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侯杰
助理教授
博士

电话:(010)8395-2487
电子邮件:jie_hou_cueb@163.com
办公室:诚明楼331
CV下载:
 

Education
Ph.D., Economics, Boston University, Boston MA, May 2014
  Dissertation Title: “Robust Parameter Estimation And Pivotal Inference under Heterogeneous And Nonstationary
  Processes”Main advisor: Pierre Perron

  Dissertation Committee: Pierre Perron, Ivan Fernandez-Val and Zhongjun Qu
M.S., Economics, HKUST, Clear Water Bay, Kowloon, Hong Kong, June 2007
M.S., Mathematics, Tsinghua University, Beijing, China, June 2006
B.A., Electronic Engineering, Tsinghua University, Beijing, China, June 2003


Fields of Interest
Econometrics, Mathematical Economics, Industrial Organization, Finance


Publications/Submitted Papers
“Triangularization of A Class of C1 Unipotent Maps,” (with Lijun Yang), the Fifth International Conference on Information
  and Management Sciences, Chengdu, China,
(2006). (ISTP Indexed).
“Modified Local Whittle Estimator for Long Memory Processes in the Presence of Low Frequency (and Other
  Contaminations,” (Jie Hou and Pierre Perron), Journal of Econometrics 182 (2014) 309–328


Working Papers

“Pivotal Inference on Structural Changes in Joint Trend Break Model with Heterogeneous Innovation,” (joint with Pierre
  Perron), April 2014, Revised October 2014

“Memory Parameter Estimation of Financial Time Series Robust to Low Frequency Contaminations,” October 2013, Revised
  September 2014


Work in Progress

“Using Interactive Effects Factor Analysis to Improve the Berry, Levinsohn and Pakes (1995) model used in industrial
  organization”


Referee Experience

Referee for Journal of Econometrics, International Journal of School and Cognitive Psychology


Honors, Fellowships and Awards
Dean’s Fellowship, Boston University, Fall 2007 to Spring 2012
Distinction in Microeconomics Qualifier Exam, Boston University, 2008
Special Research Fellowship, Boston University, Spring 2010, Fall 2010, Fall 2011.


Research Experience
Research Assistant for Prof. Pierre Perron, Department of Economics, Boston University, Fall 2009, Spring 2013


Teaching Experience
Teaching Assistant, Asset Pricing, Department of Economics, Boston University, Spring 2012
Teaching Assistant, Economic Statistics, Department of Economics, Boston University, Fall 2008, Spring 2009, Spring 2012
Teaching Fellow, Differential Equations, Department of Mathematics, Tsinghua University, Spring 2005
Teaching Fellow, Mathematical Analysis, Department of Mathematics, Tsinghua University, Fall 2004


Languages
Fluent in English, Native in Chinese


Computer Skills:
STATA, SAS, MATLAB, Gauss, Scientific WorkPlace, Microsoft Office, C, C++, JAVA, JSP, SQL, Software Engineering, Assembly Language

Citizenship:
China

References
Professor Pierre Perron
Room 449
Department of Economics
Boston University
Phone: (617) 353- 3026
Professor Zhongjun Qu
Room 312
Department of Economics
Boston University
Phone: (617) 353-3184
Email: qu@bu.edu
Professor Ivan Fernandez-Val
Room 415A
Department of Economics
Boston University
Phone: (617) 353-9670


Jie Hou’s
Papers, as of October 2014

“Local Whittle Memory Parameter Estimation of Long Memory Process in the Presence of Low Frequency Contaminations,” (Job Market Paper) (Jie Hou and Pierre Perron), Journal of Econometrics 182 (2014) 309–328
Link to a final submit version: https://dl.dropboxusercontent.com/u/22528834/LW-LFC-rev.pdf
Abstract: We propose a modified local-Whittle estimator of the memory parameter of a long memory time series process which has good properties under an almost complete collection of contamination processes that have been discussed in the literature, mostly separately. These contaminations include processes whose spectral density functions dominate at low frequencies such as random level shifts, deterministic level shifts and deterministic trends. We show that our modified estimator has the usual asymptotic distribution applicable for the standard local Whittle estimator in the absence of such contaminations. We also show how the estimator can be modified to further account for additive noise and that our modification for low frequency contamination reduces the bias due to short-memory dynamics. Through extensive simulations, we show that the proposed estimator provides substantial efficiency gains compared to existing semiparametric estimators in the presence of contaminations, with little loss of efficiency when these are absent.


“Robust Memory Parameter Estimates: A Re-examination of Daily and High-Frequency Asset Returns Volatility,” October 2013, Revised September 2014
Link: https://dl.dropboxusercontent.com/u/22528834/Emp_LWLFC_JieHou.pdf
Abstract: We apply the modified local-Whittle (LWLFC) estimator proposed in Hou and Perron (2013) to various volatilities series for stock indices and exchange rates to robustly estimate the long-memory parameter. We provide the first empirical approach for robustly estimating the memory parameters of data series that allows for coexistence of both the short-memory process and long-memory process, low frequency contaminations such as level shifts as well as additive noises. We provide a sufficient condition for the existence of long-memory and propose a mixed procedure that combines a modified Local-Whittle estimator and its perturbed and full-parametric variants to verify that sufficient condition in practice. Through our mixed procedure, we contribute to the literature by finding evidence of long-memory processes in most low frequency daily measures, suggesting a combination of a long-memory process, a noise, as well as a LFC in such data, with the relative magnitude of each of these components varying according to the specific series. We also perform extensive simulations to show the finite-sample properties of several modified LFC-robust LW estimators, including several perturbed and full-parametric estimators.

“Pivotal Inference for Structural Changes in a Joint Segmented Trend Model with Heterogeneous Innovations,” (Jie Hou and Pierre Perron), April 2014, Revised October 2014
Link:https://dl.dropboxusercontent.com/u/22528834/Trend_0419.pdf
Abstract: The issues addressed in this paper are related to testing for changes in the slope and variance of the noise in a linear time trend regression with changes in the slopes such that the series is joined at the break dates. We start with a single possible break in each and address the following issues: 1) testing for a change in trend with or without a change in variance; 2) testing for a change in variance with or without a change in trend. Asymptotically pivotal statistics are provided for each case. We then generalize some results to the case of multiple changes.


“On the Existence of a Pivotal Statistic for a Broad Range of "Searching for Missing Regressor" Problems,” April 2014, Draft available upon request
Abstract:
Using mathematical tools rarely employed in the field of Econometrics before,this paper gives a very general condition under which a pivotal statistic for coefficient break (e.g. trend break or mean shift) test is guaranteed to exist, under time-varying innovations. Since such statistic is derived from some maximized projections of a multi-dimensional standard normal variable and in its limit, Brownian motion, to a class of possible regressors, our results are also relevant to the literature of extreme value theory (EVT) in the sense that its key assumption, that the maximization has to be taken over a set of "strongly correlated" random variables, is exactly the opposite of the key assumption in EVT that the maximization is taken over a set of independent random variables. Moreover, we show that our result still holds if allowing for statistics without finite distribution before rescaling, hence includes EVT on normal variables as special case. We also point out that our results can be extended from scaling transformations to any invertible linear transformations.

To provide a proper framework for our result, we define a very general class of regression models, called "Searching for Missing Regressor" (SMR) models that incorporates all structural break models as its special cases. The innovation (idiosyncratic error) process is assumed to have a time-varying variance that covers almost all types of innovations that are not unit-root or fractionally integrated processes, which can also be included in our framework with a slight modification of argument. We argue that in SMR framework OLS in Perron and Zhu (2005) excels MLE in both estimation and testing. In the context of structural breaks, we briefly discuss the general results about consistency, rate of convergence and limit distribution of estimates of break fractions, coefficients and innovation parameters. Simulation results on a special case of "joint segmented trend break test under heterogeneous innovations" are also reported to illustrate the finite sample performance of the pivotal statistic.