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on Econometrics |
| By: | Ziyu Jiang |
| Abstract: | Identifying structural parameters in linear simultaneous equation models is a fundamental challenge in economics and related fields. Recent work leverages higher-order distributional moments, exploiting the fact that non-Gaussian data carry more structural information than the Gaussian framework. While many of these contributions still require zero-covariance assumptions for structural errors, this paper shows that such an assumption can be dispensed with. Specifically, we demonstrate that under any diagonal higher-cumulant condition, the structural parameter matrix can be identified by solving an eigenvector problem. This yields a direct identification argument and motivates a simple sample-analogue estimator that is both consistent and asymptotically normal. Moreover, when uncorrelatedness may still be plausible -- such as in vector autoregression models -- our framework offers a transparent way to test for it, all within the same higher-order orthogonality setting employed by earlier studies. Monte Carlo simulations confirm desirable finite-sample performance, and we further illustrate the method's practical value in two empirical applications. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.06777 |
| By: | Yikun Zhang; Yen-Chi Chen |
| Abstract: | Statistical methods for causal inference with continuous treatments mainly focus on estimating the mean potential outcome function, commonly known as the dose-response curve. However, it is often not the dose-response curve but its derivative function that signals the treatment effect. In this paper, we investigate nonparametric inference on the derivative of the dose-response curve with and without the positivity condition. Under the positivity and other regularity conditions, we propose a doubly robust (DR) inference method for estimating the derivative of the dose-response curve using kernel smoothing. When the positivity condition is violated, we demonstrate the inconsistency of conventional inverse probability weighting (IPW) and DR estimators, and introduce novel bias-corrected IPW and DR estimators. In all settings, our DR estimator achieves asymptotic normality at the standard nonparametric rate of convergence. Additionally, our approach reveals an interesting connection to nonparametric support and level set estimation problems. Finally, we demonstrate the applicability of our proposed estimators through simulations and a case study of evaluating a job training program. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.06969 |
| By: | ALAMI CHENTOUFI, Reda |
| Abstract: | This paper introduces a two-step procedure for convex penalized estimation in dynamic location-scale models. The method uses a consistent, non-sparse first-step estimator to construct a convex Weighted Least Squares (WLS) optimization problem compatible with the Least Absolute Shrinkage and Selection Operator (LASSO), addressing challenges associated with non-convexity and enabling efficient, sparse estimation. The consistency and asymptotic distribution of the estimator are established, with finite-sample performance evaluated through Monte Carlo simulations. The method's practical utility is demonstrated through an application to electricity prices in France, Belgium, the Netherlands, and Switzerland, effectively capturing seasonal patterns and external covariates while ensuring model sparsity. |
| Keywords: | Weighted LSE; LASSO estimation; variable selection; GARCH models |
| JEL: | C01 C22 C51 C52 C58 |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:123283 |
| By: | Yuying Sun; Feng Chen; Jiti Gao |
| Abstract: | This paper proposes a novel time-varying model averaging (TVMA) approach to enhancing forecast accuracy for multivariate time series subject to structural changes. The TVMA method averages predictions from a set of time-varying vector autoregressive models using optimal time-varying combination weights selected by minimizing a penalized local criterion. This allows the relative importance of different models to adaptively evolve over time in response to structural shifts. We establish an asymptotic optimality for the proposed TVMA approach in achieving the lowest possible quadratic forecast errors. The convergence rate of the selected time-varying weights to the optimal weights minimizing expected quadratic errors is derived. Moreover, we show that when one or more correctly specified models exist, our method consistently assigns full weight to them, and an asymptotic normality for the TVMA estimators under some regularity conditions can be established. Furthermore, the proposed approach encompasses special cases including time-varying VAR models with exogenous predictors, as well as time-varying factor augmented VAR (FAVAR) models. Simulations and an empirical application illustrate the proposed TVMA method outperforms some commonly used model averaging and selection methods in the presence of structural changes. |
| Keywords: | : Asymptotic Optimality; Consistency; Structural Change; Time-varying Weight |
| JEL: | C52 C53 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:msh:ebswps:2025-1 |
| By: | Dong, Hao; Otsu, Taisuke; Taylor, Luke |
| Abstract: | The convergence rate of an estimator can vary when applied to datasets from different populations. As the population is unknown in practice, so is the corresponding convergence rate. In this article, we introduce a method to conduct inference on estimators whose convergence rates are unknown. Specifically, we extend the subsampling approach of Bertail, Politis, and Romano (1999) to situations where the convergence rate may include logarithmic components. This extension proves to be particularly relevant in certain statistical inference problems. To illustrate the practical relevance and implementation of our results, we discuss two main examples: (i) non parametric regression with measurement error; and (ii) intercept estimation in binary choice models. In each case, our approach provides robust inference in settings where convergence rates are unknown; simulation results validate our findings. |
| Keywords: | Binary choice; convergence rate; measurement error; subsampling |
| JEL: | C14 |
| Date: | 2024–12–24 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:126066 |
| By: | Gonzalo, Jesús; Pitarakis, Jean-Yves |
| Abstract: | We propose a two-step procedure to detect cointegration in high-dimensional settings, focusing on sparse relationships. First, we use the adaptive LASSO to identify the small subset of integrated covariates driving the equilibrium relationship with a target series, ensuring model-selection consistency. Second, we adopt an information-theoretic model choice criterion to distinguish between stationarity and nonstationarity in the resulting residuals, avoiding dependence on asymptotic distributional assumptions. Monte Carlo experiments confirm robust finite-sample performance, even under endogeneity and serial correlation. |
| Keywords: | Cointegration; High Dimensional Data; Adaptive Lasso; Unit Roots |
| JEL: | C32 C52 |
| Date: | 2025–01–27 |
| URL: | https://d.repec.org/n?u=RePEc:cte:werepe:45708 |
| By: | Ryo Okui; Yutao Sun; Wendun Wang |
| Abstract: | This paper introduces a framework to analyze time-varying spillover effects in panel data. We consider panel models where a unit's outcome depends not only on its own characteristics (private effects) but also on the characteristics of other units (spillover effects). The linkage of units is allowed to be latent and may shift at an unknown breakpoint. We propose a novel procedure to estimate the breakpoint, linkage structure, spillover and private effects. We address the high-dimensionality of spillover effect parameters using penalized estimation, and estimate the breakpoint with refinement. We establish the super-consistency of the breakpoint estimator, ensuring that inferences about other parameters can proceed as if the breakpoint were known. The private effect parameters are estimated using a double machine learning method. The proposed method is applied to estimate the cross-country R&D spillovers, and we find that the R&D spillovers become sparser after the financial crisis. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.09517 |
| By: | Adam Lee; Emil A. Stoltenberg; Per A. Mykland |
| Abstract: | Inference on the parametric part of a semiparametric model is no trivial task. On the other hand, if one approximates the infinite dimensional part of the semiparametric model by a parametric function, one obtains a parametric model that is in some sense close to the semiparametric model; and inference may proceed by the method of maximum likelihood. Under regularity conditions, and assuming that the approximating parametric model in fact generated the data, the ensuing maximum likelihood estimator is asymptotically normal and efficient (in the approximating parametric model). Thus one obtains a sequence of asymptotically normal and efficient estimators in a sequence of growing parametric models that approximate the semiparametric model and, intuitively, the limiting {`}semiparametric{'} estimator should be asymptotically normal and efficient as well. In this paper we make this intuition rigorous. Consequently, we are able to move much of the semiparametric analysis back into classical parametric terrain, and then translate our parametric results back to the semiparametric world by way of contiguity. Our approach departs from the sieve literature by being more specific about the approximating parametric models, by working under these when treating the parametric models, and by taking advantage of the mutual contiguity between the parametric and semiparametric models to lift conclusions about the former to conclusions about the latter. We illustrate our theory with two canonical examples of semiparametric models, namely the partially linear regression model and the Cox regression model. An upshot of our theory is a new, relatively simple, and rather parametric proof of the efficiency of the Cox partial likelihood estimator. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.09483 |
| By: | Sooahn Shin |
| Abstract: | This paper addresses one of the most prevalent problems encountered by political scientists working with difference-in-differences (DID) design: missingness in panel data. A common practice for handling missing data, known as complete case analysis, is to drop cases with any missing values over time. A more principled approach involves using nonparametric bounds on causal effects or applying inverse probability weighting based on baseline covariates. Yet, these methods are general remedies that often under-utilize the assumptions already imposed on panel structure for causal identification. In this paper, I outline the pitfalls of complete case analysis and propose an alternative identification strategy based on principal strata. To be specific, I impose parallel trends assumption within each latent group that shares the same missingness pattern (e.g., always-respondents, if-treated-respondents) and leverage missingness rates over time to estimate the proportions of these groups. Building on this, I tailor Lee bounds, a well-known nonparametric bounds under selection bias, to partially identify the causal effect within the DID design. Unlike complete case analysis, the proposed method does not require independence between treatment selection and missingness patterns, nor does it assume homogeneous effects across these patterns. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.18772 |
| By: | Zeyang Yu |
| Abstract: | In an empirical study of persuasion, researchers often use a binary instrument to encourage individuals to consume information and take some action. We show that, with a binary Imbens-Angrist instrumental variable model and the monotone treatment response assumption, it is possible to identify the joint distribution of potential outcomes among compliers. This is necessary to identify the percentage of mobilised voters and their statistical characteristic defined by the moments of the joint distribution of treatment and covariates. Specifically, we develop a method that enables researchers to identify the statistical characteristic of persuasion types: always-voters, never-voters, and mobilised voters among compliers. These findings extend the kappa weighting results in Abadie (2003). We also provide a sharp test for the two sets of identification assumptions. The test boils down to testing whether there exists a nonnegative solution to a possibly under-determined system of linear equations with known coefficients. An application based on Green et al. (2003) is provided. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.16906 |
| By: | Massimo Franchi; Iliyan Georgiev; Paolo Paruolo |
| Abstract: | This paper proposes a novel canonical correlation analysis for semiparametric inference in $I(1)/I(0)$ systems via functional approximation. The approach can be applied coherently to panels of $p$ variables with a generic number $s$ of stochastic trends, as well as to subsets or aggregations of variables. This study discusses inferential tools on $s$ and on the loading matrix $\psi$ of the stochastic trends (and on their duals $r$ and $\beta$, the cointegration rank and the cointegrating matrix): asymptotically pivotal test sequences and consistent estimators of $s$ and $r$, $T$-consistent, mixed Gaussian and efficient estimators of $\psi$ and $\beta$, Wald tests thereof, and misspecification tests for checking model assumptions. Monte Carlo simulations show that these tools have reliable performance uniformly in $s$ for small, medium and large-dimensional systems, with $p$ ranging from 10 to 300. An empirical analysis of 20 exchange rates illustrates the methods. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.19572 |
| By: | Tinghan Zhang |
| Abstract: | Consumers are increasingly shopping online, and more and more datasets documenting consumer search are becoming available. While sequential search models provide a framework for utilizing such data, they present empirical challenges. A key difficulty arises from the inequality conditions implied by these models, which depend on multiple unobservables revealed during the search process and necessitate solving or simulating high-dimensional integrals for likelihood-based estimation methods. This paper introduces a novel representation of inequalities implied by a broad class of sequential search models, demonstrating that the empirical content of such models can be effectively captured through a specific partial ranking of available actions. This representation reduces the complexity caused by unobservables and provides a tractable expression for joint probabilities. Leveraging this insight, we propose a GHK-style simulation-based likelihood estimator that is simpler to implement than existing ones. It offers greater flexibility for handling incomplete search data, incorporating additional ranking information, and accommodating complex search processes, including those involving product discovery. We show that the estimator achieves robust performance while maintaining relatively low computational costs, making it a practical and versatile tool for researchers and practitioners. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.07514 |
| By: | Kenta Takatsu; Arun Kumar Kuchibhotla |
| Abstract: | This manuscript studies a general approach to construct confidence sets for the solution of population-level optimization, commonly referred to as M-estimation. Statistical inference for M-estimation poses significant challenges due to the non-standard limiting behaviors of the corresponding estimator, which arise in settings with increasing dimension of parameters, non-smooth objectives, or constraints. We propose a simple and unified method that guarantees validity in both regular and irregular cases. Moreover, we provide a comprehensive width analysis of the proposed confidence set, showing that the convergence rate of the diameter is adaptive to the unknown degree of instance-specific regularity. We apply the proposed method to several high-dimensional and irregular statistical problems. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.07772 |
| By: | Áureo de Paula; Elie Tamer; Weiguang Liu |
| Abstract: | Given data on a scalar random variable 𝑌, a prediction set for 𝑌 with miscoverage level 𝛼 is a set of values for 𝑌 that contains a randomly drawn 𝑌 with probability 1 − 𝛼, where 𝛼 ∈ (0, 1). Among all prediction sets that satisfy this coverage property, the oracle prediction set is the one with the smallest volume. This paper provides estimation methods of such prediction sets given observed conditioning covariates when 𝑌 is censored or measured in intervals. We first characterise the oracle prediction set under interval censoring and develop a consistent estimator for the shortest prediction interval that satisfies this coverage property. We then extend these consistency results to accommodate cases where the prediction set consists of multiple disjoint intervals. Second, we use conformal inference to construct a prediction set that achieves a particular notion of finite-sample validity under censoring and maintains consistency as sample size increases. This notion exploits exchangeability to obtain finite sample guarantees on coverage using a specially constructed conformity score function. The procedure accomodates the prediction uncertainty that is irreducible (due to the stochastic nature of outcomes), the modelling uncertainty due to partial identification and also sampling uncertainty that gets reduced as samples get larger. We conduct a set of Monte Carlo simulations and an application to data from the Current Population Survey. The results highlight the robustness and efficiency of the proposed methods. |
| Date: | 2025–01–21 |
| URL: | https://d.repec.org/n?u=RePEc:azt:cemmap:04/25 |
| By: | Ying Chen; Ziwei Xu; Kotaro Inoue; Ryutaro Ichise |
| Abstract: | Instrumental Variable (IV) provides a source of treatment randomization that is conditionally independent of the outcomes, responding to the challenges of counterfactual and confounding biases. In finance, IV construction typically relies on pre-designed synthetic IVs, with effectiveness measured by specific algorithms. This classic paradigm cannot be generalized to address broader issues that require more and specific IVs. Therefore, we propose an expertise-driven model (ETE-FinCa) to optimize the source of expertise, instantiate IVs by the expertise concept, and interpret the cause-effect relationship by integrating concept with real economic data. The results show that the feature selection based on causal knowledge graphs improves the classification performance than others, with up to a 11.7% increase in accuracy and a 23.0% increase in F1-score. Furthermore, the high-quality IVs we defined can identify causal relationships between the treatment and outcome variables in the Two-Stage Least Squares Regression model with statistical significance. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.17542 |
| By: | Li, Chenxing; Yang, Qiao |
| Abstract: | This paper introduces a novel Bayesian time series model that combines the nonparametric features of an infinite hidden Markov model with the volatility persistence captured by the GARCH framework, to effectively model and forecast short-term interest rates. When applied to US 3-month Treasury bill rates, the GARCH-IHMM reveals both structural and persistent changes in volatility, thereby enhancing the accuracy of density forecasts compared to existing benchmark models. Out-of-sample evaluations demonstrate the superior performance of our model in density forecasts and in capturing volatility dynamics due to its adaptivity to different macroeconomic environments. |
| Keywords: | Interest rates; Bayesian nonparametrics; GARCH; density forecasts |
| JEL: | C11 C14 C51 C53 C58 E43 E47 G17 |
| Date: | 2025–01–04 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:123200 |
| By: | Luo, Shikai; Yang, Ying; Shi, Chengchun; Yao, Fang; Ye, Jieping; Zhu, Hongtu |
| Abstract: | The aim of this article is to establish a causal link between the policies implemented by technology companies and the outcomes they yield within intricate temporal and/or spatial dependent experiments. We propose a novel temporal/spatio-Temporal Varying Coefficient Decision Process model, capable of effectively capturing the evolving treatment effects in situations characterized by temporal and/or spatial dependence. Our methodology encompasses the decomposition of the average treatment effect into the direct effect (DE) and the indirect effect (IE). We subsequently devise comprehensive procedures for estimating and making inferences about both DE and IE. Additionally, we provide a rigorous analysis of the statistical properties of these procedures, such as asymptotic power. To substantiate the effectiveness of our approach, we carry out extensive simulations and real data analyses. |
| Keywords: | A/B testing; policy evaluation; spatio-temporal dependent experiments; varying coefficient decision process |
| JEL: | C1 |
| Date: | 2024–07–01 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:122741 |
| By: | Jimmy Cheung; Smruthi Rangarajan; Amelia Maddocks; Xizhe Chen; Rohitash Chandra |
| Abstract: | Uncertainty quantification is crucial in time series prediction, and quantile regression offers a valuable mechanism for uncertainty quantification which is useful for extreme value forecasting. Although deep learning models have been prominent in multi-step ahead prediction, the development and evaluation of quantile deep learning models have been limited. We present a novel quantile regression deep learning framework for multi-step time series prediction. In this way, we elevate the capabilities of deep learning models by incorporating quantile regression, thus providing a more nuanced understanding of predictive values. We provide an implementation of prominent deep learning models for multi-step ahead time series prediction and evaluate their performance under high volatility and extreme conditions. We include multivariate and univariate modelling, strategies and provide a comparison with conventional deep learning models from the literature. Our models are tested on two cryptocurrencies: Bitcoin and Ethereum, using daily close-price data and selected benchmark time series datasets. The results show that integrating a quantile loss function with deep learning provides additional predictions for selected quantiles without a loss in the prediction accuracy when compared to the literature. Our quantile model has the ability to handle volatility more effectively and provides additional information for decision-making and uncertainty quantification through the use of quantiles when compared to conventional deep learning models. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.15674 |
| By: | Bailey, N.; Ditzen, J.; Holly, S. |
| Abstract: | We analyse asymmetric spatial or network dynamics in a panel framework by first distinguishing them from stronger common effects. We eliminate pervasive influences by means of a de-factoring model and then uncover the weaker cross-sectional structures by identifying units with significant residual bivariate correlation. In order to assess the effect on a given unit i from shocks to ‘neighbouring’ units, we make use of spatial econometric techniques. Given that the effects of these shocks can be directional, i.e. depend on factors such as a city’s distance from other cities and their relative sizes appropriately defined, we measure network dependencies in terms of partial correlations instead. For this, we employ GMM and use the information in a regularised version of the residual correlation matrix to identify instruments which comply with the required relevance and exclusion restrictions for instrumentation. For the jth variable in the equation for the ith unit we select elements in the jth column of this correlation matrix that represent units that are correlated with the jth variable but are not correlated with the ith variable. Translating into the terminology of the spatial or networks literature, we focus on the effects of each unit’s neighbours’ neighbours that are not their neighbours. This approach is consistent with estimating a variant of a gravity model of idiosyncratic shocks to variables such as house prices. |
| Keywords: | Spatial interconnections, housing, multiple testing, networks |
| JEL: | C21 C23 |
| Date: | 2025–01–10 |
| URL: | https://d.repec.org/n?u=RePEc:cam:camdae:2501 |
| By: | Qianli Zhao; Chao Wang; Richard Gerlach; Giuseppe Storti; Lingxiang Zhang |
| Abstract: | Realised volatility has become increasingly prominent in volatility forecasting due to its ability to capture intraday price fluctuations. With a growing variety of realised volatility estimators, each with unique advantages and limitations, selecting an optimal estimator may introduce challenges. In this thesis, aiming to synthesise the impact of various realised volatility measures on volatility forecasting, we propose an extension of the Realised GARCH model that incorporates an autoencoder-generated synthetic realised measure, combining the information from multiple realised measures in a nonlinear manner. Our proposed model extends existing linear methods, such as Principal Component Analysis and Independent Component Analysis, to reduce the dimensionality of realised measures. The empirical evaluation, conducted across four major stock markets from January 2000 to June 2022 and including the period of COVID-19, demonstrates both the feasibility of applying an autoencoder to synthesise volatility measures and the superior effectiveness of the proposed model in one-step-ahead rolling volatility forecasting. The model exhibits enhanced flexibility in parameter estimations across each rolling window, outperforming traditional linear approaches. These findings indicate that nonlinear dimension reduction offers further adaptability and flexibility in improving the synthetic realised measure, with promising implications for future volatility forecasting applications. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.17136 |
| By: | Jiaan Han; Junxiao Chen; Yanzhe Fu |
| Abstract: | We introduce CatNet, an algorithm that effectively controls False Discovery Rate (FDR) and selects significant features in LSTM with the Gaussian Mirror (GM) method. To evaluate the feature importance of LSTM in time series, we introduce a vector of the derivative of the SHapley Additive exPlanations (SHAP) to measure feature importance. We also propose a new kernel-based dependence measure to avoid multicollinearity in the GM algorithm, to make a robust feature selection with controlled FDR. We use simulated data to evaluate CatNet's performance in both linear models and LSTM models with different link functions. The algorithm effectively controls the FDR while maintaining a high statistical power in all cases. We also evaluate the algorithm's performance in different low-dimensional and high-dimensional cases, demonstrating its robustness in various input dimensions. To evaluate CatNet's performance in real world applications, we construct a multi-factor investment portfolio to forecast the prices of S\&P 500 index components. The results demonstrate that our model achieves superior predictive accuracy compared to traditional LSTM models without feature selection and FDR control. Additionally, CatNet effectively captures common market-driving features, which helps informed decision-making in financial markets by enhancing the interpretability of predictions. Our study integrates of the Gaussian Mirror algorithm with LSTM models for the first time, and introduces SHAP values as a new feature importance metric for FDR control methods, marking a significant advancement in feature selection and error control for neural networks. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.16666 |
| By: | John List |
| Abstract: | The traditional approach in experimental economics is to use a between-subject design: the analyst places each unit in treatment or control simultaneously and recovers outcome differences via differencing conditional expectations. Within-subject designs represent a significant departure from this method, as the same unit is observed in both treatment and control conditions sequentially. While some might consider the design choice straightforward (always opt for a between-subject design), I contend that researchers should meticulously weigh the advantages and disadvantages of each design. In doing so, I propose a categorization for within-subject designs based on the plausibility of recovering an internally valid estimate. In one instance, which I denote as stealth designs, the analyst should unequivocally choose a within-subject design rather than a between-subject design. |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:feb:natura:00804 |
| By: | Worapree Maneesoonthorn; David T. Frazier; Gael M. Martin |
| Abstract: | A new modular approximate Bayesian inferential framework is proposed that enables fast calculation of probabilistic predictions of future option prices. We exploit multiple information sources, including daily spot returns, high-frequency spot data and option prices. A benefit of this modular Bayesian approach is that it allows us to work with the theoretical option pricing model, without needing to specify an arbitrary statistical model that links the theoretical prices to their observed counterparts. We show that our approach produces accurate probabilistic predictions of option prices in realistic scenarios and, despite not explicitly modelling pricing errors, the method is shown to be robust to their presence. Predictive accuracy based on the Heston stochastic volatility model, with predictions produced via rapid real-time updates, is illustrated empirically for short-maturity options. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2412.00658 |
| By: | Bednall, Timothy Colin |
| Abstract: | Understanding the variance explained by statistical models is crucial for data analysis and interpretation. Traditional measures such as R-Squared, coefficient omega, and Cronbach’s alpha provide insights into model performance, but they often lack consistency and comparability across different contexts. This paper introduces a general index for variance explained (Vx), which encompasses predictive model evaluation, reliability analysis, and variable importance. The Vx framework offers a unified approach, enhancing consistency in assessing model fit and simplifying reporting across various models. It also facilitates communication of results to nonacademic audiences, including policymakers and the public. The Vx index is straightforward to calculate using widely available software and provides a model-based approach that reflects hypothesized relationships among variables. This paper demonstrates the application of the Vx framework to multiple regression models, path and mediational models, and confirmatory factor analyses, using published datasets and providing accompanying R code and Excel spreadsheets for replication. The Vx framework offers a flexible and interpretable method for evaluating model performance, which can be applied to a range of different models within the structural equation modelling family. |
| Date: | 2025–01–10 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:us346 |