This paper analyses the real-time nowcasting performance of machine learning algorithms estimated on New Zealand data. Using a large set of real-time quarterly macroeconomic indicators, we train a range of popular machine learning algorithms and nowcast real GDP growth for each quarter over the...

We introduce a new class of stochastic volatility models with autoregressive moving average (ARMA) innovations. The conditional mean process has a flexible form that can accommodate both a state space representation and a conventional dynamic regression. The ARMA component introduces serial...

Contemporary structural models of the global market for crude oil treat storage demand as a composite of precautionary responses to uncertainty and speculative behavior, due to difficulties in jointly identifying these distinct demand components. This difficulty arises because the underlying...

This paper studies the causes and effects of portfolio flows in Malaysia. We use Structural Vector Autoregression (SVAR) and Autoregressive Distributed Lag (ARDL) models to analyse the interactions among portfolio flows, global and domestic macro and financial variables within a common empirical...

Since the birth of the natural rate hypothesis, the conventional notion that short-term output simply fluctuates around a relatively stable long-term trend became the norm in modern macroeconomics, including in the standard New Keynesian DSGE model. However, the global financial crisis (GFC) led...

Large Bayesian VARs are now widely used in empirical macroeconomics. One popular shrinkage prior in this setting is the natural conjugate prior as it facilitates posterior simulation and leads to a range of useful analytical results. This is, however, at the expense of modelling exibility, as it...

This paper estimates a New Keynesian model with trend inflation and contrasts Taylor rules featuring fixed versus time-varying inflation target while allowing for passive monetary policy. The estimation is conducted over the Great Inflation and the Great Moderation periods. Time-varying...

Representative models of the macroeconomy (RMs), such as DSGE models, frequently contain unobserved variables. A finite-order VAR representation in the observed variables may not exist, and therefore the impulse responses of the RMs and SVAR models may differ. We demonstrate this divergence...

Time-varying parameter VARs with stochastic volatility are routinely used for structural analysis and forecasting in settings involving a few macroeconomic variables. Applying these models to high-dimensional datasets has proved to be challenging due to intensive computations and...

The deviance information criterion (DIC) has been widely used for Bayesian model comparison. In particular, a popular metric for comparing stochastic volatility models is the DIC based on the conditional likelihood — obtained by conditioning on the latent variables. However, some recent...