Publication activity
Articles grouped by publication year
Hover over a year or focus the chart and use arrow keys to inspect values.
2026 is year to date. 0 undated publications excluded.
Research across participating journals · All insights follow your collection filters.
Publication activity alongside incoming citations.
Articles grouped by publication year
Hover over a year or focus the chart and use arrow keys to inspect values.
2026 is year to date. 0 undated publications excluded.
OpenAlex · year of the citing work
Hover over a year or focus the chart and use arrow keys to inspect values.
2017–2026 annual-history window. 2026 is year to date. Unknown years have gaps.
Leading affiliations and themes in this collection.
Full distributions & methodology ↓Independent sources. Clear time scales.
Crossref and OpenAlex · separate totals for the same publication cohorts
Hover over a year or focus the chart and use arrow keys to inspect values.
Latest lifetime totals, grouped by the cited article’s year. These are not citations received in that year.
OpenAlex · incoming citation links grouped by citing-work year
Hover over a year or focus the chart and use arrow keys to inspect values.
2017–2026 annual-history window. 2026 is year to date. Unknown years have gaps.
Select journals to compare the same collection filters. Source totals stay separate. These measures describe research activity, not journal quality.
Articles by publication year
Latest lifetime totals · grouped by publication year
Latest lifetime totals · grouped by publication year
By year of the citing work · 2017–2026
2026 is year to date. Crossref and OpenAlex lifetime charts share a citation scale. Unknown observations appear as gaps; known zero stays zero. Annual receipts use available OpenAlex histories and may not sum to lifetime totals. 0 undated publications excluded from publication-year charts.
Crossref citation coverage: 5 / 5 publications (100.0%). Latest observation: 21 Oct 2026, 00:01 UTC. Means and percentages use records with known counts.
DOI-matched OpenAlex observations for the same selected publications. These counts are separate from Crossref and are never added together.
Observation range: 21 Oct 2026 00:01 UTC — 21 Oct 2026 00:02 UTC. Missing matches are not zero citations. Previous observations may remain if a refresh fails or a DOI no longer matches.
OpenAlex affiliations and primary-topic classification. Each publication counts once per institution or country; multi-affiliated publications appear in multiple groups, so percentages can sum above 100%. Countries describe author affiliations, not nationality. Topics use one primary topic per publication.
4 / 5 with data · 1 missing or unmatched. Percentages use all 5 selected publications.
4 / 5 with data · 1 missing or unmatched. Percentages use all 5 selected publications.
5 / 5 with data · 0 missing or unmatched. Percentages use all 5 selected publications.
Citations are the latest recorded totals for articles published in each year, not citations received during that year. Crossref and OpenAlex remain separate and share the same citation scale. 2026 is incomplete; newer articles have had less time to receive citations.
0 undated publications excluded. Unknown citation counts are shown as —, never as zero. Partial coverage can understate totals; expand each table for known / total records.
| Publication year | Journal / scope | Publications | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2021 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2022 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2023 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2025 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2026 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2021 | All selected publications | 1 | 2 | 2/2 | 2/2 |
| 2022 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2023 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | 5 | 2 | 2/2 | 2/2 |
| 2025 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2026 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2021 | All selected publications | 1 | 2 | 2/2 | 2/2 |
| 2022 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2023 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | 5 | 2 | 2/2 | 2/2 |
| 2025 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2026 | All selected publications | 0 | 1 | 1/1 | 1/1 |
Incoming citations to the selected publications, grouped by the year of the citing work. This is separate from lifetime citation totals grouped by the cited article’s publication year.
The publication filters above define which articles receive the citations. The citation-year range below only controls this chart and the incoming-citation comparison. Removing publication-year filters includes older articles’ incoming citations too.
Annual history available for 5 / 5 selected publications. Supported display window: 2017–2026; 2026 is incomplete. Within a successfully fetched ten-year window, omitted years mean zero. Missing histories and years beyond a saved observation’s window remain unknown (—); partial coverage can understate totals.
Annual-history observations (UTC): 2026-10-21T00:01:59.949630+00:00 — 2026-10-21T00:02:00.492745+00:00. Older observations may be retained after failed or incomplete refreshes.
| Citation year | Journal / scope | OpenAlex · Citations received by year | Publications | Annual history known |
|---|---|---|---|---|
| 2017 | All selected publications | 0 | 5 | 5/5 |
| 2018 | All selected publications | 0 | 5 | 5/5 |
| 2019 | All selected publications | 0 | 5 | 5/5 |
| 2020 | All selected publications | 0 | 5 | 5/5 |
| 2021 | All selected publications | 0 | 5 | 5/5 |
| 2022 | All selected publications | 0 | 5 | 5/5 |
| 2023 | All selected publications | 1 | 5 | 5/5 |
| 2024 | All selected publications | 1 | 5 | 5/5 |
| 2025 | All selected publications | 1 | 5 | 5/5 |
| 2026 | All selected publications | 3 | 5 | 5/5 |
Counts represent citation links, not unique citing papers across a journal: one paper citing two selected articles contributes two links. Older citations outside this window are excluded, so annual sums need not equal lifetime totals. OpenAlex methodology ↗
Publications by publication year. 2026 is an incomplete year.
| Period | Publications |
|---|---|
| 2021 | 2 |
| 2024 | 2 |
| 2026 | 1 |
Latest Crossref totals for the selected publications; not citations received in the selected years.
Unknown counts are excluded from the mean, median and cited-publication rate.
Same filters, source and counting rules across journals. Descriptive statistics, not a quality ranking.
| Journal / publisher | Publications | Crossref citations | OpenAlex citations | OpenAlex coverage | Crossref mean | Crossref median | Crossref cited %* | Crossref coverage |
|---|---|---|---|---|---|---|---|---|
| Bussecon Review of Social Sciences Bussecon International Academy | 5 | 6 | 6 | 5/5 | 1.2 | 1 | 60.0 | 5/5 · 100.0% |
* Percentage among publications with a known citation count.
This paper explores the nonlinear dynamics relationship between petroleum product prices and inflation in Nigeria. By focusing on the role of exchange rate fluctuations in amplifying the relationships, the paper employs a multiplicative interaction approach to address the nonlinear effects of petroleum prices on inflation. Utilizing data from 2016: Q1 to 2024: Q2 from the National Bureau of Statistics and the Central Bank of Nigeria, the study introduces interaction terms to capture how petroleum prices and exchange rates interact to influence inflation. The methodology includes static and dynamic panel estimators, with a focus on the within effect model to account for unobserved state-specific factors. Key findings reveal that AGO and PMS prices significantly drive both headline and food inflation, while DPK's impact varies, and GAS shows minimal effect. The study highlights those geographical factors—proximity to seaports and fuel depots—significantly influence inflationary pressures. States closer to these facilities experience lower inflation due to reduced distribution costs. This suggests that policies aimed at partial subsidization for states farther from key infrastructure could mitigate regional inflation disparities. The findings contribute to the understanding of inflation dynamics, emphasizing the need for targeted policy interventions tailored to regional economic conditions in Nigeria.
This study investigates the asymmetric impacts of retail energy prices on inflation in Nigeria, focusing on the period from Q1: 2010 to Q2: 2024. Utilizing secondary data sourced from the Central Bank of Nigeria (CBN) and the National Bureau of Statistics (NBS) e-library, we examine how fluctuations in petroleum prices (PMS and AGO) influence both food and headline inflation. The analysis employs a nonlinear ARDL approach to capture the distinct effects of rising versus falling energy costs, revealing significant asymmetries in their impact on inflation indices. Our findings indicate that increases in fuel prices exert a pronounced inflationary pressure, particularly on food prices, while decreases lead to muted deflationary effects. The results emphasize the need for comprehensive policy interventions to mitigate fuel-driven cost-push inflation. Specifically, we suggest revisiting the framework for fuel subsidy removal to prevent abrupt price shocks that could burden consumers. Additionally, establishing an Energy Price Stabilization Fund (EPSF) could help stabilize food prices during periods of high energy costs by providing targeted support to the food market. Moreover, managing the exchange rate to minimize extreme fluctuations could further alleviate inflation pressures associated with import costs. Finally, strengthening inflation monitoring systems will enhance the government's ability to respond proactively to inflationary trends, ensuring timely interventions. These policy recommendations aim to create a more stable economic environment, reducing the inflationary impact of volatile retail energy prices and improving overall economic resilience in Nigeria.
The aim of this study is to determine the causality relationship between energy prices, which are among the most important inputs of the economy, and selected stock market indices of developed countries. Crude oil and natural gas are used as energy variables. G7 countries were selected to represent developed countries. Stock indices used in the study are Dow & Jones (USA), DAX (Germany), CAC40 (France), FTSE250 (England), FTSE Italia All Share (Italy), NIKKEI225 (Japan), and S&P/TSX (Canada). In the study, Johansen (1988) cointegration test and Granger (1969) causality test were used to analyse the causality relationship between energy prices and selected stock market indices. The research could not find a long-term balance relationship between energy prices and developed country indices. Also, while the causality relationship was determined between crude oil prices and NIKKEI225, DAX, and CAC40 indices, a causal relationship between natural gas prices and Dow & Jones and FTSE250 indices was determined. In the study, it was found that energy prices can be used for diversification in investments to be made with stock market indices of developed countries. This study is one of the most comprehensive studies in the literature that examines the relationship between energy prices and the stock market indices of G7 countries. It is expected to contribute to the literature in this way.
The aim of this study is to examine the dynamic relationships among alternative investment instruments in Turkey using advanced time-series methods. The findings indicate evidence of granger causal relationships among some of the variables. However, the direction and statistical significance of these relationships vary across the variables examined. The analysis employs monthly data covering the period from February 2010 to December 2018. The stationarity properties of the variables were examined using the Augmented Dickey–Fuller (ADF) test. Causal relationships among the variables were subsequently investigated using the Toda–Yamamoto causality test. The findings indicate that there are statistically significant short-term and long-term relationships among alternative investment instruments. It was determined that the exchange rate and gold prices have on the stock market. Furthermore, its interest rates play in both financial markets and housing prices. This study contributes to the literature by examining the relationships between alternative investment instruments using linear methods. Moreover, the results offer researchers and policymakers important implications for financial stability for investors' portfolio diversification strategies at both the national and international levels.
There is a pool of techniques and methods in addressing dynamics behaviors in higher frequency data, prominent among them is the ARCH/GARCH techniques. In this paper, the various types and assumptions of the ARCH/GARCH models were tried in examining the dynamism of exchange rate and international crude oil prices in Nigeria. And it was observed that the Nigerian foreign exchange rates behaviors did not conform with the assumptions of the ARCH/GARCH models, hence this paper adopted Lag Variables Autoregressive (LVAR) techniques originally developed by Agung and Heij multiplier to examine the dynamic response of the Nigerian foreign exchange rates to crude oil prices. The Heij coefficient was used to calculate the dynamic multipliers while the Engel & Granger two-step technique was used for cointegration analysis. The results revealed an insignificant dynamic long-term response of the exchange rate to crude oil prices within the periods under review. The coefficient of dynamism was insignificantly in most cases of the sub-periods. The paper equally revealed that the significance of the dynamic multipliers depends greatly on external information about both market indicators which are two-way interactions. Thus, the paper recommends periodic intervention in the foreign exchange market by the monetary authorities to stabilize the market against any shocks in the international crude oil market, since crude oil is the main source of foreign exchange in Nigeria.
The aim of this study is to examine the dynamic relationships among alternative investment instruments in Turkey using advanced time-series methods. The findings indicate evidence of granger causal relationships among some of the variables. However, the direction and statistical significance of these relationships vary across the variables examined. The analysis employs monthly data covering the period from February 2010 to December 2018. The stationarity properties of the variables were examined using the Augmented Dickey–Fuller (ADF) test. Causal relationships among the variables were subsequently investigated using the Toda–Yamamoto causality test. The findings indicate that there are statistically significant short-term and long-term relationships among alternative investment instruments. It was determined that the exchange rate and gold prices have on the stock market. Furthermore, its interest rates play in both financial markets and housing prices. This study contributes to the literature by examining the relationships between alternative investment instruments using linear methods. Moreover, the results offer researchers and policymakers important implications for financial stability for investors' portfolio diversification strategies at both the national and international levels.
This study investigates the asymmetric impacts of retail energy prices on inflation in Nigeria, focusing on the period from Q1: 2010 to Q2: 2024. Utilizing secondary data sourced from the Central Bank of Nigeria (CBN) and the National Bureau of Statistics (NBS) e-library, we examine how fluctuations in petroleum prices (PMS and AGO) influence both food and headline inflation. The analysis employs a nonlinear ARDL approach to capture the distinct effects of rising versus falling energy costs, revealing significant asymmetries in their impact on inflation indices. Our findings indicate that increases in fuel prices exert a pronounced inflationary pressure, particularly on food prices, while decreases lead to muted deflationary effects. The results emphasize the need for comprehensive policy interventions to mitigate fuel-driven cost-push inflation. Specifically, we suggest revisiting the framework for fuel subsidy removal to prevent abrupt price shocks that could burden consumers. Additionally, establishing an Energy Price Stabilization Fund (EPSF) could help stabilize food prices during periods of high energy costs by providing targeted support to the food market. Moreover, managing the exchange rate to minimize extreme fluctuations could further alleviate inflation pressures associated with import costs. Finally, strengthening inflation monitoring systems will enhance the government's ability to respond proactively to inflationary trends, ensuring timely interventions. These policy recommendations aim to create a more stable economic environment, reducing the inflationary impact of volatile retail energy prices and improving overall economic resilience in Nigeria.
This paper explores the nonlinear dynamics relationship between petroleum product prices and inflation in Nigeria. By focusing on the role of exchange rate fluctuations in amplifying the relationships, the paper employs a multiplicative interaction approach to address the nonlinear effects of petroleum prices on inflation. Utilizing data from 2016: Q1 to 2024: Q2 from the National Bureau of Statistics and the Central Bank of Nigeria, the study introduces interaction terms to capture how petroleum prices and exchange rates interact to influence inflation. The methodology includes static and dynamic panel estimators, with a focus on the within effect model to account for unobserved state-specific factors. Key findings reveal that AGO and PMS prices significantly drive both headline and food inflation, while DPK's impact varies, and GAS shows minimal effect. The study highlights those geographical factors—proximity to seaports and fuel depots—significantly influence inflationary pressures. States closer to these facilities experience lower inflation due to reduced distribution costs. This suggests that policies aimed at partial subsidization for states farther from key infrastructure could mitigate regional inflation disparities. The findings contribute to the understanding of inflation dynamics, emphasizing the need for targeted policy interventions tailored to regional economic conditions in Nigeria.
There is a pool of techniques and methods in addressing dynamics behaviors in higher frequency data, prominent among them is the ARCH/GARCH techniques. In this paper, the various types and assumptions of the ARCH/GARCH models were tried in examining the dynamism of exchange rate and international crude oil prices in Nigeria. And it was observed that the Nigerian foreign exchange rates behaviors did not conform with the assumptions of the ARCH/GARCH models, hence this paper adopted Lag Variables Autoregressive (LVAR) techniques originally developed by Agung and Heij multiplier to examine the dynamic response of the Nigerian foreign exchange rates to crude oil prices. The Heij coefficient was used to calculate the dynamic multipliers while the Engel & Granger two-step technique was used for cointegration analysis. The results revealed an insignificant dynamic long-term response of the exchange rate to crude oil prices within the periods under review. The coefficient of dynamism was insignificantly in most cases of the sub-periods. The paper equally revealed that the significance of the dynamic multipliers depends greatly on external information about both market indicators which are two-way interactions. Thus, the paper recommends periodic intervention in the foreign exchange market by the monetary authorities to stabilize the market against any shocks in the international crude oil market, since crude oil is the main source of foreign exchange in Nigeria.
The aim of this study is to determine the causality relationship between energy prices, which are among the most important inputs of the economy, and selected stock market indices of developed countries. Crude oil and natural gas are used as energy variables. G7 countries were selected to represent developed countries. Stock indices used in the study are Dow & Jones (USA), DAX (Germany), CAC40 (France), FTSE250 (England), FTSE Italia All Share (Italy), NIKKEI225 (Japan), and S&P/TSX (Canada). In the study, Johansen (1988) cointegration test and Granger (1969) causality test were used to analyse the causality relationship between energy prices and selected stock market indices. The research could not find a long-term balance relationship between energy prices and developed country indices. Also, while the causality relationship was determined between crude oil prices and NIKKEI225, DAX, and CAC40 indices, a causal relationship between natural gas prices and Dow & Jones and FTSE250 indices was determined. In the study, it was found that energy prices can be used for diversification in investments to be made with stock market indices of developed countries. This study is one of the most comprehensive studies in the literature that examines the relationship between energy prices and the stock market indices of G7 countries. It is expected to contribute to the literature in this way.
Source coverage refers to this result set; a publication may occur in several sources. Sync times refer to contributing journals.
Missing abstracts: 0 · Missing authors: 0 · Missing publication dates: 0 · Dates without day precision: 0
| Source | Most recent successful journal sync | Latest attempt |
|---|---|---|
| crossref | 21 Oct 2026 00:01 UTC · brss | complete · brss · 21 Oct 2026 00:01 UTC |
| ojs-rest | 21 Oct 2026 00:01 UTC · brss | complete · brss · 21 Oct 2026 00:00 UTC |
| oai-pmh | 20 Oct 2026 19:50 UTC · brss | complete · brss · 20 Oct 2026 19:49 UTC |
| openalex-enrichment | 21 Oct 2026 00:02 UTC · brss | complete · brss · 21 Oct 2026 00:01 UTC |