Balance of opinion What about missing the weights?

Due to their early release, Business Tendency Surveys (BTS) are widely used in short term forecasting. Their questions are mainly qualitative; answers are most often used to calculate balances of opinions, which are defined as the difference between the proportions of positive answers with respect to the negative ones. These indicators are then used by forecasters as explanatory variables in econometric models. The balances of opinions are generally weighted with the firm size. However, there is no theoretical evidence of the efficiency of this kind of weighting. We propose here a model which aims at determining optimum weights; these weights should allow us to optimize the forecast of the macroeconomic variable. According to our analysis, the weights have to grow less than proportionally with the firm size. This conclusion is empirically tested through several examples derived from the French Industry BTS.Business Tendency Surveys, quantification, balance of opinion, short-term forecasting

Ptch2/Gas1 and Ptch1/Boc differentially regulate Hedgehog signalling in murine primordial germ cell migration.

Gas1 and Boc/Cdon act as co-receptors in the vertebrate Hedgehog signalling pathway, but the nature of their interaction with the primary Ptch1/2 receptors remains unclear. Here we demonstrate, using primordial germ cell migration in mouse as a developmental model, that specific hetero-complexes of Ptch2/Gas1 and Ptch1/Boc mediate the process of Smo de-repression with different kinetics, through distinct modes of Hedgehog ligand reception. Moreover, Ptch2-mediated Hedgehog signalling induces the phosphorylation of Creb and Src proteins in parallel to Gli induction, identifying a previously unknown Ptch2-specific signal pathway. We propose that although Ptch1 and Ptch2 functionally overlap in the sequestration of Smo, the spatiotemporal expression of Boc and Gas1 may determine the outcome of Hedgehog signalling through compartmentalisation and modulation of Smo-downstream signalling. Our study identifies the existence of a divergent Hedgehog signal pathway mediated by Ptch2 and provides a mechanism for differential interpretation of Hedgehog signalling in the germ cell niche

Fiscal Multipliers and Public Debt Dynamics in Consolidations

The success of a consolidation in reducing the debt ratio depends crucially on the value of the multiplier, which measures the impact of consolidation on growth, and on the reaction of sovereign yields to such a consolidation. We present a theoretical framework that formalizes the re spo nse of the public debt ratio to fiscal consolidations in relation to the value of fiscal multipliers, the starting debt level and the cyclical elasticity of the budget balance. We also assess the role of markets confidence to fiscal consolidations under al ternative scenarios. We find that with high levels of public debt and sizeable fiscal multipliers , debt ratios are likely to increase in the short term in response to fiscal consolidations. Hence, the typical horizon for a consolidation during crises episo des to reduce the debt ratio is two - three years , although this horizon depends critically on the size and persistence of fiscal multipliers and the reaction of financial markets. Anyway, such undesired debt responses are mainly short - lived. This effect is very unlikely in non - crisis times, as it requires a number of conditions difficult to observe at the same time , especially high fiscal multipliers

A Comparison of Machine Learning and Classical Demand Forecasting Methods: A Case Study of Ecuadorian Textile Industry

[EN] This document presents a comparison of demand forecasting methods, with the aim of improving demand forecasting and with it, the production planning system of Ecuadorian textile industry. These industries present problems in providing a reliable estimate of future demand due to recent changes in the Ecuadorian context. The impact on demand for textile products has been observed in variables such as sales prices and manufacturing costs, manufacturing gross domestic product and the unemployment rate. Being indicators that determine to a great extent, the quality and accuracy of the forecast, generating also, uncertainty scenarios. For this reason, the aim of this work is focused on the demand forecasting for textile products by comparing a set of classic methods such as ARIMA, STL Decomposition, Holt-Winters and machine learning, Artificial Neural Networks, Bayesian Networks, Random Forest, Support Vector Machine, taking into consideration all the above mentioned, as an essential input for the production planning and sales of the textile industries. And as a support, when developing strategies for demand management and medium-term decision making of this sector under study. Finally, the effectiveness of the methods is demonstrated by comparing them with different indicators that evaluate the forecast error, with the Multi-layer Neural Networks having the best results with the least error and the best performance.The authors are greatly grateful by the support given by the SDAS Research Group (https://sdas-group.com/).Lorente-Leyva, LL.; Alemany Díaz, MDM.; Peluffo-Ordóñez, DH.; Herrera-Granda, ID. (2021). 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Statistical analysis of arthroplasty data: II. Guidelines

It is envisaged that guidelines for statistical analysis and presentation of results will improve the quality and value of research. The Nordic Arthroplasty Register Association (NARA) has therefore developed guidelines for the statistical analysis of arthroplasty register data. The guidelines are divided into two parts, one with an introduction and a discussion of the background to the guidelines (Ranstam et al. 2011a, see pages x-y in this issue), and this one with a more technical statistical discussion on how specific problems can be handled. This second part contains (1) recommendations for the interpretation of methods used to calculate survival, (2) recommendations on howto deal with bilateral observations, and (3) a discussion of problems and pitfalls associated with analysis of factors that influence survival or comparisons between outcomes extracted from different hospitals

Enhancing survey‐based investment forecasts

We investigate the accuracy of capital investment predictors from a national business survey of South African manufacturing. Based on data available to correspondents at the time of survey completion, we propose variables that might affect the stability of their predictions. Having calibrated the survey predictors’ directional accuracy, we model the probability of a correct directional prediction using the proposed stability variables. For point forecasting, we compare the accuracy of rescaled survey forecasts with time series benchmarks and some survey/time series hybrid models. In addition, we model the magnitude of survey prediction errors using the stability variables. Directional forecast tests showed that three out of four survey predictors have value but are biased and inefficient. For shorter horizons we found survey forecasts, enhanced by time series data, significantly improved point forecasting accuracy. For longer horizons the survey predictors were as, or more, accurate than alternatives. The usefulness of the more accurate of the predictors examined is enhanced by auxiliary information: the probability of directional accuracy and the estimated error magnitude

Risk-adjusted CUSUM control charts for shared frailty survival models with application to hip replacement outcomes: a study using the NJR dataset

Background:  Continuous monitoring of surgical outcomes after joint replacement is needed to detect which brands’ components have a higher than expected failure rate and are therefore no longer recommended to be used in surgical practice. We developed a monitoring method based on cumulative sum (CUSUM) chart specifically for this application.  Methods:  Our method entails the use of the competing risks model with the Weibull and the Gompertz hazard functions adjusted for observed covariates to approximate the baseline time-to-revision and time-to-death distributions, respectively. The correlated shared frailty terms for competing risks, corresponding to the operating unit, are also included in the model. A bootstrap-based boundary adjustment is then required for risk-adjusted CUSUM charts to guarantee a given probability of the false alarm rates. We propose a method to evaluate the CUSUM scores and the adjusted boundary for a survival model with the shared frailty terms. We also introduce a unit performance quality score based on the posterior frailty distribution. This method is illustrated using the 2003-2012 hip replacement data from the UK National Joint Registry (NJR). Results:  We found that the best model included the shared frailty for revision but not for death. This means that the competing risks of revision and death are independent in NJR data. Our method was superior to the standard NJR methodology. For one of the two monitored components, it produced alarms four years before the increased failure rate came to the attention of the UK regulatory authorities. The hazard ratios of revision across the units varied from 0.38 to 2.28. Conclusions:  An earlier detection of failure signal by our method in comparison to the standard method used by the NJR may be explained by proper risk-adjustment and the ability to accommodate time-dependent hazards. The continuous monitoring of hip replacement outcomes should include risk adjustment at both the individual and unit level

The ATLAS Trigger/DAQ Authorlist, version 1.0

This is a reference document giving the ATLAS Trigger/DAQ author list, version 1.0 of 20 Nov 2008