Static Regression Models Research Articles

Drivers of S&P 500’s Profitability: Implications for Investment Strategy and Risk Management

The financial markets, shaped by dynamic forces, including macroeconomic trends and technological advancements, are influenced by a multitude of factors impacting the S&P 500 stock index, a pivotal indicator in the US equity markets. This paper highlights the significance of understanding the exogenous variables affecting the index’s profitability for academics, portfolio managers, and investment professionals. Amid the global ramifications of the S&P 500, particularly in combating the eroding purchasing power caused by inflation, investing in stock indexes emerges as a means to safeguard wealth. The study employs various statistical techniques, emphasizing a methodical approach to uncover influential variables, and using static regression and autoregressive models for immediate and time-lagged effects. In conclusion, the findings have broad practical implications beyond investment strategy, extending to portfolio construction and risk management. Acknowledging inherent uncertainties in financial market forecasts, future research endeavors should target long-term trends, specific influences, and the impact of exchange rate fluctuations on index evolution. Collaboration across regulatory bodies, academia, and the financial industry is underscored, holding the potential for effective risk monitoring and bolstering overall economic and financial market stability. This research serves as a foundational step towards enhancing market understanding and facilitating more efficient investment decision-making approaches.

Exchange Rate Volatility and Nigerian Stock Market Development

Purpose: The study aims to evaluate the impact of exchange rate volatility on the indicators of stock market, such as the returns, capitalization, liquidity, transaction volume, based on the Nigerian evidence. Theoretical framework: The paper considers extant model of exchange rate-stock market interactions. Because the diversity of the market is important the paper follows suggestions by El-Wassal (2013) to examine variety of stock market indicators in order to offer accurate depiction of how the stock market drives exchange rate volatility. Design/Methodology/Approach: The study employs a simple static regression model of stock market indicators with autoregressive adjustment component which absorb autocorrelation. The models are tested on annual data (1985-2020). Findings: The study finds that the exchange rate volatility has negative impact on stock market development - returns, capitalization and volume. The paper reveals further that other correlated controls factors impinge different impacts on the stock market indicators. Research, Practical & Social implications: One implication of the finding is that the volatility may discourage investors, reduce firm performance, and lead to reduction on the returns of firm shares. Policy makers should put in place growth-inducing infrastructural investments to make the business environment more conducive to attract foreign capital, while also positioning the capital market with several initiatives to increase trading activities. Originality/Value: The connection between the foreign exchange and financial markets is of policy importance. Although the focus is on the Nigerian markets, the evidence could be extended to other African and global markets, to serve as guide to foreign investors.

Long-term forecasting of a motor outcome following rehabilitation in chronic stroke via a hierarchical bayesian dynamic model

BackgroundGiven the heterogeneity of stroke, it is important to determine the best course of motor therapy for each patient, i.e., to personalize rehabilitation based on predictions of long-term outcomes. Here, we propose a hierarchical Bayesian dynamic (i.e., state-space) model (HBDM) to forecast long-term changes in a motor outcome due to rehabilitation in the chronic phase post-stroke.MethodsThe model incorporates the effects of clinician-supervised training, self-training, and forgetting. In addition, to improve forecasting early in rehabilitation, when data are sparse or unavailable, we use the Bayesian hierarchical modeling technique to incorporate prior information from similar patients. We use HBDM to re-analyze the Motor Activity Log (MAL) data of participants with chronic stroke included in two clinical trials: (1) the DOSE trial, in which participants were assigned to a 0, 15, 30, or 60-h dose condition (data of 40 participants analyzed), and (2) the EXCITE trial, in which participants were assigned a 60-h dose, in either an immediate or a delayed condition (95 participants analyzed).ResultsFor both datasets, HBDM accounts well for individual dynamics in the MAL during and outside of training: mean RMSE = 0.28 for all 40 DOSE participants (participant-level RMSE 0.26 ± 0.19—95% CI) and mean RMSE = 0.325 for all 95 EXCITE participants (participant-level RMSE 0.32 ± 0.31), which are small compared to the 0-5 range of the MAL. Bayesian leave-one-out cross-validation shows that the model has better predictive accuracy than static regression models and simpler dynamic models that do not account for the effect of supervised training, self-training, or forgetting. We then showcase model’s ability to forecast the MAL of “new” participants up to 8 months ahead. The mean RMSE at 6 months post-training was 1.36 using only the baseline MAL and then decreased to 0.91, 0.79, and 0.69 (respectively) with the MAL following the 1st, 2nd, and 3rd bouts of training. In addition, hierarchical modeling improves prediction for a patient early in training. Finally, we verify that this model, despite its simplicity, can reproduce previous findings of the DOSE trial on the efficiency, efficacy, and retention of motor therapy.ConclusionsIn future work, such forecasting models can be used to simulate different stages of recovery, dosages, and training schedules to optimize rehabilitation for each person.Trial registration This study contains a re-analysis of data from the DOSE clinical trial ID NCT01749358 and the EXCITE clinical trial ID NCT00057018

Just-in-time updating soft sensor model of endpoint carbon content and temperature in BOF steelmaking based on deep residual supervised autoencoder

The key of Basic oxygen furnace (BOF) endpoint control is to achieve accurate forecast of the endpoint carbon content and temperature. Due to the highly nonlinear and complex distribution characteristics of process data, the predictions of these two indicators are inaccurate. Although deep learning can extract abstract features of complex data, existing methods are pre-trained in an unsupervised manner. For soft sensor applications, the key is to highlight important information and extract features related to the target variables for prediction. Thus, this paper proposes a feature extraction model based on deep residual supervised autoencoder (DRSupAE). By incorporating important information in the feature of previous hidden layers into deeper feature learning in the form of residual connection, and each residual supervised autoencoder (RSupAE) utilizes the target variables to guide feature extraction. In this way, the stacking of new deep networks not only enhances the impact of important target-related information in layer-wise training, but also captures features related to target variables. Moreover, for the time-varying phenomenon of the BOF steelmaking process, a strategy of just-in-time updating regression network (JITRN) is proposed, which is used to solve the problem that the static regression model cannot adapt to the frequent changes of sample characteristics and causes the prediction performance to decline. An experimental study on BOF steelmaking process data is provided to demonstrate the effectiveness of the proposed method.

Prediction of landslide displacement with dynamic features using intelligent approaches

Landslide displacement prediction can enhance the efficacy of landslide monitoring system, and the prediction of the periodic displacement is particularly challenging. In the previous studies, static regression models (e.g., support vector machine (SVM)) were mostly used for predicting the periodic displacement. These models may have bad performances, when the dynamic features of landslide triggers are incorporated. This paper proposes a method for predicting the landslide displacement in a dynamic manner, based on the gated recurrent unit (GRU) neural network and complete ensemble empirical decomposition with adaptive noise (CEEMDAN). The CEEMDAN is used to decompose the training data, and the GRU is subsequently used for predicting the periodic displacement. Implementation procedures of the proposed method were illustrated by a case study in the Caojiatuo landslide area, and SVM was also adopted for the periodic displacement prediction. This case study shows that the predictors obtained by SVM are inaccurate, as the landslide displacement is in a pronouncedly step-wise manner. By contrast, the accuracy can be significantly improved using the dynamic predictive method. This paper reveals the significance of capturing the dynamic features of the inputs in the training process, when the machine learning models are adopted to predict the landslide displacement.

The relationship between profit-sharing schemes and R&D activities: evidence from German firm-level data

ABSTRACT We study the determinants of the use of profit sharing schemes (PSS) by private companies, exploiting a firm-level dataset for Germany. First, our results from static regression models replicate studies for the U.S. which report a positive correlation between R&D activity and PSS use. Similar to those studies, we find that a firm’s turnover is strongly associated with PSS use, whereas this does not hold for the age of a firm. Firms with an unionised or more qualified workforce are also associated with PSS use. By exploiting the panel structure of our data, we also provide a novel set of tests by means of dynamic models estimation. The analysis sheds light on the relation between R&D activity, past innovation and PSS use.

Dynamic and Interpretable Hazard-Based Models of Traffic Incident Durations

Understanding and predicting the duration or “return-to-normal” time of traffic incidents is important for system-level management and optimization of road transportation networks. Increasing real-time availability of multiple data sources characterizing the state of urban traffic networks, together with advances in machine learning offer the opportunity for new and improved approaches to this problem that go beyond static statistical analyses of incident duration. In this paper we consider two such improvements: dynamic update of incident duration predictions as new information about incidents becomes available and automated interpretation of the factors responsible for these predictions. For our use case, we take one year of incident data and traffic state time-series data from the M25 motorway in London. We use it to train models that predict the probability distribution of incident durations, utilizing both time-invariant and time-varying features of the data. The latter allow predictions to be updated as an incident progresses, and more information becomes available. For dynamic predictions, time-series features are fed into the Match-Net algorithm, a temporal convolutional hitting-time network, recently developed for dynamical survival analysis in clinical applications. The predictions are benchmarked against static regression models for survival analysis and against an established dynamic technique known as landmarking and found to perform favourably by several standard comparison measures. To provide interpretability, we utilize the concept of Shapley values recently developed in the domain of interpretable artificial intelligence to rank the features most relevant to the model predictions at different time horizons. For example, the time of day is always a significantly influential time-invariant feature, whereas the time-series features strongly influence predictions at 5 and 60-min horizons. Although we focus here on traffic incidents, the methodology we describe can be applied to many survival analysis problems where time-series data is to be combined with time-invariant features.

Modeling growth, development and yield of cassava: A review.

Cassava is an important crop in the developing world. The goal of this study was to review published cassava models (18) for their capability to simulate storage root biomass and to categorize them into static and dynamic models. The majority (14) are dynamic and capture within season growth dynamics. Most (13) of the dynamic models consider environmental factors such as temperature, solar radiation, soil water and nutrient restrictions. More than half (10) have been calibrated for a distinct genotype. Only one of the four static models includes environmental variables. While the static regression models are useful to estimate final yield, their application is limited to the locations or varieties used for their development unless recalibrated for distinct conditions. Dynamic models simulate growth process and provide estimates of yield over time with, in most cases, no fixed maturity date. The dynamic models that simulate the detailed development of nodal units tend to be less accurate in determining final yield compared to the simpler dynamic and statistic models. However, they can be more safely applied to novel environmental conditions that can be explored in silico. Deficiencies in the current models are highlighted including suggestions on how they can be improved. None of the current dynamic cassava models adequately simulates the starch content of fresh cassava roots with almost all models based on dry biomass simulations. Further studies are necessary to develop a new module for existing cassava models to simulate cassava quality.

IMPACT OF INFORMATION TECHNOLOGIES’ INVESTMENTS ON THE PROFITABILITY OF TUNISIAN BANKS: PANEL DATA ANALYSIS

This study examines the relationship between Information Technology investment and the profitability of Tunisian banks, via static and dynamic panel regression models. Our study focused on 15 Tunisian banks for 19 years (2001-2019). To assess the profitability of these banks, three measures were used: two traditional accounting ratios and net interest margin. Our research has shown the importance of the role played by IT in Tunisian banks since IT investments improve their profitability. This finding contradicts the “Productivity Paradox” that high IT investments are not associated with better performance. Indeed, Tunisian banks are acting on their size to boost their performance, and the more the banks take the risk by granting more loans, the more profitable they are by increasing their Return on Assets. Finally, public banks are more profitable than private banks when considering their net interest margin.
 JEL Classification Codes: B21, C58, G21, G32, O32.

Model predictive control of heat pump based on a regression model fitted to data measured in accordance to EHPA regulation

This paper presents a method for fitting a static regression model for the power consumption of a ground-sourced domestic heat pump, based on a low number of sample points extracted from a common measurement report developed in accordance to European Heat Pump Association (EHPA) regulation. Thereafter, we demonstrate how the coefficients can be updated with aRecursive Least Squaresalgorithm using only commonly accessible measurements. The regression model is designed to be used for control of a heat pump connected to an ON/OFF controlled floor heating system. The target of the method is especially systems where the flow in the floor heating circuits is unknown. The ability of the regression model to predict power consumption of the heat pump is evaluated using measurements obtained from a test-rig having the particular heat pump installed. The regression model is implemented as a module in aMixed Integer Non-linear Model Predictive Controlalgorithm to illustrate the applicability of the model for control purposes. The promising results obtained from this investigation raise the question; should quality data be available in order to enable more advanced control of domestic heat pumps?

An Adaptive State Observer for Linear Time-varying Systems with Inaccurate Parameters

The design problem of an adaptive state observer for linear time-varying systems is considered. Some parameters of a time-varying plant are assumed to be unknown numbers multiplied by known functions of time. The approach proposed below is based on identification methods of adaptation. In other words, the main idea is to transform a mathematical model in the form of a linear time-varying differential equation to a static linear regression model containing unknown parameters. In this case, both the unknown parameters of the plant and its initial conditions are the unknown parameters of the regression model under consideration. Further, standard gradient methods or other parametric identification approaches are used to estimate the unknown parameters of the regression model and to construct the observer. The results of computer simulations illustrate the effectiveness of the new state observer design method.

Inequality Within the EU-15 Countries: Have Social Benefits Been Pro-poor, Pro-middle Class or Pro-rich?

This study utilizes static and dynamic panel models in investigating the impact of social benefits on the aggregate level of income inequality as well as on the income shares of different income groups within the EU-15 countries over the period 1995-2015. While the static panel regression models are estimated with Pooled Ordinary Least Squares (POLS) and Least Squares Dummy Variables (LSDV) techniques, the dynamic panel regressions are estimated using dynamic GMM-IV technique. Diagnostic tests indicate that the results from the GMM-IV technique are consistent and the associated instrumental variables are valid; hence this study gives preference to the results from this technique. The results indicate that social benefits generally have a significantly negative impact on the aggregate level of inequality, a positive impact on the income shares of the low and middle income groups, and a negative impact on the income shares of the high income groups. In the long run, the sign and significance of the parameter estimates remain unchanged but their sizes increase considerably. This research considers a variety of theories and finds that there exists much ambiguity in the theoretical literature. Based on its findings, this study recommends that policymakers address rising income inequality by intensifying efforts towards raising social benefits and ensuring that the welfare system is efficiently managed.

Does the stock market value corporate environmental performance? Some perils of static regression models

AbstractIntangible assets not only help firms gain competitive advantages but also enable such advantages to persist when firms are not subject to imitation. Given that expenditure on environmental practices is a part of intangible assets, the absence of a dynamic perspective is a major impediment to the further development of the empirical economics of management. By exploiting fine‐grained measures of research and development assets and patent citations, this study isolates the value‐creating effect of corporate environmental performance through a dynamic lens. Using data from a balanced panel of Japanese manufacturing firms, a positive relationship between corporate environmental performance and firm value is observed even after addressing endogeneity. However, this relationship becomes statistically insignificant once dynamic panel data models are used to control for profit persistence. The results suggest that the use of static regression models can lead to the premature conclusion that it pays to be “green.”

Research of methods for design of regression models of oil and gas refinery technological units

The problem of efficient computational models design for control and scheduling problems in terms of oil and gas refinery column distillation units is discussed in the paper. Such efficient computational models can be constructed in the form of fast static regression models supplemented with dynamic models of measurement and input channels. The effectiveness of methods for constructing fast static regression models is examined in the paper. The input parameters for such regression models are determined. It is proposed to use parametric optimization methods for such models. A preliminary study showed the possibility of using an evolutionary genetic algorithm. Numerical studies were performed using data from column distillation units. The efficiency of using the methods of additional parametric optimization is shown.

Negative spatial covariation in abundance of two European ticks: diverging niche preferences or biotic interaction?

1. Understanding the ecological niches of ticks is central to predicting the risk of tick‐borne disease occurrence. Despite considerable advances in describing species distributions over the last two decades, disentangling the differences in habitat preferences from biotic interactions still remains among the main challenges.2. In boundary areas along the rivers Danube, Morava and Dyje, separating the Czech Republic, Slovakia and Austria, we studied ecological niche segregation between two European sympatric tick species, Dermacentor reticulatus and Ixodes ricinus, the abundances of which exhibited a negative spatial covariation. By applying ANCOVA analysis, we tested the effects of 19 climate, four moisture and two topographical variables derived through GIS on tick abundances.3. Dermacentor reticulatus preferred warmer and wetter sites with greater diurnal and seasonal variation in temperature but with lower seasonality in precipitation than I. ricinus.4. By adopting the static regression model based on a modified Lotka–Volterra model for interspecific competition, we examined the relative power of environmental variables to explain the negative relationship between tick log‐abundances. We found that nonzero estimates of competition coefficients α12 and α21 were not eliminated even if we included seven principal components derived from considered environmental variables.5. These results suggest that habitat conditions can shape abundance patterns of the studied ticks but their overall power to explain negative spatial covariation is low. This indicates that other critical variables were not considered in the analysis. An alternative explanation suggests that competitive interaction is not yet supported by direct field evidence obtained for these ticks.

Useful models for time series of counts or simply wrong ones?

There has been a considerable and growing interest in low integer-valued time series data leading to a diversification of modelling approaches. In addition to static regression models, both observation-driven and parameter-driven models are considered here. We compare and contrast a variety of time series models for counts using two very different data sets as a testbed. A range of diagnostic devices is employed to help inform model adequacy. Special attention is paid to dynamic structure and underlying distributional assumptions including associated dispersion properties. Competing models show attractive features, but overall no one modelling approach is seen to dominate.

Intellectual Capital and Value Creation in the Machinery and Equipment Industry

Economists have concluded that the intangible assets growth have exceeded expectations in the last years. Although there is no general agreement on its conceptualization the intangible assets's effects in the company's statements have been widely recognized, raising interest from researchers and economic agents on methods of appraisal. The purpose of this paper is to provide ideas for discussion by testing Pulic’s theory of Value Added Intellectual Capital - VAIC, for manufacturing industries operating in Brazil, in the period of 2000 to 2006. Pulic's Theory was selected mainly because it is applicable based on public data gathered from company's financial statements. The sample is comprised of Brazilian non listed and listed companies with more than 100 employees belonging to the Annual Industrial Research (Pesquisa Industrial Annual - PIA) annually undertaken by the Brazilian Institute of Geography and Statistics - IBGE). Research was undertaken in the field of quantitative methodology. Static and dynamic regression models were applied to a data panel. In general, the results achieved indicate that the VAIC model is relevant to explain value creation by the Brazilian companies.

Simulation of sap flow in a beech tree by means of a dynamic data-based model as influenced by a solar eclipse

Responses of crucial plant processes such as transpiration and photosynthesis have often been modelled by mechanistic models or static regression models. The objective of this paper was to quantify the dynamics of sap flow rate in a beech tree to the rapid variations in short-wave solar radiation, air temperature and vapour pressure deficit using an alternative dynamic data-based modelling approach. In order to have enough dynamic information, data from the solar eclipse of 11 August 1999 were used. This permitted determination of the order of the sap flow dynamics, together with the relative contribution of short-wave radiation, air temperature and vapour pressure deficit. A multiple-input and single-output transfer function was used to simulate the sap flow rate responses in three branches at 22, 16 and 9 m accurately (R2 of 0.94, 0.86 and 0.90, respectively). The appropriate model structure was the same for the three branches and was characterised by second-order dynamics. An important advantage of the dynamic modelling approach presented was that it enabled the decomposition of the total sap flow rate response into partial responses to short-wave radiation, air temperature and vapour pressure deficit, respectively.

Testing for multivariate autoregressive conditional heteroskedasticity using wavelets

Test statistics for autoregressive conditional heteroskedasticity (ARCH) in the residuals from a possibly nonlinear and dynamic multivariate regression model are considered. The new approach is based on estimation of the multivariate spectral density of squared and cross-residuals. A simple wavelet-based spectral density estimator is advocated, which is a particularly suitable analytic tool when the spectral density exhibits peaks or kinks that may arise from strong cross-dependence, seasonal patterns and other forms of periodic behaviors. In several circumstances, the spectral density may have peaks at various frequencies, such as seasonal frequencies, and the wavelet method may capture them effectively. Compared to kernel-based test statistics for multivariate ARCH effects, the weighting scheme offered by the new wavelet-based test statistics differs in several important aspects. An asymptotic analysis under the null hypothesis of no ARCH effects shows that the wavelet-based test statistic converges in distribution to a convenient standard normal distribution. Under fixed alternatives, the consistency of the wavelet-based test statistics is established in a class of static regression models with uncorrelated but dependent errors. In a Monte Carlo study comparisons are made under various alternatives between the proposed wavelet-based test statistics, the kernel-based test statistics for ARCH effects, and several popular portmanteau test statistics for ARCH effects available in the literature.

Neural network and state-space models for studying relationships among soil properties

The study of soil property relationships is of great importance in agronomy aiming for a rational management of environmental resources and an improvement of agricultural productivity. Studies of this kind are traditionally performed using static regression models, which do not take into account the involved spatial structure. This work has the objective of evaluating the relation between a time-consuming and "expensive" variable (like soil total nitrogen) and other simple, easier to measure variables (as for instance, soil organic carbon, pH, etc.). Two important classes of models (linear state-space and neural networks) are used for prediction and compared with standard uni- and multivariate regression models, used as reference. For an oat crop cultivated area, situated in Jaguariuna, SP, Brazil (22º41' S, 47º00' W) soil samples of a Typic Haplustox were collected from the plow layer at points spaced 2 m apart along a 194 m spatial transect. Recurrent neural networks and standard state-space models had a better predictive performance of soil total nitrogen as compared to the standard regression models. Among the standard regression models the Vector Auto-Regression model had a better predictive performance for soil total nitrogen.