C11 Bayesian Analysis
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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.
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.
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.
A novel spatial autoregressive model for panel data is introduced, which incor-porates multilayer networks and accounts for time-varying relationships. Moreover, the proposed approach allows the structural variance to evolve smoothly over time and enables the analysis of shock propagation in terms of time-varying spillover effects.
The framework is applied to analyse the dynamics of international relationships among the G7 economies and their impact on stock market returns and volatilities. The findings underscore the substantial impact of cooperative interactions and highlight discernible disparities in network exposure across G7 nations, along with nuanced patterns in direct and indirect spillover effects.
Climate change has become one of the most prominent concerns globally. In this paper, the authors study the transition risk of greenhouse gas emission reduction in structural environmental-macroeconomic DSGE models. First, they analyze the uncertainty in model prediction on the effect of unanticipated and pre-announced carbon price increases. Second, they conduct optimal model-robust policy in different settings. They find that reducing emissions by 40% causes 0.7% to 4% output loss with 2% on average. Pre-announcement of carbon prices affects the inflation dynamics significantly. The central bank should react slightly less to inflation and output growth during the transition risk. With optimal carbon price designs, it should react even less to inflation, and more to output growth.
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.
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.
Portfolio choice and estimation risk : a comparison of Bayesian approaches to resampled efficiency
(2002)
Estimation risk is known to have a huge impact on mean/variance (MV) optimized portfolios, which is one of the primary reasons to make standard Markowitz optimization unfeasible in practice. Several approaches to incorporate estimation risk into portfolio selection are suggested in the earlier literature. These papers regularly discuss heuristic approaches (e.g., placing restrictions on portfolio weights) and Bayesian estimators. Among the Bayesian class of estimators, we will focus in this paper on the Bayes/Stein estimator developed by Jorion (1985, 1986), which is probably the most popular estimator. We will show that optimal portfolios based on the Bayes/Stein estimator correspond to portfolios on the original mean-variance efficient frontier with a higher risk aversion. We quantify this increase in risk aversion. Furthermore, we review a relatively new approach introduced by Michaud (1998), resampling efficiency. Michaud argues that the limitations of MV efficiency in practice generally derive from a lack of statistical understanding of MV optimization. He advocates a statistical view of MV optimization that leads to new procedures that can reduce estimation risk. Resampling efficiency has been contrasted to standard Markowitz portfolios until now, but not to other approaches which explicitly incorporate estimation risk. This paper attempts to fill this gap. Optimal portfolios based on the Bayes/Stein estimator and resampling efficiency are compared in an empirical out-of-sample study in terms of their Sharpe ratio and in terms of stochastic dominance.
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.