This paper presents an evaluation of the accuracy of machine learning (ML) techniques in forecasting the realized volatility of West Texas Intermediate (WTI) crude oil prices. We compare several ML algorithms, including regularization, regression trees, random forests, and neural networks, to several heterogeneous autoregressive (HAR) models. The results show that the ML and regularization methods are efficient, even when there are only three predictors: daily, weekly, and monthly realized volatility (RV) lags. In addition, when the ML and regularization methods are applied, the results become more pronounced over longer forecasting horizons, as well as for weekly and monthly horizons. These ML methods are effective in approximating long-term realized volatility. Furthermore, we found that additional explanatory variables for real-time currency exchange contain valuable information to forecast the RV of crude oil prices.

This paper investigates whether executive boards consider firm-specific inefficiencies when they change CEOs in the Swedish electricity distribution sector. Firm-level inefficiencies are calculated using data from all Swedish electricity distributors from 2001 to 2022 and a data envelopment analysis (DEA) approach. DEA has advantages over standard financial key performance indicators since it controls for heterogeneity in inputs and outputs. It is also frequently employed by energy regulators to calculate relative cost inefficiencies. Our baseline approach uses a multilevel model and investigates the relationship between inefficiency and CEO between-effects. This analysis shows that 9–15 % of the variation in inefficiency can be attributed to the CEO effect. The second modeling approach quantifies the CEO effect using a synthetic difference-in-differences approach, focusing on firms that have changed CEOs. The results reveal that new CEOs reduce cost inefficiency more when they succeed CEOs who were forced to leave.

Recent studies in machine learning are based on models in which parameters or state variables are restricted by a restricted boundedness. These restrictions are based on prior information to ensure the validity of scientific theories or structural consistency based on physical phenomena. The valuable information contained in the restrictions must be considered during the estimation process to improve the accuracy of the estimation. Many researchers have focused on linear regression models subject to linear inequality restrictions, but generalized linear models have received little attention. In this paper, the parameters of beta Bayesian regression models subjected to linear inequality restrictions are estimated. The proposed Bayesian restricted estimator, which is demonstrated by simulated studies, outperforms ordinary estimators. Even in the presence of multicollinearity, it outperforms the ridge estimator in terms of the standard deviation and the mean squared error. The results confirm that the proposed Bayesian restricted estimator makes sparsity in parameter estimating without using the regularization penalty. Finally, a real data set is analyzed by the new proposed Bayesian estimation method.

The classical best-subset selection method has been demonstrated to be nondeterministic polynomial-time-hard and thus presents computational challenges. This problem can now be solved via advanced mixed integer optimization (MIO) algorithms for linear regression. We extend this methodology to linear instrumental variable (IV) regression and propose the best-subset instrumental variable (BSIV) method incorporating the MIO procedure. Classical IV estimation methods assume that IVs must not directly impact the outcome variable and should remain uncorrelated with nonmeasured variables. However, in practice, IVs are likely to be invalid, and existing methods can lead to a large bias relative to standard errors in certain situations. The proposed BSIV estimator is robust in estimating causal effects in the presence of unknown IV validity. We demonstrate that the BSIV using MIO algorithms outperforms two-stage least squares, Lasso-type IVs, and two-sample analysis (median and mode estimators) through Monte Carlo simulations in terms of bias and relative efficiency. We analyze two datasets involving the health-related quality of life index and proximity and the education-wage relationship to demonstrate the utility of the proposed method.

Valid instrumental variables (IVs) must not directly impact the outcome variable and must also be uncorrelated with nonmeasured variables. However, in practice, IVs are likely to be invalid. The existing methods can lead to large bias relative to standard errors in situations with many weak and invalid instruments. In this paper, we derive a LASSO procedure for the k-class IV estimation methods in the linear IV model. In addition, we propose the jackknife IV method by using LASSO to address the problem of many weak invalid instruments in the case of heteroscedastic data. The proposed methods are robust for estimating causal effects in the presence of many invalid and valid instruments, with theoretical assurances of their execution. In addition, two-step numerical algorithms are developed for the estimation of causal effects. The performance of the proposed estimators is demonstrated via Monte Carlo simulations as well as an empirical application. We use Mendelian randomization as an application, wherein we estimate the causal effect of body mass index on the health-related quality of life index using single nucleotide polymorphisms as instruments for body mass index.

Policy institutes such as central banks need accurate forecasts of key measures of economic activity to design stabilization policies that reduce the severity of economic fluctuations. Therefore, this paper develops a kernel ridge regression estimator in a mixed data sampling framework. Kernel ridge regression can handle many predictors with a nonlinear relationship to the target variable. Consequently, it has potential to improve the currently used principal component-based methods when the economic data follow a nonlinear factor structure. In a Monte Carlo study, we show that the kernel ridge regression approach is superior in terms of mean square error and is more robust than principal component-based methods to different nonlinear data generating processes. By using a dataset consisting of 24 economic indicators, we forecast Swedish gross domestic production. The results confirm the superiority of the kernel ridge regression approach. Therefore, we suggest that policy institutes consider the use of kernel-based approaches when forecasting key measures of economic activity.

This study investigates how the well-documented link between economic activity and carbon dioxide emissions, identified in previous research in energy and environmental economics, can be used to improve nowcasting carbon dioxide emissions in a data-rich environment. We compare classic and recent improvements in dynamic factor models with linear and nonlinear machine learning algorithms that have been shown to be effective in previous research. These machine learning algorithms are implemented in the mixed data sampling framework. The recent improvements in dynamic factor models include the use of nondifferenced data, which has increased prediction accuracy, especially during economically volatile periods. Additionally, there are structurally augmented dynamic factor models, which combine machine learning methods with dynamic factor models. For the structurally augmented models, we use machine learning algorithms to select the most important variables, which then augment the dynamic factor models. Our findings indicate that dynamic factor models outperform alternative approaches for carbon dioxide emission nowcasting. Specifically, models based on nondifferenced data demonstrate superior predictive ability with principal component extraction, whereas models using differenced data yield better results with Kalman filter extraction. These findings are essential for developing effective nowcasting models that enable timely emission assessments, which are critical for advancing ambitious climate policies. Our research, therefore, contributes to the ongoing discourse on the challenges of sustainable development by employing econometric models for the dynamic links between economic activities and environmental outcomes.

The negative binomial (NB) regression model is commonly used to model overdispersed count data. However, the NB regression model is not suitable for highly overdispersed data, for which the Poisson-inverse Gaussian (PIG) regression model is often used instead. The maximum likelihood (ML) estimator is typically used to estimate the coefficients of the PIG regression model. However, when multicollinearity exists among the explanatory variables, the ML estimator’s variance can become inflated. To address this issue, we propose PIG ridge regression (PIGRR) and quantile-based ridge regression estimators for the PIG regression model. We also suggest using a Wald-type method to calculate the confidence interval on the mean response function of the PIGRR. To evaluate the performance of these proposed methods, we conducted a Monte Carlo simulation study, considering mean squared error and average confidence lengths as performance criteria. Additionally, we analyzed the traffic fatalities dataset to demonstrate the benefits of the proposed estimators for practitioners dealing with multicollinearity issues in real datasets. 

Instead of applying the commonly used parametric Almon or Beta lag distribution of MIDAS, Breitung and Roling (J Forecast 34:588–603, 2015) suggested a nonparametric smoothed least-squares shrinkage estimator (henceforth SLS1) for estimating mixed-frequency models. This SLS1 approach ensures a flexible smooth trending lag distribution. However, even if the biasing parameter in SLS1 solves the overparameterization problem, the cost is a decreased goodness-of-fit. Therefore, we suggest a modification of this shrinkage regression into a two-parameter smoothed least-squares estimator (SLS2). This estimator solves the overparameterization problem, and it has superior properties since it ensures that the orthogonality assumption between residuals and the predicted dependent variable holds, which leads to an increased goodness-of-fit. Our theoretical comparisons, supported by simulations, demonstrate that the increase in goodness-of-fit of the proposed two-parameter estimator also leads to a decrease in the mean square error of SLS2, compared to that of SLS1 . Empirical results, where the inflation rate is forecasted based on the oil returns, demonstrate that our proposed SLS2 estimator for mixed-frequency models provides better estimates in terms of decreased MSE and improved R2, which in turn leads to better forecasts.

Comparisons among some new types of weighted mixed regression estimators for the linear regression model under the stochastic linear restrictions have been made in this paper. The mean squared error criterion is used to examine the superiority of different weighted mixed regression estimators. A Monte Carlo simulation study and real-life application are carried out to compare the performance of these estimators for different cases. Finally, we suggest the best weighted mixed regression estimator with collinear regressors.