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: 29 / 29 publications (100.0%). Latest observation: 21 Oct 2026, 00:21 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: 22 Oct 2026 00:25 UTC — 22 Oct 2026 06:32 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.
23 / 29 with data · 6 missing or unmatched. Percentages use all 29 selected publications.
23 / 29 with data · 6 missing or unmatched. Percentages use all 29 selected publications.
29 / 29 with data · 0 missing or unmatched. Percentages use all 29 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 |
|---|---|---|---|---|---|
| 2013 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2014 | All selected publications | 4 | 4 | 4/4 | 4/4 |
| 2015 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2016 | All selected publications | 4 | 4 | 4/4 | 4/4 |
| 2017 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| 2018 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2019 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2021 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| 2022 | All selected publications | 5 | 5 | 5/5 | 5/5 |
| 2023 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2024 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2025 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2013 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2014 | All selected publications | 11 | 4 | 4/4 | 4/4 |
| 2015 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2016 | All selected publications | 0 | 4 | 4/4 | 4/4 |
| 2017 | All selected publications | 12 | 3 | 3/3 | 3/3 |
| 2018 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2019 | All selected publications | 32 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | 18 | 2 | 2/2 | 2/2 |
| 2021 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| 2022 | All selected publications | 7 | 5 | 5/5 | 5/5 |
| 2023 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2024 | All selected publications | 1 | 2 | 2/2 | 2/2 |
| 2025 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2013 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2014 | All selected publications | 18 | 4 | 4/4 | 4/4 |
| 2015 | All selected publications | 21 | 1 | 1/1 | 1/1 |
| 2016 | All selected publications | 4 | 4 | 4/4 | 4/4 |
| 2017 | All selected publications | 23 | 3 | 3/3 | 3/3 |
| 2018 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2019 | All selected publications | 54 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | 33 | 2 | 2/2 | 2/2 |
| 2021 | All selected publications | 4 | 3 | 3/3 | 3/3 |
| 2022 | All selected publications | 18 | 5 | 5/5 | 5/5 |
| 2023 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2024 | All selected publications | 1 | 2 | 2/2 | 2/2 |
| 2025 | All selected publications | 1 | 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 29 / 29 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): 2026-10-22T00:25:55.178620+00:00 — 2026-10-22T06:32:15.077335+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 | 1 | 29 | 29/29 |
| 2018 | All selected publications | 6 | 29 | 29/29 |
| 2019 | All selected publications | 5 | 29 | 29/29 |
| 2020 | All selected publications | 19 | 29 | 29/29 |
| 2021 | All selected publications | 19 | 29 | 29/29 |
| 2022 | All selected publications | 20 | 29 | 29/29 |
| 2023 | All selected publications | 28 | 29 | 29/29 |
| 2024 | All selected publications | 31 | 29 | 29/29 |
| 2025 | All selected publications | 20 | 29 | 29/29 |
| 2026 | All selected publications | 10 | 29 | 29/29 |
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 |
|---|---|
| 2013 | 1 |
| 2014 | 4 |
| 2015 | 1 |
| 2016 | 4 |
| 2017 | 3 |
| 2018 | 1 |
| 2019 | 1 |
| 2020 | 2 |
| 2021 | 3 |
| 2022 | 5 |
| 2023 | 1 |
| 2024 | 2 |
| 2025 | 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 |
|---|---|---|---|---|---|---|---|---|
| International Journal of Finance & Banking Studies SSBFNET | 29 | 88 | 179 | 29/29 | 3.03 | 1 | 65.5 | 29/29 · 100.0% |
* Percentage among publications with a known citation count.
The main purpose of this study is to investigate inflation rate and its impact on the growth rate or to GDP growth for Eurozone countries, using panel data for the period 1997-2017, on an annual basis with a total of 257 observations. For conducting the study, and achieving results, a multiple linear regression model with the least squares regression is used. Moreover, multiple linear regression analysis has been applied in order to investigate whether Inflation rate, as an independent variable, has any significant impact on economic growth. Consequently, in order to test the data used in the model we have applied diagnostic tests, such as Durbin-Watson test to analyze the correlation of serial correlation, as well as the Breusch-Pagan test for heteroskedasticity. The tests’ results give us strong indications that the model has no relation between of serial correlation and there is no heteroskedasticity either. The study conducted shows results generated from the model, and according to the econometric results indicate that Inflation rate has positive impact on the economic growth rate for euro area.
An increase in MSMEs in large numbers is very instrumental in increasing economic growth in developing countries, including Indonesia. The research objective is based on identifying the effect of investment credit, interest rates, and labor on economic growth through the role of MSMEs. The design of this research is descriptive-verification to present a structured, factual and accurate picture and test hypotheses empirically through the MRA model. Empirical analysis proves that investment credit and interest rates through the role of MSMEs can influence positively and significantly on Indonesia's economic growth. In other results, it appears that the role of MSMEs is less able to mediate the effect of labor on Indonesia's economic growth. This fact is based on a negative and insignificant relationship. In a sentence or two, enter the implications and limitations of your research. In Indonesia, increasing bank lending to MSMEs in practice is undermined by lending policies by banks and by macroeconomic factors (economic growth, interest rates, investment credit, and labor).
<p>There is a long discussion among academics and central bankers about the theories of money supply. According to the exogenous view, central banks have the full control over money supply via policy actions including the adjustments of interest rates and reserve ratios, both of which alter commercial banks’ lending decisions. However, the theory of endogenous money supply emphasizes the role of demand for bank loans in money creation. More specifically, banks create money by meeting the demand of economic agents. In this study, we investigate which of the money supply theories holds in Turkish economy for the period 2006-2015 by employing cointegration and causality tests. Our findings show that the causality runs from bank loans to money supply both in the short and long terms, which supports the endogenous view in a sense that central bank and the banks fully meet the total demand for money in Turkish economy.</p>
The main purpose of this study is to investigate if determinants that we selected in our analysis have any effects on inflation rate in Western Balkans Countries[1] by using panel data for the period of 2001-2017, in yearly basis in total of 102 observation. The study used quantitative analysis approach and secondary data by applying the multivariate time series, respectively vector error correction model [VECM]. Multivariate time series was applied to investigate whether the budget deficit and other explanatory variable have any significant impact on inflation rate. The results from our analysis shows that three of four determinates that we used are significant on inflation rate. The model summaries statistics for inflation rate which shows that inflation rate has a moderate correlation with explanatory variables that we used in our model, that explanatory variables explain 45.5 percent of dependent variable and we can conclude that a model is a proper and fit. The results suggest that one percent point increase in budget deficit to GDP ratio is associated with about a 9.34 percent point increase in inflation rate. The overall inference is that the ratios that we selected has a significant influence on the inflation rate in Western Balkans Countries. [1] Western Balkans Countries: Albania, Bosnia & Hercegovina, Kosovo, Montenegro, North Macedonia and Serbia.
The link between stock market development and economic activity has always been the subject of considerable debate in the field of economics and it raises empirical question whether stock market development influences economic activity or whether it is a consequence of increased economic activity. This study attempts to investigate the direction of causality between stock market development and economic growth in the Indian context. Using the cointegration and causality tests for the period June 1991 to June 2013, the study confirms a well defined long-run equilibrium relationship between the stock market development indicators and economic growth in India. The empirical results show bidirectional causality between market capitalisation and economic growth and unidirectional causality from turnover ratio to economic growth in the long-run and short-run. By and large, it can be inferred that the stock market development indicators viz. market capitalisation and turnover ratio have a positive influence on economic growth in India.
This paper examines the exchange rate pass-through (ERPT) and its impact on home inflation in Bangladesh, particularly in the post-pandemic era. Examining important economic factors including the nominal exchange rate (NER), money supply (M2), industrial production (IP), and global commodity index (GCI) using monthly data from January 2002 to August 2023 helps one to grasp their combined influence on consumer price inflation (CPI). By means of Johansen cointegration and Vector Error Correction Model (VECM) approaches, the study reveals important short-term and long-term dynamics. Contrary to patterns in many emerging nations, the results show that changes in exchange rates have apparently little impact on home prices. Rather, changes in the money supply and world commodities prices mostly influence inflation; the latter becomes a major factor. The study also exposes an unexpected positive link between industrial production and CPI, implying complexity in Bangladesh's production-driven economic structure. Moreover, this investigation underlines how complex monetary expansion, external commodities shocks, and devaluation of money interact to shape inflationary pressures. These results offer legislators important new perspectives and support strong monetary policies and focused involvement in world commodities markets to help to lower inflation. This study clarifies ERPT in developing countries and provides direction on inflation control techniques for Bangladesh.
This study investigates the long-term and short-term relationships between different monetary aggregates (M0, M1, M2, and M3) and GDP in Saudi Arabia using the Engle-Granger cointegration test and the Granger causality test. The results reveal that M2 has the most stable long-term relationship with GDP, making it the most reliable aggregate for policy formulation. M1 also shows a strong relationship, while M3’s connection is comparatively weaker. The causality analysis indicates bidirectional relationships between GDP and M1, M2, and M3, suggesting that changes in these aggregates can both influence and be influenced by economic activity. In contrast, M0 shows no significant long-term relationship and only responds to changes in GDP, indicating its limited policy relevance. These findings highlight the importance of using broader aggregates like M2 for effective policy targeting in Saudi Arabia’s evolving economic landscape. The study provides crucial insights for policymakers in selecting appropriate monetary aggregates for long-term planning and short-term economic stabilization.
Although the onset of the pandemic pressured monetary and fiscal policy across the globe, deficit finance was aided by the large, established debt markets in countries like the United States. The expansionary efforts in emerging markets like Latin America put greater strains on their more limited capacity. Our study analyzes data from seven Latin American countries, three European countries, three Asian countries, and the United States. We utilize panel vector autoregressive (VAR) models to explore the dynamic interactions and feedback mechanisms among the policy variables, while accounting for unobserved country-specific effects. Our results demonstrate strong and significant interactions between monetary and fiscal policy around the time of the pandemic, with the primary causal effects running from fiscal to monetary policy. Additionally, our results yield evidence of countercyclical policy reactions to inflation for both monetary and fiscal policy over the pandemic period.
This research examines domestic debt, private sector lending, and economic development in emerging countries, focusing on Bangladesh. This analysis uses 1960–2022 Bangladeshi secondary data, World Bank World Development Indicators and Bangladesh Ministry of Finance economic statistics. Domestic debt and private sector credit affect emerging nations' economic development, according to this study's theoretical approach. This research examines the link between two factors to add to the literature and provide Bangladesh-specific insights. It is quantitative research that examines domestic debt, private sector credit, and economic development using regression analysis and statistical testing. The conclusions aids Bangladesh's economic development policymakers, economists, and stakeholders. Understanding national debt, financing from the private sector, and the advancement of the country's economy helps policymakers manage debt, allocate credit, and set economic policy. The conducted study also improves theoretical understanding of economic development in underdeveloped countries and illuminates Bangladesh's unique conditions. It examined household debt, private sector lending, and Bangladeshi economic development as well as examines the patterns of economic growth in emerging nations to enhance policy debates and broaden knowledge.
Central bank digital currencies (CBDCs) give rise to many possibilities including those of negative interest rates. A two-period decision model is presented between one central bank and one representative household. The central bank applies the Taylor (1993) rule to choose its interest rate. The household allocates its resources strategically to production, consumption, CBDC holding, and non-CBDC holding. The results are determined analytically and illustrated numerically by varying 19 parameter values. Interesting novelties of the article are that the central bank may choose negative CBDC interest rates when the household holds far more CBDC than non-CBDC, for low inflation rates, low real interest rates, low household’s potential production, low weight assigned to inflation in the Taylor (1993) rule, high target inflation rate, and high household’s production parameter. That usually causes the household to decrease its CBDC holding and increase its non-CBDC holding, production and consumption. The central bank may increase its CBDC interest rate to compete with an increasing non-CBDC interest rate if the household’s transaction efficiencies for CBDC and non-CBDC increase, or the household’s transaction efficiency for consumption decreases. Shocks to production, inflation and interest rates are analyzed.
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 00:21 UTC · ijfbs | complete · ijfbs · 21 Oct 2026 00:21 UTC |
| ojs-rest | 21 Oct 2026 00:21 UTC · ijfbs | complete · ijfbs · 21 Oct 2026 00:14 UTC |
| oai-pmh | 20 Oct 2026 19:56 UTC · ijfbs | complete · ijfbs · 20 Oct 2026 19:55 UTC |
| openalex-enrichment | 21 Oct 2026 00:22 UTC · ijfbs | complete · ijfbs · 21 Oct 2026 00:21 UTC |