Our proposed local vector autoregressive (LVAR) model has time-varying parameters that allow it to be safely used in both stationary and non-stationary situations. The estimation is conducted over an interval of local homogeneity where the parameters are approximately constant. The local...

We propose an Adaptive Dynamic Nelson-Siegel (ADNS) model to adaptively forecast the yield curve. The model has a simple yet flexible structure and can be safely applied to both stationary and nonstationary situations with different sources of change. For the 3- to 12-months ahead out-of-sample...

This paper addresses the issue of forecasting the term structure. We provide a unified state-space modelling framework that encompasses different existing discrete-time yield curve models. Within such framework we analyze the impact of two modelling choices, namely the imposition of no-arbitrage...

This paper empirically evaluates the predictive performance of the International Monetary Fund's (IMF) exchange rate assessments with respect to future exchange rate movements. The assessments of real trade-weighted exchange rates were conducted from 2006 to 2011, and were based on three...

Path forecasts, defined as sequences of individual forecasts, generated by vector autoregressions are widely used in applied work. It has been recognized that a profound econometric analysis requires, besides the path forecast, a joint prediction region that contains the whole future path with a...

This paper evaluates the ability of autoregressive models, professional forecasters, and models that incorporate unemployment flows to forecast the unemployment rate. We pay particular attention to flows-based approaches - the more reduced-form approach of Barnichon and Nekarda (2012) and the...

This paper uses a panel VAR (PVAR) approach to estimating, analysing and forecasting price dynamics in four different sectors - industry, services, construction, and agriculture - across the four largest euro area economies - Germany, France, Italy and Spain - and the euro area as a whole. By...

This paper proposes a novel and flexible framework to estimate autoregressive models with time-varying parameters. Our setup nests various adaptive algorithms that are commonly used in the macroeconometric literature, such as learning-expectations and forgetting-factor algorithms. These are...

A key aim of economics is to set goals and investigate the relationship between various socio-economic indicators. By fitting time series data using a Bayesian dynamical systems approach we identify non-linear interactions between GDP, child mortality, fertility rate and female education. We...

Global emissions beyond 44 gigatonnes of carbondioxide equivalent (GtCO2e) in 2020 can potentially lead the world to an irreversible climate change. Employing a novel dynamical system modeling approach, we predict that in a business-asusual scenario, it will reach 61 GtCO2e by 2020. Testing...