The chapter deals with parametric models for the measurement of the business cycle in economic time series. It presents univariate methods based on parametric trend - cycle decompositions and multivariate models featuring a Phillips type relationship between the output gap and inflation and the...

We propose a simple modification of the time series filter by Hamilton (2018b) that yields reliable and economically meaningful real-time output gap estimates. The original filter relies on 8-quarter ahead forecasts errors of an autoregression. While this approach yields a cyclical component of...

The authors contribute to the debate regarding the reliability of output gap estimates. As an alternative to the Hodrick-Prescott (HP) filter, they propose a simple modification of the filter proposed by Hamilton in 2018 that shares its favorable real-time properties, but leads to a more even...

We propose a simple modification of Hamilton’s (2018) time series filter that yields reliable and economically meaningful real-time output gap estimates. The original filter relies on 8 quarter ahead forecast errors of a simple autoregression of real GDP. While this approach yields a cyclical...

We employ artificial neural networks using macro-financial variables to predict recessions. We model the relationship between indicator variables and recessions 1 to 10 periods into the future and employ a procedure that penalizes a misclassified recession more than a misclassified...

This paper reviews the performance of several statistical techniques in nowcasting preliminary estimates of UK GDP, particularly during the recent depression. Traditional bridging equations, MIDAS regressions and factor models are all considered. While there are various theoretical differences...

Structural time series models are formulated in terms of components, such as trends, seasonals and cycles, that have a direct interpretation. As well as providing a framework for time series decomposition by signal extraction, they can be used for forecasting and for ‘nowcasting’. The...

This paper identifies supply and demand shocks that are specific to the oil-market, and separates them from economy-wide shocks that affect the demand for many asset classes, including oil. The shocks are identified by the sign and magnitude of the correlation between daily oil price percent...

The Beveridge-Nelson decomposition defines the trend component in terms of the eventual forecast function, as the value the series would take if it were on its long-run path. The paper in-troduces the multistep Beveridge-Nelson decomposition, which arises when the forecast function is obtained...

In this paper we extend the Stock and Watson’s (Leading economic indicators, new approaches and forecasting records, 1991) single-index dynamic factor model in an econometric framework that has the advantage of combining information from real and financial indicators published at different...