Joint Statistics and Econometrics Seminar Series

9th December 2016 - 32L.LG.03

Speaker - Javier Hidalgo (LSE)

Title - TBC

2nd December 2016 - 32L.LG.03

Speaker - Peter Robinson (LSE)

Title - Inference on trending panel data

25th November 2016 - 32L.LG.03

Speaker - Yungyoon Lee (Royal Holloway, University of London) 

Title - Misspecification testing in spatial autoregressive models

18th November 2016 - 32L.LG.03 

Speaker - Dongwoo Kim (UCL)

Title - Nonseparable unobserved hetereogeneity and partial identificaion in IV models for count outcomes

11th November 2016 - 32L.LG.03 

Speaker - Namhyun Kim (Exeter University)

Title - A new approach for dynamic event count data models   

10th November 2016 - 32L.LG.03 

Speaker - Patrick Wongsa-Art (Newcastle University)

Title - TBC

4th November 2016 -  32L.LG.03 

Speaker - Matt Masden (Duke University), joint with Alexandre Poirier

Title - Partial independence in nonseparable models
Download the paper

7th October 2016 - 32L.LG.03 

Speaker - Emmanuel Guerre (QMW), joint with Nathalie Gimenes

Title - Quantile methods for first-price auctions: a signal approach

30th September 2016 - 32L.LG.03 

Speaker - Marcelo Moreira (Fundação Getúlio Vargas (FGV/EPGE)), joint with Humberto Moreira.

Title - Optimal two-sided Tests for instrumental variables regression with heteroskedastic and autocorrelated errors.

Download the paper

Esther Ruiz

Universidad Carlos III

24th March 2017 - 12-1pm in the Leverhulme Library COL.6.15

Title - The uncertainty of principal components in dynamic factor models

Abstract - Dynamic Factor Models (DFM) are often fitted to large systems of multivariate time series to represent the evolution of underlying factors. Given that these factors are usually unobserved, to correctly interpret their estimated counterparts, one needs a measure of their uncertainty. In the context of very large systems of economic and financial variables, it is popular to extract factors using the computationally easy although non-efficient Principal Components (PC) procedure.

The asymptotic distribution of factors extracted by PC is known. However, for the sample sizes and cross-sectional dimensions usually encountered in practice, the asymptotic distribution is not an appropriate approximation to the finite sample one. We propose using bootstrap procedures to approximate the finite sample distribution of the factors extracted by PC to have a realistic picture of their associated uncertainty.

The finite sample properties of the proposed procedure are analyzed and compared with those of the asymptotic distribution and alternative bootstrap procedures previously proposed in the context of DFM. The results are empirically illustrated obtaining confidence intervals of the underlying factor in a system of Spanish macroeconomic variables and in a system of in house process of advanced and emerging markets. Joint work with Javier de Vicente. 

Lorenzo Trapani

Cass Business School

10th March 2017 - 12-1pm in the Leverhulme Library COL.6.15

Title - Sequential testing for structural stability in approximate factor models

Abstract - We develop a a family of monitoring procedures to detect a change in a large factor model. Our statistics are based on the following property of the (r+1)-th eigenvalue of the sample covariance matrix of the data: whilst under the null the (r+1)-th eigenvalue is bounded, under the alternative of a change (either in the loadings, or in the number of factors itself) it becomes spiked. Given that the sample eigenvalue does not have a known limiting distribution under the null, we regularise the problem by randomising the test statistic in conjunction with sample conditioning, obtaining a sequence of i.i.d., asymptotically chi-squared statistics which are then employed to build the monitoring scheme. Numerical evidence shows that our procedure works very well in finite samples, with a very small probability of false detections and tight detection times in presence of a genuine change point. Joint with Matteo Barigozzi.

Siegfried Hörmann

Université libre de Bruxelles

24th February 2017 - 12-1pm in the Leverhulme Library COL.6.15

Title - Detection of periodicity in functional time series

Abstract - Periodicity is one of the most important characteristics of time series, and tests for periodicity go back to the very origins of the eld. The importance of such tests has manifold reason. One of them is that most inferential pro-cedures require that the series be stationary, but classical stationarity tests (as e.g. KPSS procedures) have little power against a periodic component inthe mean.

In this account we respond to the need to develop periodicity tests for functional time series (FTS). Examples of FTS's include annual temperature or smoothed precipitation curves, daily pollution level curves, various daily curves derived from high frequency asset price data, daily bond yield curves, daily vehicle trac curves and many others. One of the important contributions of this article is the development of a fully functional ANOVA test for stationary data. If the functional time series (Yt) satises a certain weak-dependence condition, then, using a fre- quency domain approach, we obtain the asymptotic null-distribution (for the constant mean hypothesis) of the functional ANOVA statistic.

The limiting distribution has an interesting form and can be written as a sum of independent hypoexponential variables whose parameters are eigenvalues of the spectral density operator of (Yt). To the best of our knowledge, there exists no comparable asymptotic result in FDA literature. Adapting ANOVA for dependence is one way to conduct periodicity analysis. It is suitable when the periodic component has no particular form. If, however, the alternative is more specic or the period is large then we can construct simpler and more powerful tests. We hence introduce three dif- ferent models with increasing complexity and develop the appropriate test statistics.

The power-advantage will be illustrated in simulations and by a theoretical case study where we consider local consistency results for three specic alternatives.A common approach to inference for functional data is to project obser- vations onto a low dimensional basis system and then to apply a suitable multivariate procedure to the vector of projections. This approach will also be explained and discussed.

The talk is based on joint work with Piotr Kokoszka (Colorado State University) and Gilles Nisol (ULB). 

Davy Paindaveine

Université libre de Bruxelles

27th January 2017 - 12-1pm in the Leverhulme Library COL.6.15

Title - Testing uniformity on high-dimensional spheres against monotone rotationally symmetric alternatives

Abstract - We consider the problem of testing uniformity on high-dimensional unit spheres. We are primarily interested in non-null issues. We show that rotationally symmetric alternatives lead to two Local Asymptotic Normality (LAN) structures.

The first one is for fixed modal location θ and allows to derive locally asymptotically most powerful tests under specified θ. The second one, that addresses the Fisher–von Mises–Langevin (FvML) case, relates to the unspecified-θ problem and shows that the high-dimensional Rayleigh test is locally asymptotically most powerful invariant. Under mild assumptions, we derive the asymptotic non-null distribution of this test, which allows to extend away from the FvML case the asymptotic powers obtained there from Le Cam’s third lemma.

Throughout, we allow the dimension p to go to infinity in an arbitrary way as a function of the sample size n. Some of our results also strengthen the local optimality properties of the Rayleigh test in low dimensions. We perform a Monte Carlo study to illustrate our asymptotic results. Finally, we treat an application related to testing for sphericity in high dimensions.

Joint work with Christine Cutting and Thomas Verdebout.

Zudi Lu

University of Southampton

13th January 2017 - 12-1pm in the Leverhulme Library COL.6.15

Title - Some recent progress on nonlinear spatial modelling: A personal review

Abstract - Larger amounts of spatial or spatiotemporal data with more complex structures collected at irregularly spaced sampling locations are prevalent in a wide range of disciplines. With few exceptions, however, practical statistical methods for nonlinear modeling and analysis of such data remain elusive. In this talk, I provide a review on some developments and progress of the research that my co-authors and I have recently done.

In particular, we will look at some nonparametric methods for probability, including joint, density estimation, and semiparametric models for a class of spatio-temporal nonlinear regression permitting possibly nonlinear relationship between response and covariates, with location-dependent spatial neighbouring and temporal lag effects taken account of. In the setting of semiparametric spatiotemporal modelling, a computationally feasible data-driven method is also shown for spatial weight matrix estimation. For illustration, our methodology is applied to investigate some land and housing prices data sets.

Giuseppe Cavaliere

University of Bologna

13th January 2017 - 12-1pm in the Leverhulme Library COL.6.15

Title - Bootstrap inference under random distributional limits

Abstract - Asymptotic bootstrap validity is usually understood and established as consistency of the distribution of a bootstrap statistic, conditional on the data, for the unconditional limit distribution of a statistic of interest. Cases where the limit measure induced by the bootstrap is random are therefore regarded as cases where bootstrap inference is invalid.

However, apart from possessing at most one unconditional limit distribution under a fixed asymptotic scheme, a statistic in general may possess a host of conditional (random) limit distributions, depending on the choice of the conditioning sets. We discuss the appropriate probabilistic tools for establishing asymptotic bootstrap validity, in terms of asymptotic distributional uniformity of bootstrap p-values, in the case where the distribution of the bootstrap statistic conditional on the data estimates consistently a conditional limit distribution of a statistic, in a sense weaker than the usual weak convergence in probability.

We provide two general sufficient conditions for bootstrap validity in cases where weak convergence in probability fails. Finally, we apply our result to tests of parameter constancy in a general regression model based, providing a rigorous analysis of the validity of inference based on the fixed regressor bootstrap. 

Joint work with Iliyan Georgiev.