This paper evaluates the empirical performance of a medium-scale DSGE model with agents forming expectations using small forecasting models updated by the Kalman filter. The adaptive learning model fits the data better than the rational expectations (RE) model. Beliefs about the inflation persistence explain the observed decline in the mean and the volatility of inflation as well as Phillips curve flattening. Learning about inflation results in lower estimates for the persistence of the exogenous shocks that drive price and wage dynamics in the RE version of the model. Expectations based on small forecasting models are closely related to the survey evidence on inflation expectations. (JEL C53, D83, D84, E13, E17, E31)

In this paper we evaluate the empirical relevance of learning by private agents in an estimated medium-scale DSGE model. We replace the standard rational expectation assumption in the Smets and Wouters (2007) model by a constant gain learning mechanism. If agents know the correct structure of the model and only learn about the parameters, both expectation mechanisms result in a similar fit, and only the transition dynamics that are generated by specific initial beliefs are responsible for the differences between the two approaches. If, in addition, agents use only a reduced information set in forming the perceived law of motion, the implied model dynamics change and for some initial beliefs the marginal likelihood of the model is further improved. The learning models with the highest posterior probabilities add some additional persistence to the DSGE model that reduce the gap between the IRFs of the DSGE model and the more data-driven DSGE-VAR model. However, the additional dynamics that are introduced by the learning process do not systematically alter the estimated structural parameters related to the nominal and real frictions in the DSGE model.

These results suggest that a high-risk group for intervention to prevent future injection would consist of people with a history of past heroin use who are currently either in remission or using without injection and who reside on either coast, have a history of antisocial behavior and have used a variety of illicit drugs other than heroin.

This paper considers the well known Romer model of endogenous technological change and its extension where different intermediate capital goods are complementary, introduced in (Benhabib, Perli, and Xie 1994). They have shown that this modiÞcation allows indeterminate steady state for relatively mild degrees of the complementarity. The authors were able to derive analytically sufficient conditions for the indeterminacy and to Þnd speciÞc parameter values producing the indeterminate steady state.For the modiÞed Romer model of (Benhabib, Perli, and Xie 1994), I derive necessary and sufficient conditions for the steady state to be interior and strictly positive. I show that Hopf bifurcation to the absolutely stable steady state is impossible and the steady state is determinate if the model parameter values belong to a certain set. Considering a simpliÞed version of the model, I calculate necessary conditions for a Hopf bifurcation in one special case and show that it is impossible in another. Using numerical algorithm for multigoal optimization, I obtain several sets of parameter values leading to the loss of stability of the indeterminate steady state through Hopf bifurcation. AbstraktTato práce vychází z Romerova modelu endogenní technologické zmÿ eny a jeho mod-iÞkace (Benhabib, Perli a Xie, 1994), ve které jsou rû uzné kapitálové statky komplementy. Tato modiÞkace umoÿ zÿ nuje nedeterminovaný stacionární stav v pÿ rípade relativnÿ e malé míry komplementarity mezi tÿ emito statky. Autoÿ ri analyticky odvodili postaÿ cující podmínky pro nedeterminovanost a našli hodnotu parametru, která vede k nedeterminovanému stacionárnímu stavu.Pro tento model tato práce odvozuje nutné a postaÿ cující podmínky, aby stacionární stav byl interiorní a striktnÿ e positivní. Zároveÿ n ukazuje, ÿ ze Hopfova bifurkace v absolutním stacionárním stavu není moÿ zná pro jisté hodnoty parametru modelu a stacionární stav je tak determinovaný. Pro jednoduchou verzi modelu je vypoÿ cítaná nutná podmínka Hopfovy bifurkace v jednom speciÞckém pÿ rípadÿ e a pak je ukázáno, ÿ ze není moÿ zná ani v jiném pÿ rípadÿ e. Numerickými simulacemi se vypoÿ cítá nÿ ekolik hodnot parametrû u, které vedou k nestabilitÿ e nedeterminovaného stacionárního stavu prostÿ rednictvím Hopfovy bifurkace. JEL ClassiÞcation: E32, O41