Qiwei Yao

London School of Economics and Political Science

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Howell Tong

Xidian University

Jianqing Fan

Princeton University

Jinyuan Chang

Southwestern University of Finance and Economics

Liang Peng

Georgia State University

Peter Hall

Simon Fraser University

Zongwu Cai

University of Kansas

Dag Tjøstheim

University of Bergen

Wolfgang Polonik

University of California, Davis

Zudi Lu

University of Southampton

Clifford Lam

London School of Economics and Political Science

Cooperative Institutions

London School of Economics and Political Science

University of Leeds

Peking University

Chinese Academy of Sciences

University of Melbourne

National University of Singapore

University of Hong Kong

Zhejiang University of Technology

Zhejiang University

University of California, Los Angeles

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Modeling and Forecasting Daily Electricity Load Curves: A Hybrid Approach

Journal of the American Statistical Association (2012)

Haeran Cho Yannig Goude Xavier Brossat Qiwei Yao

We propose a hybrid approach for the modeling and the short-term forecasting of electricity loads. Two building blocks of our approach are (1) modeling the overall trend and seasonality by fitting a generalized additive model to the weekly averages of the load and (2) modeling the dependence structure across consecutive daily loads via curve linear regression. For the latter, a new methodology is proposed for linear regression with both curve response and curve regressors. The key idea behind the proposed methodology is dimension reduction based on a singular value decomposition in a Hilbert space, which reduces the curve regression problem to several ordinary (i.e., scalar) linear regression problems. We illustrate the hybrid method using French electricity loads between 1996 and 2009, on which we also compare our method with other available models including the Électricité de France operational model. Supplementary materials for this article are available online.

Generalized additive model

10.1080/01621459.2012.722900

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Citations (101)

Quantifying the influence of initial values on nonlinear prediction

LSE Research Online Documents on Economics (1994)

Qiwei Yao Howell Tong

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Smoothing for spatiotemporal models and its application to modeling Muskrat-Mink interaction

LSE Research Online Documents on Economics (2003)

Wenyang Zhang Qiwei Yao Howell Tong Nils Chr. Stenseth

For a set of spatially dependent dynamical models, we propose a method for estimating parameters that control temporal dynamics by spatial smoothing. The new approach is particularly relevant for analyzing spatially distributed panels of short time series. The asymptotic results show that spatial smoothing will improve the estimation in the presence of nugget effect, even when the sample size in each location is large. The proposed methodology is used to analyze the annual mink and muskrat data collected in a period of 25 years in 81 Canadian locations. Based on the proposed method, we are able to model the temporal dynamics which reflects the food chain interaction of the two species.

Smoothing

Sample (material)

Exponential Smoothing

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Large Volatility Matrix Inference via Combining Low-Frequency and High-Frequency Approaches

Journal of the American Statistical Association (2011)

Minjing Tao Yazhen Wang Qiwei Yao Jian Zou

It is increasingly important in financial economics to estimate volatilities of asset returns. However, most of the available methods are not directly applicable when the number of assets involved is large, due to the lack of accuracy in estimating high-dimensional matrices. Therefore it is pertinent to reduce the effective size of volatility matrices in order to produce adequate estimates and forecasts. Furthermore, since high-frequency financial data for different assets are typically not recorded at the same time points, conventional dimension-reduction techniques are not directly applicable. To overcome those difficulties we explore a novel approach that combines high-frequency volatility matrix estimation together with low-frequency dynamic models. The proposed methodology consists of three steps: (i) estimate daily realized covolatility matrices directly based on high-frequency data, (ii) fit a matrix factor model to the estimated daily covolatility matrices, and (iii) fit a vector autoregressive model to the estimated volatility factors. We establish the asymptotic theory for the proposed methodology in the framework that allows sample size, number of assets, and number of days go to infinity together. Our theory shows that the relevant eigenvalues and eigenvectors can be consistently estimated. We illustrate the methodology with the high-frequency price data on several hundreds of stocks traded in Shenzhen and Shanghai Stock Exchanges over a period of 177 days in 2003. Our approach pools together the strengths of modeling and estimation at both intra-daily (high-frequency) and inter-daily (low-frequency) levels.

Realized variance

Stock (firearms)

10.1198/jasa.2011.tm10276

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Citations (99)

Autoregressive Networks

arXiv (Cornell University) (2020)

Binyan Jiang Jialiang Li Qiwei Yao

We propose a first-order autoregressive (i.e. AR(1)) model for dynamic network processes in which edges change over time while nodes remain unchanged. The model depicts the dynamic changes explicitly. It also facilitates simple and efficient statistical inference methods including a permutation test for diagnostic checking for the fitted network models. The proposed model can be applied to the network processes with various underlying structures but with independent edges. As an illustration, an AR(1) stochastic block model has been investigated in depth, which characterizes the latent communities by the transition probabilities over time. This leads to a new and more effective spectral clustering algorithm for identifying the latent communities. We have derived a finite sample condition under which the perfect recovery of the community structure can be achieved by the newly defined spectral clustering algorithm. Furthermore the inference for a change point is incorporated into the AR(1) stochastic block model to cater for possible structure changes. We have derived the explicit error rates for the maximum likelihood estimator of the change-point. Application with three real data sets illustrates both relevance and usefulness of the proposed AR(1) models and the associate inference methods.

Stochastic block model

Statistical Inference

Spectral Clustering

10.48550/arxiv.2010.04492

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Efficient estimation of conditional variance functions in stochastic regression

Biometrika (1998)

Jianqing Fan Qiwei Yao

Journal Article Efficient estimation of conditional variance functions in stochastic regression Get access JIANQING FAN, JIANQING FAN Department of Statistics, University of CaliforniaLos Angeles, California 90095, U.S.A.jfan@stat.cla.edu Search for other works by this author on: Oxford Academic Google Scholar QIWEI YAO QIWEI YAO Institute of Mathematics and Statistics, University of KentCanterbury, Kent CT2 7NF, U.K.q.yao@ukc.ac.uk Search for other works by this author on: Oxford Academic Google Scholar Biometrika, Volume 85, Issue 3, September 1998, Pages 645–660, https://doi.org/10.1093/biomet/85.3.645 Published: 01 September 1998 Article history Received: 01 October 1996 Revision received: 01 November 1997 Published: 01 September 1998

Conditional expectation

10.1093/biomet/85.3.645

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Citations (469)

Nonparametric regression under dependent errors with infinite variance

LSE Research Online Documents on Economics (2004)

Liang Peng Qiwei Yao

We consider local least absolute deviation (LLAD) estimation for trend functions of time series with heavy tails which are characterised via a symmetric stable law distribution. The setting includes both causal stable ARMA model and fractional stable ARIMA model as special cases. The asymptotic limit of the estimator is established under the assumption that the process has either short or long memory autocorrelation. For a short memory process, the estimator admits the same convergence rate as if the process has the finite variance. The optimal rate of convergence n−2/5 is obtainable by using appropriate bandwidths. This is distinctly different from local least squares estimation, of which the convergence is slowed down due to the existence of heavy tails. On the other hand, the rate of convergence of the LLAD estimator for a long memory process is always slower than n−2/5 and the limit is no longer normal.

Least absolute deviations

Central limit theorem

Delta method

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Approximating conditional distribution functions using dimension reduction

The Annals of Statistics (2005)

Peter Hall Qiwei Yao

Motivated by applications to prediction and forecasting, we suggest methods for approximating the conditional distribution function of a random variable Y given a dependent random d-vector X. The idea is to estimate not the distribution of Y|X, but that of Y|θTX, where the unit vector θ is selected so that the approximation is optimal under a least-squares criterion. We show that θ may be estimated root-n consistently. Furthermore, estimation of the conditional distribution function of Y, given θTX, has the same first-order asymptotic properties that it would enjoy if θ were known. The proposed method is illustrated using both simulated and real-data examples, showing its effectiveness for both independent datasets and data from time series. Numerical work corroborates the theoretical result that θ can be estimated particularly accurately.

Conditional probability distribution

Sufficient dimension reduction

Conditional expectation

10.1214/009053604000001282

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Generalized Yule-Walker Estimation for Spatio-Temporal Models with Unknown Diagonal Coefficients

arXiv (Cornell University) (2015)

Baojun Dou Maria Lucia Parrella Qiwei Yao

We consider a class of spatio-temporal models which extend popular econometric spatial autoregressive panel data models by allowing the scalar coefficients for each location (or panel) different from each other. To overcome the innate endogeneity, we propose a generalized Yule-Walker estimation method which applies the least squares estimation to a Yule-Walker equation. The asymptotic theory is developed under the setting that both the sample size and the number of locations (or panels) tend to infinity under a general setting for stationary and alpha-mixing processes, which includes spatial autoregressive panel data models driven by i.i.d. innovations as special cases. The proposed methods are illustrated using both simulated and real data.

Endogeneity

10.48550/arxiv.1505.01177

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Methods for estimating a conditional distribution function

LSE Research Online Documents on Economics (1999)

Peter Hall Rodney Wolff Qiwei Yao

Motivated by the problem of setting prediction intervals in time series analysis, we suggest two new methods for conditional distribution estimation. The first method is based on locally fitting a logistic model and is in the spirit of recent work on locally parametric techniques in density estimation. It produces distribution estimators that may be of arbitrarily high order but nevertheless always lie between 0 and 1. The second method involves an adjusted form of the Nadaraya-Watson estimator. It preserves the bias and variance properties of a class of second-order estimators introduced by Yu and Jones but has the added advantage of always being a distribution itself. Our methods also have application outside the time series setting; for example, to quantile estimation for independent data. This problem motivated the work of Yu and Jones.

Quantile

Conditional probability distribution

Density estimation

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