Forecasting UK industrial production over the business cycle

Simpson, Paul W.; Osborn, Denise R.; Sensier, Marianne

doi:10.1002/for.797

Cited by 16 publications

(11 citation statements)

References 21 publications

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“…This equation explains the evolution of the indicator based on its own history and the statistical relationship between it and other indicators. In the same way, the economic activity one wishes to forecast is then dened based on its own historic development and the statistical relationship between it and the indicators (Eklund, 2007, Makridakis and Wheelwright, 1989, Armstrong, 1978, Clements and Hendry, 2011 Contrary to this, the Markov switching models assume that the variable one wishes to forecast can exist in 2 or more stages (Batchelor, 2004, Simpson, 2001. It then creates a model explaining the probability that the variable will switch from one stage to another.…”

Section: Econometric Methodsmentioning

confidence: 99%

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Business Cycle Forecasting

Bragg¹

2012

Financial Analysis: A Controller's Guide

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Executive SummaryHigh quality forecasts are becoming more and more important for the successful strategic planning of rms, due to increased turbulence in business conditions. Based on their position, which grants them in-depth knowledge of their industry, trade associations are considered to be in a advantageous position to create quality forecasts. This thesis investigates the possibilities for business cycle forecasting in trade associations. Indicator based methods for forecasting are evaluated from a theoretical perspective, and the possibilities for applying these in trade associations are analyzed based on an empirical study into trade associations' use of forecasting.Forecasting methods are found to have dierent properties, and the forecaster is faced with a trade o between requirements and quality. No forecasting method is found to be superior from a theoretical standpoint, and the optimal method for forecasting therefore depends on the situation in which it is applied. Industry dierences makes it impossible to forecast with a standardized set of indicators, and such industry specic indicators are needed to forecast each industry. Indicators should be selected on the basis of subjective knowledge of empirical relationships, or business cycle theories. Forecasting with the same method in all industries seems realistic.Low amounts of forecasting is currently being done in trade associations, and the demand for forecasts from member rms is relatively low. There is no signicant correlation between forecasting done, the industry's sensitivity to uctuations in the economy, and the demand for forecasts. There is a general interest in forecasting among trade associations but few resources are available.The current demand justies value creation through forecasting in trade associations, and the thesis nds that trade associations should engage in industry forecasting to improve the strategic planning of their member rms. Forecasting in trade associations should be done with composite indicators and an industry specic set of indicators. Furthermore, an automated visualization of index characteristics, to improve the interpretation process and reduce the resources required, is suggested.

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Section: Econometric Methodsmentioning

confidence: 99%

“…Composite indicators are formed as leading, lagging or coinciding indexes, and are created by summarizing the changes in each individual indicators while accounting for importance and volatility of each indicator (Niemira and Klein, 1994, Rötheli, 2007, Simpson, 2001. Mathematically this can be written as:…”

Section: Composite Indicatorsmentioning

confidence: 99%

Business Cycle Forecasting

Bragg¹

2012

Financial Analysis: A Controller's Guide

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“…The difficulties in the estimation of many previously considered (univariate and multivariate) regime switching models are typically related to the determination of the (unobserved) regimes and their conditional probabilities (see, e.g., Ang & Bekaert, 2002a, 2002bGray, 1996;Simpson et al, 2001). In our approach, parameter estimation greatly simplifies because an observable binary time series determines the regime.…”

Section: Estimationmentioning

confidence: 99%

“…This is due to the fact that the resulting conditional probabilities of the regimes can be constructed with a binary response model simplifying parameter estimation carried out with the method of maximum likelihood. This approach circumvents the difficulties reported in the parameter estimation of various previous models (see, e.g., Ang & Bekaert, 2002a, 2002bGray, 1996;Simpson, Osborn, & Sensier, 2001) where estimation requires the filtration of the latent regimes (see also the discussion in Filardo & Gordon, 1998).…”

Section: Introductionmentioning

confidence: 99%

“…The examined Monte Carlo forecasting experiments show that the proposed method is not, however, computationally burdensome and it leads forecasting gains over the single-regime VAR model. An important advantage of our model is that it facilitates multistep forecasting, whereas in the previous univariate and multivariate regime switching models, with dependence on latent regimes and time-varying transition probabilities, only one-period-ahead forecasts have been considered so far (see, e.g., Ang & Bekaert, 2002a;Filardo, 1994;Perez-Quiros & Timmermann, 2000;Simpson et al, 2001).…”

Section: Introductionmentioning

confidence: 99%

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Forecasting US interest rates and business cycle with a nonlinear regime switching VAR model

Nyberg

2017

Journal of Forecasting

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This paper introduces a regime switching vector autoregressive model with time-varying regime probabilities, where the regime switching dynamics is described by an observable binary response variable predicted simultaneously with the variables subject to regime changes. Dependence on the observed binary variable distinguishes the model from various previously proposed multivariate regime switching models, facilitating a handy simulation-based multistep forecasting method. An empirical application shows a strong bidirectional predictive linkage between US interest rates and NBER business cycle recession and expansion periods. Due to the predictability of the business cycle regimes, the proposed model yields superior out-of-sample forecasts of the US short-term interest rate and the term spread compared with the linear and nonlinear vector autoregressive (VAR) models, including the Markov switching VAR model.

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Asymmetry in the link between the yield spread and industrial production: threshold effects and forecasting

Paya

Venetis

Peel

2004

Journal of Forecasting

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We analyze the nonlinear behavior of the information content in the spread for future real economic activity. The spread linearly predicts one year ahead real growth in nine industrial production sectors of the US and four of the UK over the last forty years. However, recent investigations on the spread-real activity relation have questioned both its linear nature and its time-invariant framework. Our, in-sample, empirical evidence suggests that the spread real activity relationship exhibits asymmetries that allow for different predictive power of the spread when past spread values were above or below some threshold value. We then measure the out-of-sample forecast performance of the nonlinear model using predictive accuracy tests. The results show that significant improvement in forecasting accuracy, at least for onestep ahead forecasts, can be obtained over the linear model.

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Forecasting UK industrial production over the business cycle

Cited by 16 publications

References 21 publications

Business Cycle Forecasting

Business Cycle Forecasting

Forecasting US interest rates and business cycle with a nonlinear regime switching VAR model

Asymmetry in the link between the yield spread and industrial production: threshold effects and forecasting

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