Iskanje po katalogu digitalne knjižnice

Opis: Inflation uncertainty is a critical factor influencing not only the market mechanisms but also the economic activity efficiency. In this paper, we investigated the relationship between inflation and growth to capture the impact of inflation uncertainty in Tunisia. The study relied on a dataset covering the period 1984.01-2018.08 and was characterized by a nonlinear specification. We used Hansen’s (2001) Threshold Regression (TR) analysis to determine one threshold effect of inflation on growth while explaining the role of inflation uncertainty in the whole process. This study concluded that an optimal inflation rate does exist. Under this rate, a little rise in inflation may enhance economic growth, allowing an adverse impact of inflation uncertainty. Above the critical threshold of 3%, it was revealed that inflation and inflation uncertainty play opposite roles: while the former harms growth, the latter benefits it. Thus, we cannot sustain the Friedman-Ball hypothesis for the two regimes. To the best of the authors’ knowledge, this is the first study that aimed to investigate the simultaneous effects of inflation and inflation uncertainty on growth in Tunisia using a non-linear methodology. This study aims to fulfil the knowledge gap of such studies for developing countries.
Ključne besede: threshold regression model, inflation, inflation uncertainty, economic growth, Tunisia
Objavljeno v DKUM: 01.08.2025; Ogledov: 0; Prenosov: 6
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Opis: This research is an effort to correlate the index properties of granular soils with the California Bearing Ratio (CBR) and the compaction characteristics. Soil classification, modified proctor and CBR tests conforming to the relevant ASTM methods were performed on natural as well as composite sand samples. The laboratory test results indicated that samples used in this research lie in SW, SP and SP-SM categories based on Unified Soil Classification System and in groups A-1-b and A-3 based on the AASHTO classification system. Multiple linear regression analysis was performed on experimental data and correlations were developed to predict the CBR, maximum dry density (MDD) and optimum moisture content (OMC) in terms of the index properties of the samples. Among the various parameters, the coefficient of uniformity (Cu), the grain size corresponding to 30% passing (D30) and the mean grain size (D50) were found to be the most effective predictors. The proposed prediction models were duly validated using an independent dataset of CBR tests on sandy soils. The comparative results showed that the variation between the experimental and predicted results for CBR falls within ±4% confidence interval and that of the maximum dry density and the optimum moisture content are within ±2%. Based on the correlations developed for CBR, MDD and OMC, predictive curves are proposed for a quick estimation based on Cu , D30 and D50. The proposed models and the predictive curves for the estimation of the CBR value and the compaction characteristics would be very useful in geotechnical & pavement engineering without performing the laboratory compaction and CBR tests.
Ključne besede: CBR, regression, model, prediction, compaction characteristics
Objavljeno v DKUM: 18.06.2018; Ogledov: 1439; Prenosov: 235
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Opis: The research of the Bullwhip effect has given rise to many papers, aimed at both analysing its causes and correcting it by means of various management strategies because it has been considered as one of the critical problems in a supply chain. This study is dealing with one of its principal causes, demand forecasting. Using different simulated demand patterns, alternative forecasting methods are proposed, that can reduce the Bullwhip effect in a supply chain in comparison to the traditional forecasting techniques (moving average, simple exponential smoothing, and ARMA processes). Our main findings show that kernel regression is a good alternative in order to improve important features in the supply chain, such as the Bullwhip, NSAmp, and FillRate.
Ključne besede: bullwhip effect, supply chain, kernel regression, system dynamics model
Objavljeno v DKUM: 31.05.2017; Ogledov: 1375; Prenosov: 281
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