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on Econometrics |
| By: | Cheng Yu; Dong Li; Feiyu Jiang; Ke Zhu |
| Abstract: | Matrix-variate time series data are largely available in applications. However, no attempt has been made to study their conditional heteroskedasticity that is often observed in economic and financial data. To address this gap, we propose a novel matrix generalized autoregressive conditional heteroskedasticity (GARCH) model to capture the dynamics of conditional row and column covariance matrices of matrix time series. The key innovation of the matrix GARCH model is the use of a univariate GARCH specification for the trace of conditional row or column covariance matrix, which allows for the identification of conditional row and column covariance matrices. Moreover, we introduce a quasi maximum likelihood estimator (QMLE) for model estimation and develop a portmanteau test for model diagnostic checking. Simulation studies are conducted to assess the finite-sample performance of the QMLE and portmanteau test. To handle large dimensional matrix time series, we also propose a matrix factor GARCH model. Finally, we demonstrate the superiority of the matrix GARCH and matrix factor GARCH models over existing multivariate GARCH-type models in volatility forecasting and portfolio allocations using three applications on credit default swap prices, global stock sector indices, and future prices. |
| Date: | 2023–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2306.05169 |
| By: | Justin Dang (Department of Economics, University of San Diego); Aman Ullah (Department of Economics, University of California Riverside) |
| Abstract: | A two-step estimator of a nonparametric regression function via KRLS with parametric error covariance is proposed. The KRLS, not considering any information in the error covariance, is improved by incorporating a parametric error covariance, allowing for both heteroskedasticity and autocorrelation, in estimating the regression function. A two step procedure is used, where in the first step, the parametric error covariance is estimated from the residuals obtained by a KRLS regression and in the second step, another KRLS regression based on transformed variables from the error covariance is estimated. Theoretical results including bias, variance, and asymptotics are derived. Simulation results show that the proposed estimator outperforms the KRLS in both heteroskedastic errors and autocorrelated error cases. An empirical example is illustrated with estimating an airline cost function under a random effects model with heteroskedastic and correlated errors. The derivatives are evaluated, and the average partial effects of the inputs are determined in the application. |
| Keywords: | Nonparametric estimator; Semiparametric models; Machine Learning; Kernel Regularized Least Squares; Covariance; Heteroskedasticity; Serial Correlation |
| JEL: | C C01 C1 C13 C14 C5 C51 C52 |
| Date: | 2022–06 |
| URL: | https://d.repec.org/n?u=RePEc:ucr:wpaper:202303 |
| By: | Xuan Liang; Tao Zou |
| Abstract: | With the rapid advancements in technology for data collection, the application of the spatial autoregressive (SAR) model has become increasingly prevalent in real-world analysis, particularly when dealing with large datasets. However, the commonly used quasi-maximum likelihood estimation (QMLE) for the SAR model is not computationally scalable to handle the data with a large size. In addition, when establishing the asymptotic properties of the parameter estimators of the SAR model, both weights matrix and regressors are assumed to be nonstochastic in classical spatial econometrics, which is perhaps not realistic in real applications. Motivated by the machine learning literature, this paper proposes quasi-score matching estimation for the SAR model. This new estimation approach is still likelihood-based, but significantly reduces the computational complexity of the QMLE. The asymptotic properties of parameter estimators under the random weights matrix and regressors are established, which provides a new theoretical framework for the asymptotic inference of the SAR-type models. The usefulness of the quasi-score matching estimation and its asymptotic inference is illustrated via extensive simulation studies and a case study of an anti-conflict social network experiment for middle school students. |
| Date: | 2023–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2305.19721 |
| By: | Hyunseok Jung; Xiaodong Liu |
| Abstract: | This paper proposes an Anderson-Rubin (AR) test for the presence of peer effects in panel data without the need to specify the network structure. The unrestricted model of our test is a linear panel data model of social interactions with dyad-specific peer effects. The proposed AR test evaluates if the peer effect coefficients are all zero. As the number of peer effect coefficients increases with the sample size, so does the number of instrumental variables (IVs) employed to estimate the unrestricted model, rendering Bekker's many-IV environment. By extending existing many-IV asymptotic results to panel data, we show that the proposed AR test is asymptotically valid under the presence of both individual and time fixed effects. We conduct Monte Carlo simulations to investigate the finite sample performance of the AR test and provide two applications to demonstrate its empirical relevance. |
| Date: | 2023–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2306.09806 |
| By: | Jiti Gao; Bin Peng; Yanrong Yang |
| Abstract: | In this paper, we propose a localized neural network (LNN) model and then develop the LNN based estimation and inferential procedures for dependent data in both cases with quantitative/qualitative outcomes. We explore the use of identification restrictions from a nonparametric regression perspective, and establish an estimation theory for the LNN setting under a set of mild conditions. The asymptotic distributions are derived accordingly, and we show that LNN automatically eliminates the dependence of data when calculating the asymptotic variances. The finding is important, as one can easily use different types of wild bootstrap methods to obtain valid inference practically. In particular, for quantitative outcomes, the proposed LNN approach yields closed-form expressions for the estimates of some key estimators of interest. Last but not least, we examine our theoretical findings through extensive numerical studies. |
| Date: | 2023–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2306.05593 |
| By: | Shunsuke Imai; Yuta Okamoto |
| Abstract: | Local polynomial density (LPD) estimation has become an essential tool for boundary inference, including manipulation tests for regression discontinuity. It is common sense that kernel choice is not critical for LPD estimation, in analogy to standard kernel smoothing estimation. This paper, however, points out that kernel choice has a severe impact on the performance of LPD, based on both asymptotic and non-asymptotic theoretical investigations. In particular, we show that the estimation accuracy can be extremely poor with commonly used kernels with compact support, such as the triangular and uniform kernels. Importantly, this negative result implies that the LPD-based manipulation test loses its power if a compactly supported kernel is used. As a simple but powerful solution to this problem, we propose using a specific kernel function with unbounded support. We illustrate the empirical relevance of our results with numerous empirical applications and simulations, which show large improvements. |
| Date: | 2023–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2306.07619 |
| By: | Didier Nibbering; Matthijs Oosterveen |
| Abstract: | The effect of the full treatment is a primary parameter of interest in policy evaluation, while often only the effect of a subset of treatment is estimated. We partially identify the local average treatment effect of receiving full treatment (LAFTE) using an instrumental variable that may induce individuals into only a subset of treatment (movers). We show that movers violate the standard exclusion restriction, necessary conditions on the presence of movers are testable, and partial identification holds under a double exclusion restriction. We identify movers in four empirical applications and estimate informative bounds on the LAFTE in three of them. |
| Date: | 2023–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2306.07018 |
| By: | Runyu Dai; Yasumasa Matsuda |
| Abstract: | In this paper, we propose a parsimonious model to estimate large volatility matrices by combining DCC-GARCH, sparsity-induced weak factors (sWFs) and POET framework in Fan et al. (2013). We call this method the DCC and sWFs extended POET (DCC-ePOET). Built on the mixed factor structures, we estimate volatility matrices through the univariate volatilities of observable factors and weak latent factors with a linear transformation. We further include a sparse noise covariance estimator obtained by an aptivethreshold method proposed in POET to dressthe singularity issue when the cross-sectional dimension N is larger than the sample size T, and capture the weak correlations in the factor models'idiosyncratic terms. Simulation studies show that our proposed method achieves good finite-sample performance. Empirical studies demonstrate that the developed method is superior to several candidates in the analysis of out-of-sample minimum variance portfolio allocations. |
| Date: | 2023–06 |
| URL: | https://d.repec.org/n?u=RePEc:toh:dssraa:135 |
| By: | Alex Maynard; Katsumi Shimotsu; Nina Kuriyama |
| Abstract: | This paper studies inference in predictive quantile regressions when the predictive regressor has a near-unit root. We derive asymptotic distributions for the quantile regression estimator and its heteroskedasticity and autocorrelation consistent (HAC) t-statistic in terms of functionals of Ornstein-Uhlenbeck processes. We then propose a switching-fully modified (FM) predictive test for quantile predictability with persistent regressors. The proposed test employs an FM style correction with a Bonferroni bound for the local-to-unity parameter when the predictor has a near unit root. It switches to a standard predictive quantile regression test with a slightly conservative critical value when the largest root of the predictor lies in the stationary range. Simulations indicate that the test has reliable size in small samples and particularly good power when the predictor is persistent and endogenous, i.e., when the predictive regression problem is most acute. We employ this new methodology to test the ability of three commonly employed, highly persistent and endogenous lagged valuation regressors - the dividend price ratio, earnings price ratio, and book to market ratio - to predict the median, shoulders, and tails of the stock return distribution. |
| Date: | 2023–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2306.00296 |
| By: | Chen, Zezhun Chen; Dassios, Angelos; Tzougas, George |
| Abstract: | This article considers bivariate mixed Poisson INAR(1) regression models with correlated random effects for modelling correlations of different signs and magnitude among time series of different types of claim counts. This is the first time that the proposed family of INAR(1) models is used in a statistical or actuarial context. For expository purposes, the bivariate mixed Poisson INAR(1) claim count regression models with correlated Lognormal and Gamma random effects paired via a Gaussian copula are presented as competitive alternatives to the classical bivariate Negative Binomial INAR(1) claim count regression model which only allows for positive dependence between the time series of claim count responses. Our main achievement is that we develop novel alternative Expectation-Maximization type algorithms for maximum likelihood estimation of the parameters of the models which are demonstrated to perform satisfactory when the models are fitted to Local Government Property Insurance Fund data from the state of Wisconsin. |
| Keywords: | count data time series; Binomial-mixed Poisson INAR(1) regression models with correlated random effects; overdispersion; Gaussian copula; correlations of different signs and magnitude; Springer deal |
| JEL: | C1 |
| Date: | 2023–06–06 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:118826 |
| By: | Alain-Philippe Fortin (University of Geneva; Swiss Finance Institute); Patrick Gagliardini (Università della Svizzera italiana; Swiss Finance Institute); Olivier Scaillet (University of Geneva; Swiss Finance Institute) |
| Abstract: | We develop inferential tools for latent factor analysis in short panels. The pseudo maximum likelihood setting under a large cross-sectional dimension n and a fixed time series dimension T relies on a diagonal T x T covariance matrix of the errors without imposing sphericity or Gaussianity. We outline the asymptotic distributions of the latent factor and error covariance estimates as well as of an asymptotically uniformly most powerful invariant (AUMPI) test based on the likelihood ratio statistic for tests of the number of factors. We derive the AUMPI characterization from inequalities ensuring the monotone likelihood ratio property for positive definite quadratic forms in normal variables. An empirical application to a large panel of monthly U.S. stock returns separates date after date systematic and idiosyncratic risks in short subperiods of bear vs. bull market based on the selected number of factors. We observe an uptrend in idiosyncratic volatility while the systematic risk explains a large part of the cross-sectional total variance in bear markets but is not driven by a single factor. Rank tests reveal that observed factors struggle spanning latent factors with a discrepancy between the dimension of the two factor spaces decreasing over time. |
| Keywords: | Latent factor analysis, uniformly most powerful invariant test, panel data, large n and fixed T |
| JEL: | C12 C23 C38 C58 G12 |
| Date: | 2023–06 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2344 |
| By: | Daouia, Abdelaati; Stupfler, Gilles; Usseglio-Carleve, Antoine |
| Abstract: | The expectile is a prime candidate for being a standard risk measure in actuarial and financial contexts, for its ability to recover information about probabilities and typical behavior of extreme values as well as its excellent axiomatic properties. A series of recent papers has focused on expectile estimation at extreme levels, with a view on gathering essential information about low-probability, high-impact events that are of most interest to risk managers. The obtention of accurate confidence intervals for extreme expectiles is paramount in any decision process in which they are involved, but actual inference on these tail risk measures is still a difficult question due to their least squares nature and sensitivity to tail heaviness. This article focuses on asymptotic Gaussian inference about tail expectiles in the challenging context of heavy-tailed observations. We use an in-depth analysis of the proofs of asymptotic normality results for two classes of extreme expectile estimators to derive bias-reduced and variance-corrected Gaussian confidence intervals. These, unlike previous attempts in the literature, are well-rooted in statistical theory and can accommodate underlying distributions that display a wide range of tail behaviors. A large-scale simulation study and three real data analyses confirm the versatility of the proposed technique. |
| Keywords: | Asymptotic normality; Bias correction; Expectiles; Extreme values; Heavy tails; Inference; Variance correction |
| Date: | 2023–06–07 |
| URL: | https://d.repec.org/n?u=RePEc:tse:wpaper:128141 |
| By: | Evan Munro; David Jones; Jennifer Brennan; Roland Nelet; Vahab Mirrokni; Jean Pouget-Abadie |
| Abstract: | In online platforms, the impact of a treatment on an observed outcome may change over time as 1) users learn about the intervention, and 2) the system personalization, such as individualized recommendations, change over time. We introduce a non-parametric causal model of user actions in a personalized system. We show that the Cookie-Cookie-Day (CCD) experiment, designed for the measurement of the user learning effect, is biased when there is personalization. We derive new experimental designs that intervene in the personalization system to generate the variation necessary to separately identify the causal effect mediated through user learning and personalization. Making parametric assumptions allows for the estimation of long-term causal effects based on medium-term experiments. In simulations, we show that our new designs successfully recover the dynamic causal effects of interest. |
| Date: | 2023–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2306.00485 |
| By: | Kasper Johansson; Mehmet Giray Ogut; Markus Pelger; Thomas Schmelzer; Stephen Boyd |
| Abstract: | We consider the well-studied problem of predicting the time-varying covariance matrix of a vector of financial returns. Popular methods range from simple predictors like rolling window or exponentially weighted moving average (EWMA) to more sophisticated predictors such as generalized autoregressive conditional heteroscedastic (GARCH) type methods. Building on a specific covariance estimator suggested by Engle in 2002, we propose a relatively simple extension that requires little or no tuning or fitting, is interpretable, and produces results at least as good as MGARCH, a popular extension of GARCH that handles multiple assets. To evaluate predictors we introduce a novel approach, evaluating the regret of the log-likelihood over a time period such as a quarter. This metric allows us to see not only how well a covariance predictor does over all, but also how quickly it reacts to changes in market conditions. Our simple predictor outperforms MGARCH in terms of regret. We also test covariance predictors on downstream applications such as portfolio optimization methods that depend on the covariance matrix. For these applications our simple covariance predictor and MGARCH perform similarly. |
| Date: | 2023–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2305.19484 |
| By: | Dalia Ghanem; D\'esir\'e K\'edagni; Ismael Mourifi\'e |
| Abstract: | Quantifying the impact of regulatory policies on social welfare generally requires the identification of counterfactual distributions. Many of these policies (e.g. minimum wages or minimum working time) generate mass points and/or discontinuities in the outcome distribution. Existing approaches in the difference-in-difference literature cannot accommodate these discontinuities while accounting for selection on unobservables and non-stationary outcome distributions. We provide a unifying partial identification result that can account for these features. Our main identifying assumption is the stability of the dependence (copula) between the distribution of the untreated potential outcome and group membership (treatment assignment) across time. Exploiting this copula stability assumption allows us to provide an identification result that is invariant to monotonic transformations. We provide sharp bounds on the counterfactual distribution of the treatment group suitable for any outcome, whether discrete, continuous, or mixed. Our bounds collapse to the point-identification result in Athey and Imbens (2006) for continuous outcomes with strictly increasing distribution functions. We illustrate our approach and the informativeness of our bounds by analyzing the impact of an increase in the legal minimum wage using data from a recent minimum wage study (Cengiz, Dube, Lindner, and Zipperer, 2019). |
| Date: | 2023–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2306.04494 |
| By: | Jad Beyhum; Jonas Striaukas |
| Abstract: | GDP nowcasting commonly employs either sparse regression or a dense approach based on factor models, which differ in the way they extract information from high-dimensional datasets. This paper aims to investigate whether augmenting sparse regression with (estimated) factors can improve nowcasts. We propose an estimator for a factor-augmented sparse MIDAS regression model. The rates of convergence of the estimator are derived in a time series context, accounting for $\tau$-mixing processes and fat-tailed distributions. The application of this new technique to nowcast US GDP growth reveals several key findings. Firstly, our novel technique significantly improves the quality of nowcasts compared to both sparse regression and plain factor-augmented regression benchmarks over a period period from 2008 Q1 to 2022 Q2. This improvement is particularly pronounced during the COVID pandemic, indicating the model's ability to capture the specific dynamics introduced by the pandemic. Interestingly, our novel factor-augmented sparse method does not perform significantly better than sparse regression prior to the onset of the pandemic, suggesting that using only a few predictors is sufficient for nowcasting in more stable economic times. |
| Date: | 2023–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2306.13362 |
| By: | Hung Tran; Tien Mai |
| Abstract: | In many choice modeling applications, people demand is frequently characterized as multiple discrete, which means that people choose multiple items simultaneously. The analysis and prediction of people behavior in multiple discrete choice situations pose several challenges. In this paper, to address this, we propose a random utility maximization (RUM) based model that considers each subset of choice alternatives as a composite alternative, where individuals choose a subset according to the RUM framework. While this approach offers a natural and intuitive modeling approach for multiple-choice analysis, the large number of subsets of choices in the formulation makes its estimation and application intractable. To overcome this challenge, we introduce directed acyclic graph (DAG) based representations of choices where each node of the DAG is associated with an elemental alternative and additional information such that the number of selected elemental alternatives. Our innovation is to show that the multi-choice model is equivalent to a recursive route choice model on the DAG, leading to the development of new efficient estimation algorithms based on dynamic programming. In addition, the DAG representations enable us to bring some advanced route choice models to capture the correlation between subset choice alternatives. Numerical experiments based on synthetic and real datasets show many advantages of our modeling approach and the proposed estimation algorithms. |
| Date: | 2023–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2306.04606 |
| By: | Shadi Haj-Yahia; Omar Mansour; Tomer Toledo |
| Abstract: | Discrete choice models (DCM) are widely employed in travel demand analysis as a powerful theoretical econometric framework for understanding and predicting choice behaviors. DCMs are formed as random utility models (RUM), with their key advantage of interpretability. However, a core requirement for the estimation of these models is a priori specification of the associated utility functions, making them sensitive to modelers' subjective beliefs. Recently, machine learning (ML) approaches have emerged as a promising avenue for learning unobserved non-linear relationships in DCMs. However, ML models are considered "black box" and may not correspond with expected relationships. This paper proposes a framework that expands the potential of data-driven approaches for DCM by supporting the development of interpretable models that incorporate domain knowledge and prior beliefs through constraints. The proposed framework includes pseudo data samples that represent required relationships and a loss function that measures their fulfillment, along with observed data, for model training. The developed framework aims to improve model interpretability by combining ML's specification flexibility with econometrics and interpretable behavioral analysis. A case study demonstrates the potential of this framework for discrete choice analysis. |
| Date: | 2023–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2306.00016 |
| By: | Hernandez Roig, Harold Antonio; Aguilera Morillo, María del Carmen; Aguilera, Ana M.; Preda, Cristian |
| Abstract: | This paper deals with the "function-on-function'" or "fully functional" linear regression problem. We address the problem by proposing a novel penalized Function-on-Function Partial Least-Squares (pFFPLS) approach that imposes smoothness on the PLS weights. Our proposal introduces an appropriate finite-dimensional functional space with an associated set of bases on which to represent the data and controls smoothness with a roughness penalty operator. Penalizing the PLS weights imposes smoothness on the resulting coefficient function, improving its interpretability. In a simulation study, we demonstrate the advantages of pFFPLS compared to non-penalized FFPLS. Our comparisons indicate a higher accuracy of pFFPLS when predicting the response and estimating the true coefficient function from which the data were generated. We also illustrate the advantages of our proposal with two case studies involving two well-known datasets from the functional data analysis literature. In the first one, we predict log precipitation curves from the yearly temperature profiles recorded in 35 weather stations in Canada. In the second case study, we predict the hip angle profiles during a gait cycle of children from their corresponding knee angle profiles. |
| Keywords: | Functional Data Analysis; Partial Least Squares; Function-On-Function Regression; Roughness Penalties |
| Date: | 2023–07–05 |
| URL: | https://d.repec.org/n?u=RePEc:cte:wsrepe:37758 |
| By: | Xavier Brouty; Matthieu Garcin |
| Abstract: | Considering that both the entropy-based market information and the Hurst exponent are useful tools for determining whether the efficient market hypothesis holds for a given asset, we study the link between the two approaches. We thus provide a theoretical expression for the market information when log-prices follow either a fractional Brownian motion or its stationary extension using the Lamperti transform. In the latter model, we show that a Hurst exponent close to 1/2 can lead to a very high informativeness of the time series, because of the stationarity mechanism. In addition, we introduce a multiscale method to get a deeper interpretation of the entropy and of the market information, depending on the size of the information set. Applications to Bitcoin, CAC 40 index, Nikkei 225 index, and EUR/USD FX rate, using daily or intraday data, illustrate the methodological content. |
| Date: | 2023–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2306.13371 |
| By: | Oaxaca, Ronald L. (University of Arizona); Sierminska, Eva (LISER (CEPS/INSTEAD)) |
| Abstract: | Recently, papers have started combining the naming of two popular decomposition methods: the Oaxaca-Blinder method and the Kitagawa method, a popular method in demographics and sociology. Although the two approaches have the same objective in terms of decomposing outcome differences in some variable of interest between two populations, they are framed quite differently and do not overlap except in a special set of circumstances. Consequently, the combined labeling of these two approaches can be misleading. This note establishes the conditions under which the two methodologies are identical and when they are not. It also provides the citation history of the two methods and examples of "misuses" of the naming convention when the methods are not equivalent, accompanied by a proposal for the way forward. |
| Keywords: | decomposition methods, economics, demography |
| JEL: | A10 B41 J0 |
| Date: | 2023–05 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp16188 |
| By: | Dreber, Anna; Johannesson, Magnus |
| Abstract: | A fundamental question to the scientific enterprise is to what extent published scientific findings are credible. This question is related to the reproducibility and replicability of scientific findings where reproducibility is defined as testing if the results of an original study can be reproduced using the same data and replicability is defined as testing if the results of an original study hold in new data. We provide a framework for evaluating reproducibility and replicability in economics and divide reproducibility and replicability studies into five types: computational reproducibility, recreate reproducibility, robustness reproducibility, direct replicability and conceptual replicability, and we propose indicators to be reported for each type. |
| Date: | 2023 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:i4rdps:38 |
| By: | Cyril Bénézet (LaMME - Laboratoire de Mathématiques et Modélisation d'Evry - UEVE - Université d'Évry-Val-d'Essonne - ENSIIE - Université Paris-Saclay - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Emmanuel Gobet (CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique - X - École polytechnique - CNRS - Centre National de la Recherche Scientifique); Rodrigo Targino (FGV/EMAp - Fundação Getulio Vargas - Escola de Matemática Aplicada [Rio de Janeiro]) |
| Abstract: | In financial risk management, modelling dependency within a random vector X is crucial, a standard approach is the use of a copula model. Say the copula model can be sampled through realizations of Y having copula function C: had the marginals of Y been known, sampling X^(i) , the i-th component of X, would directly follow by composing Y^(i) with its cumulative distribution function (c.d.f.) and the inverse c.d.f. of X^(i). In this work, the marginals of Y are not explicit, as in a factor copula model. We design an algorithm which samples X through an empirical approximation of the c.d.f. of the Y marginals. To be able to handle complex distributions for Y or rare-event computations, we allow Markov Chain Monte Carlo (MCMC) samplers. We establish convergence results whose rates depend on the tails of X, Y and the Lyapunov function of the MCMC sampler. We present numerical experiments confirming the convergence rates and also revisit a real data analysis from financial risk management. |
| Keywords: | Copula models, Markov chain Monte Carlo MCMC methods, sampling |
| Date: | 2023–03 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-03334526 |