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In statistics, samples are drawn from a population in a data- generating process (DGP). Standard errors measure the uncer- tainty in sample estimates of population parameters. In sci- ence, evidence is generated to test hypotheses in an evidence- generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sam- ple. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.

We introduce the XPER (eXplainable PERformance) methodology to measure the specific contribution of the input features to the predictive or economic performance of a model. Our methodology offers several advantages. First, it is both model-agnostic and performance metric-agnostic. Second, XPER is theoretically founded as it is based on Shapley values. Third, the interpretation of the benchmark, which is inherent in any Shapley value decomposition, is meaningful in our context. Fourth, XPER is not plagued by model specification error, as it does not require re-estimating the model. Fifth, it can be implemented either at the model level or at the individual level. In an application based on auto loans, we find that performance can be explained by a surprisingly small number of features, XPER decompositions are rather stable across metrics, yet some feature contributions switch sign across metrics. Our analysis also shows that explaining model forecasts and model performance are two distinct tasks.

International audience

International audience; In this study, we systemically apply nine recent panel unit root tests to the same fourteen macroeconomic and financial series as those considered in the seminal paper by Nelson and Plosser (1982). The data cover OECD countries from 1950 to 2003. Our results clearly point out the difficulty that applied econometricians would face when they want to get a simple and clear-cut diagnosis with panel unit root tests. We confirm the fact that panel methods must be very carefully used for testing unit roots in macroeconomic or financial panels. More precisely, we find mitigated results under the cross-sectional independence assumption, since the unit root hypothesis is rejected for many macroeconomic variables. When international cross-correlations are taken into account, conclusions depend on the specification of these cross-sectional dependencies. Two groups of tests can be distinguished. The first group tests are based on a dynamic factor structure or an error component model. In this case, the non stationarity of common factors (international business cycles or growth trends) is not rejected, but the results are less clear with respect to idiosyncratic components. The second group tests are based on more general specifications. Their results are globally more favourable to the unit root assumption.

This paper introduces a new class of models for the Value-at-Risk (VaR) and Expected Shortfall (ES), called the Dynamic AutoRegressive Expectiles (DARE) models. Our approach is based on a weighted average of expectile-based VaR and ES models, i.e. the Conditional Autoregressive Expectile (CARE) models introduced by Taylor (2008a) and Kuan et al. (2009). First, we briefly present the main non-parametric, parametric and semi-parametric estimation methods for VaR and ES. Secondly, we detail the DARE approach and show how the expectiles can be used to estimate quantile risk measures. Thirdly, we use various backtesting tests to compare the DARE approach to other traditional methods for computing VaR forecasts on the French stock market. Finally, we evaluate the impact of several conditional weighting functions and determine the optimal weights in order to dynamically select the more relevant global quantile model.

This paper presents a validation framework for collateral requirements or margins on a derivatives exchange. It can be used by investors, risk managers, and regulators to check the accuracy of a margining system. The statistical tests presented in this study are based either on the number, frequency, magnitude, or timing of margin exceedances, which are defined as situations in which the trading loss of a market participant exceeds his or her margin. We also propose an original way to validate globally the margining system by aggregating individual backtesting statistics obtained for each market participant.

This paper briefly presents new empirical tests of the convergence hypothesis based on panel data. We apply a modified Evans and Karras [1996] testing procedure to three samples (Europe, OECD and World). We propose a nested tests procedure to characterize various convergence processes: absolute or conditional, with or without a common speed of convergence. In the conditional convergence context, we are particularly concerned with the information included in individual effects. We find evidence supporting the existence of an absolute and common convergence process for per capita GDP in the European Union sample (1960-1990). As concerns the OECD sample, structural disparities imply only a conditional convergence process occurring at country-specific speeds. For our World sample (86 countries) there is no convergence process at all. For OECD countries, a first study of fixed effects is proposed. It shows that only the private and public investment ratios are significantly linked to those unobservable structural disparities.

We study whether the financial analysts' concern to maintain good relationships with firms' managers in order to preserve their access to 'soft' qualitative information entice them to issue pessimistic or optimistic forecasts. We use a gravity model approach to firmsanalysts relationships and propose a measure of soft information. Our database contains the one-year ahead EPS forecasts issued by 4 648 analysts about 241 French firms (1997-2007). We find that a low (high) pair-effect is associated with a low (high) forecast error. This suggests that pessimism and optimism result from analysts' concern to preserve access to soft information released by managers., Cet article évalue dans quelle mesure le souci des analystes financiers de maintenir de bonnes relations avec les entreprises, afin de conserver un accès à de l'information financière privée, les conduit à émettre des prévisions optimistes ou pessimistes. On utilise une approche en terme de modèles de gravité des relations entreprises-analystes par une régression en panel. Les données portent sur les prévisions à un an des bénéfices par actions par 4648 analystes sur 241 entreprises françaises (1997-2007). On trouve que plus l'effet-couple est faible (fort), plus l'erreur de prévision est faible (forte). Ceci accrédité l'idée selon laquelle le pessimisme ou l'optimisme des prévisions découle du souci de conserver un accès à l'information privée révélée par les dirigeants d'entreprise.

This paper proposes a new statistical framework originating from the traditional credit-scoring literature, to evaluate currency crises Early Warning Systems (EWS). Based on an assessment of the predictive power of panel logit and Markov frameworks, the panel logit model is outperforming the Markov switching specitcations. Furthermore, the introduction of forward-looking variables clearly improves the forecasting properties of the EWS. This improvement confirms the adequacy of the second generation crisis models in explaining the occurrence of crises.

The objective of this paper is to propose a market risk measure defined in price event time and a suitable backtesting procedure for irregularly spaced data. Firstly, we combine Autoregressive Conditional Duration models for price movements and a non parametric quantile estimation to derive a semi-parametric Irregularly Spaced Intraday Value at Risk (ISIVaR) model. This ISIVaR measure gives two information: the expected duration for the next price event and the related VaR. Secondly, we use a GMM approach to develop a backtest and investigate its finite sample properties through numerical Monte Carlo simulations. Finally, we propose an application to two NYSE stocks.

This article proposes an overview of the recent developments relating to panel unit root tests. After a brief review of the first generation panel unit root tests, this paper focuses on the tests belonging to the second generation. The latter category of tests is characterized by the rejection of the cross-sectional independence hypothesis. Within this second generation of tests, two main approaches are distinguished. The first one relies on the factor structure approach and includes the contributions of Bai and Ng (2001), Phillips and Sul (2003a), Moon and Perron (2004a), Choi (2002) and Pesaran (2003) among others. The second approach consists in imposing few or none restrictions on the residuals covariance matrix and has been adopted notably by Chang (2002, 2004), who proposed the use of nonlinear instrumental variables methods or the use of bootstrap approaches to solve the nuisanceparameter problem due to cross-sectional dependency.

This paper proposes an evaluation of backtests that examine the accuracy of Value-at-Risk (VaR) forecasts. It is well known that VaR backtesting procedures outlined by the Basel Committee for Banking Supervision have limited power to control the probability of accepting an incorrect VaR forecast. In this study, we propose an original approach based on the replication of these tests on six different VaR forecasts (parametric or non parametric) for a given asset. We show that backtests generally lead to not reject the accuracy of all (or most of) these different forecasts. In other words, most of VaR forecasts are likely to be considered as valid., Dans cet article, nous proposons une démarche originale visant à évaluer la capacité des tests usuels de backtesting à discriminer différentes prévisions de Value at Risk (VaR) ne fournissant pas la même évaluation ex-ante du risque. Nos résultats montrent que, pour un même actif, ces tests conduisent très souvent à ne pas rejeter la validité, au sens de la couverture conditionnelle, de la plupart des six prévisions de VaR étudiées, même si ces dernières sont sensiblement différentes. Autrement dit, toute prévision de VaR a de fortes chances d'être validée par ce type de procédure.

The authors provide various estimates of the government net capital stocks for a panel of 26 developing countries over the period 1970-2001. Two kinds of internationally comparable series of public capital stocks are presented. The first estimates are based on the standard perpetual inventory method and various assumptions regarding initial stocks and depreciation rates. The second set of estimates takes into account the potential inefficiency of public investments in creating capital with a nonparametric approach. Three estimates of net capital stocks are provided, on the basis of three assumptions regarding the efficiency of public investment.

Using panel data models, the author examines the threshold effects of the productivity of infrastructure investment in developing countries. He considers various specifications of an augmented production function that allow for endogenous thresholds. More precisely, these specifications are tested in a panel threshold regression model. The author's main robust result is the presence of strong threshold effects in the relationship between output and private and public inputs. Whatever the transition mechanism used, the testing procedures lead to strong rejection of the linearity of this relationship. In particular, the productivity of infrastructure investment generally exhibits some network effects. When the available stock of infrastructure is very low, investment in this sector has the same productivity as noninfrastructure investment. On the contrary, when a minimum network is available, the marginal productivity of infrastructure investment is generally largely greater than the productivity of other investment. Finally, when the main network is achieved, its marginal productivity becomes similar to the productivity of other investment.

This paper proposes an extension of the Granger (1969) causality definition to panel data models with fixed coefficients. Given the heterogeneity of the data generating process, four definitions of causality relationships are proposed. A procedure of nested tests is then defined and a particular attention is done to finite sample properties of these tests. Finally, an application to the link between financial deepening and economic growth is proposed with a panel of sixteen sub-Saharan countries over the period 1967-1998.

This paper proposes an evaluation of the main empirical approaches used in the literature to estimate the productive contribution of public capital stock to private factors productivity and growth. Our analysis is based on the replication of these approaches on pseudo samples generated using a stochastic general equilibrium model with endogenous public capital, built so as to reproduce the main long run relations observed on US postwar historical data. The results suggest that the production function approach may not be reliable to determine the macroeconomic returns of public infrastructures. Two sources of biases are identi…ed. The first one stems from the presence of a common stochastic trend shared by all non stationary inputs. In this case, we show that there is a spurious asymptotic constraint which leads to the equality between the public capital and the labor elasticities. Consequently, the production function approach largely over-estimates the productive contribution of infrastructures. The standard correction proposed in the literature, based on a specification in first differences of the production function, induces a fallacious inference on the public capital productivity.

As most Exchange-Traded Funds (ETFs) engage in securities lending or are based on total return swaps, they expose their investors to counterparty risk. In this paper, we estimate empirically such risk exposures for a sample of physical and swap-based funds. We find that counterparty risk exposure is higher for swap-based ETFs, but that investors are compensated for bearing this risk. Using a difference-in-differences specification, we uncover that ETF flows respond significantly to changes in counter-party risk. Finally, we show that switching to an optimal collateral portfolio leads to substantial reduction in counterparty risk exposure.

This paper proposes a very simple test of Granger (1969) non-causality for hetero- geneous panel data models. Our test statistic is based on the individual Wald statistics of Granger non causality averaged across the cross-section units. First, this statistic is shown to converge sequentially to a standard normal distribution. Second, the semi- asymptotic distribution of the average statistic is characterized for a fixed T sample. A standardized statistic based on an approximation of the moments of Wald statistics is hence proposed. Third, Monte Carlo experiments show that our standardized panel statistics have very good small sample properties, even in the presence of cross-sectional dependence.