The Multivariate Linear Prediction Problem: Model-Based and Direct Filtering Solutions

McElroy, Tucker; Wildi, Marc

doi:10.1016/j.ecosta.2019.12.004

Cited by 5 publications

(2 citation statements)

References 41 publications

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“…While there are a variety of methods to detrend data [see Canova (1998) and McElroy & Wildi (2020) for an overview], because the timing of secular declines varies across industries, linear or deterministic trends may not be flexible enough. An alternative to UC is using the Hodrick-Prescott (HP) filter to pre-filter the data.…”

Section: Modelmentioning

confidence: 99%

Industrial Connectedness and Business Cycle Comovements

Guisinger

Owyang

Soques

2024

Econometrics and Statistics

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While aggregate shocks account for most business cycle fluctuations, sectoral shocks have become relatively more important since the 1980s. Previous studies show that sectoral shocks propagate through industry supply chains. Typically, sectors are defined by similarities in function and/or market. While some industries have supply chains within their own sector (vertical), others have supply chains across a number of sectors (horizontal). Similarity in these supply chain characteristics appear to be a determining factor in how industries comove. Using industrial production data of 82 four-digit NAICS industries over the period 1972 to 2019, this comovement is analyzed in a panel Markov-switching model incorporating a number of features relevant for sub-national analysis: (i) industry-specific trends that differentiate cyclical downturns from secular declines; (ii) a nationallevel business cycle; and (iii) factors that represent industrial comovement. While national-level shocks are typically still the most important driver of cyclical fluctuations, endogenously clustering by industry comovement highlights the role of sectoral shocks.

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Section: Modelmentioning

confidence: 99%

Industrial Connectedness and Business Cycle Comovements

Guisinger

Owyang

Soques

2024

Econometrics and Statistics

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“…(1) t } a white noise process. These same series were analyzed with this model in McElroy and Wildi (2020).…”

Section: Petrolmentioning

confidence: 99%

Ecce Signum: An R Package for Multivariate Signal Extraction and Time Series Analysis

McElroy¹,

Livsey²

2022

Preprint

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The package provides multivariate time series models for structural analysis, allowing one to extract latent signals such as trends or seasonality. Models are fitted using maximum likelihood estimation, allowing for non-stationarity, fixed regression effects, and ragged-edge missing values. Simple types of extreme values can be corrected using the device of entropy maximization. Model adequacy is assessed through residual diagnostics, and model-based signal extraction filters can be assessed in time domain and frequency domain. Extracted signals are produced with uncertainty measures that account for sample edge effects and missing values, and the signals (as well as the original time series) can be forecasted.

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Business-Cycle Analysis and Zero-Crossings of Time Series: A Generalized Forecast Approach

Wildi

2024

J Bus Cycle Res

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We propose an extension of classic time series approaches to business-cycle measurement called Simple Sign Accuracy (SSA), which addresses zero-crossings of a zero-mean stationary time series. Zero-crossings or sign-changes of the growth-rate of an economic indicator mark transitions between expansion and contraction episodes which can be related to business-cycles. The length or, more specifically, the mean duration between consecutive zero-crossings of a predictor, can be controlled in our approach by subjecting a variation of the classic optimization criterion to a novel ‘holding time’ constraint. The proposed criterion embodies a prediction trilemma which recognizes the fundamental trade-offs between accuracy, timeliness and smoothness (ATS). As a result, the SSA-criterion can address a multiplicity of design priorities in terms of ATS forecast performances, and the classic mean-square error paradigm is obtained as a special case when assigning weight only to the accuracy component. We also show that SSA can be interpreted as an extension of classic smoothing algorithms and that the approach lends itself for customization of existing benchmark predictors. The latter possibility is exploited in a real-time analysis (nowcasting) of the US business cycle, whereby SSA is plugged onto a well-known benchmark to modify the latter’s characteristics in terms of ATS performances.

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The Multivariate Linear Prediction Problem: Model-Based and Direct Filtering Solutions

Cited by 5 publications

References 41 publications

Industrial Connectedness and Business Cycle Comovements

Industrial Connectedness and Business Cycle Comovements

Ecce Signum: An R Package for Multivariate Signal Extraction and Time Series Analysis

Business-Cycle Analysis and Zero-Crossings of Time Series: A Generalized Forecast Approach

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