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Although policymakers and practitioners are particularly interested in dynamic stochastic general equilibrium (DSGE) models, these are typically too stylized to be applied directly to the data and often yield weak prediction results. Very recently, hybrid DSGE models have become popular for dealing with some of the model misspecifications. Major advances in estimation methodology could allow these models to outperform well-known time series models and effectively deal with more complex real-world problems as richer sources of data become available. In this study we introduce a Bayesian approach to estimate a novel factor augmented DSGE model that extends the model of Consolo et al. [Consolo, A., Favero, C.A., and Paccagnini, A., 2009. On the Statistical Identification of DSGE Models. Journal of Econometrics, 150, 99–115]. We perform a comparative predictive evaluation of point and density forecasts for many different specifications of estimated DSGE models and various classes of VAR models, using datasets from the US economy including real-time data. Simple and hybrid DSGE models are implemented, such as DSGE-VAR and tested against standard, Bayesian and factor augmented VARs. The results can be useful for macro-forecasting and monetary policy analysis.

This paper reconsiders the popular Brock and Hommes [Brock, W. A., and C. H. Hommes. 1997. “A Rational Route to Randomness.” Econometrica 65: 1059–1096.] framework for the study of the evolution of agents’ choices when different behavioural strategies are available. In particular, we model the intensity of choice as an endogenous variable and not a parameter as it is commonly treated in the literature. We make use of the maximum entropy inference to obtain an analogous exponential type probability function for strategies, with the intensity of choice varying over time according to the performance of each strategy. We test this approach on an existing asset pricing model, highlighting the effects on the system of the different switching pattern that originate in the endogenous switching intensity.

We examine both the degree and the structural stability of inflation persistence at different quantiles of the conditional inflation distribution. Previous research focused exclusively on persistence at the conditional mean of the inflation rate. As economic theory provides reasons for inflation persistence to differ across conditional quantiles, this is a potentially severe constraint. Conventional studies of inflation persistence cannot identify changes in persistence at selected quantiles that leave persistence at the mean of the distribution unchanged. Based on post-war US data we indeed find robust evidence for a structural break in persistence at all quantiles of the inflation process in the early 1980s. While prior to the 1980s inflation was not mean reverting, quantile autoregression based unit root tests suggest that since the end of the Volcker disinflation the unit root can be rejected at every quantile of the conditional inflation distribution.

We investigate how the entire term structure of interest rates is influenced by changes in monetary policy regimes. To do so, we develop and estimate an arbitrage-free dynamic term-structure model which accounts for regime shifts in monetary policy and price of risk. Our results for US data from 1985 through 2008 indicate that ( i ) the Federal Reserve’s reaction to inflation has changed over time, switching between “active” and “passive” monetary policy regimes; ( ii ) on average, the term spread in the “active” regime was wider than in the “passive” regime; and ( iii ) the yields in the “active” regime were considerably more volatile than in the “passive” regime. The wider term spread in the “active” regime reflects higher term premia associated with a more sensitive response of the short-term interest rate to inflation. Additionally, our analysis suggests that the model fit improves substantially when we account for regime switching in monetary policy and price of risk.

In this paper, we introduce endogenous technological change through R&D expenditure on labor-augmenting innovation in the cyclical growth model by Goodwin (Goodwin, R. 1967. “A Growth Cycle.” In Socialism, Capitalism, and Economic Growth , edited by Carl Feinstein, Cambridge, UK: Cambridge University Press.). Innovation is a costly, forward-looking process financed out of profits, and pursued by owners of capital stock (capitalists) in order to foster labor productivity and save on labor requirements. Our main findings are: (i) Goodwin-type distributive cycles arise even with dynamic optimization, but (ii) endogenous technical change has a dampening effect on economic fluctuations; (iii) steady state per capita growth, income distribution and employment rate are endogenous, and depend on the capitalists’ discount rate, the institutional variables regulating the labor market, and policy variables such as subsidies to R&D activity. Implementing the model numerically to match long run data for the US, we show that: (iv) an increase in the capitalists’ discount rate lowers per-capita growth, the employment rate and the labor share; (v) an increase in workers’ bargaining strength moderately raises the labor share and moderately decreases per-capita growth, while sharply reducing employment: quarterly US fluctuations (1948–2006) in employment and the labor share seem to support this result; (vi) a balanced budget increase in the R&D subsidy also fosters per-capita growth at the expenses of the labor share, even though the corresponding variations might be small.

This paper studies whether the relationship between monetary policy shocks of different size and output is better described by threshold autoregressive (TAR) or smooth transition autoregressive (STAR) dynamics. Using a Bayesian framework, a TAR process and a STAR process are formally compared within an unobserved components model of output, augmented with a monetary policy variable. The Bayesian model comparison favors the notion that the dynamics are nonlinear and better described by a smooth transition between regimes, which suggests that aggregation plays a role in the dynamics between monetary policy and output. This evidence is further supported by the results of a model that uses output data at the sectoral level: when more disaggregated data are employed, the transition between regimes is more abrupt. Moreover, the results show that, when the transition between regimes is smooth, large monetary policy shocks identified as the residuals of a VAR are neutral, consistent with the implications of menu-costs models.