wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We show that SPECS is able to consistently estimate an appropriate linear combination of the cointegrating vectors that may occur in the underlying DGP , while simultaneously enabling the correct recovery of sparsity patterns in the corresponding parameter space .
Revised:  We show that SPECS is able to consistently estimate an appropriate linear combination of the cointegrating vectors that may occur in the underlying DGP . In addition, SPECS is shown to enable the correct recovery of sparsity patterns in the corresponding parameter space .
Edit intent: coherence
Original:  We show that SPECS is able to consistently estimate an appropriate linear combination of the cointegrating vectors that may occur in the underlying DGP , while simultaneously enabling the correct recovery of sparsity patterns in the corresponding parameter space .
Revised:  We show that SPECS is able to consistently estimate an appropriate linear combination of the cointegrating vectors that may occur in the underlying DGP , while simultaneously enabling the correct recovery of sparsity patterns in the parameter space and to posses the same limiting distribution as the OLS oracle procedure .
Edit intent: meaning-changed
Original: In this paper, we study issues of robustness in the context of Quantitative Risk Management  . Depending on the underlying objectives, we develop a general methodology for determining whether a given risk measurement related optimization problem is robust.
Revised: We study issues of robustness in the context of Quantitative Risk Management . Depending on the underlying objectives, we develop a general methodology for determining whether a given risk measurement related optimization problem is robust.
Edit intent: coherence
Original: In this paper, we study issues of robustness in the context of Quantitative Risk Management  . Depending on the underlying objectives, we develop a general methodology for determining whether a given risk measurement related optimization problem is robust.
Revised: In this paper, we study issues of robustness in the context of Quantitative Risk Management and Optimization . Depending on the underlying objectives, we develop a general methodology for determining whether a given risk measurement related optimization problem is robust.
Edit intent: meaning-changed
Original: Currently there is little practical advice on which treatment effect estimator to use when trying to adjust for observable differences. A recent suggestion is to compare the performance of estimators in simulations that somehow mimic the empirical context. Two ways to run such "empirical Monte Carlo studies" (EMCS) have been proposed .
Revised: We consider two recent suggestions for how to perform an empirically motivated Monte Carlo study to help select a treatment effect estimator under unconfoundedness .
Edit intent: clarity
Original: We study no arbitrage conditions for financial models in which either the stocks itself or its log returns are continuous Ito processes. More precisely , we derive deterministic conditions for the existence and nonexistence of equivalent (local) martingale measures and strict martingale densities.
Revised: We derive integral tests for the existence and absence of arbitrage in a financial market with one risky asset which is either modeled as stochastic exponential of an Ito process or a positive diffusion with Markov switching. In particular , we derive deterministic conditions for the existence and nonexistence of equivalent (local) martingale measures and strict martingale densities.
Edit intent: meaning-changed
Original:  More precisely , we derive deterministic conditions for the existence and nonexistence of equivalent (local) martingale measures and strict martingale densities. For models with a random switching mechanism we also study the set of equivalent (local) martingale measures which are structure preserving. In particular, for one dimensional Markov switching models we provide sufficient and necessary conditions for the existence of structure preserving equivalent (local) martingale measures.
Revised:  More precisely , we derive deterministic conditions for the existence of the minimal martingale measure. We also show that for Markov switching models we provide sufficient and necessary conditions for the existence of structure preserving equivalent (local) martingale measures.
Edit intent: meaning-changed
Original:  In particular, for one dimensional Markov switching models we provide sufficient and necessary conditions for the existence of structure preserving equivalent (local) martingale measures. Mathematically, our proofs are based on local changes of measures and existence and uniqueness conditions .
Revised:  In particular, for one dimensional Markov switching models the minimal martingale measure preserves the independence of the noise and we study how the minimal martingale measure can be modified to change the structure of the switching mechanism. Our main mathematical tools are new criteria for the martingale and strict local martingale property of certain stochastic exponentials .
Edit intent: meaning-changed
Original:  We first deal with the estimation of a continuous time Markov chain using discrete (missing) data and derive a simpler expression for the Fisher information matrix, reducing the computation time of Wald confidence intervals to less than half of the current standard .
Revised:  We first estimate a continuous-time Markov chain using discrete (missing) data and derive a simpler expression for the Fisher information matrix, reducing the computation time of Wald confidence intervals to less than half of the current standard .
Edit intent: clarity
Original:  We first deal with the estimation of a continuous time Markov chain using discrete (missing) data and derive a simpler expression for the Fisher information matrix, reducing the computation time of Wald confidence intervals to less than half of the current standard .
Revised:  We first deal with the estimation of a continuous time Markov chain using discrete (missing) data and derive a simpler expression for the Fisher information matrix, reducing the computational time needed for the Wald confidence interval by a factor of a half .
Edit intent: clarity
Original:  We provide an efficient procedure to transfer such uncertainties to the rating migrations and probabilities of default, which is of usefulness for practitioners. When a full data set is available, we propose a tractable and parsimonious model based on self-exciting marked point processes that captures the non-Markovian effect of rating momentum.
Revised:  We provide an efficient procedure for transferring such uncertainties from the generator matrix of the Markov chain to the corresponding rating migration probabilities and, crucially, default probabilities. For our second contribution, we assume access to the full (continuous) data set and propose a tractable and parsimonious model based on self-exciting marked point processes that captures the non-Markovian effect of rating momentum.
Edit intent: meaning-changed
Original: Although stochastic volatility and GARCH models have been successful to describe the volatility dynamics of univariate asset returns, their natural extension to the multivariate models with dynamic correlations has been difficult due to several major problems.
Revised: Although stochastic volatility and GARCH (generalized autoregressive conditional heteroscedasticity) models have successfully described the volatility dynamics of univariate asset returns, their natural extension to the multivariate models with dynamic correlations has been difficult due to several major problems.
Edit intent: meaning-changed
Original: Although stochastic volatility and GARCH models have been successful to describe the volatility dynamics of univariate asset returns, their natural extension to the multivariate models with dynamic correlations has been difficult due to several major problems.
Revised: Although stochastic volatility and GARCH models have been successful to describe the volatility dynamics of univariate asset returns, extending them to the multivariate models with dynamic correlations has been difficult due to several major problems.
Edit intent: clarity
Original:  However, secondly, since multivariate asset returns are not traded synchronously , we have to use  largest time intervals so that all asset returns are observed  to compute the realized covariance matrices , where we fail to make full use of  available intraday informations when there are less frequently traded assets.
Revised:  Second, since multivariate asset returns are not traded synchronously , we have to use largest time intervals so that all asset returns are observed to compute the realized covariance matrices , where we fail to make full use of available intraday informations when there are less frequently traded assets.
Edit intent: coherence
Original:  However, secondly, since multivariate asset returns are not traded synchronously , we have to use  largest time intervals so that all asset returns are observed  to compute the realized covariance matrices , where we fail to make full use of  available intraday informations when there are less frequently traded assets.
Revised:  However, secondly, since multivariate asset returns are not synchronously traded , we have to use largest time intervals so that all asset returns are observed to compute the realized covariance matrices , where we fail to make full use of available intraday informations when there are less frequently traded assets.
Edit intent: fluency
Original:  However, secondly, since multivariate asset returns are not traded synchronously , we have to use  largest time intervals so that all asset returns are observed  to compute the realized covariance matrices , where we fail to make full use of  available intraday informations when there are less frequently traded assets.
Revised:  However, secondly, since multivariate asset returns are not traded synchronously , we have to use the largest time intervals so that all asset returns are observed to compute the realized covariance matrices , where we fail to make full use of available intraday informations when there are less frequently traded assets.
Edit intent: coherence
Original:  However, secondly, since multivariate asset returns are not traded synchronously , we have to use  largest time intervals so that all asset returns are observed  to compute the realized covariance matrices , where we fail to make full use of  available intraday informations when there are less frequently traded assets.
Revised:  However, secondly, since multivariate asset returns are not traded synchronously , we have to use largest time intervals such that all asset returns are observed to compute the realized covariance matrices , where we fail to make full use of available intraday informations when there are less frequently traded assets.
Edit intent: clarity
Original:  However, secondly, since multivariate asset returns are not traded synchronously , we have to use  largest time intervals so that all asset returns are observed  to compute the realized covariance matrices , where we fail to make full use of  available intraday informations when there are less frequently traded assets.
Revised:  However, secondly, since multivariate asset returns are not traded synchronously , we have to use largest time intervals so that all asset returns are observed in order to compute the realized covariance matrices , where we fail to make full use of available intraday informations when there are less frequently traded assets.
Edit intent: clarity
Original:  However, secondly, since multivariate asset returns are not traded synchronously , we have to use  largest time intervals so that all asset returns are observed  to compute the realized covariance matrices , where we fail to make full use of  available intraday informations when there are less frequently traded assets.
Revised:  However, secondly, since multivariate asset returns are not traded synchronously , we have to use largest time intervals so that all asset returns are observed to compute the realized covariance matrices . However, in this study, we fail to make full use of available intraday informations when there are less frequently traded assets.
Edit intent: coherence
Original:  However, secondly, since multivariate asset returns are not traded synchronously , we have to use  largest time intervals so that all asset returns are observed  to compute the realized covariance matrices , where we fail to make full use of  available intraday informations when there are less frequently traded assets.
Revised:  However, secondly, since multivariate asset returns are not traded synchronously , we have to use largest time intervals so that all asset returns are observed to compute the realized covariance matrices , where we fail to make full use of the available intraday informations when there are less frequently traded assets.
Edit intent: coherence
Original:  we make full use of intraday informations by using pairwise realized correlations, (3) the covariance matrices are guaranteed to be positive definite, (4) we avoid the arbitrariness of the ordering of asset returns, (5)  propose the flexible correlation structure model (e.g.  such as setting some correlations to be identically zeros if necessary), and (6) the parsimonious specification for the leverage effect is proposed.
Revised:  we make full use of intraday informations by using pairwise realized correlations, (3) the covariance matrices are guaranteed to be positive definite, (4) we avoid the arbitrariness of the ordering of asset returns, (5) we propose the flexible correlation structure model (e.g. such as setting some correlations to be identically zeros if necessary), and (6) the parsimonious specification for the leverage effect is proposed.
Edit intent: fluency
Original:  we make full use of intraday informations by using pairwise realized correlations, (3) the covariance matrices are guaranteed to be positive definite, (4) we avoid the arbitrariness of the ordering of asset returns, (5)  propose the flexible correlation structure model (e.g.  such as setting some correlations to be identically zeros if necessary), and (6) the parsimonious specification for the leverage effect is proposed.
Revised:  we make full use of intraday informations by using pairwise realized correlations, (3) the covariance matrices are guaranteed to be positive definite, (4) we avoid the arbitrariness of the ordering of asset returns, (5) propose the flexible correlation structure model (e.g. , such as setting some correlations to be identically zeros if necessary), and (6) the parsimonious specification for the leverage effect is proposed.
Edit intent: fluency
Original:  we make full use of intraday informations by using pairwise realized correlations, (3) the covariance matrices are guaranteed to be positive definite, (4) we avoid the arbitrariness of the ordering of asset returns, (5)  propose the flexible correlation structure model (e.g.  such as setting some correlations to be identically zeros if necessary), and (6) the parsimonious specification for the leverage effect is proposed.
Revised:  we make full use of intraday informations by using pairwise realized correlations, (3) the covariance matrices are guaranteed to be positive definite, (4) we avoid the arbitrariness of the ordering of asset returns, (5) propose the flexible correlation structure model (e.g. such as setting some correlations to be zero if necessary), and (6) the parsimonious specification for the leverage effect is proposed.
Edit intent: clarity
Original:  Our proposed models are applied to  daily returns of nine U.S. stocks with their realized volatilities and pairwise realized correlations , and are shown to outperform the existing models with regard to portfolio performances.
Revised:  Our proposed models are applied to the daily returns of nine U.S. stocks with their realized volatilities and pairwise realized correlations , and are shown to outperform the existing models with regard to portfolio performances.
Edit intent: fluency
Original:  Our proposed models are applied to  daily returns of nine U.S. stocks with their realized volatilities and pairwise realized correlations , and are shown to outperform the existing models with regard to portfolio performances.
Revised:  Our proposed models are applied to daily returns of nine U.S. stocks with their realized volatilities and pairwise realized correlations and are shown to outperform the existing models with regard to portfolio performances.
Edit intent: fluency
Original:  Our proposed models are applied to  daily returns of nine U.S. stocks with their realized volatilities and pairwise realized correlations , and are shown to outperform the existing models with regard to portfolio performances.
Revised:  Our proposed models are applied to daily returns of nine U.S. stocks with their realized volatilities and pairwise realized correlations , and are shown to outperform the existing models with respect to portfolio performances.
Edit intent: fluency
Original:  We  provide new rates of convergence for deep feedforward neural nets and, because our rates are sufficiently fast (in some cases minimax optimal), prove that semiparametric inferenceis valid using deep nets for first-step estimation .
Revised:  We prove valid inference after first-step estimation with deep learning, a result new to the literature. We provide new rates of convergence for deep feedforward neural nets and, because our rates are sufficiently fast (in some cases minimax optimal), prove that semiparametric inferenceis valid using deep nets for first-step estimation .
Edit intent: meaning-changed
Original:  We  provide new rates of convergence for deep feedforward neural nets and, because our rates are sufficiently fast (in some cases minimax optimal), prove that semiparametric inferenceis valid using deep nets for first-step estimation .
Revised:  We provide new rates of convergence for deep feedforward neural nets and, because our rates are sufficiently fast (in some cases minimax optimal), obtain valid semiparametric inference .
Edit intent: clarity
Original:  Our estimation rates and semiparametric inference results are the first in the literature to handle the current standard architecture: fully connected feedforward neural networks (multi-layer perceptrons), with the now-default rectified linear unit (ReLU) activation function and a depth explicitly diverging with the sample size.
Revised:  Our estimation rates and semiparametric inference results handle the current standard architecture: fully connected feedforward neural networks (multi-layer perceptrons), with the now-default rectified linear unit (ReLU) activation function and a depth explicitly diverging with the sample size.
Edit intent: clarity
Original:  Our estimation rates and semiparametric inference results are the first in the literature to handle the current standard architecture: fully connected feedforward neural networks (multi-layer perceptrons), with the now-default rectified linear unit (ReLU) activation function and a depth explicitly diverging with the sample size.
Revised:  Our estimation rates and semiparametric inference results are the first in the literature to handle the current standard architecture: fully connected feedforward neural networks (multi-layer perceptrons), with the now-common rectified linear unit (ReLU) activation function and a depth explicitly diverging with the sample size.
Edit intent: clarity
Original:  Our estimation rates and semiparametric inference results are the first in the literature to handle the current standard architecture: fully connected feedforward neural networks (multi-layer perceptrons), with the now-default rectified linear unit (ReLU) activation function and a depth explicitly diverging with the sample size.
Revised:  Our estimation rates and semiparametric inference results are the first in the literature to handle the current standard architecture: fully connected feedforward neural networks (multi-layer perceptrons), with the now-default rectified linear unit activation function and a depth explicitly diverging with the sample size.
Edit intent: clarity
Original:  We prove valid inference after first-step estimation with deep learning, a result new to the literature. We provide new rates of convergence for deep feedforward neural nets and, because our rates are sufficiently fast (in some cases minimax optimal) , obtain valid semiparametric inference .
Revised:  We establish novel rates of convergence for deep feedforward neural nets and, because our rates are sufficiently fast (in some cases minimax optimal) , obtain valid semiparametric inference .
Edit intent: clarity
Original:  We provide new rates of convergence for deep feedforward neural nets and, because our rates are sufficiently fast (in some cases minimax optimal) , obtain valid semiparametric inference .
Revised:  We provide new rates of convergence for deep feedforward neural nets and, because our rates are sufficiently fast (in some cases minimax optimal) to allow us to establish valid second-step inference after first-step estimation with deep learning, a result also new to the literature .
Edit intent: meaning-changed
Original:  The relaxation time scale of the population to its steady state is determined from the distribution of single-cell growth rates independent of the details of the division process . We propose an experimental method to measure single-cell growth variability by observing how long it takes for the population to reach its steady state or balanced growth.
Revised:  The relaxation time scale of the population to its steady state is determined from the distribution of single-cell growth rates independent of the details of the division process such as the noise in the timing of division and the mechanism of cell-size regulation. We validate the predictions of our model using existing experimental data and propose an experimental method to measure single-cell growth variability by observing how long it takes for the population to reach its steady state or balanced growth.
Edit intent: meaning-changed
Original: In their seminal work `Robust Replication of Volatility Derivatives,' Carr and Lee  show how to robustly price and replicate a variety of claims written on the quadratic variation of a risky asset under the assumption that the asset's volatility process is independent of the Brownian motion that drives the asset's price.
Revised: In their seminal work Carr and Lee show how to robustly price and replicate a variety of claims written on the quadratic variation of a risky asset under the assumption that the asset's volatility process is independent of the Brownian motion that drives the asset's price.
Edit intent: clarity
Original: In their seminal work `Robust Replication of Volatility Derivatives,' Carr and Lee  show how to robustly price and replicate a variety of claims written on the quadratic variation of a risky asset under the assumption that the asset's volatility process is independent of the Brownian motion that drives the asset's price.
Revised: In their seminal work `Robust Replication of Volatility Derivatives,' Carr and Lee (2008) show how to robustly price and replicate a variety of claims written on the quadratic variation of a risky asset under the assumption that the asset's volatility process is independent of the Brownian motion that drives the asset's price.
Edit intent: meaning-changed
Original:  In this paper  , we perform a number of Monte Carlo experiments to test the effectiveness of Carr and Lee's immunization strategy.
Revised:  In this paper , we show that the correlation immunization strategy is the only strategy among the class of strategies discussed in Carr and Lee (2008) that results in real-valued hedging portfolios when the correlation between the asset's price and volatility is nonzero. Additionally , we perform a number of Monte Carlo experiments to test the effectiveness of Carr and Lee's immunization strategy.
Edit intent: meaning-changed