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Smooth mixtures, i.e. mixture models with covariate-dependent mixing weights, are very useful flexible models for conditional densities. Previous work shows that using too simple mixture components for modeling heteroscedastic and/or heavy tailed data can give a poor fit, even with a large number of components. This paper explores how well a smooth mixture of symmetric components can capture skewed data. Simulations and applications on real data show that including covariate-dependent skewness in the components can lead to substantially improved performance on skewed data, often using a much smaller number of components. Furthermore, variable selection is effective in removing unnecessary covariates in the skewness, which means that there is little loss in allowing for skewness in the components when the data are actually symmetric. We also introduce smooth mixtures of gamma and log-normal components to model positively-valued response variables.

In this paper our main aim is to quantify the role that housing collateral plays for the monetary transmission mechanism. Furthermore, we want to explore the implications of the increase in household indebtedness, and specifically the loan-to-value ratio, in the last two decades. We set up a two sector DSGE model with production of goods and housing. Households can only borrow by using their houses as collateral. The structure of the model closely follows Iacoviello and Neri (2010). To be able to do quantitatively relevant exercises we estimate the model using Bayesian methods on Swedish data for 1986q1-2008q3. We quantify the reinforcement of the monetary transmission mechanism that housing used as collateral implies in the presence of nominal loan contracts. This mechanism functions through the effects of the interest rate on house prices as well as on inflation and thereby the real value of nominal debt. This component of the monetary transmission mechanism becomes stronger the higher the loan-to-value ratio is. A change in the maximum loan-to-value ratio from 85% to 95%, all else being equal, implies that the effect of a monetary policy shock is increased by 4% for inflation, 8% for GDP and 24% for consumption. We conclude that to properly understand the monetary transmission mechanism and its changing nature over time, we need to take into account the effects of housing related collateral constraints.

A general model is proposed for flexibly estimating the density of a continuous response variable conditional on a possibly high-dimensional set of covariates. The model is a finite mixture of asymmetric student-t densities with covariate dependent mixture weights. The four parameters of the components, the mean, degrees of freedom, scale and skewness, are all modelled as functions of the covariates. Inference is Bayesian and the computation is carried out using Markov chain Monte Carlo simulation. To enable model parsimony, a variable selection prior is used in each set of covariates and among the covariates in the mixing weights. The model is used to analyse the distribution of daily stock market returns, and shown to more accurately forecast the distribution of returns than other widely used models for financial data.

This paper aims to evaluate if frictions in credit markets are important for business cycles in the U.S. and the Euro area. For this purpose, I modify the DSGE financial accelerator model developed by Bernanke, Gertler and Gilchrist (1999) by adding frictions such as price indexation to past inflation, sticky wages, consumption habits and variable capital utilization. When I estimate the model with Bayesian methods, I find that financial frictions are relevant in both areas. According to the posterior odds ratio, the data clearly favors the model with financial frictions both in the U.S. and the Euro area. Moreover, consistent with common perceptions, financial frictions are larger in the Euro area.

This paper addresses two important questions that have, so far, been studied separately in the literature. First, the paper aims at explaining the high volatility of long-term interest rates observed in the data, which is hard to replicate using standard macro models. Building a small-scale macroeconomic model and estimating it on U.S. and U.K. data, I show that the policy responses of a central bank that is uncertain about the natural rate of unemployment can explain this volatility puzzle. Second, the paper aims at shedding new light on the distinction between rules and discretion in monetary policy. My empirical results show that using yield curve data may facilitate the empirical discrimination between different monetary policy regimes and that U.S. monetary policy is best understood as originating from a discretionary regime since 1960.

The substantial fluctuations in house prices recently experienced by many industrialized economies have stimulated a vivid debate on the possible implications for monetary policy. In this paper, we ask whether the U.S. Fed, the Bank of Japan and the Bank of England have reacted to house prices. We study the responses of these central banks by estimating a structural model for each country where credit constrained agents borrow against real estate. The main result is that house price movements did play a separate role in the U.S., U.K.. and Japanese central bank reaction functions.

We consider forecast combination and, indirectly, model selection for VAR models when there is uncertainty about which variables to include in the model in addition to the forecast variables. The key difference from traditional Bayesian variable selection is that we also allow for uncertainty regarding which endogenous variables to include in the model. That is, all models include the forecast variables, but may otherwise have differing sets of endogenous variables. This is a difficult problem to tackle with a traditional Bayesian approach. Our solution is to focus on the forecasting performance for the variables of interest and we construct model weights from the predictive likelihood of the forecast variables. The procedure is evaluated in a small simulation study and found to perform competitively in applications to real world data.