Dickey-Fuller Research Articles

Short‐term predictions of PM10 and NO2 concentrations in urban environments based on ARIMA search grid modeling

AbstractAir pollution poses a persistent challenge for urban management departments and policymakers due to its significant health and economic impacts. Various cities worldwide have implemented diverse strategies and initiatives to enhance air quality monitoring and modeling standards. However, the outcomes of these efforts often manifest over the long term, leading to a preference for short‐term statistical methods. The autoregressive integrated moving average (ARIMA) search grid modeling approach has gained widespread use for forecasting air quality. This paper presents a comprehensive time series analysis conducted to predict air quality in urban areas of Budapest, Hungary, with a focus on nitrogen dioxide (NO2) and particulate matter (PM10), using air quality data spanning from 2018 to 2022 for four monitoring categories: Urban traffic, industrial background, urban background, and suburban background. The study employs the ARIMA search grid method to forecast concentrations of these pollutants at multiple air quality monitoring stations based on Akaike information criteria (AIC) and the Bayesian information criteria (BIC) criteria along with the results of augmented Dickey–Fuller (ADF) test. The results demonstrate varying levels of forecast accuracy across different stations, indicating the model's effectiveness in short‐term predicting of air quality. These findings are essential for assessing the reliability of air quality forecasts in Budapest and can inform decisions regarding air quality management and the development of strategies to address air pollution and particulate matter concerns in the region.

Cointegration analysis of GDP indicators per capita between the Republic of Azerbaijan and Turkey on purchasing power parity

The article analyses the interrelationships between the main components of socio-economic development levels in Turkey and the Republic of Azerbaijan. The purpose of this work is to construct a cross-country regression model of per capita economic growth in Turkey and the Republic of Azerbaijan and to conduct a cross-country comparative analysis of the main components of inclusive growth under the strong influence of both countries. The aim of this research is to enable empirical study of the joint movement of both indicators, determine cause-effect relationships and co-integration dependence, establish long-term integration relationships, and take necessary steps and measures for a positive outcome. The level of economic development was measured using GDP per capita with purchasing power parity (PPP). The study analyses the dynamic interaction of indicators using data from January 1992 to December 2022. Econometric methodology was employed, including the extended Dickey-Fuller test to detect a single root, and the Granger test. The Johansen test, Error correction model, and ARDL model standard diagnostic tests were used to examine the cointegration relationship. Stationarity, causality, and cointegration tests were conducted on the entire sample at a significance level of 10%.

Framework for Examining the Correlation Between Trade Liberalization and Employment in Nigeria

This research examines the relationship between trade liberalization and employment in Nigeria using a retrospective research approach. The study considers variables such as the employment rate, trade openness index, foreign direct investment, and currency exchange rate. The analytical framework combines classical linear regression and Auto Regressive Distributed Lag (ARDL) models to capture both short-term and long-term dynamics. Diagnostic tests, including descriptive statistics, Augmented Dickey-Fuller (ADF) test, Perron Unit Root Test, ARDL-Bound test, and Error Correction Model (ECM)-ARDL test, are conducted to analyze the collected data. The study utilizes secondary data from the statistical bulletin of the Central Bank of Nigeria (2018) covering the period from 1985 to 2018. The findings of the study are mixed. The error correction estimates indicate a negative relationship between the trade openness index and the employment rate. On the other hand, foreign direct investment shows a negative relationship with employment rate, but this relationship is not statistically significant. The exchange rate demonstrates a positive and significant correlation with the employment rate. It was recommended that, Diversifying Nigeria's economy by promoting domestic industries like agriculture, manufacturing, and services to create job opportunities and reduce import dependence. Implement effective labor market policies including job training, worker protections, and competitive labor market measures to mitigate negative impacts of trade liberalization on employment. Target FDI to sectors with high job creation potential, incentivizing investments, supporting technology transfer, and fostering partnerships for employment growth in Nigeria.

EGG PRICE RESPONSE TO INCREASE IN THE PRICES OF MAIZE AND SOYBEAN MEAL IN NIGERIA USING ECONOMETRIC TOOLS

Poultry egg productionn depends greatly on maize and soybean meal which affect the price of egg directly. Egg has been confirmed as one of the cheapest sources of protein. This study was aimed at estimating the relationship between egg price (EP) response to maize price (MP) and soybean meal price (SBMP) changes in Nigeria from 1990 to 2021Time series data in respect of SBMP (N/Kilogramme), MP (N /Kilogramme) and EP (N /Crate) in local currency were obtained. Augmented Dickey Fuller (ADF) test and, the Johansen cointegration technique were employed to test for the stationarity of the variables and the long-run relationship between variables, respectively. Result of the study shows the presence of one co integrating long run equilibrium relationship between the variables. The Vector Error Correction Model (VECM) estimates showed that MP and SBMP had a negative influence on egg production which consequently transform to high price of egg. Policies on reduction of MP and SBMP should be implemented so as to reduce the EP and make the produce more available to the Nigeria populace.

From resource curse to green growth: Exploring the role of energy utilization and natural resource abundance in economic development

AbstractThis study delves into the profound repercussions of the resource curse hypothesis within the Brazil, Russia, India, China, and South Africa (BRICS) nations from 1991 to 2022, examining the intricate interplay among natural resource abundance, energy consumption, and economic development (ED). Methodologically, it employs the cross‐sectionally augmented Dickey–Fuller test to assess stationarity and utilizes the Westerlund cointegration technique to analyze cointegration. Subsequently, the cross‐sectionally augmented autoregressive distributive lag model is deployed to explore the impact of natural resource availability, renewable and non‐renewable energy utilization, and carbon emissions on ED within these countries. The findings reveal a stark reality wherein both carbon emissions and non‐renewable energy consumption wield a consistently positive influence on short‐ and long‐term economic growth across the BRICS economies. Particularly striking is the dominant impact of non‐renewable energy consumption. However, this comes in stark contrast to the adverse effects identified with excessive resource and coal rents, signifying potential economic setbacks arising from rampant natural resource exploitation. Furthermore, the suboptimal utilization of renewable energy resources hints at a detrimental effect on ED. These results transcend the confines of developing nations, underscoring the universality of the resource curse hypothesis, affecting both developing and developed countries. The study illuminates the grave risks inherent in overreliance and overexploitation of natural resources, elucidating heightened competition that severely impedes the ED trajectory of the BRICS countries in both short and long terms. Policymakers must prioritize economic diversification, implement sustainable resource management, and invest in innovative technologies to mitigate the resource curse in BRICS nations, fostering resilience and sustainable economic growth. In conclusion, This study highlights the severe impact of the resource curse in BRICS nations, stressing the imperative for adept resource management to counter the risks linked with overdependence on non‐renewable resources and bolster sustainable economic growth.

Research & Development and Employees Productivity in Public Service of Nigerian Fourth Republic

Research & Development and Employees Productivity in Public Service of Nigerian Fourth Republic 1Casey Ngozi Egbon-Charles 2 Danjuma Bwese Tanko, 3 Nasamu Gambo (PhD), 4 Judith Aimiebenomo Ehiaguina, 5 Fredrick Yakubu 1,2,3,4,5Nile University of Nigeria, Abuja, Nigeria doi: https://doi.org/10.37745/gjhrm.2013/vol12n4108130 Published July 14, 2024 Citation: Egbon-Charles C.N., Tanko D.B., Gambo N., Ehiaguina J.A.,and Yakubu F. (2024) Research & Development and Employees Productivity in Public Service of Nigerian Fourth Republic, Global Journal of Human Resource Management, Vol.12, No.4, pp.108-129 Abstract: This study examines the impact of research & development on employees’ productivity in public service of Nigerian fourth republic from 1999 to 2023. The annual data for the study is sourced from World Bank’s Development Indicators (WDI) and Central Bank of Nigeria Statistical Bulletin. The dependent variable is employee’s productivity proxied by total factor productivity while the explanatory variables are research & development expenditure as percentage of GDP, employee compensation proxied for income and institutional quality components of governance effectiveness and regulatory quality. The preliminary test of Augmented Dickey Fuller (ADF) unit root test reveals mixed order of integration among the series under consideration, which reinforces the choice of Autoregressive Distributive Lag (ARDL) as estimation technique. The ARDL bounds result reveal the existence of long-run among the series under consideration. On the result, the series of research & development exhibit negative and significant relationship with employee productivity in the Nigerian public service. The complementary series of employee compensation exert negative effect and significant impact on employee productivity. Also, governance effectiveness further exerts negative and insignificant nexus while regulatory quality demonstrates significant positive effect on employee productivity. This study concludes that research & development has not enhanced employee productivity in the Nigeria public service within the period considered. Therefore, the research recommends the need for government agencies and parastatals to make adequate provision for resources to fund high-quality training programs for public sector employees. This can include providing on-the-job training, skill development workshops, and opportunities for continuous learning and professional development.

Climate change and crop production nexus: assessing the role of technological development for sustainable agriculture in Vietnam

PurposeThis study aims to examine the effects of climate change and agricultural technologies on crop production in Vietnam for the period 1990–2018.Design/methodology/approachSeveral econometric techniques – such as the augmented Dickey–Fuller, Phillips–Perron, the autoregressive distributed lag (ARDL) bounds test, variance decomposition method (VDM) and impulse response function (IRF) are used for the empirical analysis.FindingsThe results of the ARDL bounds test confirm the significant dynamic relationship among the variables under consideration, with a significance level of 1%. The primary findings indicate that the average annual temperature exerts a negative influence on crop yield, both in the short term and in the long term. The utilization of fertilizer has been found to augment crop productivity, whereas the application of pesticides has demonstrated the potential to raise crop production in the short term. Moreover, both the expansion of cultivated land and the utilization of energy resources have played significant roles in enhancing agricultural output across both in the short term and in the long term. Furthermore, the robustness outcomes also validate the statistical importance of the factors examined in the context of Vietnam.Research limitations/implicationsThis study provides persuasive evidence for policymakers to emphasize advancements in intensive agriculture as a means to mitigate the impacts of climate change. In the research, the authors use average annual temperature as a surrogate measure for climate change, while using fertilizer and pesticide usage as surrogate indicators for agricultural technologies. Future research can concentrate on the impact of ICT, climate change (specifically pertaining to maximum temperature, minimum temperature and precipitation), and agricultural technological improvements that have an impact on cereal production.Originality/valueTo the best of the authors’ knowledge, this study is the first to examine how climate change and technology effect crop output in Vietnam from 1990 to 2018. Various econometrics tools, such as ARDL modeling, VDM and IRF, are used for estimation.

Analysis of the Effects of Money Market Fund Mobilization on the Dynamics of Nigerian Economic Growth

Economic growth is a function of some productive efforts among which savings mobilizations is considered very vital. This study examines savings mobilization strategy and its impact on Nigeria’s economic growth during the year 2001-2022.This is vested with the objectives to investigate the effect of banking density, savings rates and money supply on economic growth. The study relies on time series data sourced from the publication of the Central Bank of Nigeria. Gross Domestic Product Growth Rate (GDPGR) was adopted as dependent variable, while Banking Density (BD), Savings Rates (SR) and Money Supply (MS) were the independent variables. Augmented Dickey Fuller (ADF) unit root test was employed, to test the stationarity. The Auto Regressive Distributive Lag (ADRL) was used to ascertain the relationship between the variables alongside Vector Error Correction Model (VCM). Post estimation diagnostic tools used include Breuch-Godfrey serial correlation LM test and the CUSUM test for stability. From the ARDL, β-coefficient and the associated probabilities were adopted to determine the extent and direction of relationship on economic growth. Data were tested at 5% level of significance; it was discovered that banking density and savings rates affected economic growth positively but with insignificant effects, while money supply affected economic growth negatively with insignificant effect. The study therefore recommends amongst others, that stakeholders, be directed towards entrenching higher banking density as opposed to banking desert, and that money supply should be made dynamic in accordance with economic realities.

Empirical Approach to Modelling and Forecasting Inflation using ARIMA Model: Evidence from Tanzania

This study investigates the dynamics of the inflation rate (INFL) in Tanzania spanning from 1990 to 2021 using advanced time series analysis techniques. The dataset sourced from the World Bank online database serves as the basis for analytical exploration. Initially, the Autoregressive Integrated Moving Average (ARIMA) model is applied to understand underlying patterns in the INFL data. Transitioning to Seasonal ARIMA (SARIMA) modeling encounters challenges due to the absence of pronounced seasonality or clear trends. Unit root tests, including the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests, assess the stationarity of the data. Following the identification of non-stationarity, differencing is employed to achieve stationarity. Estimation of ARIMA models (ARIMA (1,1,1) and ARIMA (2,1,1)) is conducted, with diagnostic checks confirming the suitability of the ARIMA (1,1,1) model. The study contributes to the understanding of inflation dynamics and facilitates evidence-based economic policymaking in Tanzania.

The Contribution of Sustainable Tourism to Economic Growth and Employment in Kazakhstan

In the global economy, tourism stands out as a prominent and expanding sector, playing a pivotal role in enhancing a nation's economic prospects. The surge in tourism has the potential to yield positive economic outcomes, particularly in gross domestic product (GDP) and employment opportunities. Within Central Asian countries, the tourism industry serves as a catalyst for economic development and GDP growth, with Kazakhstan being a focal point. This research delves into the impact of tourism on Kazakhstan's economic growth and employment during the period from 2015 to 2023. Augmented Dickey–Fuller and Phillips–Perron unit root tests were conducted to assess the stationarity of variables. Utilizing regression techniques and the Johansen cointegration approach, the analysis reveals a significant and positive correlation between tourism, economic growth, and the employment sector in Kazakhstan. The study underscores the importance of formulating policies that promote tourism throughout the country, suggesting that policymakers should concentrate on strategies harnessing the immense potential of the tourism sector. The findings of this research offer valuable policy implications and avenues for future exploration.

Analisis Kausalitas Nilai Tukar Rupiah/USD, Dow Jones Industrial Average (DJIA), Nikkei225, Shanghai Index Composite (SSEC) Terhadap Indeks Harga Saham Gabungan (IHSG)

The COVID-19 pandemic has had a negative effect on global stock markets. In addition, the increase in the number of confirmed cases of COVID-19 further increased the volatility of the exchange rate. This study aims to determine whether there is a causal relationship between the Exchange Rate, the Dow Jones Industrial Average (DJIA), the Nikkei225 Index, and the Shanghai Stock Exchange Composite Index (SSEC) and the Composite Stock Price Index (CSPI). The research method used is the Augmented Dickey Fuller Unit Root Test, and Granger Causality. The results of the study using Granger Causality indicate that the Exchange Rate, DJIA Index, and Nikkei225 Index have a causal relationship to the Composite Stock Price Index. The SSEC index does not have a causal relationship with the Composite Stock Price Index. This means that the JCI movement has the influence of the Exchange Rate, the DJIA Index, and the Nikkei225 Index.

Air Quality and Public Health Risk Assessment: A Case of an Industrial Area in Haridwar City, Uttarakhand (India).

Air pollution is a significant issue for a developing country like India and the air quality index (AQI) forecasting helps to predict air quality levels in advance and allows individuals to take precautionary measures to protect their health. The study aimed to forecast the AQI for an industrial area (SIDCUL, Haridwar City) using a time series regression model. Three years of existing AQI data points (post-COVID-19) were collected from the Uttarakhand Pollution Control Board for the SIDCUL area of Haridwar City and tried to know the status of AQI values for the following 12 months. Trend and seasonality components were seen through the decomposition process. Further, the augmented Dickey-Fuller test was applied to check the stationarity of the series before finalizing the best-suited time series model for forecasting the AQI values. With the help of autocorrelation function (ACF)/partial ACF plots, a seasonal autoregressive integrated moving average (ARIMA) (0,1,0) (1,0,0)[12] model was selected with the minimum akaike information criterion (253.143) and mean absolute percentage error (17.42%). The AQI values have also been forecasted for this industrial area (SIDCUL) for the following year. The seasonal ARIMA (0,1,0) (1,0,0)[12] model may be helpful to forecast the AQI values for a nonstationary time series dataset. Research indicates that the air of the SIDCUL area will become moderately polluted and may cause breathing discomfort to asthma patients' health. The scientists might apply this model to other polluted regions of the country so that the public and the government can take preventive measures in advance.

An Accident Prediction Model Based on ARIMA in Kuala Lumpur, Malaysia, Using Time Series of Actual Accidents and Related Data

Recently, there has been an emerging trend to analyse time series data and utilise sophisticated tools for optimally fitting time series models. To date, Malaysian industrial accident data is underutilised and lacks informative records. Thus, this paper aims to investigate the Malaysian accident database and further evaluate the optimal forecasting models in accident prediction. The model’s input was based on available data from the Department of Occupational Safety and Health, Malaysia (DOSH), from 2018 until 2021, with 80% of the dataset to train the models and the remaining 20% for validation. The negative binomial and Poisson distribution prediction showed a mean absolute percentage error (MAPE) of 33% and 51%, respectively. It indicated that the negative binomial performed better than the Poisson distribution in accident frequency prediction. The available time series accident data were gathered for four years, and stationarity was checked in R Studio software for the Augmented Dickey-Fuller test. The lowest Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and other error values were used to justify the best model, which was the ARIMA(2,0,2)(2,0,0)(12) model. The ARIMA models were considered after the data showed autocorrelation. The MAPE for both ARIMA in R and manual time series were 40% and 49%, respectively. Therefore, the accident prediction by using R Studio would outperform the manually negative binomial and Poisson distribution. Based on the findings, industrial safety practitioners should report accidents to DOSH truthfully in the era of digitalisation. It could enable future data-driven accident predictions to be carried out.

ANALYSIS OF REGIONAL STOCK MARKETS FOR INVESTMENT & DIVERSIFICATION PERSPECTIVE: EVIDENCE FROM PAKISTAN, CHINA & INDIAN STOCK MARKETS

The academic ramification of research is that it is one of few that provides a comprehensive analysis of regional market cointegration. One of this study methods is application of pairwise cointegration that is useful for figuring out strength of long-term link amid two variables. In order to reap benefits of diversity, it is better to invest locally rather than traveling to another region, which is why this study investigates new options for academicians to examine long-term interaction between regional stock markets. Study employs most efficient pairwise cointegrated approach to illustrate precise link amid two variables. Daily data of these countries' stock market indices from 2002-2018, used to study stock market co-movement/co-integration. The statistical tests were applied like Dickey Fuller test, regression & co-variance. Pairwise co-integration is examined after confirming combine co integration of three markets. Results show that there is no cointegration amid stock markets of Pakistan China & India. So, Pakistani investors have chance to participate in other two markets at a time when it appears to be lucrative investment portfolio.

The Impact of Foreign Direct Investment on Economic Growth: Empirical Evidence

Foreign direct investments are an instrument widely used by countries, both developed and emergent, to achieve their economic development. Among others benefits, discussed in the specialized literature, investment inflows accelerate production, improve productivity, facilitates the transfer of technology, raise the qualification oh human capital, contributes to create new jobs and to boost the economic growth of the host countries. The main purpose of the research is to examine the short and long runs relationships between foreign direct investment (FDI) and economic growth in Algeria. To carry out our investigation, the time series data of the two variables are used over the period 1990-2023. This period covers the span time in which data on the volume of foreign direct investments are available. To achieve the research purpose, an ARDL model is built based on the unit root methodology of the Augmented Dickey Fuller and Phillips  Perron tests. The findings of the study show that the foreign direct investment (FDI) and economic growth are cointegrated, indicating the existence of a long relationship among them. They establish also that foreign direct investment (FDI) has a positive impact on economic growth. Building on the research results, Algerian decision makers should take the required actions to improve the climate business, making it more attractive for foreign investors.

Modelling The Volatility of Frankfurt Stock Exchange (DAX) Returns Using hybrid Models

Recently, the interest of researchers in the use of hybrid models in the process of analyzing model time series with fluctuations and forecasting fluctuations in financial time series has increased significantly. Hybrid ARMA-GARCH models were created for medium- and long-term forecasts of time series of financial market index prices: ARMA models are used to analyze their linear component, which is a combination of autoregressive models and moving average models, and GARCH models are used to analyze the nonlinear component. which are generalized autoregressive models that depend on the nonconstancy of variance models. Hybrid ARMA-GARCH models eliminate the weaknesses and gaps that exist in each group of models (ARMA and GARCH) separately, which increases their forecasting accuracy and reliability, so they have already been successfully applied to model and forecast daily stock returns for three standard indices in the USA. The purpose of this article is to investigate which of the hybrid ARMA-GARCH models is optimal for forecasting the return of the DAX index, which is the most important stock index of the German securities market. It is the German equivalent of the American Dow Jones Index, has been calculated since 1988 by Deutsche Börse AG and reflects the total return on capital of the largest stock companies listed on the Frankfurt Stock Exchange (currently 40; by 2021 – 30): calculated as a weighted average of capitalization of the value of Free Float share prices on the Xetra electronic exchange, and also takes into account dividends on shares, assuming that the dividend is reinvested in the share on which it was accrued. The database of this study consisted of the daily closing prices of the DAX index presented on the official website of the Frankfurt Stock Exchange during the period from 01.01.2018 to 09.30.2023 (altogether about 1,500 observations), the stability of the time series was assessed using Expanded Dickey Fuller Liquidity (ADF). The article proposes 7 hybrid models, from which the one that is best suited for modeling the volatility of the DAX index is selected. It is an ARMA (2,3)-EGARCH (1,1) model because it captures volatility and leverage effects on DAX returns and its expected returns more than other models. The selection of the best alternative from the developed array of hybrid models was carried out according to the following criteria: AIC (Akaike Information Criterion), BIC (Bayesian Information Criterion), H-QIC (Hannan-Quinn Information Criterion).

Effect of Selected Macro-Economic Variables on Securities Performance at the Nairobi Securities Exchange, Kenya

The equity securities market is an integral component of any country's financial system, and its performance is intertwined with the broader macro-economic environment. Equity securities market determines the state of the economy; this is through stock and asset prices fluctuations as well as company earnings with corresponding dividend policies, and multiple macroeconomic variables. The inherent problem has been how do changes in specific macro-economic variables affect the performance of the equity securities market at the Security Exchange. This study sought to establish the influence of the selected macro-economic variables on equity securities market performance at Nairobi Securities Exchange (NSE). The specific objectives were to establish the effects of exchange rate; interest rate; inflation rate; money supply; and Gross Domestic Product on equity share prices fluctuations based on the NSE-20 from when the economy was liberalized. The study was guided by the Capital Asset Pricing Model, the Arbitrage Pricing Theory and the Efficient Market Hypothesis. An explanatory research design was used to determine the effect of selected macro-economic variables on securities exchange market performance. Annual time series secondary data for the periods 1986 to 2022 was used. Findings of diagnostic test demonstrated that there was no multicollinearity among the independent variables, residuals were homoscedastic, and there was no autocorrelation among the residuals. The results of Shapiro wilk normality test showed that the study's variables were normally distributed. The Augmented dickey fuller unit root test both showed that there was no unit root and that the variables had a short run relationship. The study findings were: the exchange rate (indicating negative and significant effect on equity security indices; the interest rate indicating negative and significant effect on equity security indices; inflation rate indicating positive and significant effect on equity security indices; money supply which indicates a negative and significant effect on equity security indices; and gross domestic product had a positive and significant effect on equity security indices. The study concluded that exchange rate, interest rate and money supply significantly affect equity security indices negatively while inflation rate and gross domestic product significantly affect equity security indices positively. The study therefore recommends the Central bank of Kenya to employ measures intervening foreign exchange markets to stabilize the currency, implement appropriate interest rate policies, and maintaining adequate foreign exchange reserves. The policy makers should prioritize price stability and monitor the growth of money supply to ensure it remains in line with the country's economic fundamentals. Policy makers should also strike a delicate balance between controlling inflation and supporting economic growth by closely monitoring economic indicators and adjusting interest rates accordingly.

Monetary Policy Measures and Commodity Price in Nigeria: An Auto-Regressive Distributive Lag Approach

This study investigated the impact of monetary policy measures on commodity price in Nigeria from 1990 to 2022. Data for the study were obtained from the Central Bank of Nigeria data source and the World Bank’s World Development Indicators 2022. Real interest rate, cash reserve ratio, Broad money supply, Treasury bill rate, and exchange rate are adopted to proxy monetary policy while commodity price index is employed to capture commodity price. The individual series were subjected to unit root tests using the Augmented Dickey Fuller approach and the diagnoses established mixed orders of I(0) and I(1) integrations, thereby necessitating application of the Auto-Regressive Distributive lag bounds test as well as the long-run and error correction versions. The analyzed model’s result established that real interest rate is positive but statistically insignificant with commodity price in the long-run and the most current year period of the short-run while cash reserve ratio had a positive and significant relationship with regress and in the long-run. Also, it was evident that broad money supply had a negative but significant relationship with commodity price in the current period of the short-run. Moreso, Treasury bill rate and commodity price revealed that the former had a negative but significant relationship with the latter in the current period of the short-run. Finally, the official exchange rate exerted a positive but insignificant ramification on commodity price both in the long-run and previous year period of the short-run. Hence, it is concluded that monetary policy fundamentals as captured here had significant push-up effect on commodity price in Nigeria. Thus, the need for suggesting amongst others that the monetary policy authority should attractively peg the price of federal government’s securities, moderate money supply and interest rate to enhance monetary stability for driving diversified private sector investments which if sustained will dampen commodity price in the long-run. Keywords: Monetary Policy, Commodity Price, Real Interest Rate, Cash Reserve Ratio, Treasury Bill, Exchange Rate Broad Money Supply.

A Time Series Analysis of the Total Program Expenditures to Support the Supplemental Nutrition Assistance Program in the United States

The Supplemental Nutrition Assistance Program (SNAP, also known as food stamps) offers tens of millions of American beneficiaries a crucial lifeline. This welfare benefit has been associated with better nutritional outcomes and has been linked to an efficient tool to fight hunger, improve labour market outcomes for the beneficiaries, achieve higher birth weight for children born to the beneficiary mothers, improve height and health outcomes for the beneficiaries. SNAP has been found to be an essential tool to ensure the availability of vital resources during times of need, etc. Despite the program’s numerous advantages, serious doubts exist about its viability and stability of the program especially, given the steep rise in program costs over the years. Over the past 50 years, SNAP has experienced tremendous growth both in terms of the number of beneficiaries and amount of spending. From over $1.82 billion in 1969 to over $113 billion in 2022, the total cost of the SNAP initiative has increased over 62 times in 53 years. This research uses data from the US Department of Agriculture to investigate the time series properties in the rise in the total expenditure devoted to the SNAP between 1969 and 2022. With and without trends, Augmented Dickey-Fuller tests are run with carefully chosen lag lengths. The existence of a unit root cannot be rejected in all specifications pointing to the possibility that the program might have grown in an unstable manner over time. An overwhelming amount of evidence points to an unstable and unstable growth in the overall amount spent on SNAP recipients. This unchecked growth may present substantial difficulties for policymakers especially since the program competes with other welfare programs in an environment of rapidly rising national debt and persistent budget deficits. The report does not attempt to estimate program fraud or abuse which may partially contribute to higher expenditures.

Построение краткосрочного прогноза количества вагонов на станции и путях необщего пользования

Objective: collect raw data for building predictive models. Analyze the initial data, identify data outliers and outliers, divide the data into time intervals, calculate correlation coefficients, partial autocorrelation, cross-correlation, analyze the trend and seasonality of the obtained time series. Using autoregressive models, machine learning models, neuro-fuzzy models to build forecasts of time series and determine the quality of the obtained forecasts. Methods: point density, autocorrelation, partial autocorrelation, cross-correlation, Foster-Stewart test, Dickey-Fuller test, ARMA, MLP, Encoder-Decoder LTSM, TSK, Fuzzy-Partitions, SCRG, Transformers. Results: we obtained estimates of the prediction accuracy of the selected models, compared the results of the predictive models trained on different samples of initial data. Conclusions are made about the efficiency and methods of building predictive models. Practical significance: the 
 significance of building accurate predictive models for the key quantitative indicators of stations and nonpublic routes operation is shown. The factors influencing the accuracy of the obtained forecasts are analyzed.