This paper develops a structural VAR model to measure how a shock to one country can affect the GDP of other countries. It uses trade linkages to estimate the multiplier effects of a shock as it is transmitted through other countries' output fluctuations. The paper introduces a new specification...

Over the postwar, the U.S., Europe and Japan have experienced what may be thought of as medium frequency oscillations between persistent periods of robust growth and persistent periods of relative stagnation. These medium frequency movements, further, appear to bear some relation to the high...

We propose a Bayesian procedure for exploiting small, possibly long-lag linear predictability in the innovations of a finite order autoregression. We model the innovations as having a log-spectral density that is a continuous mean-zero Gaussian process of order 1/√T. This local embedding makes...

This paper develops a vector autoregression (VAR) for time series which are observed at mixed frequencies - quarterly and monthly. The model is cast in state-space form and estimated with Bayesian methods under a Minnesota-style prior. We show how to evaluate the marginal data density to...

We model the conditional mean and volatility of stock returns as a latent vector autoregressive (VAR) process to study the contemporaneous and intertemporal relationship between expected returns and risk in a flexible statistical framework and without relying on exogenous predictors. We find a...

When the VAR representation of a times series has a non-fundamental representation, standard SVAR techniques cannot be used to exactly identify the effects of structural shocks. This problem is know to potentially arise when one of the structural shocks represents news about the future. However,...

This paper considers VAR models incorporating many time series that interact through a few dynamic factors. Several econometric issues are addressed including estimation of the number of dynamic factors and tests for the factor restrictions imposed on the VAR. Structural VAR identification based...

We propose a new strategy for dissecting the macroeconomic time series, provide a template for the business-cycle propagation mechanism that best describes the data, and use its properties to appraise models of both the parsimonious and the medium-scale variety. Our findings support the...

This paper illustrates how to handle a sequence of extreme observations—such as those recorded during the COVID-19 pandemic—when estimating a Vector Autoregression, which is the most popular time-series model in macroeconomics. Our results show that the ad-hoc strategy of dropping these...

The stochastic process for earnings is the key element of incomplete markets models in modern quantitative macroeconomics. We show that a simple modification of the canonical process used in the literature leads to a dramatic improvement in the measurement of earnings dynamics in administrative...