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research.html
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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1">
<meta name="description" content="Research by Yuxuan Wnag">
<meta name="author" content="Yuxuan Wang">
<meta name="keywords" content="Yuxuan Wang, Computer Science, MSc student, CUHK, Hong Kong, Golang, dubbo-go, Microservice, Distributed System" />
<link rel="shortcut icon" type="image/x-icon" href="siteicon.ico">
<title>Yuxuan Wang - Research</title>
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<h2 class="page-header"> Working Papers </h2>
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<!-- <div class="panel-heading">-->
<!-- <h4>Bayesian Estimation of Fractionally Integrated Vector Autoregressions and an Application to Identified Technology Shocks</h4>-->
<!-- <h5>(with <a href="https://sites.google.com/site/rossdoppelt/" target="_blank">Ross Doppelt</a>)</h5>-->
<!-- <h5><em>Revise and Resubmit at Journal of Econometrics</em></h5>-->
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<!-- <!– <hr /> –>-->
<!-- <h4><strong style="font-size: 90%;"><a data-toggle="collapse" href="#collapse1">Abstract</a> | <a href="docs/Doppelt_OHara_FIVARs.pdf" target="_blank">Paper</a> </strong></h4>-->
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<!-- <div id="collapse1" class="panel-collapse collapse">-->
<!-- <div class="panel-body">-->
<!-- <p>We introduce a new method for Bayesian estimation of fractionally integrated vector autoregressions (FIVARs). -->
<!-- The FIVAR, which nests a standard VAR as a special case, allows each series to exhibit long memory, meaning that -->
<!-- low frequencies can play a dominant role — a salient feature of many macroeconomic and financial time series. -->
<!-- Although the parameter space is typically high-dimensional, our inferential procedure is computationally tractable -->
<!-- and relatively easy to implement. We apply our methodology to the identification of technology shocks, an empirical -->
<!-- problem in which business-cycle predictions depend on carefully accounting for low-frequency fluctuations.</p>-->
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<!-- <h4>Posterior Sampling in Two Classes of Multivariate Fractionally Integrated Models: Corrigendum to Ravishanker, N. and B. K. Ray (1997) Australian Journal of Statistics 39 (3), 295-311</h4>-->
<!-- <h5>(with <a href="https://sites.google.com/site/rossdoppelt/" target="_blank">Ross Doppelt</a>)</h5>-->
<!-- <h5><em>Provisionally accepted at Australian & New Zealand Journal of Statistics</em></h5>-->
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<!-- <!– <hr /> –>-->
<!-- <h4><strong style="font-size: 90%;"><a data-toggle="collapse" href="#collapse2">Abstract</a> | <a href="docs/Doppelt_OHara_VARFIMA.pdf" target="_blank">Paper</a> </strong></h4>-->
<!-- </div>-->
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<!-- <div class="panel-body">-->
<!-- <p>We discuss posterior sampling for two distinct multivariate generalizations of the univariate ARIMA model with fractional integration. -->
<!-- The existing approach to Bayesian estimation, introduced by Ravishanker and Ray (1997), claims to provide a posterior-sampling algorithm -->
<!-- for fractionally integrated vector autoregressive moving averages (FIVARMAs). We show that this algorithm produces posterior draws for -->
<!-- vector autoregressive fractionally integrated moving averages (VARFIMAs), a model of independent interest that has not previously received -->
<!-- attention in the Bayesian literature.</p>-->
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<!-- <h4>Yogurts Choose Consumers? Estimation of Random Utility Models via Two-Sided Matching</h4>-->
<!-- <h5>(with O. Bonnet, A. Galichon, and M. Shum)</h5>-->
<!-- <h5><em>Submitted</em></h5>-->
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<!-- <!– <hr /> –>-->
<!-- <h4><strong style="font-size: 90%;"><a data-toggle="collapse" href="#collapse3">Abstract</a> | <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2928876" target="_blank">Paper</a> </strong></h4>-->
<!-- </div>-->
<!-- <div id="collapse3" class="panel-collapse collapse">-->
<!-- <div class="panel-body">-->
<!-- <p>In this paper we show that the problem of demand inversion in multinomial choice models is equivalent to the -->
<!-- determination of stable outcomes in matching models. This result is very general and applies to random utility -->
<!-- models that are not necessarily additive or smooth. Based on this equivalence, we argue that the algorithms for -->
<!-- the determination of stable matchings can provide effective computational methods to inverse multinomial choice -->
<!-- models, and we give a numerical benchmark of these algorithms. Our approach allows to estimate models that were -->
<!-- previously difficult to estimate, such as the pure characteristics model, as well as nonadditive random utility -->
<!-- models. The equivalence also allows to exploit the theory of stable matchings in order to describe important -->
<!-- properties of the set of utilities solution to the demand inversion problem, and to study the cases of existence -->
<!-- and uniqueness of identified utilities, as well as obtain consistency results.</p>-->
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