Conditional inference with a complex sampling: exact computations and Monte Carlo estimations

In survey statistics, the usual technique for estimating a population total consists in summing appropriately weighted variable values for the units in the sample. Different weighting systems exit: sampling weights, GREG weights or calibration weights for example. In this article, we propose to use the inverse of conditional inclusion probabilities as weighting system. We study examples where an auxiliary information enables to perform an a posteriori stratification of the population. We show that, in these cases, exact computations of the conditional weights are possible. When the auxiliary information consists in the knowledge of a quantitative variable for all the units of the population, then we show that the conditional weights can be estimated via Monte-Carlo simulations. This method is applied to outlier and strata-Jumper adjustments

Image Reconstruction in Optical Interferometry

This tutorial paper describes the problem of image reconstruction from interferometric data with a particular focus on the specific problems encountered at optical (visible/IR) wavelengths. The challenging issues in image reconstruction from interferometric data are introduced in the general framework of inverse problem approach. This framework is then used to describe existing image reconstruction algorithms in radio interferometry and the new methods specifically developed for optical interferometry.Comment: accepted for publication in IEEE Signal Processing Magazin

"Forecasting stochastic Volatility using the Kalman filter: an application to Canadian Interest Rates and Price-Earnings Ratio"

In this paper, we aim at forecasting the stochastic volatility of key financial market variables with the Kalman filter using stochastic models developed by Taylor (1986,1994) and Nelson (1990). First, we compare a stochastic volatility model relying on the Kalman filter to the conditional volatility estimated with the GARCH model. We apply our models to Canadian short-term interest rates. When comparing the profile of the interest rate stochastic volatility to the conditional one, we find that the omission of a constant term in the stochastic volatility model might have a perverse effect leading to a scaling problem, a problem often overlooked in the literature. Stochastic volatility seems to be a better forecasting tool than GARCH(1,1) since it is less conditioned by autoregressive past information. Second, we filter the S&P500 price-earnings(P/E) ratio in order to forecast its value. To make this forecast, we postulate a rational expectations process but our method may accommodate other data generating processes. We find that our forecast is close to a GARCH(1,1) profile.Stochastic volatility, Kalman filter, P/E ratio forecast, Interest rate forecast

The Effects of IFRS on Financial Ratios: Early Evidence in Canada

This paper provides preliminary evidence of the impact on financial ratios caused by the transition to International Financial Reporting Standards (IFRS) in Canada. The main features of IFRS are explained in the context of a shift from Canadian Generally Accepted Accounting Principles (GAAP) while the main differences between the two sets of rules are underscored – heavier reliance of IFRS on fair value accounting and comprehensive income, and the use of the entity theory for consolidation. The effects of IFRS on financial ratios in the areas of liquidity, leverage, coverage and profitability are discussed and verified using a sample cohort of early adopters in Canada. The preliminary evidence reveals significantly higher volatility to most of the ratios under IFRS when compared to those derived under pre-changeover Canadian GAAP. While the means and medians of IFRS ratios differ from the means and medians of the same ratios under pre-changeover Canadian GAAP, the differences are not statistically significant overall. However, important individual discrepancies are in some cases observed. Naturally, analysts using ratios for analytical purposes during the transition period need to be vigilant as ratios computed under IFRS are not directly comparable with those derived under pre-changeover Canadian GAAP. It is recommended that heightened attention be directed to the new feature – comprehensive income – which incorporates unrealized gains and losses that bypass the income statement. The suggested analytical tools best suited to mitigate the contributing effect include reliance on comprehensive-Return on Assets (ROA) and comprehensive-Return on Equity (ROE).IFRS, financial ratios, first application of IFRS

Financiarisation de la stratégie d’entreprise et restructuration de l’industrie forestière. Étude de l’entreprise Tembec

Cet article analyse la montée en puissance des investisseurs financiers dans le capital des entreprises, à partir de l’étude du cas de l’entreprise Tembec. La dynamique de la stratégie de la compagnie est expliquée à travers une analyse de trois discours qui contribuent à la définition de la stratégie industrielle de l’entreprise : le discours sur les caractéristiques de l’industrie, le discours de l’entreprise sur sa stratégie, et les conférences téléphoniques entre les dirigeants de l’entreprise et les analystes financiers. La financiarisation de l’activité industrielle est ainsi illustrée à travers la redéfinition des outils de mesure, de la finalité et des dimensions de la stratégie d’entreprise.This article analyzes the rise in power of financial investors in business capital, based on a case study of the company Tembec. The dynamic of the company’s corporate strategy is explained through an analysis of three exchanges and communications that contributed to shaping that strategy. These were : views expressed on the characteristics of the industry, the company’s own stance regarding its strategy, and telephone conferences held between the company’s managers and financial analysts. The financialization of the activity of an industry is illustrated through the redefinition of the measurement tools, the business purpose and aspects of corporate strategy

Can we define a best estimator in simple one-dimensional cases?

International audienceWhat is the best estimator for assessing a parameter of a probability distribution from a small number of measurements? Is the same answer valid for a location parameter like the mean as for a scale parameter like the variance? It is sometimes argued that it is better to use a biased estimator with low dispersion than an unbiased estimator with a higher dispersion. In which cases is this assertion correct? To answer these questions, we will compare, on a simple example, the determination of a location parameter and a scale parameter with three "optimal" estimators: the minimum-variance unbiased estimator, the minimum square error estimator, and the a posteriori mean