that use panel data face two simultaneous challenges. One is associated with the potential presence of unobserved cross-country heterogeneity, while the other is due to the use of aggregate data. In this paper, we apply both the dynamic fixed effects and iterative empirical Bayes estimators to show first that when country heterogeneity is accurately accounted for in the estimation, it is possible to obtain significant impacts of trade variables on the environment, even though we use aggregate data. Second, using both the empirical Bayes parameter estimates and indicators of stringency of environmental regulations, we show that at low levels of stringency, the probability of having pollution-intensive foreign direct investments (FDIs) increases with a decrease in stringency. However, at high levels of regulatory stringency, more stringent regulations may lead to more pollution-intensive FDIs. This implies that pollution havens may exist only if environmental regulations are very lax or nonexistent.