C11 Bayesian Analysis
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The author proposes a Differential-Independence Mixture Ensemble (DIME) sampler for the Bayesian estimation of macroeconomic models.It allows sampling from particularly challenging, high-dimensional black-box posterior distributions which may also be computationally expensive to evaluate. DIME is a “Swiss Army knife”, combining the advantages of a broad class of gradient-free global multi-start optimizers with the properties of a Monte Carlo Markov chain (MCMC). This includes fast burn-in and convergence absent any prior numerical optimization or initial guesses, good performance for multimodal distributions, a large number of chains (the “ensemble”) running in parallel, an endogenous proposal density generated from the state of the full ensemble, which respects the bounds of the prior distribution. The author shows that the number of parallel chains scales well with the number of necessary ensemble iterations.
DIME is used to estimate the medium-scale heterogeneous agent New Keynesian (“HANK”) model with liquid and illiquid assets, thereby for the first time allowing to also include the households’ preference parameters. The results mildly point towards a less accentuated role of household heterogeneity for the empirical macroeconomic dynamics.
In this paper we adapt the Hamiltonian Monte Carlo (HMC) estimator to DSGE models, a method presently used in various fields due to its superior sampling and diagnostic properties. We implement it into a state-of-theart, freely available high-performance software package, STAN. We estimate a small scale textbook New-Keynesian model and the Smets-Wouters model using US data. Our results and sampling diagnostics confirm the parameter estimates available in existing literature. In addition, we find bimodality in the Smets-Wouters model even if we estimate the model using the original tight priors. Finally, we combine the HMC framework with the Sequential Monte Carlo (SMC) algorithm to create a powerful tool which permits the estimation of DSGE models with ill-behaved posterior densities.
In this paper we adopt the Hamiltonian Monte Carlo (HMC) estimator for DSGE models by implementing it into a state-of-the-art, freely available high-performance software package. We estimate a small scale textbook New-Keynesian model and the Smets-Wouters model on US data. Our results and sampling diagnostics confirm the parameter estimates available in existing literature. In addition we combine the HMC framework with the Sequential Monte Carlo (SMC) algorithm which permits the estimation of DSGE models with ill-behaved posterior densities.
Using a nonlinear Bayesian likelihood approach that fully accounts for the zero lower bound on nominal interest rates, the authors analyze US post-crisis business cycle dynamics and provide reference parameter estimates. They find that neither the inclusion of financial frictions nor that of household heterogeneity improve the empirical fit of the standard model, or its ability to provide a joint explanation for the post-2007 dynamics. Associated financial shocks mis-predict an increase in consumption. The common practice of omitting the ZLB period in the estimation severely distorts the analysis of the more recent economic dynamics.
Based on OECD evidence, equity/housing-price busts and credit crunches are followed by substantial increases in public consumption. These increases in unproductive public spending lead to increases in distortionary marginal taxes, a policy in sharp contrast with presumably optimal Keynesian fiscal stimulus after a crisis. Here we claim that this seemingly adverse policy selection is optimal under rational learning about the frequency of rare capital-value busts. Bayesian updating after a bust implies massive belief jumps toward pessimism, with investors and policymakers believing that busts will be arriving more frequently in the future. Lowering taxes would be as if trying to kick a sick horse in order to stand up and run, since pessimistic markets would be unwilling to invest enough under any temporarily generous tax regime.
The authors relax the standard assumption in the dynamic stochastic general equilibrium (DSGE) literature that exogenous processes are governed by AR(1) processes and estimate ARMA (p,q) orders and parameters of exogenous processes. Methodologically, they contribute to the Bayesian DSGE literature by using Reversible Jump Markov Chain Monte Carlo (RJMCMC) to sample from the unknown ARMA orders and their associated parameter spaces of varying dimensions.
In estimating the technology process in the neoclassical growth model using post war US GDP data, they cast considerable doubt on the standard AR(1) assumption in favor of higher order processes. They find that the posterior concentrates density on hump-shaped impulse responses for all endogenous variables, consistent with alternative empirical estimates and the rigidities behind many richer structural models. Sampling from noninvertible MA representations, a negative response of hours to a positive technology shock is contained within the posterior credible set. While the posterior contains significant uncertainty regarding the exact order, the results are insensitive to the choice of data filter; this contrasts with the authors’ ARMA estimates of GDP itself, which vary significantly depending on the choice of HP or first difference filter.
We theoretically and empirically study large-scale portfolio allocation problems when transaction costs are taken into account in the optimization problem. We show that transaction costs act on the one hand as a turnover penalization and on the other hand as a regularization, which shrinks the covariance matrix. As an empirical framework, we propose a flexible econometric setting for portfolio optimization under transaction costs, which incorporates parameter uncertainty and combines predictive distributions of individual models using optimal prediction pooling. We consider predictive distributions resulting from highfrequency based covariance matrix estimates, daily stochastic volatility factor models and regularized rolling window covariance estimates, among others. Using data capturing several hundred Nasdaq stocks over more than 10 years, we illustrate that transaction cost regularization (even to small extent) is crucial in order to produce allocations with positive Sharpe ratios. We moreover show that performance differences between individual models decline when transaction costs are considered. Nevertheless, it turns out that adaptive mixtures based on high-frequency and low-frequency information yield the highest performance. Portfolio bootstrap reveals that naive 1=N-allocations and global minimum variance allocations (with and without short sales constraints) are significantly outperformed in terms of Sharpe ratios and utility gains.
he predictive likelihood is of particular relevance in a Bayesian setting when the purpose is to rank models in a forecast comparison exercise. This paper discusses how the predictive likelihood can be estimated for any subset of the observable variables in linear Gaussian state-space models with Bayesian methods, and proposes to utilize a missing observations consistent Kalman filter in the process of achieving this objective. As an empirical application, we analyze euro area data and compare the density forecast performance of a DSGE model to DSGE-VARs and reduced-form linear Gaussian models.
How do changes in market structure affect the US business cycle? We estimate a monetary DSGE model with endogenous
rm/product entry and a translog expenditure function by Bayesian methods. The dynamics of net business formation allow us to identify the 'competition effect', by which desired price markups and inflation decrease when entry rises. We
find that a 1 percent increase in the number of competitors lowers desired markups by 0.18 percent. Most of the cyclical variability in inflation is driven by markup fluctuations due to sticky prices or exogenous shocks rather than endogenous changes in desired markups.
Credit boom detection methodologies (such as threshold method) lack robustness as they are based on univariate detrending analysis and resort to ratios of credit to real activity. I propose a quantitative indicator to detect atypical behavior of credit from a multivariate system - a monetary VAR. This methodology explicitly accounts for endogenous interactions between credit, asset prices and real activity and detects atypical credit expansions and contractions in the Euro Area, Japan and the U.S. robustly and timely. The analysis also proves useful in real time.
