C32 Time-Series Models; Dynamic Quantile Regressions (Updated!)
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I provide a solution method in the frequency domain for multivariate linear rational expectations models. The method works with the generalized Schur decomposition, providing a numerical implementation of the underlying analytic function solution methods suitable for standard DSGE estimation and analysis procedures. This approach generalizes the time-domain restriction of autoregressive-moving average exogenous driving forces to arbitrary covariance stationary processes. Applied to the standard New Keynesian model, I find that a Bayesian analysis favors a single parameter log harmonic function of the lag operator over the usual AR(1) assumption as it generates humped shaped autocorrelation patterns more consistent with the data.
Empirical estimates of equilibrium real interest rates are so far mostly limited to advanced economies, since no statistical procedure suitable for a large set of countries is available. This is surprising, as equilibrium rates have strong policy implications in emerging markets and developing economies as well; current estimates of the global equilibrium rate rely on only a few countries; and estimates for a more diverse set of countries can improve understanding of the drivers. The authors propose a model and estimation strategy that decompose ex ante real interest rates into a permanent and transitory component even with short samples and high volatility. This is done with an unobserved component local level stochastic volatility model, which is used to estimate equilibrium rates for 50 countries with Bayesian methods.
Equilibrium rates were lower in emerging markets and developing economies than in advanced economies in the 1980s, similar in the 1990s, and have been higher since 2000. In line with economic integration and rising global capital markets, synchronization has been rising over time and is higher among advanced economies. Equilibrium rates of countries with stronger trade linkages and similar demographic and economic trends are more synchronized.
We analyze cyclical co-movement in credit, house prices, equity prices, and longterm interest rates across 17 advanced economies. Using a time-varying multi-level dynamic factor model and more than 130 years of data, we analyze the dynamics of co-movement at different levels of aggregation and compare recent developments to earlier episodes such as the early era of financial globalization from 1880 to 1913 and the Great Depression. We find that joint global dynamics across various financial quantities and prices as well as variable-specific global co-movements are important to explain fluctuations in the data. From a historical perspective, global co-movement in financial variables is not a new phenomenon, but its importance has increased for some variables since the 1980s. For equity prices, global cycles play currently a historically unprecedented role, explaining more than half of the fluctuations in the data. Global cycles in credit and housing have become much more pronounced and longer, but their importance in explaining dynamics has only increased for some economies including the US, the UK and Nordic European countries. We also include GDP in the analysis and find an increasing role for a global business cycle.
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 examine both the degree and the structural stability of inflation persis tence at different quantiles of the conditional inflation distribution. Previous research focused exclusively on persistence at the conditional mean of the inflation rate. Economic theory, however, provides various reasons -for example downward wage rigidities or menu costs- to expect higher inflation persistence at the upper than at the lower tail of the conditional inflation distribution.
Based on post-war US data we indeed find slower mean reversion in response to positive than to negative shocks. We find robust evidence for a structural break in persistence at all quantiles of the inflation process in the early 1980s. Inflation persistence has decreased and become more homogeneous across quantiles. Persistence at the conditional mean became more informative about the degree of persistence across the entire conditional inflation distribution. While prior to the 1980s inflation was not mean reverting in response to large positive shocks, our evidence strongly suggests that since the end of the Volcker disinflation the unit root can be rejected at every quantile including the upper tail of the conditional inflation distribution.