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on Econometrics |
| By: | Francisco Blasques (Vrije Universiteit Amsterdam); Noah Stegehuis (Vrije Universiteit Amsterdam) |
| Abstract: | This paper proposes a score-driven model for filtering time-varying causal parameters through the use of instrumental variables. In the presence of suitable instruments, we show that we can uncover dynamic causal relations between variables, even in the presence of regressor endogeneity which may arise as a result of simultaneity, omitted variables, or measurement errors. Due to the observation-driven nature of score models, the filtering method is simple and practical to implement. We establish the asymptotic properties of the maximum likelihood estimator and show that the instrumental-variable score-driven filter converges to the unique unknown causal path of the true parameter. We further analyze the finite sample properties of the filtered causal parameter in a comprehensive Monte Carlo exercise. Finally, we reveal the empirical relevance of this method in an application to aggregate consumption in macroeconomic data. |
| Keywords: | observation-driven models, time-varying parameters, causal inference, endogeneity, instrumental variables |
| JEL: | C01 C22 C26 |
| Date: | 2024–02–29 |
| URL: | https://d.repec.org/n?u=RePEc:tin:wpaper:20240016 |
| By: | Zhiqiang Liao |
| Abstract: | We study the problem of variable selection in convex nonparametric least squares (CNLS). Whereas the least absolute shrinkage and selection operator (Lasso) is a popular technique for least squares, its variable selection performance is unknown in CNLS problems. In this work, we investigate the performance of the Lasso CNLS estimator and find out it is usually unable to select variables efficiently. Exploiting the unique structure of the subgradients in CNLS, we develop a structured Lasso by combining $\ell_1$-norm and $\ell_{\infty}$-norm. To improve its predictive performance, we propose a relaxed version of the structured Lasso where we can control the two effects--variable selection and model shrinkage--using an additional tuning parameter. A Monte Carlo study is implemented to verify the finite sample performances of the proposed approaches. In the application of Swedish electricity distribution networks, when the regression model is assumed to be semi-nonparametric, our methods are extended to the doubly penalized CNLS estimators. The results from the simulation and application confirm that the proposed structured Lasso performs favorably, generally leading to sparser and more accurate predictive models, relative to the other variable selection methods in the literature. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.01911 |
| By: | Guanghui Pan |
| Abstract: | In this paper, we introduce a doubly doubly robust estimator for the average and heterogeneous treatment effect for left-truncated-right-censored (LTRC) survival data. In causal inference for survival functions in LTRC survival data, two missing data issues are noteworthy: one is the missing data of counterfactuals for causal inference, and the other is the missing data due to truncation and censoring. Based on previous research on non-parametric deep learning estimation in survival analysis, this paper proposes an algorithm to obtain an efficient estimate of the average and heterogeneous causal effect. We simulate the data and compare our methods with the marginal hazard ratio estimation, the naive plug-in estimation, and the doubly robust causal with Cox Proportional Hazard estimation and illustrate the advantages and disadvantages of the model application. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.01412 |
| By: | Nan Liu; Yanbo Liu; Yuya Sasaki |
| Abstract: | This paper proposes methods of estimation and uniform inference for a general class of causal functions, such as the conditional average treatment effects and the continuous treatment effects, under multiway clustering. The causal function is identified as a conditional expectation of an adjusted (Neyman-orthogonal) signal that depends on high-dimensional nuisance parameters. We propose a two-step procedure where the first step uses machine learning to estimate the high-dimensional nuisance parameters. The second step projects the estimated Neyman-orthogonal signal onto a dictionary of basis functions whose dimension grows with the sample size. For this two-step procedure, we propose both the full-sample and the multiway cross-fitting estimation approaches. A functional limit theory is derived for these estimators. To construct the uniform confidence bands, we develop a novel resampling procedure, called the multiway cluster-robust sieve score bootstrap, that extends the sieve score bootstrap (Chen and Christensen, 2018) to the novel setting with multiway clustering. Extensive numerical simulations showcase that our methods achieve desirable finite-sample behaviors. We apply the proposed methods to analyze the causal relationship between mistrust levels in Africa and the historical slave trade. Our analysis rejects the null hypothesis of uniformly zero effects and reveals heterogeneous treatment effects, with significant impacts at higher levels of trade volumes. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.06654 |
| By: | Chun Pong Lau |
| Abstract: | In dynamic discrete choice models, some parameters, such as the discount factor, are being fixed instead of being estimated. This paper proposes two sensitivity analysis procedures for dynamic discrete choice models with respect to the fixed parameters. First, I develop a local sensitivity measure that estimates the change in the target parameter for a unit change in the fixed parameter. This measure is fast to compute as it does not require model re-estimation. Second, I propose a global sensitivity analysis procedure that uses model primitives to study the relationship between target parameters and fixed parameters. I show how to apply the sensitivity analysis procedures of this paper through two empirical applications. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.16330 |
| By: | Eric Beutner (Vrije Universiteit Amsterdam); Julia Schaumburg (Vrije Universiteit Amsterdam); Barend Spanjers (Vrije Universiteit Amsterdam) |
| Abstract: | This study reflects on the inconsistency of the fixed-design residual bootstrap procedure for GARCH models under dependent innovations. We introduce a novel recursive-design residual block bootstrap procedure to accurately quantify the uncertainty around parameter estimates and volatility forecasts. A simulation study provides evidence for the validity of the recursive-design residual block bootstrap in the presence of dependent innovations. The resulting bootstrap confidence intervals are not only valid but also potentially narrower than the ones obtained from the inconsistent fixed design bootstrap, depending on the underlying data-generating process and the sample size. In an application to financial time series, we illustrate the empirical relevance of our proposed methods, showing evidence for the residual dependence and demonstrating notable differences between the confidence intervals obtained by the fixed- and the recursive-design bootstrap procedure. |
| Keywords: | GARCH, Dependent Innovations, Residual Block Bootstrap |
| Date: | 2024–01–25 |
| URL: | https://d.repec.org/n?u=RePEc:tin:wpaper:20240008 |
| By: | Matias D. Cattaneo; Yingjie Feng; Boris Shigida |
| Abstract: | This paper presents uniform estimation and inference theory for a large class of nonparametric partitioning-based M-estimators. The main theoretical results include: (i) uniform consistency for convex and non-convex objective functions; (ii) optimal uniform Bahadur representations; (iii) optimal uniform (and mean square) convergence rates; (iv) valid strong approximations and feasible uniform inference methods; and (v) extensions to functional transformations of underlying estimators. Uniformity is established over both the evaluation point of the nonparametric functional parameter and a Euclidean parameter indexing the class of loss functions. The results also account explicitly for the smoothness degree of the loss function (if any), and allow for a possibly non-identity (inverse) link function. We illustrate the main theoretical and methodological results with four substantive applications: quantile regression, distribution regression, $L_p$ regression, and Logistic regression; many other possibly non-smooth, nonlinear, generalized, robust M-estimation settings are covered by our theoretical results. We provide detailed comparisons with the existing literature and demonstrate substantive improvements: we achieve the best (in some cases optimal) known results under improved (in some cases minimal) requirements in terms of regularity conditions and side rate restrictions. The supplemental appendix reports other technical results that may be of independent interest. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.05715 |
| By: | Anton Skrobotov |
| Abstract: | This review discusses methods of testing for a panel unit root. Modern approaches to testing in cross-sectionally correlated panels are discussed, preceding the analysis with an analysis of independent panels. In addition, methods for testing in the case of non-linearity in the data (for example, in the case of structural breaks) are presented, as well as methods for testing in short panels, when the time dimension is small and finite. In conclusion, links to existing packages that allow implementing some of the described methods are provided. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.08908 |
| By: | Lenin Arango-Castillo; Francisco J. Martínez-Ramírez; María José Orraca |
| Abstract: | Persistence is the speed with which a time series returns to its mean after a shock. Although several measures of persistence have been proposed in the literature, when they are empirically applied, the different measures indicate incompatible messages, as they differ both in the level and the implied evolution of persistence. One plausible reason why persistence estimators may differ is the presence of data particularities such as trends, cycles, measurement errors, additive and temporary change outliers, and structural changes. To gauge the usefulness and robustness of different measures of persistence, we compare them in a univariate time series framework using Monte Carlo simulations. We consider nonparametric, semiparametric, and parametric time-domain and frequency-domain persistence estimators and investigate their performance under different anomalies found in practice. Our results indicate that the nonparametric method is, on average, less affected by the different types of time series anomalies analyzed in this work. |
| Keywords: | Persistence;Monte-Carlo simulations;time series |
| JEL: | C15 C53 C22 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:bdm:wpaper:2024-11 |
| By: | Zhongjian Lin; Francis Vella |
| Abstract: | We address the estimation of endogenous treatment models with social interactions in both the treatment and outcome equations. We model the interactions between individuals in an internally consistent manner via a game theoretic approach based on discrete Bayesian games. This introduces a substantial computational burden in estimation which we address through a sequential version of the nested fixed point algorithm. We also provide some relevant treatment effects, and procedures for their estimation, which capture the impact on both the individual and the total sample. Our empirical application examines the impact of an individual's exercise frequency on her level of self-esteem. We find that an individual's exercise frequency is influenced by her expectation of her friends'. We also find that an individual's level of self-esteem is affected by her level of exercise and, at relatively lower levels of self-esteem, by the expectation of her friends' self-esteem. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.13971 |
| By: | Philipp Gersing |
| Abstract: | We prove that under the condition that the eigenvalues are asymptotically well separated and stable, the normalised principal components of a r-static factor sequence converge in mean square. Consequently, we have a generic interpretation of the principal components estimator as the normalised principal components of the statically common space. We illustrate why this can be useful for the interpretation of the PC-estimated factors, performing an asymptotic theory without rotation matrices and avoiding singularity issues in factor augmented regressions. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.11676 |
| By: | Rangika Peiris; Minh-Ngoc Tran; Chao Wang; Richard Gerlach |
| Abstract: | A long memory and non-linear realized volatility model class is proposed for direct Value at Risk (VaR) forecasting. This model, referred to as RNN-HAR, extends the heterogeneous autoregressive (HAR) model, a framework known for efficiently capturing long memory in realized measures, by integrating a Recurrent Neural Network (RNN) to handle non-linear dynamics. Loss-based generalized Bayesian inference with Sequential Monte Carlo is employed for model estimation and sequential prediction in RNN HAR. The empirical analysis is conducted using daily closing prices and realized measures from 2000 to 2022 across 31 market indices. The proposed models one step ahead VaR forecasting performance is compared against a basic HAR model and its extensions. The results demonstrate that the proposed RNN-HAR model consistently outperforms all other models considered in the study. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.13588 |
| By: | Kory Kroft; Ismael Mourifié; Atom Vayalinkal |
| Abstract: | This paper investigates the causal effect of job training on wage rates in the presence of firm heterogeneity. When training affects worker sorting to firms, sample selection is no longer binary but is “multilayered”. This paper extends the canonical Heckman (1979) sample selection model - which assumes selection is binary - to a setting where it is multilayered, and shows that in this setting Lee bounds set identifies a total effect that combines a weighted-average of the causal effect of job training on wage rates across firms with a weighted-average of the contrast in wages between different firms for a fixed level of training. Thus, Lee bounds set identifies a policy-relevant estimand only when firms pay homogeneous wages and/or when job training does not affect worker sorting across firms. We derive sharp bounds for the causal effect of job training on wage rates at each firm which leverage information on firm-specific wages. We illustrate our partial identification approach with an empirical application to the Job Corps Study. Results show that while conventional Lee bounds are strictly positive, our within-firm bounds include 0 showing that canonical Lee bounds may be capturing a pure sorting effect of job training. |
| JEL: | C01 C21 C26 H0 H50 J0 J24 J32 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32952 |
| By: | Henk Keffert; Nikolaus Schweizer |
| Abstract: | When it comes to structural estimation of risk preferences from data on choices, random utility models have long been one of the standard research tools in economics. A recent literature has challenged these models, pointing out some concerning monotonicity and, thus, identification problems. In this paper, we take a second look and point out that some of the criticism - while extremely valid - may have gone too far, demanding monotonicity of choice probabilities in decisions where it is not so clear whether it should be imposed. We introduce a new class of random utility models based on carefully constructed generalized risk premia which always satisfy our relaxed monotonicity criteria. Moreover, we show that some of the models used in applied research like the certainty-equivalent-based random utility model for CARA utility actually lie in this class of monotonic stochastic choice models. We conclude that not all random utility models are bad. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.00704 |
| By: | Jamie L. Cross (University of Melbourne); Lennart Hoogerheide (Vrije Universiteit Amsterdam); Paul Labonne (BI Norwegian Business School); Herman K. van Dijk (Erasmus University Rotterdam) |
| Abstract: | In several scientific fields, such as finance, economics and bioinformatics, important theoretical and practical issues exist involving multimodal and asymmetric count data distributions due to heterogeneity of the underlying population. For accurate approximation of such distributions we introduce a novel class of flexible mixtures consisting of shifted negative binomial distributions, which accommodates a wide range of shapes that are commonly seen in these data. We further introduce a convenient reparameterization which is more closely related to a moment interpretation and facilitates the specification of prior information and the Monte Carlo simulation of the posterior. This mixture process is estimated by the sparse finite mixture Markov chain Monte method since it can handle a flexible number of non- empty components. Given loan payment, inflation expectation and DNA count data, we find coherent evidence on number and location of modes, fat tails and implied uncertainty measures, in contrast to conflicting evidence obtained from well-known frequentist tests. The proposed methodology may lead to more accurate measures of uncertainty and risk which improves prediction and policy analysis using multimodal and asymmetric count data. |
| Keywords: | Count data, multimodality, mixtures, shifted negative binomial, Markov chain Monte Carlo, Bayesian inference, sparse finite mixture |
| JEL: | C11 C14 C63 |
| Date: | 2024–09–13 |
| URL: | https://d.repec.org/n?u=RePEc:tin:wpaper:20240056 |
| By: | Yaojun Zhang; Lanpeng Ji; Georgios Aivaliotis; Charles C. Taylor |
| Abstract: | This paper proposes three types of Bayesian CART (or BCART) models for aggregate claim amount, namely, frequency-severity models, sequential models and joint models. We propose a general framework for the BCART models applicable to data with multivariate responses, which is particularly useful for the joint BCART models with a bivariate response: the number of claims and aggregate claim amount. To facilitate frequency-severity modeling, we investigate BCART models for the right-skewed and heavy-tailed claim severity data by using various distributions. We discover that the Weibull distribution is superior to gamma and lognormal distributions, due to its ability to capture different tail characteristics in tree models. Additionally, we find that sequential BCART models and joint BCART models, which incorporate dependence between the number of claims and average severity, are beneficial and thus preferable to the frequency-severity BCART models in which independence is assumed. The effectiveness of these models' performance is illustrated by carefully designed simulations and real insurance data. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.01908 |
| By: | Mitrodima, Gelly; Oberoi, Jaideep |
| Abstract: | We consider alternative specifications of conditional autoregressive quantile models to estimate Value-at-Risk and Expected Shortfall. The proposed specifications include a slow moving component in the quantile process, along with aggregate returns from heterogeneous horizons as regressors. Using data for 10 stock indices, we evaluate the performance of the models and find that the proposed features are useful in capturing tail dynamics better. |
| Keywords: | value-at-risk; expected shortfall; CAViaR-type models; component models; long range dependency; long range dependence |
| JEL: | C50 G11 |
| Date: | 2024–01–01 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:120880 |
| By: | Zhao, X.; Hong, S. Y.; Linton, O. B. |
| Abstract: | We study the different origins of two closely related extreme financial risk factors: volatility bursts and price jumps. We propose a new method to separate these quantities from ultra-high-frequency data via a novel endogenous thresholding approach in the presence of market microstructure noise and staleness. Our daily jump statistic proxies volatility bursts when intraday jumps are accurately controlled by our local jump test (which proves to be highly powerful with extremely low misclassification rates due to its timely detections). We find that news is more related to volatility bursts; while high-frequency trading variables, especially volume and bid/ask spread, are prominent signals for price jumps. |
| Keywords: | Price Jumps, Volatility Bursts, Market Microstructure Noise, Endogenous Sampling, High-Frequency Trading, News Sentiment |
| JEL: | G12 G14 C14 |
| Date: | 2024–09–06 |
| URL: | https://d.repec.org/n?u=RePEc:cam:camdae:2449 |