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on Econometrics |
| By: | Sungwon Lee (Department of Economics, Sogang University, Seoul, Korea) |
| Abstract: | This paper considers binary choice models with panel data. We extend the correlated random effects binary choice models for panel data in Chamberlain (1980) to semiparametric models in which the conditional expectation projection of the unobserved time-invariant heterogeneity onto the space of functions of time-varying covariates for all time periods is nonparametrically specified. This class of models is tractable for identification and estimation of the model parameters with short panel data. We provide a set of mild conditions under which the parameters are identified. We propose to use the penalized sieve minimum distance (PSMD) estimation and develop the asymptotic theory. The PSMD estimators of finite dimensional parameters are shown to be semiparametrically efficient when the weighting matrix is the optimal one. We also show the bootstrap validity. The Monte Carlo simulation results confirm that the proposed estimator performs well in finite samples. |
| Keywords: | binary choice models, correlated random effects, sieve estimation, semiparametric efficiency, bootstrap |
| JEL: | C13 C14 C31 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:sgo:wpaper:2302&r=ecm |
| By: | Young, Alwyn |
| Abstract: | Randomization inference provides exact finite sample tests of sharp null hypotheses which fully specify the distribution of outcomes under counterfactual realizations of treatment, but the sharp null is often considered restrictive as it rules out unspecified heterogeneity in treatment response. However, a growing literature shows that tests based upon permutations of regressors using pivotal statistics can remain asymptotically valid when the assumption regarding the permutation invariance of the data generating process used to motivate them is actually false. For experiments where potential outcomes involve the permutation of regressors, these results show that permutation-based randomization inference, while providing exact tests of sharp nulls, can also have the same asymptotic validity as conventional tests of average treatment effects with unspecified heterogeneity and other forms of specification error in treatment response. This paper extends this work to the consideration of interactions between treatment variables and covariates, a common feature of published regressions, as well as issues in the construction of confidence intervals and testing of subsets of treatment effects. |
| Keywords: | randomization inference; sharp null; confidence intervals; subset testing; Elsevier deal |
| JEL: | J1 |
| Date: | 2024–04–01 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:120933&r=ecm |
| By: | Jackson Bunting; Paul Diegert; Arnaud Maurel |
| Abstract: | We provide semiparametric identification results for a broad class of learning models in which continuous outcomes depend on three types of unobservables: i) known heterogeneity, ii) initially unknown heterogeneity that may be revealed over time, and iii) transitory uncertainty. We consider a common environment where the researcher only has access to a short panel on choices and realized outcomes. We establish identification of the outcome equation parameters and the distribution of the three types of unobservables, under the standard assumption that unknown heterogeneity and uncertainty are normally distributed. We also show that, absent known heterogeneity, the model is identified without making any distributional assumption. We then derive the asymptotic properties of a sieve MLE estimator for the model parameters, and devise a tractable profile likelihood based estimation procedure. Monte Carlo simulation results indicate that our estimator exhibits good finite-sample properties. |
| JEL: | C14 C50 D83 |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:32164&r=ecm |
| By: | Sungwon Lee (Department of Economics, Sogang University, Seoul, Korea) |
| Abstract: | This paper proposes a one-step sieve estimator of the parameter in the semiparametric triangular model for quantile regression of Lee (2007). The proposed estimator is a penalized sieve minimum distance (PSMD) estimator developed by Chen and Pouzo (2009). We develop the asymptotic theory for the PSMD estimator under a set of low-level conditions. The PSMD estimator is shown to be semiparametrically efficient, and the validity of a weighted bootstrap is established. A small Monte Carlo simulation study shows that our estimator performs well in finite samples. |
| Keywords: | quantile regression, endogeneity, sieve estimation, semiparametric efficiency |
| JEL: | C13 C14 C31 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:sgo:wpaper:2301&r=ecm |
| By: | Zhang, Xinyu; Li, Dong; Tong, Howell |
| Abstract: | Most threshold models to-date contain a single threshold variable. However, in many empirical applications, models with multiple threshold variables may be needed and are the focus of this article. For the sake of readability, we start with the Two-Threshold-Variable Autoregressive (2-TAR) model and study its Least Squares Estimation (LSE). Among others, we show that the respective estimated thresholds are asymptotically independent. We propose a new method, namely the weighted Nadaraya-Watson method, to construct confidence intervals for the threshold parameters, that turns out to be, as far as we know, the only method to-date that enjoys good probability coverage, regardless of whether the threshold variables are endogenous or exogenous. Finally, we describe in some detail how our results can be extended to the K-Threshold-Variable Autoregressive (K-TAR) model, K > 2. We assess the finite-sample performance of the LSE by simulation and present two real examples to illustrate the efficacy of our modeling. |
| Keywords: | compound Poisson process; degeneracy of a spatial process; multiple threshold variables; TAR model; weighted Nadaraya-Watson method |
| JEL: | C1 |
| Date: | 2023–02–23 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:118377&r=ecm |
| By: | Jonas E. Arias; Juan F. Rubio-Ramirez; Minchul Shin; Daniel F. Waggoner |
| Abstract: | We propose an approach for Bayesian inference in time-varying SVARs identified with sign restrictions. The linchpin of our approach is a class of rotation-invariant time-varying SVARs in which the prior and posterior densities of any sequence of structural parameters belonging to the class are invariant to orthogonal transformations of the sequence. Our methodology is new to the literature. In contrast to existing algorithms for inference based on sign restrictions, our algorithm is the first to draw from a uniform distribution over the sequences of orthogonal matrices given the reduced-form parameters. We illustrate our procedure for inference by analyzing the role played by monetary policy during the latest inflation surge |
| Keywords: | time-varying parameters; structural vector autoregressions; identification |
| JEL: | C11 C51 E52 E58 |
| Date: | 2024–02–27 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedpwp:97853&r=ecm |
| By: | Alexander Mayer; Dominik Wied; Victor Troster |
| Abstract: | We propose a new framework for assessing Granger causality in quantiles in unstable environments, for a fixed quantile or over a continuum of quantile levels. Our proposed test statistics are consistent against fixed alternatives, they have nontrivial power against local alternatives, and they are pivotal in certain important special cases. In addition, we show the validity of a bootstrap procedure when asymptotic distributions depend on nuisance parameters. Monte Carlo simulations reveal that the proposed test statistics have correct empirical size and high power, even in absence of structural breaks. Finally, two empirical applications in energy economics and macroeconomics highlight the applicability of our method as the new tests provide stronger evidence of Granger causality. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.09744&r=ecm |
| By: | Jikai Jin; Vasilis Syrgkanis |
| Abstract: | Average treatment effect estimation is the most central problem in causal inference with application to numerous disciplines. While many estimation strategies have been proposed in the literature, recently also incorporating generic machine learning estimators, the statistical optimality of these methods has still remained an open area of investigation. In this paper, we adopt the recently introduced structure-agnostic framework of statistical lower bounds, which poses no structural properties on the nuisance functions other than access to black-box estimators that attain small errors; which is particularly appealing when one is only willing to consider estimation strategies that use non-parametric regression and classification oracles as a black-box sub-process. Within this framework, we prove the statistical optimality of the celebrated and widely used doubly robust estimators for both the Average Treatment Effect (ATE) and the Average Treatment Effect on the Treated (ATTE), as well as weighted variants of the former, which arise in policy evaluation. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.14264&r=ecm |
| By: | Tadao Hoshino |
| Abstract: | This study introduces a novel spatial autoregressive model in which the dependent variable is a function that may exhibit functional autocorrelation with the outcome functions of nearby units. This model can be characterized as a simultaneous integral equation system, which, in general, does not necessarily have a unique solution. For this issue, we provide a simple condition on the magnitude of the spatial interaction to ensure the uniqueness in data realization. For estimation, to account for the endogeneity caused by the spatial interaction, we propose a regularized two-stage least squares estimator based on a basis approximation for the functional parameter. The asymptotic properties of the estimator including the consistency and asymptotic normality are investigated under certain conditions. Additionally, we propose a simple Wald-type test for detecting the presence of spatial effects. As an empirical illustration, we apply the proposed model and method to analyze age distributions in Japanese cities. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.14763&r=ecm |
| By: | Juan Carlos Escanciano; Ricardo Parra |
| Abstract: | Though out-of-sample forecast evaluation is systematically employed with modern machine learning methods and there exists a well-established classic inference theory for predictive ability, see, e.g., West (1996, Asymptotic Inference About Predictive Ability, \textit{Econometrica}, 64, 1067-1084), such theory is not directly applicable to modern machine learners such as the Lasso in the high dimensional setting. We investigate under which conditions such extensions are possible. Two key properties for standard out-of-sample asymptotic inference to be valid with machine learning are (i) a zero-mean condition for the score of the prediction loss function; and (ii) a fast rate of convergence for the machine learner. Monte Carlo simulations confirm our theoretical findings. For accurate finite sample inferences with machine learning, we recommend a small out-of-sample vs in-sample size ratio. We illustrate the wide applicability of our results with a new out-of-sample test for the Martingale Difference Hypothesis (MDH). We obtain the asymptotic null distribution of our test and use it to evaluate |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.12838&r=ecm |
| By: | Suparna Biswas; Rituparna Sen |
| Abstract: | Left truncated and right censored data are encountered frequently in insurance loss data due to deductibles and policy limits. Risk estimation is an important task in insurance as it is a necessary step for determining premiums under various policy terms. Spectral risk measures are inherently coherent and have the benefit of connecting the risk measure to the user's risk aversion. In this paper we study the estimation of spectral risk measure based on left truncated and right censored data. We propose a non parametric estimator of spectral risk measure using the product limit estimator and establish the asymptotic normality for our proposed estimator. We also develop an Edgeworth expansion of our proposed estimator. The bootstrap is employed to approximate the distribution of our proposed estimator and shown to be second order ``accurate''. Monte Carlo studies are conducted to compare the proposed spectral risk measure estimator with the existing parametric and non parametric estimators for left truncated and right censored data. Based on our simulation study we estimate the exponential spectral risk measure for three data sets viz; Norwegian fire claims data set, Spain automobile insurance claims and French marine losses. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.14322&r=ecm |
| By: | Pablo Geraldo Bast\'ias |
| Abstract: | Causal inference with observational data critically relies on untestable and extra-statistical assumptions that have (sometimes) testable implications. Well-known sets of assumptions that are sufficient to justify the causal interpretation of certain estimators are called identification strategies. These templates for causal analysis, however, do not perfectly map into empirical research practice. Researchers are often left in the disjunctive of either abstracting away from their particular setting to fit in the templates, risking erroneous inferences, or avoiding situations in which the templates cannot be applied, missing valuable opportunities for conducting empirical analysis. In this article, I show how directed acyclic graphs (DAGs) can help researchers to conduct empirical research and assess the quality of evidence without excessively relying on research templates. First, I offer a concise introduction to causal inference frameworks. Then I survey the arguments in the methodological literature in favor of using research templates, while either avoiding or limiting the use of causal graphical models. Third, I discuss the problems with the template model, arguing for a more flexible approach to DAGs that helps illuminating common problems in empirical settings and improving the credibility of causal claims. I demonstrate this approach in a series of worked examples, showing the gap between identification strategies as invoked by researchers and their actual applications. Finally, I conclude highlighting the benefits that routinely incorporating causal graphical models in our scientific discussions would have in terms of transparency, testability, and generativity. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.11659&r=ecm |
| By: | Lucas Girard; Yannick Guyonvarch |
| Abstract: | In the 1990s, Joshua Angrist and Guido Imbens studied the causal interpretation of Instrumental Variable estimates (a widespread methodology in economics) through the lens of potential outcomes (a classical framework to formalize causality in statistics). Bridging a gap between those two strands of literature, they stress the importance of treatment effect heterogeneity and show that, under defendable assumptions in various applications, this method recovers an average causal effect for a specific subpopulation of individuals whose treatment is affected by the instrument. They were awarded the Nobel Prize primarily for this Local Average Treatment Effect (LATE). The first part of this article presents that methodological contribution in-depth: the origination in earlier applied articles, the different identification results and extensions, and related debates on the relevance of LATEs for public policy decisions. The second part reviews the main contributions of the authors beyond the LATE. J. Angrist has pursued the search for informative and varied empirical research designs in several fields, particularly in education. G. Imbens has complemented the toolbox for treatment effect estimation in many ways, notably through propensity score reweighting, matching, and, more recently, adapting machine learning procedures. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.13023&r=ecm |
| By: | Steven Y. K. Wong; Jennifer S. K. Chan; Lamiae Azizi |
| Abstract: | Time-series with time-varying variance pose a unique challenge to uncertainty quantification (UQ) methods. Time-varying variance, such as volatility clustering as seen in financial time-series, can lead to large mismatch between predicted uncertainty and forecast error. Building on recent advances in neural network UQ literature, we extend and simplify Deep Evidential Regression and Deep Ensembles into a unified framework to deal with UQ under the presence of volatility clustering. We show that a Scale Mixture Distribution is a simpler alternative to the Normal-Inverse-Gamma prior that provides favorable complexity-accuracy trade-off. To illustrate the performance of our proposed approach, we apply it to two sets of financial time-series exhibiting volatility clustering: cryptocurrencies and U.S. equities. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.14476&r=ecm |
| By: | Kaori Narita; J.D. Tena; Claudio Detotto |
| Abstract: | When treatment cannot be manipulated, propensity score analysis provides a practical approach to making causal claims. However, it is still rarely utilised in leadership and applied psychology research. The purpose of this paper is threefold. First, it explains and discusses the application of the method with a particular focus on propensity score weighting. This approach is readily implementable since a weighted regression is available in most statistical software. Moreover, using a double robust estimator can offer protection against the misspecification of the model by including confounding variables both in the treatment and response equations. A second aim is to discuss how propensity score analysis has been conducted in recent management studies and examine future challenges. Finally, we illustrate the method by showing how it can be employed to estimate the causal impact of leadership succession on performance using data from Italian football. The case also exemplifies how to extend the standard single treatment analysis to estimate the separate impact of different managerial characteristic changes between the old and the new manager. |
| Keywords: | causality, propensity score, leadership succession, observational data, football |
| JEL: | C31 J24 J63 M51 Z22 |
| Date: | 2022–11 |
| URL: | http://d.repec.org/n?u=RePEc:liv:livedp:202225&r=ecm |
| By: | Cathy W. S. Chen; Takaaki Koike; Wei-Hsuan Shau |
| Abstract: | This research incorporates realized volatility and overnight information into risk models, wherein the overnight return often contributes significantly to the total return volatility. Extending a semi-parametric regression model based on asymmetric Laplace distribution, we propose a family of RES-CAViaR-oc models by adding overnight return and realized measures as a nowcasting technique for simultaneously forecasting Value-at-Risk (VaR) and expected shortfall (ES). We utilize Bayesian methods to estimate unknown parameters and forecast VaR and ES jointly for the proposed model family. We also conduct extensive backtests based on joint elicitability of the pair of VaR and ES during the out-of sample period. Our empirical study on four international stock indices confirms that overnight return and realized volatility are vital in tail risk forecasting. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.07134&r=ecm |
| By: | Julian Granna; Stefan Lang; Nikolaus Umlauf |
| Abstract: | Modeling real estate prices in the context of hedonic models often involves fitting a Generalized Additive Model, where only the mean of a (lognormal) distribution is regressed on a set of variables without taking other parameters of the distribution into account. Thus far, the application of regression models that model the full conditional distribution of the prices, has been infeasible for large data sets, even on powerful machines. Moreover, accounting for heterogeneity of effects regarding time and location, is often achieved by naive stratification of the data rather than on a model basis. A novel batchwise backfitting algorithm is applied in the context of a structured additive distributional regression model, which enables us to efficiently model all distributional parameters of the price distribution. Using a large German dataset of apartment asking prices with over one million observations, we employ a model-based clustering algorithm to capture the heterogeneity of covariate effects on the parameters with respect to location. We thus identify clusters that are homogeneous with respect to the influence of location on price. A boosting type algorithm of the batchwise backfitting algorithm is then used to automatically determine the variables relevant for modelling the location and scale parameters in each regional cluster. This allows for a different influence of variables on the distribution of prices depending on the location and price segment of the dwelling. |
| Keywords: | IWLS proposals, MCMC, multiplicative interaction effects, structured additive predictor |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:inn:wpaper:2024-02&r=ecm |
| By: | Guglielmo Maria Caporale; Luis Alberiko Gil-Alana |
| Abstract: | This paper proposes a long-memory model including multiple cycles in addition to the long-run component. Specifically, instead of a single pole or singularity in the spectrum, it allows for multiple poles and thus different cycles with different degrees of persistence. It also incorporates non-linear deterministic structures in the form of Chebyshev polynomials in time. Simulations are carried out to analyse the finite sample properties of the proposed test, which is shown to perform well in the case of a relatively large sample with at least 1000 observations. The model is then applied to weekly data on the S&P500 from 1 January 1970 to 26 October 2023 as an illustration. The estimation results based on the first differenced logged values (i.e., the returns) point to the existence of three cyclical structures in the series with a length of approximately one month, one year and four years respectively, and to orders of integration in the range (0, 0.20), which implies stationary long memory in all cases. |
| Keywords: | fractional integration, multiple cycles, stock market indices, S&P500 |
| JEL: | C22 C15 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10947&r=ecm |
| By: | Kei Nakagawa; Kohei Hayashi; Yugo Fujimoto |
| Abstract: | In this paper, we propose the Continuous Time Fractional Topic Model (cFTM), a new method for dynamic topic modeling. This approach incorporates fractional Brownian motion~(fBm) to effectively identify positive or negative correlations in topic and word distribution over time, revealing long-term dependency or roughness. Our theoretical analysis shows that the cFTM can capture these long-term dependency or roughness in both topic and word distributions, mirroring the main characteristics of fBm. Moreover, we prove that the parameter estimation process for the cFTM is on par with that of LDA, traditional topic models. To demonstrate the cFTM's property, we conduct empirical study using economic news articles. The results from these tests support the model's ability to identify and track long-term dependency or roughness in topics over time. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.01734&r=ecm |
| By: | Edward S. Knotek; Saeed Zaman |
| Abstract: | This chapter summarizes the mixed-frequency methods commonly used for nowcasting inflation. It discusses the importance of key high-frequency data in producing timely and accurate inflation nowcasts. In the US, consensus surveys of professional forecasters have historically provided an accurate benchmark for inflation nowcasts because they incorporate professional judgment to capture idiosyncratic factors driving inflation. Using real-time data, we show that a relatively parsimonious mixed-frequency model produces superior point and density nowcasting accuracy for headline inflation and competitive nowcasting accuracy for core inflation compared with surveys of professional forecasters over a long sample spanning 1999–2022 and over a short sample focusing on the period since the start of the pandemic. |
| Keywords: | inflation; nowcasting; mixed-frequency models; survey nowcasts; real-time data |
| JEL: | C53 E3 E37 |
| Date: | 2024–03–07 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedcwq:97908&r=ecm |
| By: | R. Zelli; P.L.Conti; M.G. Pittau |
| Abstract: | The paper addresses the issue of comparing deprivation distributions, when poverty is measured by a sum of binary variables. To accomplish this task, it provides a graphical device, the Three I's of Deprivation (TID) curve, that summarizes incidence, intensity and inequality aspects of deprivation in a society, and it is the natural counterpart of the TIP curve widely used in income poverty analysis. Uncertainty around the estimated deprivation curves is evaluated through simultaneous confidence bands. An hypothesis test of dominance is presented to facilitate the comparison and the ordering of deprivation curves across groups and over time. An extension of the Sen-Shorrocks poverty index that summarizes the three I's of deprivation is characterized and confidence intervals are developed. As a substantive illustration the evolution of material and social deprivation across European countries over the period of the outbreak of the pandemic is analysed. |
| Keywords: | EU-SILC data;binary variables;stochastic dominance;deprivation curves |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:cns:cnscwp:202405&r=ecm |