The Israeli-Palestinian conflict constitutes a prominent example of a long-lasting political conflict which has major consequences for the livelihoods of the people on both sides. The agricultural sectors of the Palestinian and Israeli economies are tightly connected. However, various security...

We propose an extended time-varying parameter Vector Autoregression that allows for an evolving relationship between the variances of the shocks. Using this model, we show that the relationship between the conditional variance of GDP growth and the long-term interest rate has become weaker over...

We introduce a new model for time-varying spatial dependence. The model extends the well-known static spatial lag model. All parameters can be estimated conveniently by maximum likelihood. We establish the theoretical properties of the model and show that the maximum likelihood estimator for the...

We introduce a new and general methodology for analyzing vector autoregressive models with time-varying coefficient matrices and conditionally heteroskedastic disturbances. Our proposed method is able to jointly treat a dynamic latent factor model for the autoregressive coefficient matrices and...

We analyze cyclical co-movement in credit, house prices, equity prices, and long-term 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...

After the introduction of an exchange rate commitment and an immediate 7% depreciation of the Czech koruna of in 2013, output growth resumed but inflation remained low. Consequently, the Czech National Bank did not return policy to normal for more than three years. Using a time-varying parameter...

This paper proposes a novel approach to introduce time-variation in structural parameters of DSGE models. Structural parameters are allowed to evolve over time via an observation-driven updating equation. The estimation of the resulting DSGE model can be easily performed by maximum likelihood...

We develop a dynamic factor model with time-varying parameters and stochastic volatility, estimate it with several variables for a large number of countries and decompose the variance of each variable in terms of contributions from uncertainty common to all countries (global uncertainty),...

We build a dynamic factor model with time-varying parameters and stochastic volatility and use it to decompose the variance of a large set of financial and macroeconomic variables for 22 OECD countries spanning from 1960 onwards into contributions from country-specific uncertainty,...

In this paper we develop a general framework to analyze state space models with timevarying system matrices where time variation is driven by the score of the conditional likelihood. We derive a new filter that allows for the simultaneous estimation of the state vector and of the time-varying...