When constructing unconditional point forecasts, both direct- and iterated-multistep (DMS and IMS) approaches are common. However, in the context of producing conditional forecasts, IMS approaches based on vector autoregressions (VAR) are far more common than simpler DMS models. This is despite...

Sign-restricted Structural Vector Autoregressions (SVARs) are increasingly common. However, they usually result in a set of structural parameters that have very different implications in terms of impulse responses, elasticities, historical decomposition and forecast error variance decomposition...

The primary goal of this paper is to describe several models that are currently used at the National Bank of the Republic of Macedonia (NBRM) for short-term forecasting of inflation - Autoregressive integrated moving average models (aggregated and disaggregated approach), three equation...

Economic policies are generally formulated on the basis of data available in real time, which might subsequently be revised. Implicitly, the possibility of data revisions creates an element of uncertainty around the very same data driving policy decisions. Given that such uncertainty could be...

While conditional forecasting has become prevalent both in the academic literature and in practice (e.g., bank stress testing, scenario forecasting), its applications typically focus on continuous variables. In this paper, we merge elements from the literature on the construction and...

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...

Aggregate demand forecasting, also known as nowcasting when it applies to current quarter assessment, is of notable interest to policy makers. This paper concentrates on the empirical methods dealing with mixed-frequency data. In particular, it focuses on the MIDAS approach and its later...

High-frequency financial and economic activity indicators are usually time aggregated before forecasts of low-frequency macroeconomic events, such as recessions, are computed. We propose a mixed-frequency modelling alternative that delivers high-frequency probability forecasts (including their...

This study explores the benefits of incorporating fat-tailed innovations, asymmetric volatility response, and an extended information set into crude oil return modeling and forecasting. To this end, we utilize standard volatility models such as Generalized Autoregressive Conditional...

When do financial markets help in predicting economic activity? With incomplete markets, the link between financial and real economy is state-dependent and financial indicators may turn out to be useful particularly in forecasting "tail" macroeconomic events. We examine this conjecture by...