The 2015-16 Joint Statistics and Econometrics Seminar Series starts in October 2015. Further details will be published here soon.
This seminar series is a joint partnership with the STICERD econometrics programme.
During Michaelmas term, they take place on Friday mornings at 12pm in 32L.LG.03 (32 Lincoln's Inn Fields)
16 October 2015
Hiroaki Kaido (Boston University)
Robust cionfidence regions for incomplete methods
23 October 2015
Sebastian Kripfganz
Unconditional transformed likelihood estimation of time-space dynamic panel data model
13 November 2015
Mingli Chen (University of Warwick)
Estimation of nonlinear panel models with multiple unobserved effects
20 November 2015
Daniel Wilhelm (UCL)
Nonparametric instrumental variable estimation under monotonicity
27 November 2015
Ying-Ying Lee (Oxford University)
Welfare analysis for discrete choice with intervaldata on income
04 December 2015
Stepana Lazarova (Queen Mary University)
Data-driven GMM test for parameter instability
11 December 2015
Dennis Nekipelov (UC Berkeley)
Title to be confirmed
For full details of the seminars in October-December 2015 please visit the Department of Economics website here.
2016 Joint Statistics and Econometrics Seminars in the Department of Statistics
All Statistics and Joint Econometrics seminars during Lent Term will take place from 12.00pm to 1.00pm and will be followed by a buffet lunch from 1.00pm to 2.00pm. Unless otherwise specified they will take place in COL 6.15 (Leverhulme Library), Department of Statistics, 6th Floor Columbia House (69 Aldwych).
LSE maps and directions
Weichi Wu
Research Associate, Department of Statistical Sciences, UCL
Structural Change Detection for M-estimation Regression Under Time Series Non-stationarity
Abstract: We consider the structural change testing for a wide class of M-estimation regressions with non-stationary regressors and errors. New uniform Bahadur representations are established with nearly optimal approximation rates. A CUSUM-type test statistics based on the gradient vectors are considered. Two of the most frequently used change point testing procedures, pivotalization and independent wild bootstrap, are shown to be inconsistent for non-stationary time series M-estimation regression. In this paper, a simple bootstrap method is proposed and is proved to be consistent in the context of M-estimation regression for structural change detection under both abrupt and smooth non-stationarity and temporal dependence. Our bootstrap procedure is shown to have certain asymptotically optimal properties in terms of accuracy and power. Our methodology is applied to the Hong Kong circulatory and respiratory data, and asymmetry of structural changes in different quantiles and conditional mean are found.
Friday 29 January 2016
Andrew Harvey
Professor of Econometrics, University of Cambridge
Heavy Tails and Conditional Volatility
Abstract: Dynamic Conditional Score (DCS) models provide a unified framework for constructing nonlinear time series models. The emphasis is on models in which the conditional distribution of an observation may be heavy-tailed and the location and/or scale changes over time. In a multivariate context,correlation may change over time and, more generally, the parameters of a copula may change. The defining feature of DCS models is that the dynamics are driven by the score of the conditional distribution. When a suitable link function is employed for the changing parameter, analytic expressions may be derived for unconditional moments, autocorrelations and moments of multi-step forecasts. Furthermore a full asymptotic distribution theory for maximum likelihood estimators can be developed, including analytic expressions for asymptotic covariance matrices of the estimators.The talk will give an introduction to DCS models. Further details, including a link to the new CUP monograph on the topic, can be found on the website here
Friday 12 February 2016
Roel Oomen
Visiting Senior Fellow, Department of Statistics, LSE
Title (subject to change): The practice of FX spot trading and competition amongst liquidity providers
Friday 26 February 2016
Rajen Shah
Lecturer in Statistics, University of Cambridge
Goodness of fit tests for high-dimensional linear models
Abstract: In this talk I will introduce a framework for constructing goodness of fit tests in both low and high-dimensional linear models. The idea involves applying regression methods to the scaled residuals following either an ordinary least squares or Lasso fit to the data, and using some proxy for prediction error as the final test statistic. We call this family Residual Prediction (RP) tests. We show that simulation can be used to obtain the critical values for such tests in the low-dimensional setting, and demonstrate that some form of the parametric bootstrap can do the same when the high-dimensional linear model is under consideration. We show that RP tests can be used to test for significance of groups or individual variables as special cases, and here they compare favourably with state of the art methods, but we also argue that they can be designed to test for as diverse model misspecifications as heteroscedasticity and different types of nonlinearity.This is joint work with Peter Bühlmann.
Friday 11 March 2016, Room COL 6.15
Valentina Corradi
Professor of Econometrics, University of Surrey
Robust Forecast Comparison
Joint with Sainan Jin and Norman Swanson
Abstract: Forecast accuracy is typically measured in terms of a given loss function. However, as a consequence of the use of misspecified models in multiple model comparisons, relative forecast rankings are loss function dependent. This paper addresses this issue by using a novel criterion for forecast evaluation which is based on the entire distribution of forecast errors. We introduce the concepts of general-loss (GL) forecast superiority and convex-loss (CL) forecast superiority; and develop tests for GL (CL) superiority that are based on an out-of-sample generalization of the tests introduced by Linton,Maasoumi and Whang (2005). The asymptotic null distributions of our test statistics are nonstandard, and resampling procedures are used to obtain critical values. Additionally, the tests are consistent and have nontrivial local power under a sequence of local alternatives. In addition to the stationary case, we outline theory extending our tests to the case of heterogeneity induced by distributional change overtime. Monte Carlo simulations suggest that the tests perform reasonably well in finite samples; and an application to exchange rate data indicates that our tests can help identify superior forecasting models, regardless of loss function.
You can view the paper related to this talk here.