Dynamic Regression Model Research Articles

Economic value added and working capital efficiency linkages: an Indian context

Purpose This paper aims to explore the relationship between the economic value added (EVA) and the working capital efficiency (WCE) of the listed firms in India. Design/methodology/approach This paper uses annual data of 401 listed companies for the period 2012–2019. Furthermore, the dynamic panel data regression model was used to investigate the relationship between the variables of interest. Findings The results reveal that the net trade cycle (NTC) is significantly and negatively associated with listed firms’ economic value in India, indicating that a shorter NTC generates higher EVA for Indian firms. The authors further explore the association between individual components of the NTC with EVA. The authors also found that an inverse, and significant relationship exists between EVA and the individual components of the NTC. The findings also reported a meaningful relationship between EVA and control variables except for leverage and age. For listed firms, the results suggest that sales growth and firm size are crucial factors driving firms’ EVA. Practical implications Higher WCE enhances shareholder value creation, forming positive stakeholders’ expectations toward the company. Originality/value Over the years, many studies have been conducted to determine the relationship between WCE and traditional measures of firms’ profitability. However, hardly any study finds out the impact of WCE on the value-based measures of firms’ performance. This study fills the gap in the existing literature by analyzing the impact of WCE on firms’ EVA.

Influence of Asset Quality on the Financial Performance of Insurance Companies in Kenya

The research aimed to investigate the influence of asset quality on the financial performance of insurance companies. The study was anchored on the efficiency structure theory. Positivism research philosophy was employed with a descriptive research design that targeted a population of 56 general insurance and life insurance firms in Kenya. Secondary balanced panel data spanning 15 years from 2008 to 2022 was also used in the study. The secondary data was sourced from the financial statements of the firms and industry publications from the Insurance Regulatory Authority (IRA). The quantitative data collected was analyzed using descriptive statistics inform of percentages, frequencies, mean, and standard deviation. Additionally, a balanced dynamic panel data regression model was performed. Prior to fitting the panel regression model, diagnostic tests were undertaken to evaluate linearity, stationarity, normality of residuals, serial correlation, and homoscedasticity. The research included 41 insurance firms that met the inclusion criteria. The study results determined that asset quality has a statistically significant and negative influence on the financial performance of insurance companies in Kenya (β = -0.1956, t = -2.93, p = 0.003). The study concluded that asset quality is a collective consideration of diversification, liquidity, risk and liability profiles and thus plays a crucial role in influencing profitability. The study recommends that additional regulations should be instituted by the IRA that entail developing a range of targets on each of the aspects of asset quality.

Hantavirus Pulmonary Syndrome Outbreak Anticipation by a Rapid Synchronous Increase in Rodent Abundance in the Northwestern Argentina Endemic Region: Towards an Early Warning System for Disease Based on Climate and Rodent Surveillance Data.

Hantavirus pulmonary syndrome (HPS) is an American emerging disease caused by the rodent-borne virus genus Orthohantavirus (Family: Hantaviridae: Order: Elliovirales Class: Bunyaviricetes). In Argentina, almost half of the HPS infections occur in the northwestern endemic region. In this study, we monitored rodent abundance during 2022 and 2023 in three sites with different sampling methods (removal trapping, live trapping and hunted rodents by domestic cats) to evaluate their relationship with human infections. We found a similar pattern of variation in rodent abundance across time, and particularly a synchronous rise of rodent abundance that anticipated an HPS outbreak in 2023. Our dynamic regression models revealed a positive relationship between HPS cases and rodent abundance with a three-month lag, as well as rainfall with an eight-month lag. Our results provide a framework for the planning and implementation of public health prevention campaigns based on climatology and rodent monitoring. Domestic cats bringing rodents into houses can be an overlooked risk factor, particularly if viral shedding of infected rodents is magnified by stress. HPS is a disease of public health concern due to its high mortality rate, the lack of a specific therapeutic treatment and no vaccine. Thus, prevention of infections is of the utmost importance.

Carbon Emission Prediction Algorithm for Community Electricity Consumption Based on Improved PSO Algorithm with Optimized Autoregressive Moving Average Models

Abstract The anticipation of carbon emissions stemming from community electricity usage stands as a pivotal field of study. Such analysis holds the potential to furnish communities with informed pathways towards crafting viable carbon reduction strategies, thereby contributing significantly to the overarching goals of carbon peaking and eventual neutrality. The intricacies of carbon emissions from community electricity consumption are manifold, entangled with variables like energy consumption patterns, energy mix, and carbon emission coefficients. Hence, the imperative lies in crafting a predictive framework adept at holistically integrating these variables to enhance prognostic precision and reliability. Within this research, we propose a dynamic carbon emission factor regression model. This model is uniquely poised to capture real-time shifts influenced by changes in energy structure and policy landscapes, thereby amplifying predictive sensitivity. Leveraging the Particle Swarm Optimization (PSO) algorithm, we synchronously optimize the autoregressive terms (p) of the Autoregressive Integrated Moving Average (ARIMA) model and the moving average terms (q) of the MA model to attain a globally optimal solution. Crucially, this approach obviates the need for manual intervention and arbitrary parameter selection. In contrast to conventional optimization methodologies, our paper advances the PSO’s weight calculation mechanism. By assigning greater weight values during the initial iterations, the algorithm maintains robust global search capabilities. Subsequently, the inertia weight diminishes progressively throughout the iteration process, fostering precise local exploration.

Has the Digital Economy Boosted Carbon Reduction in Livestock Farming in China?

Carbon reduction in livestock is a necessary path for the green transformation of the livestock industry. Has the digital economy as an emerging productive force in recent years driven carbon reduction in the livestock sector? This paper employs dynamic panel regression models, mediation effect models, and spatial econometric models to investigate the impact mechanisms of the digital economy on carbon emission reduction in livestock husbandry. The results indicate the following: (1) The digital economy has a positive impact on carbon reduction in livestock farming by fostering technical innovation, enhancing the level of human capital, and transforming the mode of production. (2) From 2013 to 2021, the digital economy did not show a significant carbon reduction effect until 2018. It has had a substantial impact on carbon reduction in livestock husbandry in the northeastern and western regions, while its influence in the eastern and central regions has not been significant. The digital economy positively affects carbon emission reduction in non-dairy cows and pigs, negatively affects poultry, and shows no significant effect on dairy cows and sheep. (3) In terms of spatial effects, the digital economy is not only driving carbon emission reductions from livestock farming in the local region but is also significantly driving carbon emission reductions from livestock farming in the surrounding provinces. The findings of this article provide some insights into future policy formulation for low-carbon development in the livestock sector.

Regarding the finance-investment nexus in sub-Saharan Africa: an issue of too little or too much finance?

PurposeThis study investigates the finance-investment nexus in sub-Saharan Africa using data from 41 countries spanning the period from 2000 to 2022. The central question addressed is whether there is a “too little” or “too much” finance problem in the region.Design/methodology/approachThis study employs a system-generalised method of moments (GMM) approach to analyse the association between finance and private investment. Additionally, a dynamic threshold regression model is used to uncover potential nonlinearities in this relationship.FindingsInitially, the study identifies a negative correlation between increased finance and private investment. However, further analysis using the dynamic threshold regression model reveals a critical threshold level of finance. Specifically, the threshold is found to be 6.52% of domestic credit to the private sector and 23.18% using the financial development index. Below this threshold, finance negatively impacts private investment, while surpassing this threshold leads to positive growth in private investment. These findings indicate an issue of “too little” finance in the finance and private investment nexus in sub-Saharan Africa. The results are robust across different model specifications.Research limitations/implicationsThe implications of this study highlight the importance of identifying critical thresholds for financing to enhance investment expenditures in the region.Originality/valueThis study contributes to the literature by uncovering nonlinearities in the finance-investment nexus in sub-Saharan Africa. The identification of critical thresholds provides valuable insights for policymakers, emphasising the need to strengthen the financial sector in countries operating below these thresholds to promote private investment and economic growth.

Exploring causal interactions between macroprudential policy and financial cycles in South Africa

This study investigated the causal interactions between macroprudential policy, measured by the Macroprudential Policy Index (MPI), and financial cycles represented by the Aggregate Financial Cycle (AFC) and the Credit and Asset Prices Financial Cycle (CAFC) in South Africa from 1970q1 to 2023q2. Additionally, the study explored the effects of macroprudential policy during different phases of financial cycles. Using the time-varying Granger-causality model, the study found that the MPI Granger-caused financial cycles during the 2003/07 credit boom in South Africa, while the AFC and CAFC Granger-caused macroprudential policy during the Covid-19-induced collapse of financial markets. The results suggest that macroprudential policy is employed more proactively during financial booms and more reactively during financial busts in South Africa. The Markov switching dynamic regression model used to assess the MPI's effects revealed that macroprudential policy's effectiveness is stronger during financial busts and weaker during financial booms in South Africa. This is because financial institutions in South Africa tend to resist stricter regulations during boom phases due to heightened optimism about future prospects. Conversely, they are more receptive to stimulatory interventions during bust phases. Based on these findings, it is recommended that the South African Reserve Bank and the Prudential Authority use macroprudential policy more assertively during financial booms to enhance its effectiveness. This could involve setting more stringent parameters upon activating macroprudential policy tools or complementing macroprudential policy with monetary policy during financial booms.

Extraction evolutionary features of continuous light-induced plasmonic nanobubbles by a dynamic spectral regression model

A comprehensive understanding of the nature of light-induced plasmonic nanobubbles (PNBs) generated around noble metal nanoparticles is essential for optimizing PNB-based applications, such as light-driven microfluidic control. To investigate the overall evolution pattern of plasmonic nanobubbles (PNBs) under continuous light illumination, a computational model based on the least-squares method is established. Meanwhile, the variations in nanofluid transmittance and vaporization mass under continuous light illumination are measured by an immersion fiber and an electronic balance, respectively. The effects of nanoparticle concentration on the nanofluid transmittance and vaporization mass are analyzed. Considering the variations in nanofluid concentration, the overall evolution pattern of PNBs is preliminarily analyzed by monitoring the real-time dynamic change in nanofluid transmittance. The experimental results indicate that the nanoparticle concentration has an important effect on the nanofluid vaporization process. During the illumination stage, an increased nanofluid concentration intensifies the synergistic effect of water vaporization and PNB growth on transmittance reduction. The change in nanofluid transmittance caused by PNB scattering greatly exceeds that caused by increasing nanoparticle concentration. The nanofluid concentration gradually increases and peaks at the end of the cooling stage, and the rate of change of the nanofluid concentration is proportional to the nanofluid concentration. The numerical calculations show that the average size and the range of the PNB size distribution continuously increase with increasing irradiation time, and decrease during the cooling stage. A higher nanofluid concentration corresponds to a wider range of PNB size distribution.

Spatially organized striatal neuromodulator release encodes trajectory errors.

Goal-directed navigation requires animals to continuously evaluate their current direction and speed of travel relative to landmarks to discern whether they are approaching or deviating from their goal. Striatal dopamine and acetylcholine are powerful modulators of goal-directed behavior, but it is unclear whether and how neuromodulator dynamics at landmarks incorporate relative motion for effective behavioral guidance. Using optical measurements in mice, we demonstrate that cue-evoked striatal dopamine release encodes bi-directional 'trajectory errors' reflecting relationships between ongoing speed and direction of locomotion and visual flow relative to optimal goal trajectories. Striatum-wide micro-fiber array recordings resolved an anatomical gradient of trajectory error signaling across the anterior-posterior axis, distinct from trajectory error independent cue signals. Dynamic regression modeling revealed that positive and negative trajectory error encoding emerges early and late respectively during learning and over different time courses in the medial and lateral striatum, enabling region specific contributions to learning. Striatal acetylcholine release also encodes trajectory errors, but encoding is more spatially restricted, opposite polarity, and delayed relative to dopamine, supporting distinct roles in modulating striatal output and behavior. Dopamine trajectory error signaling and task performance were reproduced in a reinforcement learning model incorporating a conjunctive state space representation, suggesting a potential neural substrate for trajectory error generation. Our results establish region specific neuromodulator signals positioned to guide the speed and direction of locomotion to reach goals based on environmental landmarks during navigation.

Assessing the effect of digital platforms on innovation quality: mechanism identification and threshold characteristics

In the digital era, digital platforms are gradually changing the ways of life and social production and are becoming the centre of digital services in various industries. Additionally, they break the established institutional framework of social recognition and promote technological change and innovation. Thus, examining the quality of innovation facilitated by digital platforms has become an important area of research. This paper utilises China’s 2011–2021 provincial panel data and examines digital platforms, resource mismatches and innovation quality within the same research framework. The direct, indirect and nonlinear effects of digital platforms on innovation quality are explored using the fixed effects model, intermediary effect model and dynamic threshold regression model. The findings indicate that digital platforms have a significant positive direct effect on innovative quality, and resource mismatches play intermediary and threshold roles in the process. These findings offer policy guidance for constructing robust digital platform ecosystems and enhancing innovation quality.

The Impact of Inflation on Financial Sector Performance: Evidence from OECD Countries

Abstract This research article investigates the relationship between inflation and the performance of the financial sector within OECD countries, a group critical to global economic stability. The financial sector is a basis of sustainable economic growth, and its performance is heavily influenced by the macroeconomic environment, particularly inflation. This study analyses how inflation, alongside other significant factors such as real GDP growth, government size, trade openness, and interest rates, impacts the financial sector development. By examining data from 38 OECD countries over a 20-year period (2002-2021), this research employs a comprehensive approach using both static and dynamic panel regression models. The results consistently indicate a negative correlation between inflation and the key financial sector variables, suggesting that higher inflation levels undermine financial sector performance. These findings underscore the importance of maintaining low and stable inflation to foster an efficient and stable financial sector. This study makes a valuable contribution to the literature by focusing specifically on OECD countries, which are often seen as benchmarks for economic policies and financial systems.

Modeling Determinants of Profitability in Ethiopia Manufacturing Companies: A Dynamic Panel Model Approach

Background: Profitability is the primary measure of the overall success of company. The analysis of profitability ratios is important for the shareholders, creditors, prospective investors, manufacturers and government alike. Objective: The objective of this study was fitted a dynamic panel regression model that efficiently fit the data and further to identify the determinants contributing significantly to profitability in Ethiopia. Methods: This study was conducted on secondary data of 32 sample manufacturing companies collected from the audited financial statements of large tax payers’ office and national bank of Ethiopia. The study was covered a period of seven years from 2011 to 2017. The panel unit root test of Levin-Lin-Chu tests was made for each variable and applied first difference transformation for the variables that had unit root. A dynamic panel regression model was utilized for data handling technique using generalized method of moments (GMM) estimation. Results: We compared the results when one unit increase in lagged profitability, managerial efficiency, capital intensity, GDP, exchange rate and one ratio decrease in leverage, ceteris paribus, turn out were found to increase the profitability of manufacturing companies by around 0.69, 0.179, 4.52E-06, 3.844, 0.04 and 0.393 ratio, respectively. The model reveled that previous profitability, leverage, capital intensity, managerial efficiency, GDP and exchange rate had a statistically significant (P<0.05) effect on companies’ profitability in Ethiopia. Conclusion: As per our findings, the manufacturing companies should minimize leverage financing from its capitals and should emphasize the management of appropriate financing to increase profitability. Finally, we recommended that policy makers should coming up with better policies on improvement of profitability.

Forecasting Turkey’s Primary Energy Demand Based on Fuzzy Auto-regressive Distributed Lag Models with Symmetric and Non-symmetric Triangular Coefficients

AbstractThis study aims to guide policymakers in allocating resources and planning for the future by consistently estimating energy data trends. Because of the complexity and uncertainty of energy demand behavior and many influencing factors, we decide to take advantage of a fuzzy regression model to determine the actual relationships in the energy demand system and provide an accurate forecast of energy demand. For this purpose, because of energy demand drivers, fuzzy possibilistic approaches with symmetric and non-symmetric triangular coefficients are integrated with the autoregressive distributed lag (ARDL) model, each in a time-series format with feedback mechanisms inside. After regularizing the L1 (Lasso regression) and L2 (ridge regression) metrics to minimize the overfitting problem, the optimal fuzzy-ARDL model is obtained. Turkey’s primary energy consumption is projected based on the best model by benchmarking the static and dynamic possibilistic fuzzy regression models according to their training and test values.

Shrinkage Estimation and Forecasting in Dynamic Regression Models Under Structural Instability

Abstract This paper introduces a Stein-like shrinkage method for estimating slope coefficients and forecasting in first order dynamic regression models under structural breaks. The model allows for unit root and non-stationary regressors. The proposed shrinkage estimator is a weighted average of a restricted estimator that ignores the break in the slope coefficients, and an unrestricted estimator that uses the observations within each regime. The restricted estimator is the most efficient estimator but inconsistent when there is a break. However, the unrestricted estimator is consistent but not efficient. Therefore, the proposed shrinkage estimator balances the trade-off between the bias and variance efficiency of the restricted estimator. The averaging weight is proportional to the weighted distance of the restricted estimator, and the unrestricted estimator. We derive the analytical large-sample approximation of the bias, mean squared error, and risk for the shrinkage estimator, the unrestricted estimator, and the restricted estimator. We show that the risk of the shrinkage estimator is lower than the risk of the unrestricted estimator under any break size and break points. Moreover, we extend the results for the model with a unit root and non-stationary regressors. We evaluate the finite sample performance of our proposed method via extensive simulation study, and empirically in forecasting output growth.

IMPACT OF CAPITAL STRUCTURE ON PROFITABILITY: AN EMPIRICAL SEARCH

In today's corporate world, every company needs to survive in the long run by becoming financially sustainable. Firms must cope with a variety of financial variables for a successful long-term financial existence, where the two most crucial issues are capital structure and profitability. The finance manager must design the capital structure in such a way so that the company can earn a good profit to satisfy all of its stakeholders. A modest attempt has been made in this study to understand how far profitability is affected by the capital structure design. This study considered 100 manufacturing companies of India's National Stock Exchange (NSE) as the sample companies. The study has been purely based on secondary data, and the required data has been gathered from the 'CAPITALINE' database for a period of 10 years, i.e., from 2011 to 2020. This study considered ROIC ( proxy of profitability) as the dependent variable and leverage (proxy of capital structure) as the independent variable. Besides, some control variables such as firm size, Tangibility, uniqueness, growth, and firm quality have also been taken into account. A dynamic panel data regression model has been used in this study to examine the relationship between profitability and capital structure. The findings of the study clearly revealed that profitability is affected by the choice of capital structure design. KEYWORDS: Capital Structure, Leverage, Profitability, ROIC, Dynamic Panel, NSE

Does the gender dissimilarity of female mayors with their political government team affect municipalities’ budgets? Evidence from Spain

ABSTRACT Recent research has considered whether the growing presence of women in political institutions has affected public budgets. This study takes a different gender perspective. The aim is to assess how gender dissimilarity between female mayors and their government teams can affect municipal budgetary policies. We use data from Spanish municipalities with more than 20,000 inhabitants in the period from 2004 to 2018, a dynamic regression model and take into account the possible moderating effect of ideology. Our main findings are that gender dissimilarity of female mayors increased current social spending and reduced nonsocial spending in line with female gender preferences, while it decreased tax revenues. However, when we analyze only the years of economic growth, we observe that the gender dissimilarity of female mayors increased tax revenue in accordance with their gender preferences. We also see that the interaction between right-wing ideology and the dissimilarity of female mayors substantially and significantly accentuated the decrease in nonsocial spending. We confirm our findings in different population segments.

Impact of Xpert MTB/RIF implementation in tuberculosis case detection and control in Brazil: a nationwide intervention time-series analysis (2011–2022)

Since 2014, Brazil has gradually implemented the Xpert MTB/RIF (Xpert) test to enhance early tuberculosis (TB) and drug-resistant (DR-TB) detection and control, yet its nationwide impact remains underexplored. Our study conducts an intervention time-series analysis (ITSA) to evaluate how the Xpert's implementation has improved TB and DR-TB detection nationwide. 1,061,776 cases from Brazil's National TB Registry (2011-2022) were reviewed and ITSA (2011-2019) was used to gauge the impact of the Xpert's adoption on TB and DR-TB notification. Granger Causality and dynamic regression modelling determined if incorporating Xpert testing as an external regressor enhanced forecasting accuracy for Brazil's future TB trends. Xpert implementation resulted in a 9.7% increase in TB notification and substantial improvements in DR-TB (63.6%) and drug-susceptible TB (92.1%) detection compared to expected notifications if it had not been implemented. Xpert testing counts also presented a time-dependent relationship with DR-TB detection post-implementation, and improved predictions in forecasting models, which depicted a potential increase in TB and DR-TB detection in the next six years. This study underscores the critical role of Xpert's adoption in boosting TB and DR-TB detection in Brazil, reinforcing the case for its widespread use in disease control. Improvements in prediction accuracy resulting from integrating Xpert data are crucial for allocating resources and reducing the incidence of TB. By acknowledging Xpert's role in both disease control and improving predictions, we advocate for its expanded use and further research into advanced molecular diagnostics for effective TB and DR-TB control. FIOCRUZ.

Bayesian dynamic modelling for probabilistic prediction of pavement condition

Significant funds have been allocated to maintain road networks each year in developed countries. Performance prediction is crucial for pavement management systems to adjust working plans and budget allocation. As a Bayesian nonparametric method, Gaussian process regression (GPR) is powerful in predicting nonlinear time series and quantifying uncertainty. However, it remains computationally intensive and fails to adapt to the time-varying characteristics. To address such issues, a dynamic GPR model is proposed for probabilistic prediction of the International Roughness Index (IRI) for flexible pavements. A moving window strategy is developed to substantially shrink the size of training data, which effectively alleviates computational cost and thus leads to a dynamic GPR. A genetic algorithm is then adopted to determine the optimal window size by considering the trade-off between computational efficiency and accuracy. A dataset acquired from Long-Term Pavement Performance (LTPP) is used to demonstrate the feasibility of the dynamic GPR. Its performance is compared to traditional GPR as well as dynamic and static Bayesian linear regression (BLR) models. The comparison results indicate that the proposed dynamic GPR can increase the accuracy by 0.86, 1.52, and 2.27 times for dynamic BLR, static GPR, and static BLR, respectively. It exhibits the best results in terms of accuracy and uncertainty metrics due to its nonlinear modelling and time-varying ability.

Analisis Determinan Struktur Modal pada Perusahaan Property dan Real Estate

The aim of this research is to determine the influence of asset tangibility, liquidity, non-debt tax shield, business risk, growth and firm size on the capital structure of property and real estate sector companies listed on the Indonesia Stock Exchange in 2014-2021. A dynamic panel data regression model with the Generalized Method of Moment (GMM) estimator is applied in this research. The results show that determinants simultaneously influence capital structure. Asset tangibility, growth and firm size partially have a significant effect on capital structure, while liquidity, non-debt tax shield and business risk partially have no effect on capital structure. Therefore, the formation of an optimal capital structure is expected to take into account tangible assets, growth rate and company size in order to maximize company value.

Forecasting the UK top 1% income share in a shifting world

AbstractUK top income shares have varied hugely over the past two centuries, ranging from more than 30% to less than 7% of pre‐tax national income allocated to the top 1 percentile. We build a congruent dynamic linear regression model of the top 1% income share allowing for economic, political and social factors. Saturation estimation is used to model outliers and trend breaks, proxying underlying structural changes driving income inequality in the UK. We use the model to forecast the top 1% income share over the last 15 years, and compare to a range of forecast devices. Despite a well‐specified constant parameter model conditioning on significant explanatory variables, the best performing forecasts are obtained from a random walk and a smoothed random walk. These results are explained by the presence of shifts in the income share over the forecast period, resulting in forecasts from equilibrium correction models converging to the wrong equilibrium. Our best prediction for 2026 based on the most recent data from 2021 (a 5‐year ahead projection) is that the pre‐tax top 1% income share will remain at the most recent realized value of 12.7%, but there is a large degree of uncertainty, with a 95% confidence band ranging from 10% to 15.7%.