Autoregressive Research Articles

Regulation mechanism of droplets wetting on banana leaf surface and its dynamic contact angle wetting model

The effective wetting and deposition of pesticide droplets on the leaf surface significantly affect the control of pests and diseases, and its mainly affected by the leaf surface properties and pesticide formulation properties. This article aims to investigate the wettability regulation mechanism and dynamic wetting properties of banana leaves, in order to intelligently design appropriate pesticide formulations. The micro-morphology of the banana leaves exhibits a micro-nano dual-scale structure with different microstructures and roughness on the adaxial side and abaxial side. Correspondingly, the wettability on the adaxial side of the banana leaf with higher roughness (Rq = 71.1 nm) is always better than that on the abaxial side (Rq = 42.5 nm). The droplet contact performance of droplets exhibits a strong concentration dependence of surfactants, which regulate the inter-species differences in the wettability of different pesticide formulations on both sides. Besides, the dynamic wetting process of pesticide droplets exhibits strong time dependence, by using a time series method to establish an AR(2) model, precise simulation and prediction of the contact angle changes on the adaxial side (R2 = 0.9560) and abaxial side (R2 = 0.8903) during the dynamic wetting process were achieved. The dynamic wetting model provides new insights into the spreading and deposition processes of droplets on leaves. This work provides a favorable reference for the study of the dynamic balance between wetting and adhesion properties of pesticide spray on leaf surfaces.

Heterogeneity, Jumps and Co-Movements in Transmission of Volatility Spillovers Among Cryptocurrencies

Abstract We analyze properties identified in the price volatility of Bitcoin and some of the leading cryptocurrencies namely Litecoin, Ripple, and Ethereum. We employ Heterogeneous Autoregressive models (HAR) in both a univariate and multivariate level of analysis. First, the significance of heterogeneity and jumps is examined, considering the ability of several univariate HAR models, to predict realized volatility of cryptocurrencies. Second, we examine the relevance of realized volatility jumps and covariances in the transmission of volatility spillovers among cryptocurrencies. We perform a comparative spillover analysis of the multivariate HAR models in two versions, considering variances only and covariances as well. Our results indicate that covariances and jumps inclusion lead to an increase in spillovers. The time-varying spillover analysis indicates higher dependency between Bitcoin and the other cryptocurrencies mostly at short frequencies.

Displacement residuals reveal landslide regime shifts

AbstractDespite significant progress in the development of advanced technologies for detecting and monitoring unstable slopes, accurately predicting catastrophic landslides remains a challenge. To tackle this challenge, our research integrates advanced prediction models and granular systems theory to provide insights into regime shifts within slow-moving deep-seated landslide dynamics. Our approach is designed to discern exceptional departures from historical landslide dynamics. The approach leverages the “group dynamics,” crucial for identifying precursory failure indicators, according to the generic dynamics of the precursory failure regime in granular systems. We select three different monitored slow-moving landslides as test cases. We employ an error correction cointegration vector autoregression model together with an exogenous regressor to encode historical spatiotemporal landslide dynamics and predict displacement at multiple locations by considering the historical landslide motion and relationship with external triggers. Displacement residuals are obtained by computing the difference between predicted and measured displacement for a given historical calibration time window. Threshold values for the displacement residuals are determined by analyzing the historical distribution of these residuals. Lastly, persistence in time of the threshold exceedance and the number of monitoring points that exceed the threshold at the same time are considered to encode the group dynamics. This approach offers several advantages, including the effective identification of critical regime shifts, adaptability, and transferability, and it introduces regime shift information into local landslide early warning systems. This approach can enhance confidence in the resultant alert, particularly when integrated with conventional alert systems, thereby improving the reliability of landslide warning systems.

Estimation in functional partially linear spatial autoregressive model

Functional regression has been a hot topic in statistical research. However, not much work has been done when response variables are cross-sectionally dependent variables and explanatory variables contain a real-valued scalar variable and a functional-valued random variable. In this paper, we consider a new functional partially linear spatial autoregressive model. Based on the functional principal components analysis and basis function approximation, we obtain the estimators of the unknown parameter and functions through the instrumental variables estimation method. The asymptotic normality and convergence rates of estimators are proved under some mild conditions. In addition, we illustrate the finite sample performance of the proposed estimation method through simulation study and a real data analysis.

Forecasting of Inland Fish Production: Case of Catla (<em>Catla catla</em>) and Gift Tilapia (<em>Oreochromis niloticus</em>) Production in Udawalawa Reservoir in Sabaragamuwa Province, Sri Lanka

This study focused on assessing and forecasting Catla (Catla catla) and Tilapia (Oreochromis niloticus) production in the Udawalawe reservoir in Sri Lanka using an 11-year time series data from 2010 to 2021. The relevant secondary data were collected from the National Aquaculture Development Authority of Sri Lanka (NAQDA). Unit root tests, Dickey-Fuller tests, and Seasonal Auto Regressive Integrated Moving Average (SARIMA) models and trend analysis were applied for the data analyses. According to the results, stationarity behaviour was observed in both Catla and Tilapia production information series. Hence, SARIMA (2,2,1) (1,0,1)12 is the appropriate empirical model for forecasting Catla production. Furthermore, the maximum average landings of Catla production were observed in the March-June time period. SARIMA (1,0,1) (1,1,1)12 was indicated as the most suitable model to forecast Tilapia production in the reservoir. The maximum average of Tilapia production was also observed in the March-June period. Both the SARIMA model and trend analysis gave approximately equal production information regarding Catla and Tilapia. Thus, these findings showed that the SARIMA model has more accuracy than trend analysis when analysing the time series information for short durations. However, when the duration becomes larger, forecasting is not suitable using the SARIMA model, and trend analysis gives a clear view up to a certain level. Both Catla and Tilapia production exhibited significant changing patterns and multiple seasonalities. Thus, trend analysis gives valuable insights than SARIMA model in long term predictions in case of this study. These findings are highly important for enhancing fish production in this reservoir, as they predict future fish catches under the current reservoir conditions.

Impact of Macroeconomic Indicators on The Indonesia Stock Exchange (IDX) Composite Index Performance

This study examines the impact of macroeconomic indicators on the performance of the Indonesia Stock Exchange (IDX) Composite Index from 2014 to 2023, focusing on four key variables: interest rates, inflation, money supply (M2), and Gross Domestic Product (GDP). Using a Vector Autoregressive (VAR) model, the research investigates the short-term dynamics between these variables. The findings show that in the VAR estimation, only the lag 1 M2 value has a statistically significant coefficient. However, the Impulse Response Function (IRF) analysis reveals that shocks to the Bank Indonesia rate, inflation, money supply, and GDP initially cause significant fluctuations in the Jakarta Stock Exchange price index, but these effects are short-lived, with the index stabilizing around zero after several periods. These results provide valuable insights for policymakers and investors, emphasizing the importance of understanding macroeconomic conditions for making informed stock market decisions.

Modelling Petrol Prices in Kenya from 2014 to 2023 Using Sarimax Model: A Case Study of Nairobi County

The requirement for petrol price information is crucial for majority of enterprises. This is because fluctuations in petrol prices impact inflation hence affecting daily lives of citizens. In analyzing the prices of petrol, researchers have employed several models but encountered various limitations. These limitations include; the Error Correction Model can examine only one co-integrating association. The Vector Autoregression (VAR) model does not account for the structural changes in the data. Additionally, the AutoRegressive Integrated Moving Average (ARIMA) model does not take into consideration the seasonal component in the data. The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model assumes that over time the volatility is constant. Moreover, the Seasonal Autoregressive Integrated Moving Average (SARIMA) model does not integrate the external factors. Hence in this study Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) model was employed since it captures seasonality in data and incorporates the exogenous variables. The research’s aim was to model prices of petrol in Kenya for the period between 2014 to 2023 with exchange rates as an external factor. Secondary data was obtained from Energy and Petroleum Regulatory Authority (EPRA), Kenya National Bureau of Statistics (KNBS) and Central Bank of Kenya (CBK) websites. R software was used to analyze the data. By the use of historical data of petrol prices and exchange rates, the study sought to fit the best Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) model, validate the model and predict the petrol prices. The petrol price data was found to be non-stationary using Augmented Dickey Fuller test (ADF). Regular differencing was conducted to make the data stationary. Seasonal differencing due to seasonality component available in the data was also performed. Best SARIMAX model was chosen from various SARIMAX models according to Box-Jenkins methodology which uses least Akaike Information Criterion (AIC) value. SARIMAX (0,1,1)(2,1,2)<sub>12</sub> model was selected since it had least Akaike Information Criterion (AIC) value of 656.3733 and the model validated using the hold out technique. The forecasts errors from the training set were; Mean Squared Error (MSE)=10.4970, Root Mean Square Error (RMSE)=3.239911, Mean Absolute Percentage Error (MAPE)=2.309268% while those from the testing set were; Mean Squared Error (MSE)=3271.1012, Root Mean Square Error (RMSE)=57.193542, Mean Absolute Percentage Error (MAPE)=26.695390%. There was less error in the training set than in the testing set as it was expected hence the model suited the data well and could be used for future predictions. The model was then used for five year forecast into the future. This study’s findings will offer sound suggestions to policymakers, businesses and consumers. This study recommends a model to be fitted using other factors affecting petrol prices and fitting Fourier terms, Behavioral Assessment Tools (BATS) and Trigonometric Box-Cox ARMA Trend Seasonal (TBATS) models.

Cycle Fluctuations in the U.S. Real GDP and National Income and Product Accounts Aggregates

This article provides a new set of empirical regularities describing the U.S. macroeconomy, focusing on business cycle fluctuations in the real GDP and eleven major aggregates from the U.S. National Income and Product Accounts (NIPAs). Patterns of the cyclical fluctuations are assessed using filtering methodologies that adapt to the properties of the series being studied. We employ a recent dataset that includes the Great Recession caused by the 2008 financial crisis and the ensuing recovery. We aim to (1) examine the lead-lag relations via cross-correlations between the aggregate cycle in the U.S. real GDP and the cyclical movements in the eleven major NIPA aggregates; (2) investigate econometrically the inter-linkage between the cyclical component of real GDP and that of each NIPA aggregate by way of the Granger-causality test; and (3) evaluate the capability or the predictive power of each NIPA aggregate to forecast real GDP growth via three forecasting models. Comparisons are made with popular nonparametric HP and Baxter-King (BK) filters. The basic conclusion from the empirical analysis is that the adaptive model-based filters have demonstrated advantages over the commonly used HP and BK filters for business cycle analysis across very diverse economic series from the U.S. national accounts, and the structural time series model using smoothed trend and cycle estimates produced more accurate forecasts than the AR(p) model that forecasts directly using the unfiltered time series.

Revealing the New Nexus in Urban Unemployment Dynamics: The Relationship between Institutional Variables and Long-Term Unemployment in Colombia

Abstract The aim of this study is to assess the impact of various institutional variables on the long-term unemployment rate (LAPU) in the Colombian urban labor market. Vector autoregressive models are estimated using microdata from the Great Integrated Household Survey (GEIH), which has national coverage. Monthly data were analyzed for the 13 major capital cities and their metropolitan areas, as well as for the group of 11 intermediate capital cities, resulting in a total of 24 main urban labor markets in Colombia. The dataset includes unionized individuals, individuals with verbal and written contracts, non-labor income, unemployed individuals with subsidies, and individuals receiving severance payments. The results indicate that the growth in the number of unionized employees and non-labor income contributes to increasing the persistence in the duration of unemployment in Colombia. A key finding is that a positive growth in the ratio – gap between individuals with written contracts versus verbal contracts reduces LAPU. This provides evidence of how reducing information asymmetries in the Colombian labor market can improve labor market outcomes and contribute to medium- and long-term social welfare in Colombian urban centers.

Longer-term impacts of the COVID-19 pandemic on obstructive sleep apnoea (OSA)-related healthcare: a province-based study

RationaleFollowing marked reductions in sleep medicine care early in the COVID-19 pandemic, there is limited information about the recovery of these services. We explored long-term trends in obstructive sleep apnoea...

Modeling the Long-Term Variability in the Surfaces of Three Lakes in Morocco with Limited Remote Sensing Image Sources

Satellite imagery has become a widespread resource for modeling variability in lake surfaces. However, the extended monitoring of a lake’s perimeter faces significant challenges due to atmospheric obstacles that cannot be rectified. Due to the atmosphere’s everchanging opacity, only half of the acquired satellite images have reliable qualitative accuracy making it possible to identify a lake’s contour. Consequently, approximately 50% of the monthly lake outline values can be determined using remote sensing methods, leaving the remaining 50% unknown. This situation is applicable to three lakes in Morocco (Abakhan, Ouiouan, and Tiglmanine), the subjects of the current research for the period between 1984 and 2022. What can we do if, during a period of time in which we monitored the evolution of the surface of a lake by satellite means, we obtain only about 50% of the possible images? Shall we just settle for this and stop the analysis? Although it may be challenging to believe, the present study introduces two statistical methods for interpolating and validating the monthly values of the lake outline: the iterative ratio method based on the autocorrelation of the monthly water balance and the Kalman filter. We estimated the reconstruction errors of the missing values and validated the methodology using an inverse philosophy, reconstructing the initial data from the table of the simulation results. Given that the difference between the initial values and the reconstructed initial values resembles white noise or an AR (1) process with a low coefficient, we deemed the methodological approach acceptable. Several comparison criteria between the two interpolation methods were employed, yet determining the more appropriate one remains challenging. Based on our surface reconstruction method, Lake Abakhan, with an average area of 22 hectares, experienced significant fluctuations, ranging from a maximum of 34 hectares in 2010 to a minimum of 0.8 hectares in 2022. Lake Ouiouan, with an average area of 14 hectares, displayed much lower variation, with a maximum of 17 hectares in 2020 and a minimum of 6.5 hectares in 1988. Lake Tiglmanine showed a pattern similar to that of Lake Abakhan but with less pronounced fluctuations. With an average area of 6.1 hectares, its maximum was 9.2 hectares in 2011 and its minimum was 4.1 hectares in 1984.

Sparse-structured time-varying parameter vector autoregression for high-dimensional network connectedness measurement

This paper proposes a new sparse-structured time-varying parameter vector autoregression (SS-TVP-VAR) model to effectively measure high-dimensional network connectedness of financial enterprises. Previous VAR based network connectedness methods require high computational cost and are not feasible for high-dimensional networks of a large number of enterprises. The proposed SS-TVP-VAR not only reduces computational cost but also selects key coefficients when building the VAR model. As a result, it produces a more adaptive and effective connectedness to measure systemic risk caused by extreme events. Studies on 57 listed major financial enterprises in China mainland during a 15-year period show that the proposed SS-TVP-VAR achieves reliable and useful results, and it successfully identifies all the major market events during this time.

Metaverse and financial markets: A quantile-time-frequency connectedness analysis

Amidst increasing interest from investors and scholars in the emerging Metaverse market, this paper marks a pioneering attempt to investigate the volatility connections between the Metaverse stock index and traditional financial markets such as Gold, Crude Oil, the Volatility Index, Bitcoin, and the Nasdaq. Utilizing a novel Quantile Vector Autoregressive (QVAR) model, the study assesses the transmission of shocks between the Metaverse market and its counterparts during bearish, normal, and bullish market conditions. The results highlight a significant increase in connectivity during extreme conditions compared to median levels. Notably, the Nasdaq emerges as a principal volatility transmitter to the Metaverse index, while Bitcoin shows minimal influence, suggesting that technological innovations, rather than cryptocurrencies, predominantly drive the Metaverse market. This investigation provides valuable insights for investors and policymakers, considering the nascent stage of Metaverse-related empirical research.

Coordinated measurement of marine economy: High-quality and low-carbon development in China

The marine economy needs high-quality development, as severe greenhouse effect has become a global concern and carbon emission reduction has been an essential target of marine economy growth. However, a lack of synergistic research in the field of marine economy development and carbon emissions has hindered progress. This study explores the critical interactive relationship between the high-quality and low-carbon development in China's marine economy, aiming to foster green development in coastal urban agglomerations and enhance ecological security alongside economic growth. Despite the importance, comprehensive analysis across multiple dimensions of high-quality and low-carbon development of marine economy remains scarce. This paper establishes a systematic and scientific evaluation index system, utilizing text analysis technology and the Panel Vector Autoregression Model (PVAR) to quantitatively assess the complex system relationship between these dimensions. The results including: (1) The development of a 23 nodes tree structure through a bottom-up, multi-level coding method, effectively addressing the complexity of the current evaluation system for high-quality development of the marine economy. (2) Moran's I of subsystems are all negative, presenting an obvious high-low agglomeration characteristics. The discovery that intra-group differences exceed inter-group ones in marine economic development, indicates that the need for synergistic development among the provinces within marine economic circles. (3) The identification of significant bidirectional interactions between open and shared high-quality development and low-carbon development, with a notable lag effect in responses. The predominant role of innovation in all subsystems underscores the centrality of the marine innovation environment and factors as key drivers in the synergy of high-quality and low-carbon development in the marine economy.

Assessing the Effects of Exchange Rate Volatility on Zambia’s Economic Growth: Evidence from ARDL and NARDL Models

This study investigated the interplay between exchange rate volatility, inflation rates, and real interest rates on Zambia’s economic growth from 1992 to 2022, utilizing annualized time series data. The study was necessitated by the limited published literature and relatively varying findings on the variables’ relationships in resource-dependent countries, such as Zambia. Diagnostic tests, including stationarity and co-integration analyses, were employed to determine integration orders and potential long-run relationships. The linear and nonlinear autoregressive distributed lag models were employed to assess short- and long-run dynamics of the variables on economic growth. The results established a positive short-run relationship between inflation rates and Gross Domestic Product (GDP) growth in the linear autoregressive distributive lag model, while an inverse relationship was observed in the nonlinear autoregressive distributive lag model, suggesting that negative shocks in inflation rates had a highly significant positive impact on economic growth. Furthermore, interest rates exhibited a positive relationship with economic growth, further suggesting that positive shocks had a greater significant direct effect on economic growth in comparison to negative shocks in the short and long run, respectively. Finally, exchange rates in both models exhibited an inverse relationship with economic growth irrespective of positive or negative shocks in the long run, highlighting the adverse effect of exchange rate volatility on economic growth prospects in developing countries, such as Zambia. The speed of adjustment to convergence following any disruptions was determined to be 75.18% (ARDL) and 89.19% (NARDL), highlighting relatively fast speeds of adjustments from any short-run disruptions. Notably, some of the policy recommendations included regular assessments of exchange rate volatility influences on import prices, domestic inflation, and production costs in key sectors. Additionally, the implementation of currency hedging options and forwards as well as bulking of foreign exchange reserves will ensure the stability of exchange rates against other major currencies in various economic conditions.

Forecasting Lattice and Point Spatial Data: Comparison of Unilateral and Multilateral SAR Models

Spatial auto-regressive (SAR) models are widely used in geosciences for data analysis; their main feature is the presence of weight (W) matrices, which define the neighboring relationships between the spatial units. The statistical properties of parameter and forecast estimates strongly depend on the structure of such matrices. The least squares (LS) method is the most flexible and can estimate systems of large dimensions; however, it is biased in the presence of multilateral (sparse) matrices. Instead, the unilateral specification of SAR models provides triangular weight matrices that allow consistent LS estimates and sequential prediction functions. These two properties are strictly related and depend on the linear and recursive nature of the system. In this paper, we show the better performance in out-of-sample forecasting of unilateral SAR (estimated with LS), compared to multilateral SAR (estimated with maximum likelihood, ML). This conclusion is supported by numerical simulations and applications to real geological data, both on regular lattices and irregularly distributed points.

The effects of weather factors on road traffic casualties: Analysis on provincial panel data of China from 2006 to 2021

IntroductionRoad traffic injuries stands as a major concern globally, as they result in significant loss of life, economic impact, and erode trust in government and societal safety. The influence of weather on road traffic safety is undeniable, impacting road conditions, individuals, and vehicles. However, the specific influence of weather on road traffic casualties has seldom been explored. MethodThis study assesses the effect of weather factors on road traffic casualties in China from 2006 to 2021. Vector error correction models (VECM) were utilized to determine the Granger causality between weather factors and covariates. Furthermore, panel autoregressive distribution lag models (ARDL) were applied to quantify the association between weather factors and road traffic casualties. ResultsThe findings indicate that rainfall and temperature exert a short-term negative impact on casualty risk, which intriguingly becomes positive in the long term. A standout discovery is the significant role of health investments, which are shown to reduce casualty numbers in both the short and long-terms. In the long run, the gross domestic product significantly enhances casualties, while expressway mileage notably decreases them. ConclusionsThese results demonstrate the significant influence of weather on road traffic casualties and highlight the critical roles played by factors such as gross domestic product, health investment, and expressway mileage. The evidence presented in the study underscores the urgent need for more effective strategies to mitigate road traffic casualties. Thus, some effective measures are proposed to reduce road traffic casualties. This study is conducive to the improvement of traffic in severe weather in China and provides guidance for traffic management departments.

A novel stochastic ProFiVaS model based on decomposition of stochastic Vasicek differential equation for modeling and simulating of financial indicators

The main purpose of this study is to develop a new discrete time stochastic ProFiVaS model based on decomposition of stochastic Vasicek differential equation which follows bouncing autoregressive AR1, with fixed variance and trend for modeling and simulating of international financial time series forecasting.This study offers the four discrete time stochastic models. The first model is determined as autoregressive integrated moving average-Arima1,1,1, and the parameter estimates of this model are found using the maximum likelihood method (MLM). Monte Carlo simulation is applied to the obtained parameters. This model is named MoCArima for short. Second, the MoCArima model whose parameters are estimated with particle swarm optimization (PSO) is determined as Proposed Monte Carlo (MC) Arima1,1,1, and is briefly named as ProMoCArima. The third model is determined as Fixed Variance Stochastic Model which has a bouncing autoregressive AR1 structure with constant variance and trend component. This model is briefly named as FiVaS. The parameter estimates are found using the MLM. The fourth model is determined as the Proposed Fixed Variance Stochastic Model. This model is briefly named as ProFiVaS. Structurally the same as the third model. The parameter estimates of both trend and stochastic parts of the FiVaS model are found by the PSO.Proposed models are applied to forecasting international different financial indicators to show its superiority and applicability. The performance and usage efficiency of the proposed four stochastic models are examined on a total of six different international financial data types, five of which are stock index data BIST100 (Türkiye), S&P500 (USA), DAX (Germany), FTSE100 (UK), NIKKEI225 (Japan), and one of which is the exchange rate EURO/USD. The ProFiVaS among all models provides the best performance on the all indices. The ProFiVaS can easily be extended to any real-world time series prediction requirements.The proposed models can be extended to all stationary and non-stationary linear or nonlinear autoregression models opening the door to modeling data types obtained from different sector fields. A part of the future plan of this work is to design a decision support system software that assigns a value of 1 when an investor needs to invest in a financial indicator, and 0 otherwise.

Application of Vector Autoregressive (VAR) Model on the Interaction of Inflation Rates and Public Debt in Kenya from 2011 to 2021

This study examines the relationship between public debt and inflation rates in Kenya from 2011 to 2021 using the Vector Autoregressive (VAR) model. Despite the models likeAutoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average (SARIMA), and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) gaining popularity in time series analysis, the Vector Autoregressive model, being multivariate, is relevant in analyzing two or more time series variables simultaneously, benefiting from the bi-directional causality and providing a better outlook into the flow of the dynamic interaction between inflation and public debt. The main objectives are modelling the Vector Autoregressive model and forecasting future trends to provide insights for policymakers. Additionally, the methodological approach comprises descriptive statistics, stationarity tests, normality tests, and the Vector Autoregressive model. Descriptive statistics reveal significant variations, with public debt increasing from 1.35 trillion KES to a peak of 8.2 trillion KES, and inflation rates ranging from 3.2% to 19.72% for the period from 2011 to 2021. The Augmented Dickey-Fuller (ADF) test confirmed that both time series were stationary at their levels. The Vector Autoregressive model, chosen for its ability to analyze dynamic interactions, indicated a significant relationship between the variables, with inflation showing strong self-persistence (coefficient of 0.8731, <i>p</i> < 2 × 10<sup>−16</sup>), though public debt did not significantly impact inflation in the model (<i>p</i> = 0.5592). The models R-squared values, 95.82% for public debt and 84.74% for inflation, highlight its strong explanatory power. Moreover, findings indicate that while public debt does not directly affect inflation within the model lag structure, inflation exhibits a strong self-persistence. The model R-squared values are 95.82% for public debt and 84.74% for inflation, demonstrating high explanatory power. Recommendations include the implementation of a robust debt management strategy, emphasizing sustainable borrowing and enhancing revenue generation to mitigate inflationary pressures. Further research is recommended to explore the broader macroeconomic impacts of public debt on economic growth and employment in Kenya.

Fiscal policy for economic growth and environmental quality: insights from Pakistan’s fiscal decentralization

Fiscal decentralization is perceived as a viable strategy for fostering economic growth in Pakistan. Concerns persist regarding its potential impact on environmental quality, particularly in relation to increased carbon emissions. Employing diverse analytical methods such as structural vector autoregressive (SVAR) modeling and autoregressive moving average model evaluations, this study comprehensively explores the dynamic relationships among fiscal decentralization, economic development, and environmental quality over the period 1979–2019. Intriguing trends emerge, revealing positive correlations between environmental efficiency and variables, including revenue collection, tax autonomy, and transfer payments. Conversely, a negative correlation is observed between government spending and economic growth. The interplay of these variables results in oscillations in the influence of fiscal decentralization on carbon emissions, presenting a complex challenge for policymakers. Notably, positive fiscal decentralization shocks lead to an increase in carbon emissions, while positive expenditure decentralization and economic development correlate with decreased carbon emissions. Despite economic growth's statistically significant negative impact on carbon emissions, the intricate nature of the relationships underscores the complexity of the link between fiscal decentralization, economic expansion, and environmental outcomes. From a policy perspective, the study emphasizes the need to adjust fiscal policies and coordinate intergovernmental fiscal ties across all government levels to enhance environmental efficiency. These findings provide policymakers with valuable insights to navigate the intricate landscape of fiscal decentralization and its environmental implications.