Forecasting turning points in Canada

Economists have long been involved in the search for a few key indicators that predict the behavior of market economies. For Canada, it has been shown that the yield curve reliably tilts down and that real M1 growth declines before economic contraction, but this has been demonstrated almost exclusively in the context of single estimation equations or atheoretical VARs. This paper offers an alternative approach to the study of economic turning points. To qualify as a business-cycle indicator, a variable must behave differently when an economy is approaching or in recession than it does during economic expansions. That simple logic admits a variety of parametric and nonparametric tests of a variable’s usefulness, in forecasting. We examine the behavior around recessions of sixteen Canadian and U.S. time series. In the end we find that only the slopes of the Canadian and the U.S. term structure meet the prespecified criteria; the change in the nominal MCI and in real M1 follow behind.business cycles monetary policy yield curve interest rates

Turning points or turning around: Family coach work with 'troubled families'

The study aimed to discover how family coaches work intensively with families with moderately complex problems bringing together perceptions from 20 families, 20 coaches and six other professionals, and exploring potential savings for 50 family cases. The Family Coaching Service is part of the English government’s ‘Troubled Families’ payment by results initiative, seeking to help families ‘turn their lives around’ to save state spending on anti-social behaviour, worklessness and school absence. Results show the work to be a staged process, over six months with the coach combining practical help with relationship building to engage families, set and achieve goals and negotiate endings. Cost savings were made in 82% of cases. Family coaches find the work rewarding but emotionally demanding. Families say their coach is special and different, and describe potential turning point experiences stemming from the work with their coach. There is clear congruence in the perceptions of the service from families, coaches and other professionals. Some tensions were evident in the work with other professionals and in managing relationship boundaries with families. Relationship-based help offered by para-professionals may offer a promising model of family support that statutory social workers in particular can learn from and engage with

Dating Business Cycle Turning Points

This paper discusses formal quantitative algorithms that can be used to identify business cycle turning points. An intuitive, graphical derivation of these algorithms is presented along with a description of how they can be implemented making very minimal distributional assumptions. We also provide the intuition and detailed description of these algorithms for both simple parametric univariate inference as well as latent-variable multiple-indicator inference using a state-space Markov-switching approach. We illustrate the promise of this approach by reconstructing the inferences that would have been generated if parameters had to be estimated and inferences drawn based on data as they were originally released at each historical date. Waiting until one extra quarter of GDP growth is reported or one extra month of the monthly indicators released before making a call of a business cycle turning point helps reduce the risk of misclassification. We introduce two new measures for dating business cycle turning points, which we call the %u201Cquarterly real-time GDP-based recession probability index%u201D and the %u201Cmonthly real-time multiple-indicator recession probability index%u201D that incorporate these principles. Both indexes perform quite well in simulation with real-time data bases. We also discuss some of the potential complicating factors one might want to consider for such an analysis, such as the reduced volatility of output growth rates since 1984 and the changing cyclical behavior of employment. Although such refinements can improve the inference, we nevertheless find that the simpler specifications perform very well historically and may be more robust for recognizing future business cycle turning points of unknown character.

Forecasting Metals Returns A Bayesian Decision Theoretic Approach

Turning points in commodity returns are important for decisions of policy makers, commodity producers and consumers reliant on medium term outcomes. However, forecasting of turning points has been a neglected feature of forecasting, especially in commodity markets. I forecast turning points in metals price returns using Bayesian Decision Theory. The method produces a probabilistic statement about our belief of a turning point occurring in the next period which, combined with a decision rule based on a loss function generates optimal turning point forecasts. This method produces positive results in forecasting turning points in metals returns, with the simple linear models investigated producing more accurate turning point forecasts than naive models across a number of different evaluation methods for the general case and for the specific example of a producing firm.

Quantum Gravity and Turning Points in the Semiclassical Approximation

The wavefunctional in quantum gravity gives an amplitude for 3-geometries and matter fields. The four-space is usually recovered in a semiclassical approximation where the gravity variables are taken to oscillate rapidly compared to matter variables; this recovers the Schrodinger evolution for the matter. We examine turning points in the gravity variables where this approximation appears to be troublesome. We investigate the effect of such a turning point on the matter wavefunction, in simple quantum mechanical models and in a closed minisuperspace cosmology. We find that after evolving sufficiently far from the turning point the matter wavefunction recovers to a form close to that predicted by the semiclassical approximation, and we compute the leading correction (from `backreaction') in a simple model. We also show how turning points can appear in the gravitational sector in dilaton gravity. We give some remarks on the behavior of the wavefunctional in the vicinity of turning points in the context of dilaton gravity black holes.Comment: 32 pages, 3 Postscript figures, uses epsf.tex and fps.sty, some discussion, references and Acknowledgements added, version to appear in Phys. Rev.

A Classifying Procedure for Signaling Turning Points

A Hidden Markov Model (HMM) is used to classify an out of sample observation vector into either of two regimes. This leads to a procedure for making probability forecasts for changes of regimes in a time series, i.e. for turning points. Instead o maximizing a likelihood, the model is estimated with respect to known past regimes. This makes it possible to perform feature extraction and estimation for different forecasting horizons. The inference aspect is emphasized by including a penalty for a wrong decision in the cost function. The method is tested by forecasting turning points in the Swedish and US economies, using leading data. Clear and early turning point signals are obtained, contrasting favourable with earlier HMM studies. Some theoretical arguments for this are given.Business Cycle; Feature Extraction; Hidden Markov Switching-Regime Model; Leading Indicator; Probability Forecast.