Showing papers by "Oliver Linton published in 2022"
31 Jan 2022
TL;DR: In this paper , the authors investigate how to improve efficiency using regression adjustments with covariates in covariate-adaptive randomizations (CARs) with imperfect subject compliance, and propose an optimal but potentially misspecified linear adjustment and its further improvement via a nonlinear adjustment, both of which lead to more efficient estimators than the one without adjustments.
Abstract: We investigate how to improve efficiency using regression adjustments with covariates in covariate-adaptive randomizations (CARs) with imperfect subject compliance. Our regression-adjusted estimators, which are based on the doubly robust moment for local average treatment effects, are consistent and asymptotically normal even with heterogeneous probability of assignment and misspecified regression adjustments. We propose an optimal but potentially misspecified linear adjustment and its further improvement via a nonlinear adjustment, both of which lead to more efficient estimators than the one without adjustments. We also provide conditions for nonparametric and regularized adjustments to achieve the semiparametric efficiency bound under CARs.
2 citations
TL;DR: In this article , a statistical dynamic factor model for a large number of daily returns across multiple time zones is proposed, and three related estimators for practical use; Monte Carlo simulations reveal that two of them work well.
Abstract: Most stock markets are open for 6-8 hours per trading day. The Asian, European and American stock markets are separated in time by time-zone differences. We propose a statistical dynamic factor model for a large number of daily returns across multiple time zones. Our model has a common global factor as well as continent factors. Under a mild fixed-signs assumption, our model is identified and has a structural interpretation. We derive the asymptotic theories of the quasi-maximum likelihood estimator (QMLE) of our model. As QMLE is inefficient by definition in this article, we outline three related estimators for practical use; Monte Carlo simulations reveal that two of them work well. We then apply our model to two real data sets - the equity portfolio returns of Japan, Europe and US and MSCI equity indices of 41 developed and emerging markets. Some new insights about linkages between different markets are drawn. Last, a Bayesian estimator (i.e., the Gibbs sampling) is also explained and suitable for estimation when the number of stocks is not too big.
2 citations
TL;DR: In this paper , nonparametric tests for the null hypothesis of time stochastic dominance are proposed for ranking investment strategies or environmental policies based on the expected net present value of the future benefits.
1 citations
1 citations
TL;DR: In this article , a score test statistic for whether there is a stochastic trend in conditional variances of a GARCH process is proposed, which can be generalized to other conditional variance models.
TL;DR: The authors applied the Hole algorithm to evaluate the The authors2021 output quality exercise and found that implied journal ranking agrees quite closely with the ABS-SCOB journal ranking, and in particular the GPA's agree with a 91% correlation.
Abstract: We apply the Hole algorithm to evaluate the REF2021 output quality exercise. We find that the implied journal ranking agrees quite closely with the ABS-SCOB journal ranking, and in particular the GPA’s agree with a 91% correlation.
TL;DR: In this article , a regression adjusted local average treatment effect (LATE) estimator is proposed to improve efficiency in the estimation of LATEs under covariate-adaptive randomizations (CARs).
Abstract: We study regression adjustments with additional covariates in randomized experiments under covariate-adaptive randomizations (CARs) when subject compliance is imperfect. We develop a regression-adjusted local average treatment effect (LATE) estimator that is proven to improve efficiency in the estimation of LATEs under CARs. Our adjustments can be parametric in linear and nonlinear forms, nonparametric, and high-dimensional. Even when the adjustments are misspecified, our proposed estimator is still consistent and asymptotically normal, and their inference method still achieves the exact asymptotic size under the null. When the adjustments are correctly specified, our estimator achieves the minimum asymptotic variance. When the adjustments are parametrically misspecified, we construct a new estimator which is weakly more efficient than linearly and nonlinearly adjusted estimators, as well as the one without any adjustments. Simulation evidence and empirical application confirm efficiency gains achieved by regression adjustments relative to both the estimator without adjustment and the standard two-stage least squares estimator.
TL;DR: This paper develops new econometric methods for the estimation of high-dimensional panel data models with interactive fixed effects based on similar ideas as the very popular common correlated effects (CCE) estimator which is frequently used in the low-dimensional case.
Abstract: Interactive fixed effects are a popular means to model unobserved heterogeneity in panel data. Models with interactive fixed effects are well studied in the low-dimensional case where the number of parameters to be estimated is small. However, they are largely unexplored in the high-dimensional case where the number of parameters is large, potentially much larger than the sample size itself. In this paper, we develop new econometric methods for the estimation of high-dimensional panel data models with interactive fixed effects. Our estimator is based on similar ideas as the very popular common correlated effects (CCE) estimator which is frequently used in the low-dimensional case. We thus call our estimator a high-dimensional CCE estimator. We derive theory for the estimator both in the large-T-case, where the time series length T tends to infinity, and in the small-T-case, where T is a fixed natural number. The theoretical analysis of the paper is complemented by a simulation study which evaluates the finite sample performance of the estimator.
TL;DR: In this paper , the authors considered the case where the submatrices 1, . . . , K may be of full rank and the Kronecker product part of the time series was captured in a different way from that in (1); specifically, the eigenvalues of do not necessarily have a spike as in the factor model case, but nevertheless it can allow weak or strong dependence in the cross section depending on the parameters of the dk × dk matrices k.
Abstract: The difference is that in (2) the submatrices 1, . . . , K may be of full rank. The Kronecker product part in (2) captures the comovement of the time series in a different way from that in (1); specifically, the eigenvalues of do not necessarily have a spike as in the factor model case, but nevertheless it can allow weak or strong dependence in the cross section depending on the parameters of the dk × dk matrices k. Linton and Tang (2021) considered the case where σ 2 = 0 and where K → ∞ with dk finite, but allowing for σ 2 > 0 is relatively straightforward since this only adds to the diagonal elements of . The Kronecker product model (2) could be estimated by the quadratic form estimator of Linton and Tang (2021). As in Linton and Tang (2021), we shall impose tr( j) = dj for j = 1, . . . , K. Then, tr = (σ 2 + 1)d, where d = d1 ×· · ·× dK , so that
TL;DR: Li et al. as mentioned in this paper contributed to the first discussion meeting on statistical aspects of the Covid-19 Pandemic, which was held at the Royal Statistical Society (RSSA).
Abstract: Journal Article Shaoran Li, Oliver Linton and Shuyi Ge's Contribution to the ‘First Discussion Meeting on Statistical Aspects of the Covid-19 Pandemic’ Get access Shaoran Li, Shaoran Li Faculty of Economics, University of Cambridge, Cambridge, UK Search for other works by this author on: Oxford Academic Google Scholar Oliver Linton, Oliver Linton Faculty of Economics, University of Cambridge, Cambridge, UK Correspondence: Oliver Linton, Faculty of Economics, University of Cambridge, Cambridge, UK. Email: obl20@cam.ac.uk Search for other works by this author on: Oxford Academic Google Scholar Shuyi Ge Shuyi Ge Faculty of Economics, University of Cambridge, Cambridge, UK Search for other works by this author on: Oxford Academic Google Scholar Journal of the Royal Statistical Society Series A: Statistics in Society, Volume 185, Issue 4, October 2022, Pages 1836–1837, https://doi.org/10.1111/rssa.12933 Published: 18 November 2022 Article history Received: 10 December 2020 Accepted: 13 July 2021 Published: 18 November 2022
15 Oct 2022
TL;DR: In this article , generalized weighting estimators with weights estimated by solving an expanding set of equations were proposed to investigate causal mechanisms, and the proposed estimators are consistent and asymptotically normal.
Abstract: To investigate causal mechanisms, causal mediation analysis decomposes the total treatment effect into the natural direct and indirect effects. This paper examines the estimation of the direct and indirect effects in a general treatment effect model, where the treatment can be binary, multi-valued, continuous, or a mixture. We propose generalized weighting estimators with weights estimated by solving an expanding set of equations. Under some sufficient conditions, we show that the proposed estimators are consistent and asymptotically normal. Specifically, when the treatment is discrete, the proposed estimators attain the semiparametric efficiency bounds. Meanwhile, when the treatment is continuous, the convergence rates of the proposed estimators are slower than O ( N − 1 / 2 ) ; however, they are still more efficient than that constructed from the true weighting function. A simulation study reveals that our estimators exhibit a satisfactory finite-sample performance, while an application shows their practical value.
01 Jul 2022
TL;DR: In this article , the authors define an estimator that is meaningful in the non-parametric model and that specialises in the plausible model to a slope coefficient, but the estimator proposed is not the semi- parametric efficient estimator of that slope coefficient under the semi parametric model.
Abstract: there are no meaningful assump-tions made The that we have some plausible semi- parametric model, which is a special case of a more general non- parametric model, but we to allow for misspecification and in particular define an estimand that is meaningful in the non- parametric model and that specialises in the plausible model to a slope coefficient. However, the estimator that is proposed is not the semi- parametric efficient estimator of that slope coefficient under the semi- parametric model, we what is the role of the model at all? The it for example E ( Y | A , L ) is consistently parametric The machine learning methods estimate Y , the model? A , the
DOI•
TL;DR: In this article , the authors consider a panel data model which allows for heterogeneous time trends at different locations and propose a new estimation method for the model before establishing an asymptotic theory for the proposed estimation method.
Abstract: In this paper, we consider a panel data model which allows for heterogeneous time trends at different locations. We propose a new estimation method for the panel data model before we establish an asymptotic theory for the proposed estimation method. For inferential purposes, we develop a bootstrap method for the case where weak correlation presents in both dimensions of the error terms. We examine the finite–sample properties of the proposed model and estimation method through extensive simulated studies. Finally, we use the newly proposed model and method to investigate rainfall, temperature and sunshine data of U.K. respectively. Overall, we find the weather of winter has changed dramatically over the past fifty years. Changes may vary with respect to locations for the other seasons. Reference Details 2239 Cambridge Working Papers in Economics 2215 Janeway Institute Working Paper Series Published 20 June 2022
08 Feb 2022
TL;DR: In this paper , the authors proposed a statistical dynamic factor model for a large number of daily returns across multiple time zones and applied it to two real data sets: (1) equity portfolio returns from Japan, Europe and the US; (2) MSCI equity indices of 41 developed and emerging markets.
Abstract: Most stock markets are open for 6-8 hours per trading day. The Asian, European and American stock markets are separated in time by time-zone differences. We propose a statistical dynamic factor model for a large number of daily returns across multiple time zones. Our model has a common global factor as well as continental factors. Under a mild fixed-signs assumption, our model is identified and has a structural interpretation. We propose several estimators of the model: the maximum likelihood estimator-one day (MLE-one day), the quasi-maximum likelihood estimator (QMLE), an improved estimator from QMLE (QMLE-md), the QMLEres (similar to MLE-one day), and a Bayesian estimator (Gibbs sampling). We establish consistency, the rates of convergence and the asymptotic distributions of the QMLE and the QMLE-md. We next provide a heuristic procedure for conducting inference for the MLE-one day and the QMLE-res. Monte Carlo simulations reveal that the MLE-one day, the QMLE-res and the QMLE-md work well. We then apply our model to two real data sets: (1) equity portfolio returns from Japan, Europe and the US; (2) MSCI equity indices of 41 developed and emerging markets. Some new insights about linkages among different markets are drawn.
TL;DR: Shuyi Ge, Oliver Linton and Shaoran Li as discussed by the authors contributed to the first discussion meeting on statistical aspects of the Covid-19 Pandemic, which was held at the Royal Statistical Society (RSSA).
Abstract: Journal Article Shuyi Ge, Oliver Linton and Shaoran Li's Contribution to the ‘First Discussion Meeting on Statistical Aspects of the Covid-19 Pandemic’ Get access Shuyi Ge, Shuyi Ge Faculty of Economics, University of Cambridge, Cambridge, UK Search for other works by this author on: Oxford Academic Google Scholar Oliver Linton, Oliver Linton Faculty of Economics, University of Cambridge, Cambridge, UK Correspondence: Oliver Linton, Faculty of Economics, University of Cambridge, Cambridge, UK. Email: obl20@cam.ac.uk Search for other works by this author on: Oxford Academic Google Scholar Shaoran Li Shaoran Li Faculty of Economics, University of Cambridge, Cambridge, UK Search for other works by this author on: Oxford Academic Google Scholar Journal of the Royal Statistical Society Series A: Statistics in Society, Volume 185, Issue 4, October 2022, Pages 1831–1832, https://doi.org/10.1111/rssa.12929 Published: 18 November 2022 Article history Received: 10 December 2020 Accepted: 13 July 2021 Published: 18 November 2022