Evaluating point and density forecasts of DSGE models : [Version 13 März 2012]

This paper investigates the accuracy of point and density forecasts of four DSGE models for inflation, output growth and the federal funds rate. Model parameters are estimated and forecasts are derived successively from historical U.S. data vintages synchronized with the Fed’s Greenbook projections. Point forecasts of some models are of similar accuracy as the forecasts of nonstructural large dataset methods. Despite their common underlying New Keynesian modeling philosophy, forecasts of different DSGE models turn out to be quite distinct. Weighted forecasts are more precise than forecasts from individual models. The accuracy of a simple average of DSGE model forecasts is comparable to Greenbook projections for medium term horizons. Comparing density forecasts of DSGE models with the actual distribution of observations shows that the models overestimate uncertainty around point forecasts

Stability under Learning of Equilibria in Financial Markets with Supply Information

In a recent paper Ganguli and Yang [2009] demonstrate, that there can exist multiple equilibria in a financial market model á la Grossman and Stiglitz [1980] if traders possess private information regarding the supply of the risky asset. The additional equilibria differ in some important respects from the usual equilibrium of the Grossman–Stiglitz type which still exists in this model. This note shows that these additional equilibria are always unstable under learning. This is true for both eductive learning following Guesnerie [2002] and adaptive learning via least–squares estimation (cf. Marcet and Sargent [1988] or Evans and Honkapohja [2001]). Regarding the original Grossman–Stiglitz type equilibrium, the stability results are less clear cut, since this equilibrium might be unstable under eductive learning while it is always stable under adaptive learning.Recursive Least Squares Learning, Eductive Stability, Rational Expectations, Private Information

E–stability and stability of adaptive learning in models with asymmetric information

The paper demonstrates how the E–stability principle introduced by Evans and Honkapohja [2001] can be applied to models with heterogeneous and private information in order to assess the stability of rational expectations equilibria under learning. The paper extends already known stability results for the Grossman and Stiglitz [1980] model to a more general case with many differentially informed agents and to the case where information is endogenously acquired by optimizing agents. In both cases it turns out that the rational expectations equilibrium of the model is inherently E-stable and thus locally stable under recursive least squares learning.Adaptive Learning, Eductive Stability, Rational Expectations

Adaptive circular deconvolution by model selection under unknown error distribution

We consider a circular deconvolution problem, in which the density $f$ of a circular random variable $X$ must be estimated nonparametrically based on an i.i.d. sample from a noisy observation $Y$ of $X$. The additive measurement error is supposed to be independent of $X$. The objective of this work was to construct a fully data-driven estimation procedure when the error density $\varphi$ is unknown. We assume that in addition to the i.i.d. sample from $Y$, we have at our disposal an additional i.i.d. sample drawn independently from the error distribution. We first develop a minimax theory in terms of both sample sizes. We propose an orthogonal series estimator attaining the minimax rates but requiring optimal choice of a dimension parameter depending on certain characteristics of $f$ and $\varphi$, which are not known in practice. The main issue addressed in this work is the adaptive choice of this dimension parameter using a model selection approach. In a first step, we develop a penalized minimum contrast estimator assuming that the error density is known. We show that this partially adaptive estimator can attain the lower risk bound up to a constant in both sample sizes $n$ and $m$. Finally, by randomizing the penalty and the collection of models, we modify the estimator such that it no longer requires any previous knowledge of the error distribution. Even when dispensing with any hypotheses on $\varphi$, this fully data-driven estimator still preserves minimax optimality in almost the same cases as the partially adaptive estimator. We illustrate our results by computing minimal rates under classical smoothness assumptions.Comment: Published in at http://dx.doi.org/10.3150/12-BEJ422 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm

Estimating Monetary Policy Reaction Functions Using Quantile Regressions

Monetary policy rule parameters are usually estimated at the mean of the interest rate distribution conditional on inflation and an output gap. This is an incomplete description of monetary policy reactions when the parameters are not uniform over the conditional distribution of the interest rate. I use quantile regressions to estimate parameters over the whole conditional distribution of the Federal Funds Rate. Inverse quantile regressions are applied to deal with endogeneity. Realtime data of inflation forecasts and the output gap are used. I find significant and systematic variations of parameters over the conditional distribution of the interest rate.monetary policy rules; IV quantile regression; real-time data

The Larger the Better? The Role of Interest-Group Size in Legislative Lobbying

We develop a model of legislative lobbying where policy proposals are endogenous. We show that a policy proposer with preferences tilted towards one lobby may be induced by an increase in that interest group's size to propose policies geared towards the opposing lobby. Hence, a larger lobby size can have adverse effects on policy outcomes for this same lobby. This provides another rationale as to why some interests do not organize. Moreover, we find that a second-mover advantage in Groseclose and Snyder (1996)-type lobbying models with exogenous policy proposals can turn into a second-mover disadvantage when the proposal is endogenous.legislative lobbying, vote buying, legislatures, interest groups, political economy