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The time series of the federal funds rate has recently been extended back to 1928, now including several episodes during which interest rates remained near the lower bound of zero. This series is analyzed, using the method of indirect inference, by applying recent research on bounded time series to estimate a set of bounded parametric diffusion models. This combination uncouples the specification of the bounds from the law of motion. Although Louis Bachelier was the first to use arithmetic Brownian motion to model financial time series, he has often been criticized for this proposal, since the process can take on negative values. Most researchers favor processes such as geometric Brownian motion (GBM), which remains positive. Under this framework, Bachelier's proposal remains valid when specified with bounds and is shown to compare favorably when modeling the federal funds rate.

We propose in this paper a novel reliable detection method to recognize forged inpainting images. Detecting potential forgeries and authenticating the content of digital images is extremely challenging and important for many applications. The proposed approach involves developing new probabilistic support vector machines (SVMs) kernels from a flexible generative statistical model named “bounded generalized Gaussian mixture model”. The developed learning framework has the advantage to combine properly the benefits of both discriminative and generative models and to include prior knowledge about the nature of data. It can effectively recognize if an image is a tampered one and also to identify both forged and authentic images. The obtained results confirmed that the developed framework has good performance under numerous inpainted images.

A successful process of design concept evaluation has positive influence on subsequent processes. This study aims to consider the evaluation information at multiple stages and the interaction among evaluators and improve the credibility of evaluation results.

This paper proposes a multi-stage approach for design concept evaluation based on complex network and bounded confidence. First, a network is constructed according to the evaluation data. Depending on the consensus degree of evaluation opinions, the number of evaluation rounds is determined. Then, bounded confidence rules are applied for the modification of preference information. Last, a planning function is constructed to calculate the weight of each stage and aggregate information at multiple evaluation stages.

The results indicate that the opinions of the evaluators tend to be consistent after multiple stages of interactive adjustment, and the ordering of design concept alternatives tends to be stable with the progress of the evaluation.

Updating preferences according to the bounded confidence rules, only the opinions within the trust threshold are considered. The attribute information of the node itself is inadequately considered.

This method addresses the need for considering the evaluation information at each stage and minimizes the impact of disagreements within the evaluation group on the evaluation results.

This study aims to explore the short-run and long-run relationships and causality between economic growth and financialization in the new member states (NMS-11) and to provide some policy implications drawn from the empirical findings.

The autoregressive distributed lag (ARDL) bounds test approach to cointegration with the vector error correction model and the cumulative sum of squares (CUSUMQ) test for stability of functions is used between 1995q1 and 2021q4 to examine the existence of cointegration, relationships and causality between economic growth and financialization.

The findings of the ARDL bounds test demonstrate that the variables included in the models are bound together in the long run, as confirmed by the associated equilibrium correction. The estimated models indicate that the association between selected variables and economic growth is stronger and more statistically significant in the short run compared with the long run. Also, for NMS-11 understudied countries, short-run causality prevails over long-run causality. The changes in the level of financialization have a significant negative effect on the growth rates in the short run, which aligns with findings from previous empirical studies.

This study extends the existing very limited literature about short-run and long-run relationships and causality among economic growth and financialization, including inflation and unemployment variables, to determine their link in the NMS-11. Specifically, the present study reveals that the current level of financialization hampers economic growth and promoting such economic policies further can have adverse effects on the overall economic growth.

Fintech plays a prominent role in augmenting the financial inclusion of the population and increasing the money supply, which calls for the intervention of monetary policy. This article is an attempt to examine the relationship between the financial inclusion, fintech and monetary policy effectiveness of the Indian economy, within the framework of wealth creation and transmission mechanism through the cost of capital.

On the quarterly data retrieved from multiple sources, autoregressive distributed lagged regression is used to examine the relationship between different variables as explained in four set models; after which the Toda–Yamamoto causality test is employed to capture the direction of the relationship.

The study finds a positive relationship between financial inclusion, fintech and inflation taken as a proxy for Monetary Policy Effectiveness (MPE) in the short as well as in the long run. However, the relationship between fintech and inflation is negative once the cost of capital is included in the models. The causality test exhibits the uni-directional causality from fintech to MPE and MPE to financial inclusion. Bi-directional causality exists between wealth and MPE. Similarly, bank rate and interbank rate are bound by bi-directional causality.

Being financially included facilitates ease and boosts public access to more financial services and credit, leading to increased demand and hence inflation. Hence government and regulators need to take mindful measures to enhance the fintech development and financial inclusion to make the monetary policy effective.

As per the author's best knowledge, this is the first study to examine the relationship between fintech, financial inclusion and monetary policy effectiveness in the context of the Indian economy.

This study presents a framework to estimate throughput and cost of additive manufacturing (AM) as related to process parameters, material thermodynamic properties and machine specifications. Taking a 3D model of the part design as input, the model uses a parametrization of the rate-limiting physics of the AM build process – herein focusing on laser powder bed fusion (LPBF) and scaling of LPBF melt pool geometry – to estimate part- and material-specific build time. From this estimate, per-part cost is calculated using a quantity-dependent activity-based production model.

Analysis tools that assess how design variables and process parameters influence production cost increase our understanding of the economics of AM, thereby supporting its practical adoption. To this aim, our framework produces a representative scaling among process parameters, build rate and production cost.

For exemplary alloys and LPBF system specifications, predictions reveal the underlying tradeoff between production cost and machine capability, and look beyond the capability of currently commercially available equipment. As a proxy for build quality, the number of times each point in the build is re-melted is derived analytically as a function of process parameters, showcasing the tradeoff between print quality due to increased melting cycles, and throughput.

Typical cost models for AM only assess single operating points and are not coupled to models of the representative rate-limiting process physics. The present analysis of LPBF elucidates this important coupling, revealing tradeoffs between equipment capability and production cost, and looking beyond the limits of current commercially available equipment.

The purpose of this research is to evaluate corruption in the context of India, spanning the period between 1988 and 2021. Additionally, it aims to provide an in-depth comprehension of the factors that drive its prevalence and to propose policy directives for addressing these underlying issues.

The study instead of relying on perception-based measures, takes a distinct approach by formulating a corruption index derived from reported instances, thus ensuring a more objective assessment. Furthermore, we employ stochastic frontier analysis to tackle the issue of under-reporting within the corruption index based on reported cases. Subsequently, an auto regressive distributed lag (ARDL) methodology is applied to ascertain the principal drivers of corruption, encompassing both long and short factors.

This study reveals that corruption in India is notably influenced by economic growth and income inequality. Conversely, government effectiveness and globalization display a tendency to mitigate corruption. However, our rigorous analysis demonstrates that financial development does not wield a substantial influence in our study. Moreover, our inquiry uncovers a nonlinear relationship between economic growth and corruption. Additionally, we ascertain that the long run and short run impacts of corruption remain relatively stable across both models utilized in our study.

This study differs from previous research in the subsequent manners. Primarily, we employed an objective measure to formulate the corruption index, coupled with addressing the underreporting issues via stochastic frontier analysis. Moreover, this study pioneers the identification of a non-linear relationship between corruption and economic growth within the Indian context, a facet unexplored in previous investigations.

This study aims to critically evaluate hospitality and tourism demand research and introduce a behavioral economics approach to solve the problems faced by researchers.

Current issues in hospitality and tourism demand analysis are identified through critical reflection, and a behavioral economics approach is adopted to develop a new conceptual framework.

Four issues in hospitality and tourism studies are identified from the microeconomic theory and econometric modeling perspectives. The study’s demand framework provides both a theoretical underpinning and quantitative models to resolve the identified issues. With a focus on consumers’ cost–benefit assessments in light of individual differences and environmental factors, the authors’ conceptual framework represents a new effort to quantify hospitality and tourism demand at the disaggregate level with interactive multiple demand curve estimations.

The study’s analytical framework for hospitality and tourism demand analysis is unique, and it fills the research gap. However, this research is still in the conceptual stage, and the authors leave it to future studies to empirically test the framework.

The proposed demand framework at the disaggregate level will benefit both private and public sectors involved in hospitality and tourism businesses in terms of pricing, marketing and policymaking.

The authors offer a new conceptual model that bridges the gap between aggregate and disaggregate hospitality and tourism demand analyses. Specifically, the authors identify research directions for future hospitality and tourism demand research involving individual tourists/consumers at the disaggregate level.

Tourism–economy relationship in India has been studied extensively in the past literature using a single equation approach. However, the present paper diverted from this trend and examined the tourism–economy relationship using the specific to general modelling approach over the 1990–2018 time period. The study also accounts for the influence of merchandise trade, capital formation, foreign investment inflows and inflation on economic growth to achieve the robustness of the coefficient estimates.

To achieve the objective, the study utilised a specific to general modelling strategy. First, the regression equation includes only three core variables: gross domestic product (GDP), international tourist receipts and international tourist expenditures. Next, the authors include other control variables in the regression equation one by one, leading us to test five model types for investigating the cointegration among the variables. As for the estimation technique, the authors employed autoregressive distributed lag (ARDL) approach.

The paper's findings highlight that tourism receipts and expenditures exert a positively significant impact on economic growth. Moreover, including the additional independent variables does not substantially change the tourism and economic growth relationship. The existence of one-way causality from tourism expenditures to economic growth supports the tourism-led growth hypothesis. These findings highlight the rationale for intervention by the government and policymakers to promote tourism potential and facilities to accelerate the overall growth performance of the country. While the existence of one-way causal effect from economic growth to tourism revenues supports the growth-led tourism development hypothesis, implying that economic expansion is necessary for tourism development.

This research article tried to present a comprehensive picture of India's tourism–economy relationship. However, the present study is organised as an aggregate economy-level analysis. It assumed that the aggregate tourism sector is homogenous. However, different tourism sectors exert different levels of influence on the economy. The authors expect future research can take the disaggregated analysis of the tourism–economy relationship.

This study provides valuable insights into the tourism-led growth hypothesis in India. The study highlights comprehensive intervention by the government and policymakers for accelerating tourism development to invigorate the overall growth performance of the country over the long run. The principal recommendation emerging from the present research is that the tourism growth potential can be depended upon to stimulate the economic performance of the Indian economy.

The present study diverted from the previous empirical studies by following a specific to general modelling strategy. First, the regression model includes only three core variables such as economic growth, tourism receipts and tourism expenditure. Next, the authors include other control variables in the regression equation one by one, leading us to test five model types for investigating the cointegrating relationship among the variables. GDP growth rate is used as a dependent variable in all five specifications. The idea is to expand the model to capture every feature of the data generating process.

Unemployment is one of the major challenges facing most countries, including Africa as a continent. Seeking how to reduce unemployment, debt, inflation and increase gross domestic product (GDP), foreign direct investment (FDI) and gross capital formation in the continent has been an agenda of governments, policy makers and economists to. This study examines the relationship between economic growth, inflation, debt, FDI, gross capital formation, labor force, population and unemployment in Africa.

An updated panel dataset of 29 African countries was selected from different regions from 1991 to 2019. These countries were selected based on their unemployment, population growth and inflation rates. The Pesaran cross-sectional dependence and panel unit root test (the Dickey–Fuller cross-sectional supplemented and the Im-Pesaran-Shin cross-sectional) were applied. Further, the panel Autoregressive Distributed Lag (ARDL) model (Bounds test) and pooled mean group (PMG) estimator were utilized in this work.

This shows that economic growth, debt, labor force and population have a positive relationship with unemployment in the long run. Therefore, an increase in these variables generates an increase in the selected African countries' unemployment growth. In contrast, inflation, FDI and gross capital formation have a negative relationship with unemployment in the long run, which implies that an increase in these variables reduces unemployment in the selected African countries.

This study has potential limitations because some data from the countries are not up to date and some years are missing from the data.

This study contributes to understanding unemployment and Okun's law in the African economy. This study shows that an increase in economic growth leads to a rise in unemployment, while an increase in inflation leads to a decrease in unemployment.

This paper provides an insight into the major factors that increase and reduces unemployment for government and policy marker to take the adequate measure.