|
on Econometrics |
| By: | Zhewen Pan; Zhengxin Wang; Junsen Zhang; Yahong Zhou |
| Abstract: | We propose a method for defining, identifying, and estimating the marginal treatment effect (MTE) without imposing the instrumental variable (IV) assumptions of independence, exclusion, and separability (or monotonicity). Under a new definition of the MTE based on reduced-form treatment error that is statistically independent of the covariates, we find that the relationship between the MTE and standard treatment parameters holds in the absence of IVs. We provide a set of sufficient conditions ensuring the identification of the defined MTE in an environment of essential heterogeneity. The key conditions include a linear restriction on potential outcome regression functions, a nonlinear restriction on the propensity score, and a conditional mean independence restriction that will lead to additive separability. We prove this identification using the notion of semiparametric identification based on functional form. We suggest consistent semiparametric estimation procedures, and provide an empirical application for the Head Start program to illustrate the usefulness of the proposed method in analyzing heterogenous causal effects when IVs are elusive. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.17595&r=ecm |
| By: | Zhenhao Gong (Shanxi University of Finance and Economics); Min Seong Kim (University of Connecticut) |
| Abstract: | This paper proposes an inference procedure for the interactive fixed effects model that is valid in the presence of cross-sectional dependence. When the error terms are cross-sectionally dependent, the Least Square (LS) estimator of this model is asymptotically biased and therefore the associated confidence interval tends to have a large coverage error. To address this, we propose a bias correction of the LS estimator and a cross-sectional dependence robust variance estimator to construct associated test statistics. The paper also discusses practical issues in implementing the proposed method, including the construction of distance that reflects the decaying pattern of cross-sectional dependence and the selection of the bandwidth parameters. Monte Carlo simulations show our procedure works well in finite samples. As empirical illustrations, we apply our procedure to study the effect of divorce law reforms on divorce rates and the impact of clean water and sewerage interventions on child mortality. |
| Keywords: | Bandwidth selection, Bias correction, Robust inference, Spatial HAC method |
| JEL: | C12 C13 C15 C31 C33 J12 I18 |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:uct:uconnp:2024-02&r=ecm |
| By: | Chang, Jinyuan; Zhang, Henry; Yang, Lin; Yao, Qiwei |
| Abstract: | We consider to model matrix time series based on a tensor canonical polyadic (CP)-decomposition. Instead of using an iterative algorithm which is the standard practice for estimating CP-decompositions, we propose a new and one-pass estimation procedure based on a generalized eigenanalysis constructed from the serial dependence structure of the underlying process. To overcome the intricacy of solving a rank-reduced generalized eigenequation, we propose a further refined approach which projects it into a lower-dimensional full-ranked eigenequation. This refined method can significantly improve the finite-sample performance. We show that all the component coefficient vectors in the CP-decomposition can be estimated consistently. The proposed model and the estimation method are also illustrated with both simulated and real data, showing effective dimension-reduction in modelling and forecasting matrix time series. |
| Keywords: | dimension-reduction; generalized eigenanalysis; tensor CP-decomposition; matrix time series |
| JEL: | C1 |
| Date: | 2023–02–01 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:117644&r=ecm |
| By: | Joao M.C. Santos Silva (University of Surrey); Rainer Winkelmann (University of Zurich) |
| Abstract: | Exponential regressions are frequently used when outcomes are non-negative. They are attractive because they are easy to interpret and to estimate, using pseudo maximum likelihood (PML). However, the validity of these methods depends on the correct specification of the conditional expectation, and little is known regarding their properties when the conditional expectation is misspecified. We show that PML estimators of misspecified exponential models provide optimal approximations to the conditional expectation, in a weighted mean squared error sense, and we give conditions under which their Poisson PML estimator identifies average marginal effects. |
| JEL: | C13 C21 C25 C51 |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:sur:surrec:0124&r=ecm |
| By: | Klebel, Thomas; Traag, Vincent |
| Abstract: | Sound causal inference is crucial for advancing the study of science. Incorrectly interpreting predictive effects as causal might be ineffective or even detrimental to policy recommendations. Many publications in science studies lack appropriate methods to substantiate their causal claims. We here provide an introduction to structural causal models. Such models, usually represented in a graphical form, allow researchers to make their causal assumptions transparent and provide a foundation for causal inference. We illustrate how to use structural causal models to conduct causal inference using regression models based on simulated data of a hypothetical structural causal model of Open Science. The graphical representation of structural causal models allows researchers to clearly communicate their assumptions and findings, thereby fostering further discussion. We hope our introduction helps more researchers in science studies to consider causality explicitly. |
| Date: | 2024–02–09 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:4bw9e&r=ecm |
| By: | Marc Hallin; Simos Meintanis; Klaus Nordhausen |
| Abstract: | We propose a family of tests of the validity of the assumptions underlying independent component analysis methods. The tests are formulated as L2–type procedures based on characteristic functions and involve weights; a proper choice of these weights and the estimation method for the mixing matrix yields consistent and affine-invariant tests. Due to the complexity of the asymptotic null distribution of the resulting test statistics, implementation is based on permutational and resampling strategies. This leads to distribution-free procedures regardless of whether these procedures are performed on the estimated independent components themselves or the componentwise ranks of their components. A Monte Carlo study involving various estimation methods for the mixing matrix, various weights, and a competing test based on distance covariance is conducted under the null hypothesis as well as under alternatives. A real-data application demonstrates the practical utility and effectiveness of the method. |
| Keywords: | Characteristic function; total independence; independent component model; rank test |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:eca:wpaper:2013/368952&r=ecm |
| By: | E. Otranto |
| Abstract: | Modern approaches to financial time series aim to model in a multivariate framework the volatility of different indices or assets, which could influence each other, creating spillover effects. Furthermore, the integration of financial markets provides a similar dynamics (co-movement). We propose a new model for volatility vectors, belonging to the family of Multiplicative Error Models, which incorporates spillover and co-movement effects. By adopting an appropriate parameterization, it is possible to estimate this model even for high dimensional vectors of volatility. To reduce the number of unknown coefficients, we propose a 3-step model-based clustering procedure. The proposed model is applied to a set of seventeen world financial indices, providing a useful interpretation of spillover effects and co- movements. Furthermore, the proposed parameterization is compared with two alternatives, showing significantly better performance. |
| Keywords: | high-dimensional time series;vector of volatility;multiplicative factors;model-based clustering;high-low range |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:cns:cnscwp:202404&r=ecm |
| By: | Dennis Schroers |
| Abstract: | This article addresses the robust measurement of covariations in the context of solutions to stochastic evolution equations in Hilbert spaces using functional data analysis. For such equations, standard techniques for functional data based on cross-sectional covariances are often inadequate for identifying statistically relevant random drivers and detecting outliers since they overlook the interplay between cross-sectional and temporal structures. Therefore, we develop an estimation theory for the continuous quadratic covariation of the latent random driver of the equation instead of a static covariance of the observable solution process. We derive identifiability results under weak conditions, establish rates of convergence and a central limit theorem based on infill asymptotics, and provide long-time asymptotics for estimation of a static covariation of the latent driver. Applied to term structure data, our approach uncovers a fundamental alignment with scaling limits of covariations of specific short-term trading strategies, and an empirical study detects several jumps and indicates high-dimensional and time-varying covariations. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.16286&r=ecm |
| By: | Liyang Sun; Eli Ben-Michael; Avi Feller |
| Abstract: | The synthetic control method (SCM) is a popular approach for estimating the impact of a treatment on a single unit with panel data. Two challenges arise with higher frequency data (e.g., monthly versus yearly): (1) achieving excellent pre-treatment fit is typically more challenging; and (2) overfitting to noise is more likely. Aggregating data over time can mitigate these problems but can also destroy important signal. In this paper, we bound the bias for SCM with disaggregated and aggregated outcomes and give conditions under which aggregating tightens the bounds. We then propose finding weights that balance both disaggregated and aggregated series. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.12084&r=ecm |
| By: | Daniel Lewis |
| Abstract: | Identification based on higher moments has drawn increasing theoretical attention and been widely adopted in empirical practice in macroeconometrics in the last two decades. This article reviews two parallel strands of the literature: identification strategies based on heteroskedasticity and strategies based on non-Gaussianity more generally. I outline the seminal identification results and discuss recent extensions, parametric and non-parametric implementations, and prominent empirical applications. I additionally describe key issues for the adoption of such strategies, including weak identification and interpretability of statistically identified structural shocks. I further outline key areas of ongoing research. |
| Date: | 2024–02–20 |
| URL: | http://d.repec.org/n?u=RePEc:azt:cemmap:03/24&r=ecm |
| By: | Einmahl, John (Tilburg University, Center For Economic Research); Zhou, C. (Tilburg University, Center For Economic Research) |
| Keywords: | Extreme value statistics; functional limit theorems; non-identical distributions; tail empirical process; tail dependence |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:tiu:tiucen:6bcb09c5-8b19-48b8-9320-b80e0d9db36b&r=ecm |
| By: | Jasin Machkour; Daniel P. Palomar; Michael Muma |
| Abstract: | In high-dimensional data analysis, such as financial index tracking or biomedical applications, it is crucial to select the few relevant variables while maintaining control over the false discovery rate (FDR). In these applications, strong dependencies often exist among the variables (e.g., stock returns), which can undermine the FDR control property of existing methods like the model-X knockoff method or the T-Rex selector. To address this issue, we have expanded the T-Rex framework to accommodate overlapping groups of highly correlated variables. This is achieved by integrating a nearest neighbors penalization mechanism into the framework, which provably controls the FDR at the user-defined target level. A real-world example of sparse index tracking demonstrates the proposed method's ability to accurately track the S&P 500 index over the past 20 years based on a small number of stocks. An open-source implementation is provided within the R package TRexSelector on CRAN. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.15139&r=ecm |
| By: | Goldfayn-Frank, Olga; Kieren, Pascal; Trautmann, Stefan |
| Abstract: | In macroeconomic surveys, inflation expectations are commonly elicited via density forecasts in which respondents assign probabilities to pre-specified ranges in inflation. This question format is increasingly subject to criticism. In this study, we propose a new method to elicit inflation expectations which is based on prior decision theoretic research. We demonstrate that it leads to well-defined expectations with central tendencies close to the corresponding point forecasts and to lower forecast uncertainty than density forecasts. In contrast to currently employed methods, the approach is robust to differences in the state of the economy and thus allows comparisons across time and across countries. Additionally, the method is not very time consuming and portable in the sense that it can be applied to different macroeconomic measures. |
| Keywords: | Inflation expectations; measurement; surveys |
| Date: | 2024–02–14 |
| URL: | http://d.repec.org/n?u=RePEc:awi:wpaper:0742&r=ecm |
| By: | Brendan K Beare; Massimo Franchi; Phil Howlett |
| Abstract: | In this article we provide a complete description of the set of all solutions to an autoregressive law of motion in a finite-dimensional complex vector space. Every solution is shown to be the sum of three parts, each corresponding to a directed flow of time. One part flows forward from the arbitrarily distant past; one flows backwards from the arbitrarily distant future; and one flows outward from time zero. The three parts are obtained by applying three complementary spectral projections to the solution, these corresponding to a separation of the eigenvalues of the autoregressive operator according to whether they are inside, outside or on the unit circle. We provide a finite-dimensional parametrization of the set of all solutions. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:syd:wpaper:2024-01&r=ecm |