Financial Frictions, Propagation of Shocks, and Macroeconomic Volatility

I study the evolution of aggregate volatility in the US during the postwar period by assessing the relative role played by financial shocks, technological progress, and changes in the financial system. Balance-sheet variables of firms have been characterized by greater volatility since the early 1970s. This Financial Immoderation has coexisted with the so-called Great Moderation, which refers to the slowdown in volatility of real and nominal variables since the mid 1980s. In the second chapter, I study the moderation in real variables calibrating a real business cycle model with two technology shocks. I consider several statistical specifications for technological progress. A deterministic trend model outperforms in accounting for volatilities, but a stochastic trend model accounts better for the correlation structure of the data. In the third chapter, I account for the divergent patterns in volatility analyzing the role played by financial factors. To do so, I estimate a DSGE model including financial rigidities, allowing for structural breaks in a subset of parameters. I conclude that the Financial Immoderation is driven by larger financial shocks and that the estimated reduction in the size of the financial accelerator in the mid 1980s accounts for 30% of the decline in the volatilities of investment growth and the nominal interest rate. In the last chapter, I focus on analyzing financial shocks. Using the estimation output, I obtain that the contribution of financial shocks to the variance of investment is increasing over time, reducing the relative importance of the investment-specific technology shock. The estimated reduction in the level of financial rigidities has a signifficant impact on the model implied propagation dynamics. Given that the model implies a negative response upon impact of consumption in response to a positive business wealth shock, I empirically characterize the effects of such a financial shock on consumption using sign restrictions. I conclude that documenting the effects on consumption is not a trivial matter since the results vary signifficantly depending on the variables used to measure business wealth and the cost of external borrowing

Technology Shocks, Statistical Models, and The Great Moderation

In this paper we compare the cyclical features implied by an RBC model with two technology shocks under several statistical specifications for the stochastic processes governing technological change. We conclude that while a trend-stationary model accounts better for the observed volatilities, a difference-stationary model does a relatively better job of accounting for the correlation of the variables of interest with output. We also explore some counterfactuals to assess the ability of our model to replicate the volatility slowdown of the mid 1980s. First, we conclude that the stochastic growth model outperforms the deterministic growth model in accounting for the Great Moderation. Finally, we obtain that even though the neutral technology shock is the main driving force in the volatility slowdown, allowing for a larger financial flexibility in the form of a smaller volatility for the investment-specific innovation improves the ability of our model to account for the magnitude of the Great Moderation.Business Cycle; Aggregate fluctuations; Technology Shocks; Unit Roots

Technology Shocks, Statistical Models, and The Great Moderation

In this paper we compare the cyclical features implied by an RBC model with two technology shocks under several statistical specifications for the stochastic processes governing technological change. We conclude that while a trend-stationary model accounts better for the observed volatilities, a difference-stationary model does a relatively better job of accounting for the correlation of the variables of interest with output. We also explore some counterfactuals to assess the ability of our model to replicate the volatility slowdown of the mid 1980s. First, we conclude that the stochastic growth model outperforms the deterministic growth model in accounting for the Great Moderation. Finally, we obtain that even though the neutral technology shock is the main driving force in the volatility slowdown, allowing for a larger financial flexibility in the form of a smaller volatility for the investment-specific innovation improves the ability of our model to account for the magnitude of the Great Moderation

Technology Shocks, Statistical Models, and The Great Moderation

In this paper we compare the cyclical features implied by an RBC model with two technology shocks under several statistical specifications for the stochastic processes governing technological change. We conclude that while a trend-stationary model accounts better for the observed volatilities, a difference-stationary model does a relatively better job of accounting for the correlation of the variables of interest with output. We also explore some counterfactuals to assess the ability of our model to replicate the volatility slowdown of the mid 1980s. First, we conclude that the stochastic growth model outperforms the deterministic growth model in accounting for the Great Moderation. Finally, we obtain that even though the neutral technology shock is the main driving force in the volatility slowdown, allowing for a larger financial flexibility in the form of a smaller volatility for the investment-specific innovation improves the ability of our model to account for the magnitude of the Great Moderation

Methods versus substance: measuring the effects of technology shocks on hours

In this paper, we employ both calibration and modern (Bayesian) estimation methods to assess the role of neutral and investment-specific technology shocks in generating fluctuations in hours. Using a neoclassical stochastic growth model, we show how answers are shaped by the identification strategies and not by the statistical approaches. The crucial parameter is the labor supply elasticity. Both a calibration procedure that uses modern assessments of the Frisch elasticity and the estimation procedures result in technology shocks accounting for 2% to 9% of the variation in hours worked in the data. We infer that we should be talking more about identification and less about the choice of particular quantitative approaches.Business cycles ; Technology - Economic aspects

Households´ balance sheets and the effect of fiscal policy

En este trabajo identificamos seis tipos de hogares en Estados Unidos en función de la composición de su balance financiero en el Panel Survey of Income Dynamics. Desde 1999 se observa una acusada disminución en la proporción de hogares ahorradores y un aumento en la proporción de hogares endeudados, en particular aquellos que presentan una riqueza neta negativa. Utilizando como marco teórico un modelo neokeynesiano con estos seis tipos de hogares, así como con imperfecciones en los mercados de crédito y de trabajo, exploramos cómo los cambios en la distribución de los hogares en función de su balance afectan la transmisión de los shocks del gasto público al consumo agregado, al empleo y al PIB. Encontramos que la proporción de hogares en la cola izquierda de la distribución de la riqueza desempeña un papel clave en la propensión marginal agregada al consumo, la magnitud de los multiplicadores fiscales y las consecuencias distributivas de los shocks del gasto público. Si bien el valor de los multiplicadores de producción y consumo está correlacionado positivamente con la proporción de hogares con riqueza negativa, el tamaño del multiplicador de empleo decrece con la proporción de este tipo de consumidoresUsing households’ balance sheet composition in the Panel Survey of Income Dynamics, we identify six household types. Since 1999, there has been a decline in the share of patient households and an increase in the share of impatient households with negative wealth. Using a six-agent New Keynesian model with search and matching frictions, we explore how changes in households’ shares affect the transmission of government spending shocks. We show that the relative share of households in the left tail of the wealth distribution plays a key role in the aggregate marginal propensity to consume, the magnitude of fiscal multipliers, and the distributional consequences of government spending shocks. While the output and consumption multipliers are positively correlated with the share of households with negative wealth, the size of the employment multiplier is negatively correlated. Moreover, our calibrated model delivers jobless fiscal expansion

Methods versus Substance: Measuring the Effects of Technology Shocks on Hours

In this paper, we employ both calibration and modern (Bayesian) estimation methods to assess the role of neutral and investment-specific technology shocks in generating fluctuations in hours. Using a neoclassical stochastic growth model, we show how answers are shaped by the identification strategies and not by the statistical approaches. The crucial parameter is the labor supply elasticity. Both a calibration procedure that uses modern assessments of the Frisch elasticity and the estimation procedures result in technology shocks accounting for 2% to 9% of the variation in hours worked in the data. We infer that we should be talking more about identification and less about the choice of particular quantitative approaches.

Households’ balance sheets and the effect of fiscal policy

Using households' balance-sheet composition in the Panel Survey of Income Dynamics, we identify six household types. Since 1999, there has been a decline in the share of patient households and an increase in the share of impatient households with negative wealth. Using a six-agent New Keynesian model with search and matching frictions, we explore how changes in households' shares affect the transmission of government spending shocks. We show that the relative share of households in the left tail of the wealth distribution plays a key role in the aggregate marginal propensity to consume, the magnitude of fiscal multipliers, and the distributional consequences of government spending shocks. While the output and consumption multipliers are positively correlated with the share of households with negative wealth, the size of the employment multiplier is negatively correlated. Moreover, our calibrated model can deliver jobless fiscal expansions

Methods for Computing Marginal Data Densities from the Gibbs Output

We introduce two new methods for estimating the Marginal Data Density (MDD) from the Gibbs output, which are based on exploiting the analytical tractability condition. Such a condition requires that some parameter blocks can be analytically integrated out from the conditional posterior densities. Our estimators are applicable to densely parameterized time series models such as VARs or DFMs. An empirical application to six-variate VAR models shows that the bias of a fully computational estimator is sufficiently large to distort the implied model rankings. One estimator is fast enough to make multiple computations of MDDs in densely parameterized models feasible