nep-ecm New Economics Papers
on Econometrics
Issue of 2023‒10‒09
twenty-six papers chosen by
Sune Karlsson, Örebro universitet

  1. Donut Regression Discontinuity Designs By Cladia Noack; Chistoph Rothe
  2. A Combination Forecast for Nonparametric Models with Structural Breaks By Zongwu Cai; Gunawan
  3. Causal inference in network experiments: regression-based analysis and design-based properties By Mengsi Gao; Peng Ding
  4. Forecasted Treatment Effects By Irene Botosaru; Raffaella Giacomini; Martin Weidner
  5. Optimal Local Model Averaging for Divergent-Dimensional Functional-Coefficient Regressions By Yuying Sun; Shaoxin Hong; Zongwu Cai
  6. Consistency, distributional convergence, and optimality of score-driven filters By Eric A. Beutner; Yicong Lin; Andre Lucas
  7. Moment-Based Estimation of Diffusion and Adoption Parameters in Networks By L. S. Sanna Stephan
  8. The Local Projection Residual Bootstrap for AR(1) Models By Amilcar Velez
  9. Mean Group Distributed Lag Estimation of Impulse Response Functions in Large Panels By Chi-Young Choi; Alexander Chudik
  10. Robust bootstrap inference for linear time-varying coefficient models: Some Monte Carlo evidence By Yicong Lin; Mingxuan Song
  11. Generalized Information Criteria for Structured Sparse Models By Eduardo F. Mendes; Gabriel J. P. Pinto
  12. On the use of U-statistics for linear dyadic interaction models By G. M. Szini
  13. The Robust F-Statistic as a Test for Weak Instruments By Frank Windmeijer
  14. The Mundlak Spatial Estimator By Badi H. Baltagi
  15. A Trimming Estimator for the Latent-Diffusion-Observed-Adoption Model By L. S. Sanna Stephan
  16. Kernel-Based Stochastic Learning of Large-Scale Semiparametric Monotone Index Models with an Application to Aging and Household Risk Preference By Qingsong Yao
  17. Structural Econometric Estimation of the Basic Reproduction Number for Covid-19 Across U.S. States and Selected Countries By Johnsson, I.; Pesaran, M. H.; Yang, C. F.
  18. Bandwidth Selection for Treatment Choice with Binary Outcomes By Takuya Ishihara
  19. iCOS: Option-Implied COS Method By Evgenii Vladimirov
  20. A Causal Perspective on Loan Pricing: Investigating the Impacts of Selection Bias on Identifying Bid-Response Functions By Christopher Bockel-Rickermann; Sam Verboven; Tim Verdonck; Wouter Verbeke
  21. Introducing the $\sigma$-Cell: Unifying GARCH, Stochastic Fluctuations and Evolving Mechanisms in RNN-based Volatility Forecasting By German Rodikov; Nino Antulov-Fantulin
  22. Peer Effects Heterogeneity and Social Networks in Education By Livia Shkoza; Derya Uysal; Winfried Pohlmeier
  23. Fourier Neural Network Approximation of Transition Densities in Finance By Rong Du; Duy-Minh Dang
  24. DeepVol: A Deep Transfer Learning Approach for Universal Asset Volatility Modeling By Chen Liu; Minh-Ngoc Tran; Chao Wang; Richard Gerlach; Robert Kohn
  25. Kinks Know More: Policy Evaluation Beyond Bunching with an Application to Solar Subsidies By Stefan Pollinger
  26. Rethinking “Distance From”: Lessons from Wittenberg and Mainz By Zhao, Qiyi C.

  1. By: Cladia Noack; Chistoph Rothe
    Abstract: We study the econometric properties of so-called donut regression discontinuity (RD) designs, a robustness exercise which involves repeating estimation and inference without the data points in some area around the treatment threshold. This approach is often motivated by concerns that possible systematic sorting of units, or similar data issues, in some neighborhood of the treatment threshold might distort estimation and inference of RD treatment effects. We show that donut RD estimators can have substantially larger bias and variance than contentional RD estimators, and that the corresponding confidence intervals can be substantially longer. We also provide a formal testing framework for comparing donut and conventional RD estimation results.
    Date: 2023–08
  2. By: Zongwu Cai (Department of Economics, The University of Kansas, Lawrence, KS 66045, USA); Gunawan (Department of Economics, The University of Kansas, Lawrence, KS 66045, USA)
    Abstract: Structural breaks in time series forecasting can cause inconsistency in the conventional OLS estimator. Recent research suggests combining pre and post-break estimators for a linear model can yield an optimal estimator for weak breaks. However, this approach is limited to linear models only. In this paper, we propose a weighted local linear estimator for a nonlinear model. This estimator assigns a weight based on both the distance of observations to the predictor covariates and their location in time. We investigate the asymptotic properties of the proposed estimator and choose the optimal tuning parameters using multifold cross-validation to account for the dependence structure in time series data. Additionally, we use a nonparametric method to estimate the break date. Our Monte Carlo simulation results provide evidence for the forecasting outperformance of our estimator over the regular nonparametric post-break estimator. Finally, we apply our proposed estimator to forecast GDP growth for nine countries and demonstrate its superior performance compared to the conventional estimator using Diebold-Mariano tests.
    Keywords: Combination Forecasting; Local Linear Fitting; Multifold Cross-Validation; Nonparametric Model; Structural Break Model
    JEL: C14 C22 C53
    Date: 2023–09
  3. By: Mengsi Gao; Peng Ding
    Abstract: Network experiments have been widely used in investigating interference among units. Under the ``approximate neighborhood interference" framework introduced by \cite{Leung2022}, treatments assigned to individuals farther from the focal individual result in a diminished effect on the focal individual's response, while the effect remains potentially nonzero. \cite{Leung2022} establishes the consistency and asymptotic normality of the inverse-probability weighting estimator for estimating causal effects in the presence of interference. We extend these asymptotic results to the Hajek estimator which is numerically identical to the coefficient from the weighted-least-squares fit based on the inverse probability of the exposure mapping. The numerically equivalent regression-based approach offers two notable advantages: it can provide standard error estimators through the same weighted-least-squares fit, and it allows for the integration of covariates into the analysis. Furthermore, we introduce the regerssion-based network-robust variance estimator, adopting the form of the Heteroskedasticity and Autocorrelation Consistent estimator, and analyze its asymptotic bias. Recognizing that the variance estimator can be anti-conservative, we propose an adjusted variance estimator to improve empirical coverage. Although we focus on regression-based point and variance estimators, our theory holds under the design-based framework, which assumes that the randomness comes solely from the design of network experiments and allows for arbitrary misspecification of the regression models.
    Date: 2023–09
  4. By: Irene Botosaru; Raffaella Giacomini; Martin Weidner
    Abstract: We consider estimation and inference about the effects of a policy in the absence of a control group. We obtain unbiased estimators of individual (heterogeneous) treatment effects and a consistent and asymptotically normal estimator of the average treatment effects, based on forecasting counterfactuals using a short time series of pre-treatment data. We show that the focus should be on forecast unbiasedness rather than accuracy. Correct specification of the forecasting model is not necessary to obtain unbiased estimates of the individual treatment effects. Instead, simple basis function (e.g., polynomial time trends) regressions deliver unbiasedness under a broad class of data-generating processes for the individual counterfactuals. Basing the forecasts on a model can introduce misspecification bias and does not necessarily improve performance even under correct specification. Consistency and asymptotic normality of the Forecasted Average Treatment effects (FAT) estimator attains under an additional assumption that rules out common and unforecastable shocks occurring between the treatment date and the date at which the effect is calculated.
    Keywords: polynomial regressions
    JEL: C32 C53
    Date: 2023–08–31
  5. By: Yuying Sun (School of Economics and Management, University of Chinese Academy of Sciences and Academy of Mathematics and Systems Science, Chinese Academy of Sciences, China); Shaoxin Hong (Center for Economic Research, Shandong University, Jinan, Shandong 250100, China); Zongwu Cai (Department of Economics, The University of Kansas, Lawrence, KS 66045, USA)
    Abstract: This paper proposes a novel local model averaging estimator for divergent-dimensional functional-coefficient regressions, which selects optimal functional combination weights by minimizing a local leave-h-out forward-validation criterion. It is shown that the proposed leave-h-out forward-validation model averaging (FVMA) estimator is asymptotically optimal in the sense of achieving the lowest possible local squared error loss in a class of functional model averaging estimators, which is also extended to the ultra-high dimensional framework. The rate of the FVMA-based varying-weights converging to the optimal weights minimizing the expected local quadratic errors is derived. Besides, when correctly specified models are included in the candidate model set, the proposed FVMA asymptotically assigns all varying weights to the correctly specified models. Furthermore, a simulation study and an empirical application highlight the merits of the proposed FVMA estimator relative to a variety of popular estimators with constant model averaging weights and model selection.
    Keywords: Asymptotic optimality; Functional-coefficient models; Forward-validation; Model averaging; Varying-weights
    JEL: C2 C13
    Date: 2023–09
  6. By: Eric A. Beutner (Vrije Universiteit Amsterdam); Yicong Lin (Vrije Universiteit Amsterdam); Andre Lucas (Vrije Universiteit Amsterdam)
    Abstract: We study the in-fill asymptotics of score-driven time series models. For general forms of model mis-specification, we show that score-driven filters are consistent for the Kullback-Leibler (KL) optimal time-varying parameter path, which minimizes the pointwise KL divergence between the statistical model and the unknown dynamic data generating process. This directly implies that for a correctly specified predictive conditional density, score-driven filters consistently estimate the time-varying parameter path even if the model is mis-specified in other respects. We also obtain distributional convergence results for the filtering errors and derive the filter that minimizes the asymptotic filter error variance. Score-driven filters turn out to be optimal under correct specification of the predictive conditional density. The results considerably generalize earlier findings on the continuous-time consistency of volatility filters under mis-specification: they apply to biased filters, use weaker assumptions, allow for more general forms of mis-specification, and consider general time-varying parameters in non-linear time series models beyond the volatility case. Several examples are used to illustrate the theory, including time-varying tail shape models, dynamic copulas, and time-varying regression models.
    Keywords: score-driven models, information theoretic optimality, Kullback-Leibler divergence, pseudo true time-varying parameters, in-fill asymptotics.
    JEL: C22 C32
    Date: 2023–08–30
  7. By: L. S. Sanna Stephan
    Abstract: According to standard econometric theory, Maximum Likelihood estimation (MLE) is the efficient estimation choice, however, it is not always a feasible one. In network diffusion models with unobserved signal propagation, MLE requires integrating out a large number of latent variables, which quickly becomes computationally infeasible even for moderate network sizes and time horizons. Limiting the model time horizon on the other hand entails loss of important information while approximation techniques entail a (small) error that. Searching for a viable alternative is thus potentially highly beneficial. This paper proposes two estimators specifically tailored to the network diffusion model of partially observed adoption and unobserved network diffusion.
    Date: 2023–09
  8. By: Amilcar Velez
    Abstract: This paper contributes to a growing literature on confidence interval construction for impulse response coefficients based on the local projection (LP) approach. We propose an LP-residual bootstrap method to construct confidence intervals for the impulse response coefficients of AR(1) models. The method uses the LP approach and a residual bootstrap procedure to compute critical values. We present two theoretical results. First, we prove the uniform consistency of the LP-residual bootstrap under general conditions, which implies that the proposed confidence intervals are uniformly asymptotically valid. Second, we show that the LP-residual bootstrap can provide asymptotic refinements to the confidence intervals under certain conditions. We illustrate our results with a simulation study.
    Date: 2023–09
  9. By: Chi-Young Choi; Alexander Chudik
    Abstract: This paper develops Mean Group Distributed Lag (MGDL) estimation of impulse responses in large panels with one or two cross-section dimensions. Sufficient conditions for asymptotic consistency and asymptotic normality are derived, and satisfactory small sample performance is documented using Monte Carlo experiments. MGDL estimators are used to estimate the effects of crude oil price increases on U.S. city- and product-level retail prices.
    Keywords: panel data; impulse response functions; estimation; inference; Mean Group Distributed Lag (MGDL)
    JEL: C23
    Date: 2023–09–22
  10. By: Yicong Lin (Vrije Universiteit Amsterdam); Mingxuan Song (Vrije Universiteit Amsterdam)
    Abstract: We propose two robust bootstrap-based simultaneous inference methods for time series models featuring time-varying coefficients and conduct an extensive simulation study to assess their performance. Our exploration covers a wide range of scenarios, encompassing serially correlated, heteroscedastic, endogenous, nonlinear, and nonstationary error processes. Additionally, we consider situations where the regressors exhibit unit roots, thus delving into a nonlinear cointegration framework. We find that the proposed moving block bootstrap and sieve wild bootstrap methods show superior, robust small sample performance, in terms of empirical coverage and length, compared to the sieve bootstrap introduced by Friedrich and Lin (2022) for stationary models. We then revisit two empirical studies: herding effects in the Chinese new energy market and consumption behaviors in the U.S. Our findings strongly support the presence of herding behaviors before 2016, aligning with earlier studies. However, we diverge from previous research by finding no substantial herding evidence between around 2018 and 2021. In the second example, we find a time-varying cointegrating relationship between consumption and income in the U.S.
    Keywords: time-varying models, bootstrap inference, simultaneous confidence bands, energy market, nonlinear cointegration.
    JEL: C14 C22 C63 Q56
    Date: 2023–08–23
  11. By: Eduardo F. Mendes; Gabriel J. P. Pinto
    Abstract: Regularized m-estimators are widely used due to their ability of recovering a low-dimensional model in high-dimensional scenarios. Some recent efforts on this subject focused on creating a unified framework for establishing oracle bounds, and deriving conditions for support recovery. Under this same framework, we propose a new Generalized Information Criteria (GIC) that takes into consideration the sparsity pattern one wishes to recover. We obtain non-asymptotic model selection bounds and sufficient conditions for model selection consistency of the GIC. Furthermore, we show that the GIC can also be used for selecting the regularization parameter within a regularized $m$-estimation framework, which allows practical use of the GIC for model selection in high-dimensional scenarios. We provide examples of group LASSO in the context of generalized linear regression and low rank matrix regression.
    Date: 2023–09
  12. By: G. M. Szini
    Abstract: Even though dyadic regressions are widely used in empirical applications, the (asymptotic) properties of estimation methods only began to be studied recently in the literature. This paper aims to provide in a step-by-step manner how U-statistics tools can be applied to obtain the asymptotic properties of pairwise differences estimators for a two-way fixed effects model of dyadic interactions. More specifically, we first propose an estimator for the model that relies on pairwise differencing such that the fixed effects are differenced out. As a result, the summands of the influence function will not be independent anymore, showing dependence on the individual level and translating to the fact that the usual law of large numbers and central limit theorems do not straightforwardly apply. To overcome such obstacles, we show how to generalize tools of U-statistics for single-index variables to the double-indices context of dyadic datasets. A key result is that there can be different ways of defining the Hajek projection for a directed dyadic structure, which will lead to distinct, but equivalent, consistent estimators for the asymptotic variances. The results presented in this paper are easily extended to non-linear models.
    Date: 2023–09
  13. By: Frank Windmeijer
    Abstract: Montiel Olea and Pflueger (2013) proposed the effective F-statistic as a test for weak instruments in terms of the Nagar bias of the two-stage least squares (2SLS) estimator relative to a benchmark worst-case bias. We show that their methodology applies to a class of linear generalized method of moments (GMM) estimators with an associated class of generalized effective F-statistics. The standard nonhomoskedasticity robust F-statistic is a member of this class. The associated GMMf estimator, with the extension f for first-stage, is a novel and unusual estimator as the weight matrix is based on the first-stage residuals. As the robust F-statistic can also be used as a test for underidentification, expressions for the calculation of the weak-instruments critical values in terms of the Nagar bias of the GMMf estimator relative to the benchmark simplify and no simulation methods or Patnaik (1949) distributional approximations are needed. In the grouped-data IV designs of Andrews (2018), where the robust F-statistic is large but the effective F-statistic is small, the GMMf estimator is shown to behave much better in terms of bias than the 2SLS estimator, as expected by the weak-instruments test results.
    Date: 2023–09
  14. By: Badi H. Baltagi (Center for Policy Research, Maxwell School, Syracuse University, 426 Eggers Hall, Syracuse, NY 13244)
    Abstract: The spatial Mundlak model first considered by Debarsy (2012) is an alternative to fixed effects and random effects estimation for spatial panel data models. Mundlak modelled the correlated random individual effects as a linear combination of the averaged regressors over time plus a random time-invariant error. This paper shows that if spatial correlation is present whether spatial lag or spatial error or both, the standard Mundlak result in panel data does not hold and random effects does not reduce to its fixed effects counterpart. However, using maximum likelihood one can still estimate these spatial Mundlak models and test the correlated random effects specification of Mundlak using Likelihood ratio tests as demonstrated by Debarsy for the Mundlak spatial Durbin model.
    Keywords: Mundlak Regression, Panel Data, Fixed and Random Effects, Spatial error model, Spatial Durbin model
    JEL: C33
    Date: 2023–09
  15. By: L. S. Sanna Stephan
    Abstract: Network diffusion models are applicable to many socioeconomic interactions, yet network interaction is hard to observe or measure. Whenever the diffusion process is unobserved, the number of possible realizations of the latent matrix that captures agents' diffusion statuses grows exponentially with the size of network. Due to interdependencies, the log likelihood function can not be factorized in individual components. As a consequence, exact estimation of latent diffusion models with more than one round of interaction is computationally infeasible. In the present paper, I propose a trimming estimator that enables me to establish and maximize an approximate log likelihood function that almost exactly identifies the peak of the true log likelihood function whenever no more than one third of eligible agents are subject to trimming.
    Date: 2023–09
  16. By: Qingsong Yao
    Abstract: This paper studies semiparametric estimation of monotone index models in a data-rich environment, where the number of covariates ($p$) and sample size ($n$) can both be large. Motivated by the mini-batch gradient descent algorithm (MBGD) that is widely used as a stochastic optimization tool in the machine learning field, this paper proposes a novel subsample- and iteration-based semiparametric estimation procedure. Starting from any initial guess of the parameter, in each round of iteration we draw a random subsample from the data set, and use such subsample to update the parameter based on the gradient of some well-chosen loss function, where the nonparametric component is replaced with its kernel estimator. Our proposed algorithm essentially generalizes MBGD algorithm to the semiparametric setup. Compared with the KBGD algorithm proposed by Khan et al. (2023) whose computational complexity is of order $O(n^2)$ in each update, the computational burden of our new estimator can be made close to $O(n)$, so can be easily applied when the sample size $n$ is large. Moreover, we show that if we further conduct averages across the estimators produced during iterations, the difference between the average estimator and KBGD estimator will be $n^{-1/2}$-trivial. Consequently, the average estimator is $n^{-1/2}$-consistent and asymptotically normally distributed. In other words, our new estimator substantially improves the computational speed, while at the same time maintains the estimation accuracy. We finally apply our new method to study how household age structure affects its risk preference and investing behavior. Using Chinese 2019 national survey data, we find that household with more elderly people is more likely to be risk averse and prefer risk-free assets.
    Date: 2023–09
  17. By: Johnsson, I.; Pesaran, M. H.; Yang, C. F.
    Abstract: This paper proposes a structural econometric approach to estimating the basic reproduction number (R0) of Covid-19. This approach identifies R0 in a panel regression model by filtering out the effects of mitigating factors on disease diffusion and is easy to implement. We apply the method to data from 48 contiguous U.S. states and a diverse set of countries. Our results reveal a notable concentration of R0 estimates with an average value of 4.5. Through a counterfactual analysis, we highlight a significant underestimation of the R0 when mitigating factors are not appropriately accounted for.
    Keywords: basic reproduction number, Covid-19, panel threshold regression model
    JEL: C13 C33 I12 I18 J18
    Date: 2023–09–19
  18. By: Takuya Ishihara
    Abstract: This study considers the treatment choice problem when outcome variables are binary. We focus on statistical treatment rules that plug in fitted values based on nonparametric kernel regression and show that optimizing two parameters enables the calculation of the maximum regret. Using this result, we propose a novel bandwidth selection method based on the minimax regret criterion. Finally, we perform a numerical analysis to compare the optimal bandwidth choices for the binary and normally distributed outcomes.
    Date: 2023–08
  19. By: Evgenii Vladimirov
    Abstract: This paper proposes the option-implied Fourier-cosine method, iCOS, for non-parametric estimation of risk-neutral densities, option prices, and option sensitivities. The iCOS method leverages the Fourier-based COS technique, proposed by Fang and Oosterlee (2008), by utilizing the option-implied cosine series coefficients. Notably, this procedure does not rely on any model assumptions about the underlying asset price dynamics, it is fully non-parametric, and it does not involve any numerical optimization. These features make it rather general and computationally appealing. Furthermore, we derive the asymptotic properties of the proposed non-parametric estimators and study their finite-sample behavior in Monte Carlo simulations. Our empirical analysis using S&P 500 index options and Amazon equity options illustrates the effectiveness of the iCOS method in extracting valuable information from option prices under different market conditions.
    Date: 2023–09
  20. By: Christopher Bockel-Rickermann; Sam Verboven; Tim Verdonck; Wouter Verbeke
    Abstract: In lending, where prices are specific to both customers and products, having a well-functioning personalized pricing policy in place is essential to effective business making. Typically, such a policy must be derived from observational data, which introduces several challenges. While the problem of ``endogeneity'' is prominently studied in the established pricing literature, the problem of selection bias (or, more precisely, bid selection bias) is not. We take a step towards understanding the effects of selection bias by posing pricing as a problem of causal inference. Specifically, we consider the reaction of a customer to price a treatment effect. In our experiments, we simulate varying levels of selection bias on a semi-synthetic dataset on mortgage loan applications in Belgium. We investigate the potential of parametric and nonparametric methods for the identification of individual bid-response functions. Our results illustrate how conventional methods such as logistic regression and neural networks suffer adversely from selection bias. In contrast, we implement state-of-the-art methods from causal machine learning and show their capability to overcome selection bias in pricing data.
    Date: 2023–09
  21. By: German Rodikov; Nino Antulov-Fantulin
    Abstract: This paper introduces the $\sigma$-Cell, a novel Recurrent Neural Network (RNN) architecture for financial volatility modeling. Bridging traditional econometric approaches like GARCH with deep learning, the $\sigma$-Cell incorporates stochastic layers and time-varying parameters to capture dynamic volatility patterns. Our model serves as a generative network, approximating the conditional distribution of latent variables. We employ a log-likelihood-based loss function and a specialized activation function to enhance performance. Experimental results demonstrate superior forecasting accuracy compared to traditional GARCH and Stochastic Volatility models, making the next step in integrating domain knowledge with neural networks.
    Date: 2023–09
  22. By: Livia Shkoza (University of Konstanz, GSDS); Derya Uysal (University of Munich, CESifo); Winfried Pohlmeier (University of Konstanz, CASCB, ICEA)
    Abstract: This study focuses on the role of heterogeneity in network peer effects by accounting for network-specific factors and different driving mechanisms of peer behavior. We propose a novel Multivariate Instrumental Variable (MVIV) estimator which is consistent for a large number of networks keeping the individual network size bounded. We apply this approach to estimate peer effects on school achievement exploiting the network structure of friendships within classrooms. The empirical evidence presented is based on a unique network dataset from German upper secondary schools. We show that accounting for heterogeneity is not only crucial from a statistical perspective, but also yields new structural insights into how class size and gender composition affect school achievement through peer behavior.
    Keywords: network heterogeneity; peer effects; multivariate instrumental variables; minimum distance estimation; school achievement;
    JEL: D85 L14 I21 C30 C36
    Date: 2023–09–06
  23. By: Rong Du; Duy-Minh Dang
    Abstract: This paper introduces FourNet, a novel single-layer feed-forward neural network (FFNN) method designed to approximate transition densities for which closed-form expressions of their Fourier transforms, i.e. characteristic functions, are available. A unique feature of FourNet lies in its use of a Gaussian activation function, enabling exact Fourier and inverse Fourier transformations and drawing analogies with the Gaussian mixture model. We mathematically establish FourNet's capacity to approximate transition densities in the $L_2$-sense arbitrarily well with finite number of neurons. The parameters of FourNet are learned by minimizing a loss function derived from the known characteristic function and the Fourier transform of the FFNN, complemented by a strategic sampling approach to enhance training. Through a rigorous and comprehensive error analysis, we derive informative bounds for the $L_2$ estimation error and the potential (pointwise) loss of nonnegativity in the estimated densities. FourNet's accuracy and versatility are demonstrated through a wide range of dynamics common in quantitative finance, including L\'{e}vy processes and the Heston stochastic volatility models-including those augmented with the self-exciting Queue-Hawkes jump process.
    Date: 2023–09
  24. By: Chen Liu; Minh-Ngoc Tran; Chao Wang; Richard Gerlach; Robert Kohn
    Abstract: This paper introduces DeepVol, a promising new deep learning volatility model that outperforms traditional econometric models in terms of model generality. DeepVol leverages the power of transfer learning to effectively capture and model the volatility dynamics of all financial assets, including previously unseen ones, using a single universal model. This contrasts to the prevailing practice in econometrics literature, which necessitates training separate models for individual datasets. The introduction of DeepVol opens up new avenues for volatility modeling and forecasting in the finance industry, potentially transforming the way volatility is understood and predicted.
    Date: 2023–09
  25. By: Stefan Pollinger (ECON - Département d'économie (Sciences Po) - Sciences Po - Sciences Po - CNRS - Centre National de la Recherche Scientifique)
    Abstract: This paper demonstrates that kinks or discontinuities in incentive schemes (e.g., taxes, subsidies, or prices) simultaneously identify agents' intensive and participation margin responses. The proposed semi-nonparametric estimator enables the evaluation of such schemes when existing kink and discontinuity methods are inapplicable due to the presence of both margins. The paper applies the estimator to evaluate the German subsidy for rooftop solar panels, a cornerstone in the global efforts to transit towards a carbon-free economy. Compared to a linear scheme, the government's nonlinear subsidy reduces costs by 0.14 per cent; an optimal nonlinear scheme would more than triple this gain. Ignoring the participation margin when optimising the subsidy would increase costs substantially. The results highlight the importance of estimating both margins for optimal policy design.
    Keywords: Participation Margin, Solar Subsidies, Nonlinear Incentive Schemes, Bunching
    Date: 2023–08–17
  26. By: Zhao, Qiyi C.
    Abstract: An influential literature in early modern economic history uses “distance from” as an instrumental or a control variable. I show that “distance from Wittenberg” and “distance from Mainz, ” two prominent instruments for the adoption of Protestantism and printing technology, have historical and econometric drawbacks that engender misleading conclusions. Historical data challenge the assumption that distance determined access to ideas or technology. Placebo tests and simulations reveal that “distance from” variables frequently produce falsely significant estimates in first stage and OLS regressions. My findings suggest caution in using “distance from” instruments for the adoption of ideas and technologies.
    Keywords: distance from, Reformation, printing, religion, Protestantism, idea and technology diffusion, instrumental variable, early modern economic history
    JEL: C18 C36 N0 N10 N13 N3 N30 N33 N70 N73 N93 O14 O15 O30 O33 O43
    Date: 2023–06–28

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