Impact Of Exogenous Variables Research Articles

The seasonal model of chili price movement with the effect of long memory and exogenous variables for improving time series model accuracy

This study aimed to explore big-time series data on agricultural commodities with an autocorrelation model comprising long-term processes, seasonality, and the impact of exogenous variables. Among the agricultural commodities with a large amount of data, chili prices exemplified criteria for long-term memory, seasonality, and the impact of various factors on production as an exogenous variable. These factors included the month preceding the new year and the week before the Eid al-Fitr celebration in Indonesia. To address the factors affecting price fluctuations, the Seasonal Autoregressive Fractionally Integrated Moving Average (SARFIMA) model was used to manage seasonality and long-term memory effects in the big data analysis. It improved with the addition of exogenous variables called SARFIMAX (SARFIMA with exogenous variables is known as SARFIMAX). After comparing the accuracy of both models, it was discovered that the SARFIMAX performed better, indicating the influence of seasonality and previous chili prices for an extended period in conjunction with exogenous variables. The SARFIMAX model gives an improvement in model accuracy by adding the effect of exogenous variables. Consequently, this observation concerning price dynamics established the cornerstone for maintaining the sustainability of chili supply even with the big data case.

Data forecasting performance evaluation of threshold spatial vector autoregressive with exogenous variables

One time series model developed to predict economic data is Spatial Vector Autoregressive with Exogenous Variables (SpVARX). This model can accommodate simultaneously the interrelationships between variables, the impact of exogenous variables, and Inter-regional linkages. However, this model has not adjusted the nonlinearity relationships between variables. The relationship between economic variables is usually not linear. Therefore, we introduce the Threshold Spatial Vector Autoregressive with exogenous variables (TSpVARX). This paper assesses the forecasting performance of TSpVARX and compares it with SpVARX models. We conducted a simulation study by generating 100 times the simulation data with twelve scenarios. We found that the forecasting performance of the TSpVARX model is better than SpVARX when there is a nonlinear relationship between variables. In addition, we find that the forecasting performance of TSpVARX models will improve as the sample size increases.

Peroxymonosulfate-based photodegradation of naproxen by stimulating (Mo, V, and Zr)-carbide nanoparticles

Molybdenum carbide (Mo2C), vanadium carbide (VC), and zirconium carbide (ZrC) nanoparticles (NPs) were investigated systematically as photocatalysts for the stimulation of peroxymonosulfate (PMS) for the degradation of naproxen (NX) under visible light. Characterizations of the samples by XRD, XPS, SEM, UV/PL, and BET were performed. In particular, the impact of exogenous variables on NX degradation was thoroughly and methodically investigated. 80.47 % of NX was decomposed over 60 min with 0.75 g/L PMS and ZrC at pH 3. ZrC NPs showed higher photocatalytic/photo-piezocatalytic properties in the degradation of NX compared to VC and Mo2C NPs. The substantial rise in the reaction rate of NX in ZrC/PMS-based photo-degradation can be attributed to the larger surface area, mesoporous, narrow energy bandgap (Eg), and lower intensity of photoelectrons and holes recombining of ZrC NPs. Due to their substantial photocatalytic efficacy and significant piezo-photocatalytic properties, all NPs are viable candidates for piezo-photocatalytic/photocatalytic degradation. The degradation of NX was then seen in the active species scavenging studies, in which sulfate radicals, hydroxyl radicals, holes, and superoxide radicals were involved. According to continuous cycle studies, the ZrC NPs also had excellent durability and reusability. Additionally, the potential paths for NX degradation were proposed based on the study of LC-MS, and the degradation mechanism of NX was clarified. Based on the superior stability and reusability of ZrC NPs after 5 successive cycles, this material is anticipated to be utilized in the future for the treatment of wastewater, containing pharmaceuticals.

Perilaku Perjalanan Kota Makassar Selama Masa Pandemi Covid-19

The Covid-19 pandemic is a new phenomenon that has an impact on the whole world, including Indonesia. The rapid transmission of the virus prompted the government to issue regulations on limiting physical activity. This restriction policy has an impact not only on the health sector but also occurs in various other sectors such as the economy, education and transportation. Restrictions by the government and public concern about the Covid-19 virus resulted in a decrease in daily routines. People switch to using technology to meet their daily needs, for example for work purposes. This research specifically wants to see and measure the influence of work from home activities on travel behavior during the Covid-19 pandemic. This influence is measured using the Structural Equation Modeling Partial Least Square (SEM-PLS) quantitative analysis to see the impact of exogenous variables on endogenous variables through variables work from home that are composed of certain constructs as a whole. The results of the study show changes and influences between work from home activities and travel behavior after it occurred during the Covid-19 pandemic.

Food prices response to global and national factors: Evidence beyond asymmetry

The current series of studies examine how local food prices are affected by domestic and international factors. This research advances the existing body of knowledge by examining this effect at different quantiles, frequencies, and times. We use research data from January 1999 to August 2022 using three local and six global variables as independent variables. Additionally, our study uses recent econometric methods, including Wavelet Coherence, Quantile-on-Quantile Regression (QQR), and Granger Causality in Quantiles (GCQ). Moreover, this research uses the Quantile Regression (QR) approach to determine how reliable the findings are. Based on the GCQ approach, the results demonstrate that the correlation persists at most of the quantiles. Moreover, the WC results demonstrate a substantial association between local prices of food and the independent factors across various frequencies and times. Additionally, QQR estimates demonstrate that the impact of exogenous variables on food prices vary among quantiles. These findings are also supported by the QR method. Last but not least, our study offers policy suggestions obtained based on the results of this study.

Exploring injury severity of pedestrian-vehicle crashes at intersections: unbalanced panel mixed ordered probit model

Although the pedestrian deaths have been declining in recent years, the pedestrian-vehicle death rate in Croatia is still pretty high. This study intended to explore the injury severity of pedestrian-vehicle crashes with panel mixed ordered probit model and identify the influencing factors at intersections. To achieve this objective, the data were collected from Ministry of the Interior, Republic of Croatia from 2015 to 2018. Compared to the equivalent random-effects and random parameter ordered probit models, the proposed model showed better performance on goodness-of-fit, while capturing the impact of exogenous variables to vary among the intersections, as well as accommodating the heterogeneity issue due to unobserved effects. Results revealed that the proposed model can be considered as an alternative to deal with the heterogeneity issue and to decide the factor determinants. The results may provide beneficial insight for reducing the injury severity of pedestrian-vehicle crashes.

LIQUIDITY AND SOLVENCY ANALYSIS OF THE REAL SECTOR OF THE ECONOMY OF SERBIA FOR 2018-2019

Issues of liquidity and solvency of companies, and not only issues of profitability, are crucial in the context of considering the future business of companies, and thus the economic sectors and the economy as a whole. For several years now, the required short-term funds are still not enough to service current liabilities. Liquidity rates are at half of a satisfactory level, while the growth of long-term loans, together with equity, is still not sufficient to finance fixed assets (solvency). Liquidity and solvency issues affect both the efficiency and productivity of the company, and indirectly the profitability. Based on the available information, the paper discusses the results of the real sector of the Serbian economy for the period 2018-2019, in the context of liquidity and solvency, as well as selected individual sectors, using generally accepted indicators. In the new crisis caused by the COVID 19 virus, companies have found themselves in a position where many of them need additional capital in order to improve and harmonize their financial structure. The illiquidity of the economy is a chronic problem, it is structural in its nature, which is the result of business inefficiency and a high degree of volatility on the impact of exogenous variables. In terms of solvency, we can conclude that one of the basic problems of all sectors is efficiency in inventory management, slow collection of receivables and late payment of liabilities. If we add the IMF forecasts, to this already disrupted structure of the solvency of the economy, ie the real sector, in which, the share of insolvent companies in developing countries will increase by between 14% and 30% in 2020, (due to the pandemic), we can be not optimistic. On the other hand, we must emphasize that the forecasts of economic growth for the countries of the region in 2021 are fairly optimistic, and for Serbia the predicted growth is 6.1%. The result of the analysis points to the conclusion that there will be no significant changes in terms of revenue and profitability of the real sector in the short term, but that the crisis caused by coronavirus will mostly affect small, micro and medium enterprises when it comes to liquidity and solvency, in this respect. In the long run, impaired solvency, and after that liquidity, will affect the still intact profitability of the sector. In that regard, solvency issues have a more significant impact, because in the long run, if corrective measures are not being taken, they will cause the inability of the companies to pay long-term overdue liabilities, and increase the number of companies operating without equity and the number of blocked companies. The sectors that will have the biggest problems in terms of liquidity and solvency are certainly transport, accommodation and tourism, art, entertainment and recreation, and very likely construction.

Time Series Forecasting of Univariate Agrometeorological Data: A Comparative Performance Evaluation via One-Step and Multi-Step Ahead Forecasting Strategies.

High-frequency monitoring of agrometeorological parameters is quintessential in the domain of Precision Agriculture (PA), where timeliness of collected observations and the ability to generate ahead-of-time predictions can substantially impact the crop yield. In this context, state-of-the-art internet-of-things (IoT)-based sensing platforms are often employed to generate, pre-process and assimilate real-time data from heterogeneous sensors and streaming data sources. Simultaneously, Time-Series Forecasting Algorithms (TSFAs) are responsible for generating reliable forecasts with a pre-defined forecast horizon and confidence. These TSFAs often rely on modelling the correlation between endogenous variables, the impact of exogenous variables on latent form and structural properties of data such as autocorrelation, periodicity, trend, pattern, and causality to approximate the model parameters. Traditionally, TSFAs such as the Holt–Winters (HW) and Autoregressive family of models (ARIMA) apply a linear and parametric approach towards model approximation, whilst models like Support Vector Regression (SVRs) and Neural Networks (NNs) adhere to a non-linear, non-parametric approach for modelling the historical data. Recently, Deep-Learning-based TSFAs such as Recurrent Neural Networks (RNNs), and Long-Short-Term-Memory (LSTMS) have gained popularity due to their capability to model long sequences of highly non-linear and stochastic data effectively. However, the evolution of TSFAs for predicting agrometeorological parameters pivots around one-step-ahead forecasting, which often overestimates the performance metrics defined for validating forecast capabilities of potential TSFAs. Hence, this paper attempts to evaluate and compare the performance of different machine learning (ML) and deep learning (DL) based TSFAs under one-step and multi-step-ahead forecast scenarios, thereby estimating the generalization capabilities of TSFA models over unseen data. The data used in this study are collected from an Automatic Weather Station (AWS), sampled at an interval of 15 min, and range over one month. Temperature (T) and Humidity (H) observations from the AWS are further converted into univariate, supervised time-series diurnal data profiles. Finally, walk-forward validation is used to evaluate recursive one-step-ahead forecasts until the desired prediction horizon is achieved. The results show that the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) and SVR models outperform their DL-based counterparts in one-step and multi-step ahead settings with a fixed forecast horizon. This work aims to present a baseline comparison between different TSFAs to assist the process of model selection and facilitate rapid ahead-of-time forecasting for end-user applications.

Micro Level Speed Choice Behavior on a Rural Highway in an Heterogeneous Traffic Environment: Latent Class Modeling Approach

The modeling of driver speed choice behavior on a rural highway with heterogeneous traffic environment in a developing country is reported. The study explores the application of a latent class model as an alternative methodological approach, to relate factors such as road geometry, roadside environment, traffic mix and operational characteristics with speed choice at the micro level. In the conventional analysis based on lane-based homogeneous traffic environments, the impact of exogenous variables is kept constant across road segments; whereas those impacts might vary across different segments for different conditions. To check this possible variation, the study compares latent class linear regression with ordinary least square, as well as the random parameter linear regression model. The proposed models were estimated using data collected from two sections of a major national highway in Bangladesh. The entire roadway section was divided into several small segments and the critical segment length was estimated based on several sensitivity tests. The small segmental level exogenous variables were considered for the model building and estimation. The results obtained could be used to understand better the speed choice factors of drivers in the non-lane-based traffic environments of developing countries. Law enforcement agencies, traffic operation and maintenance officials, as well as roadway design and planning authorities, could gainfully use the findings of this study to inform more responsive speed management strategies.

Evaluating temporal variability of exogenous variable impacts over 25 years: An application of scaled generalized ordered logit model for driver injury severity

The current study undertakes a unique research effort to quantify the impact of various exogenous factors on crash severity over time. Specifically, we examine if over time, the impact of exogenous variables has changed and if so what is the magnitude of the change. The research contributes to driver injury severity analysis both methodologically and empirically by proposing a framework that addresses the challenges associated with pooled (or pseudo-panel) data. For our analysis, we draw data from the General Estimates System (GES) over a span of twenty-five years. The data is compiled for driver injury severity in single or two vehicle crashes from 1989 through 2014 in 5-year increments (1989, 1994, 1999, 2004, 2009 and 2014). The alternative econometric frameworks considered for the analysis include ordered logit, generalized ordered logit, scaled generalized ordered logit and mixed generalized ordered logit models. A host of comparison metrics are computed to evaluate the performance of these alternative models in examining the pooled data. The model development exercise is conducted with a host of exogenous variables including driver characteristics, vehicle characteristics, roadway attributes, environmental factors, crash characteristics and temporal attributes. The model estimation results are further augmented by performing a detailed policy scenario analysis, probability profile representations and elasticity effects for different driving and situational conditions across different years.

Risk–Return Relationship in BRIC Equity Markets: Evidence from Markov Regime Switching Model with Time-varying Transition Probabilities

A rich literature supports the existence of both positive and negative relationship between the risk and return in the developed equity markets. However, the present study attempts to capture the risk–return relationship in the most promising and opportunities-instilled emerging market club, the “BRIC” equity markets, by employing a Markov regime switching model with time-varying transition probabilities, further taking St. Louis Fed Financial Stress Index (the US financial market stress) as an economic variable. The weekly benchmark index values are used in the analysis, spanning from the year 2004 to 2013. The results report the existence of time-varying transition probabilities with respect to the Brazilian and Indian markets only and fixed transition probabilities for the other countries undertaken. The Markov results support the existence of two regimes, wherein regime-1 reports a positive risk–return relationship, and regime-2 reports a negative relationship between the risk and return. Ironically, the Chinese equity market is found to be the riskiest but a perfect hedge instrument amongst others, considering its risk–return interactions in both the regimes. Furthermore, a lower level of financial stress in the US financial market is associated with a higher probability of remaining in the “Bullish” regime-1 in the Indian market as well as Brazilian market. Moreover, there is a positive co-movement between the US financial stress and the expected time-varying duration of remaining in the “Bearish” regime. This shows that due to the growing interdependence among the worldwide economies, a financial stress in one economy does have an impact on the other markets and risk–return relationship in their equity markets. An understanding of the risk–return dynamics coupled with the impact of exogenous variables is an imperative task that a portfolio manager must undertake so as to justify and manage the investments made in the equity markets.

A Semi-Structural Method to Estimate the NATREX for a Small Open Economy

Abstract This paper lays out a simple semi-structural method to estimate a small country version of the Natural Real Exchange Rate approach. Applying the model to the Finnish economy suggests that it works quite well in empirical applications. The predictions of the model regarding the impact of exogenous variables are generally confirmed by the data. Moreover, the implied exchange rate misalignments are consistent with previous research. The paper also highlights the benefits and drawbacks of the model both from a theoretical and an empirical perspective and compares it to other equilibrium real exchange rate approaches commonly employed in empirical research.

Who Manages Technological Innovation in Italy? Policy Entrepreneurship and Railway Policy-Making (1970-98)

The first section of this article provides some conceptual sketches to identify entrepreneurial roles in policy making, to classify them, and to hypothesize their fundamental dynamics. The second section offers an analysis of Italian railway policy-making, with particular stress on technological innovation in the last 30 years. After examining the impact of exogenous variables and the essential phases of domestic policy-making, the study will investigate actors and resources of the policy field, other than network and interactions. The third section will explore the roles and dynamics of policy entrepreneurs in railway technological innovation, describing some of the principal tensions and dilemmas associated with those networking actors. Towards an empirical theory of policy entrepreneurship, the concluding remarks will develop some additional hypotheses about conditions facilitating (or inhibiting) entrepreneurial roles in policy making.

Anticipating Change in Development Activity Levels

This study demonstrates how cointegration analysis of privately-owned housing within disparate areas of the United States can aid developers in anticipating changes in the level of market activity. The study analyzes change in the number of housing units within four geographic regions: the Northeast, the Midwest, the South and the West. Whereas most studies of regional variation in real estate activity have focused on short-run analysis, this research extends the examination to consider the impact of exogenous variables over a longer time frame. The study uses Citibase data from 1959 through 1995. Results indicate that the four regions move together in the long run and are driven by one common factor, but that change in the South and the West lead those in the other two regions. Results have widespread policy implications for residential and commercial developers nationwide, because change within the dominant areas may serve as indicators of developing change elsewhere.