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on Econometrics |
| By: | Matteo Barigozzi; Luca Trapin |
| Abstract: | This paper considers an approximate dynamic matrix factor model that accounts for the time series nature of the data by explicitly modelling the time evolution of the factors. We study Quasi Maximum Likelihood estimation of the model parameters based on the Expectation Maximization (EM) algorithm, implemented jointly with the Kalman smoother which gives estimates of the factors. This approach allows to easily handle arbitrary patterns of missing data. We establish the consistency of the estimated loadings and factor matrices as the sample size $T$ and the matrix dimensions $p_1$ and $p_2$ diverge to infinity. The finite sample properties of the estimators are assessed through a large simulation study and an application to a financial dataset of volatility proxies. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.04112 |
| By: | Jean-Yves Pitarakis |
| Abstract: | This paper introduces a new method for testing the statistical significance of estimated parameters in predictive regressions. The approach features a new family of test statistics that are robust to the degree of persistence of the predictors. Importantly, the method accounts for serial correlation and conditional heteroskedasticity without requiring any corrections or adjustments. This is achieved through a mechanism embedded within the test statistics that effectively decouples serial dependence present in the data. The limiting null distributions of these test statistics are shown to follow a chi-square distribution, and their asymptotic power under local alternatives is derived. A comprehensive set of simulation experiments illustrates their finite sample size and power properties. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.00475 |
| By: | Chen, Jia; Cui, Guowei; Sarafidis, Vasilis; Yamagata, Takashi |
| Abstract: | This paper develops a Mean Group Instrumental Variables (MGIV) estimator for spatial dynamic panel data models with interactive effects, under large N and T asymptotics. Unlike existing approaches that typically impose slope-parameter homogeneity, MGIV accommodates cross-sectional heterogeneity in slope coefficients. The proposed estimator is linear, making it computationally efficient and robust. Furthermore, it avoids the incidental parameters problem, enabling asymptotically valid inferences without requiring bias correction. The Monte Carlo experiments indicate strong finite-sample performance of the MGIV estimator across various sample sizes and parameter configurations. The practical utility of the estimator is illustrated through an application to regional economic growth in Europe. By explicitly incorporating heterogeneity, our approach provides fresh insights into the determinants of regional growth, underscoring the critical roles of spatial and temporal dependencies. |
| Keywords: | Dynamic panel data, spatial interactions, heterogeneous slopes, interactive effects, latent common factors, instrumental variables, large N and T asymptotics. |
| JEL: | C3 C33 C55 O47 |
| Date: | 2025–01–30 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:123497 |
| By: | Masahiro Kato; Fumiaki Kozai; Ryo Inokuchi |
| Abstract: | The estimation of average treatment effects (ATEs), defined as the difference in expected outcomes between treatment and control groups, is a central topic in causal inference. This study develops semiparametric efficient estimators for ATE estimation in a setting where only a treatment group and an unknown group-comprising units for which it is unclear whether they received the treatment or control-are observable. This scenario represents a variant of learning from positive and unlabeled data (PU learning) and can be regarded as a special case of ATE estimation with missing data. For this setting, we derive semiparametric efficiency bounds, which provide lower bounds on the asymptotic variance of regular estimators. We then propose semiparametric efficient ATE estimators whose asymptotic variance aligns with these efficiency bounds. Our findings contribute to causal inference with missing data and weakly supervised learning. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.19345 |
| By: | Higgins, Ayden; Jochmans, Koen |
| Abstract: | Inference in linear panel data models is complicated by the presence of fixed effects when (some of) the regressors are not strictly exogenous. Under asymptotics where the number of cross-sectional observations and time periods grow at the same rate, the within-group estimator is consistent but its limit distribution features a bias term. In this paper we show that a panel version of the moving block bootstrap, where blocks of adjacent cross-sections are resampled with replacement, replicates the limit distribution of the within-group estimator. Confidence ellipsoids and hypothesis tests based on the reverse-percentile bootstrap are thus asymptotically valid without the need to take the presence of bias into account. |
| Keywords: | Asymptotic bias; bootstrap; dynamic model; fixed effects; inference |
| JEL: | C23 |
| Date: | 2025–02–13 |
| URL: | https://d.repec.org/n?u=RePEc:tse:wpaper:130347 |
| By: | Eoghan O'Neill |
| Abstract: | This paper introduces Type 2 Tobit Bayesian Additive Regression Trees (TOBART-2). BART can produce accurate individual-specific treatment effect estimates. However, in practice estimates are often biased by sample selection. We extend the Type 2 Tobit sample selection model to account for nonlinearities and model uncertainty by including sums of trees in both the selection and outcome equations. A Dirichlet Process Mixture distribution for the error terms allows for departure from the assumption of bivariate normally distributed errors. Soft trees and a Dirichlet prior on splitting probabilities improve modeling of smooth and sparse data generating processes. We include a simulation study and an application to the RAND Health Insurance Experiment data set. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.03600 |
| By: | David M. Kaplan; Xin Liu |
| Abstract: | We propose and study three confidence intervals (CIs) centered at an estimator that is intentionally biased to reduce mean squared error. The first CI simply uses an unbiased estimator's standard error; compared to centering at the unbiased estimator, this CI has higher coverage probability for confidence levels above 91.7%, even if the biased and unbiased estimators have equal mean squared error. The second CI trades some of this "excess" coverage for shorter length. The third CI is centered at a convex combination of the two estimators to further reduce length. Practically, these CIs apply broadly and are simple to compute. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.00450 |
| By: | Abhimanyu Gupta; Xi Qu; Sorawoot Srisuma; Jiajun Zhang |
| Abstract: | We present simple to implement Wald-type statistics that deliver a general nonparametric inference theory for linear restrictions on varying coefficients in a range of spatial autoregressive models. Our theory covers error dependence of a general form, allows for a degree of misspecification robustness via nonparametric spatial weights and permits inference on both varying regression and spatial coefficients. One application of our method finds evidence for constant returns to scale in the production function of the Chinese nonmetal mineral industry, while another finds a nonlinear impact of the distance to the employment center on housing prices in Boston. A simulation study confirms that our tests perform well in finite-samples. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.03084 |
| By: | Zhipeng Liao; Bin Wang; Wenyu Zhou |
| Abstract: | This paper explores the estimation and inference of the minimum spanning set (MSS), the smallest subset of risky assets that spans the mean-variance efficient frontier of the full asset set. We establish identification conditions for the MSS and develop a novel procedure for its estimation and inference. Our theoretical analysis shows that the proposed MSS estimator covers the true MSS with probability approaching 1 and converges asymptotically to the true MSS at any desired confidence level, such as 0.95 or 0.99. Monte Carlo simulations confirm the strong finite-sample performance of the MSS estimator. We apply our method to evaluate the relative importance of individual stock momentum and factor momentum strategies, along with a set of well-established stock return factors. The empirical results highlight factor momentum, along with several stock momentum and return factors, as key drivers of mean-variance efficiency. Furthermore, our analysis uncovers the sources of contribution from these factors and provides a ranking of their relative importance, offering new insights into their roles in mean-variance analysis. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.19213 |
| By: | Ertian Chen |
| Abstract: | Estimating dynamic discrete choice models with large state spaces poses computational difficulties. This paper develops a novel model-adaptive approach to solve the linear system of fixed point equations of the policy valuation operator. We propose a model-adaptive sieve space, constructed by iteratively augmenting the space with the residual from the previous iteration. We show both theoretically and numerically that model-adaptive sieves dramatically improve performance. In particular, the approximation error decays at a superlinear rate in the sieve dimension, unlike a linear rate achieved using conventional methods. Our method works for both conditional choice probability estimators and full-solution estimators with policy iteration. We apply the method to analyze consumer demand for laundry detergent using Kantar's Worldpanel Take Home data. On average, our method is 51.5% faster than the conventional methods in solving the dynamic programming problem, making the Bayesian MCMC estimator computationally feasible. The results confirm the computational efficiency of our method in practice. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.18746 |
| By: | Blazsek, Szabolcs; Ayala, Astrid |
| Abstract: | Score-driven filters are updated by the scaled gradient of the log-likelihood (LL). The gradient is with respect to a dynamic parameter and the scaling parameter is 1, or the information quantity or its square root in the literature. The information quantity is minus the expected value of the Hessian of the LL with respect to a dynamic parameter, i.e. the Hessianis smoothed using a probability-weighted average for each period. We suggest an alternative approach and scale the gradients using novel Hessian-driven filters, i.e. Hessian smoothing is performed over time. The method can be used for score-driven models in general. We illustrate it for Beta-t-EGARCH (exponential generalized autoregressive conditional heteroscedasticity). Weuse Standard & Poor's 500 (S&P 500) data. We show empirical results for in-sample statistical performance from 2015 to 2025, and out-of-sample forecasting performance from 2021 to 2025. We find for the S&P 500 that the Hessian-driven scaling is superior to the existing scaling methods for Beta-t-EGARCH. We find similar results for a Monte Carlo simulation experimentwhere misspecified Beta-t-EGARCH models with constant and Hessian-driven gradient scaling are estimated for returns generated by a Markov-switching (MS) Beta-t-EGARCH. Hessianbased gradient scaling captures regime-switching dynamics better than constant gradient scaling. |
| Keywords: | Dynamic conditional score (DCS); Generalized autoregressive score (GAS); Dynamic gradient scaling parameters in score driven filters; Gradient descent; Newton's method |
| JEL: | C22 C32 |
| Date: | 2025–02–17 |
| URL: | https://d.repec.org/n?u=RePEc:cte:werepe:45978 |
| By: | Konrad Menzel |
| Abstract: | In contrast to problems of interference in (exogenous) treatments, models of interference in unit-specific (endogenous) outcomes do not usually produce a reduced-form representation where outcomes depend on other units' treatment status only at a short network distance, or only through a known exposure mapping. This remains true if the structural mechanism depends on outcomes of peers only at a short network distance, or through a known exposure mapping. In this paper, we first define causal estimands that are identified and estimable from a single experiment on the network under minimal assumptions on the structure of interference, and which represent average partial causal responses which generally vary with other global features of the realized assignment. Under a fixed-population, design-based approach, we show unbiasedness, consistency and asymptotic normality for inverse-probability weighting (IPW) estimators for those causal parameters from a randomized experiment on a single network. We also analyze more closely the case of marginal interventions in a model of equilibrium with smooth response functions where we can recover LATE-type weighted averages of derivatives of those response functions. Under additional structural assumptions, these "agnostic" causal estimands can be combined to recover model parameters, but also retain their less restrictive causal interpretation. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.19394 |
| By: | Cilliers, Jacobus; Nour Elashmawy; David McKenzie |
| Abstract: | The post-double selection Lasso estimator has become a popular way of selecting control variables when analyzing randomized experiments. This is done to try to improve precision, and reduce bias from attrition or chance imbalances. This paper re-estimates 780 treatment effects from published papers to examine how much difference this approach makes in practice. PDS Lasso is found to reduce standard errors by less than one percent compared to standard Ancova on average and does not select variables to model treatment in over half the cases. The authors discuss and provide evidence on the key practical decisions researchers face in using this method. |
| Date: | 2024–09–26 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:wbrwps:10931 |
| By: | Angelo Mele |
| Abstract: | Exponential random graph models (ERGMs) are very flexible for modeling network formation but pose difficult estimation challenges due to their intractable normalizing constant. Existing methods, such as MCMC-MLE, rely on sequential simulation at every optimization step. We propose a neural network approach that trains on a single, large set of parameter-simulation pairs to learn the mapping from parameters to average network statistics. Once trained, this map can be inverted, yielding a fast and parallelizable estimation method. The procedure also accommodates extra network statistics to mitigate model misspecification. Some simple illustrative examples show that the method performs well in practice. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.01810 |
| By: | John A. 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 treatment effects 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 many might consider the choice straightforward (always opt for a between-subject design), given the distinct benefits of within-subject designs, I argue that researchers should meticulously weigh the advantages and disadvantages of each design type. 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. |
| JEL: | C9 C90 C91 C92 C93 C99 |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33456 |
| By: | Timoth\'ee Fabre; Ioane Muni Toke |
| Abstract: | This work focuses on a self-exciting point process defined by a Hawkes-like intensity and a switching mechanism based on a hidden Markov chain. Previous works in such a setting assume constant intensities between consecutive events. We extend the model to general Hawkes excitation kernels that are piecewise constant between events. We develop an expectation-maximization algorithm for the statistical inference of the Hawkes intensities parameters as well as the state transition probabilities. The numerical convergence of the estimators is extensively tested on simulated data. Using high-frequency cryptocurrency data on a top centralized exchange, we apply the model to the detection of anomalous bursts of trades. We benchmark the goodness-of-fit of the model with the Markov-modulated Poisson process and demonstrate the relevance of the model in detecting suspicious activities. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.04027 |
| By: | Peralta, Isabel Molina |
| Abstract: | This paper reviews the main methods for small area estimation of welfare indicators. It begins by discussing the importance of small area estimation methods for producing reliable disaggregated estimates. It mentions the baseline papers and describes the contents of the different sections. Basic direct estimators obtained from area-specific survey data are described first, followed by simple indirect methods, which include synthetic procedures that do not account for the area effects and composite estimators obtained as a composition (or weighted average) of a synthetic and a direct estimator. The previous estimators are design-based, meaning that their properties are assessed under the sampling replication mechanism, without assuming any model to be true. The paper then turns to proper model-based estimators that assume an explicit model. These models allow obtaining optimal small area estimators when the assumed model holds. The first type of models, referred to as area-level models, use only aggregated data at the area level to fit the model. However, unit-level survey data were previously used to calculate the direct estimators, which act as response variables in the most common area-level models. The paper then switches to unit-level models, describing first the usual estimators for area means, and then moving to general area indicators. Semi-parametric, non-parametric, and machine learning procedures are described in a separate section, although many of the procedures are applicable only to area means. Based on the previous material, the paper identifies gaps or potential limitations in existing procedures from a practitioner’s perspective, which could potentially be addressed through research over the next three to five years. |
| Date: | 2024–06–26 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:wbrwps:10828 |
| By: | Huber, Johannes; Meyer-Gohde, Alexander |
| Abstract: | The standard approach to solving linear DSGE models is to apply the QZ method. It is a one-shot algorithm that leaves the researcher with little alternative than to seek a different algorithm should the result be numerically unsatisfactory. We develop an iterative implementation of QZ that delivers the standard result as its first iteration and further refinements at each subsequent iteration. We demonstrate that our algorithm successful corrects for accuracy losses identified in particular cases of a macro finance model and does not erroneously attempt to refine sufficiently accurate solutions. |
| Keywords: | Numerical accuracy, DSGE, Solution methods |
| JEL: | C61 C63 E17 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:imfswp:311846 |
| By: | Peter H. Egger; Yulong Wang |
| Abstract: | This paper develops a novel method to estimate firm-specific market-entry thresholds in international economics, allowing fixed costs to vary across firms alongside productivity. Our framework models market entry as an interaction between productivity and observable fixed-cost measures, extending traditional single-threshold models to ones with set-valued thresholds. Applying this approach to Chinese firm data, we estimate export-market entry thresholds as functions of domestic sales and surrogate variables for fixed costs. The results reveal substantial heterogeneity and threshold contours, challenging conventional single-threshold-point assumptions. These findings offer new insights into firm behavior and provide a foundation for further theoretical and empirical advancements in trade research. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.03406 |
| By: | Haruki Kono |
| Abstract: | Slutsky symmetry and negative semidefiniteness are necessary and sufficient conditions for the rationality of demand functions. While the empirical implications of Slutsky negative semidefiniteness in repeated cross-sectional demand data are well understood, the empirical content of Slutsky symmetry remains largely unexplored. This paper takes an important first step toward addressing this gap. We demonstrate that the average Slutsky matrix is not identified and that its identified set always contains a symmetric matrix. A key implication of our findings is that the symmetry of the average Slutsky matrix is untestable, and consequently, individual Slutsky symmetry cannot be tested using the average Slutsky matrix. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.18923 |