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Search Results: 1 - 10 of 33984 matches for " ccapm model "
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Choques transitórios em variáveis econ?micas
Figueirêdo, Erik Alencar de;Leite Filho, Paulo Amilton Maia;
Economia Aplicada , 2005, DOI: 10.1590/S1413-80502005000400006
Abstract: this study aims to test the existence of near unit roots and local persistence in several important variables of economic models (product market, ccapm and black-scholes' formula). it is argued that the rejection of the unit root hypothesis will not necessarily imply in accepting a stationary and ergodic behavior for the time series. in order to do that, the near unit root model developed by phillips, moon and xiao (2001) was selected and an estimation strategy was used. such strategy is described as follows: a) the df-gls test, suggested by elliott, rothenberg and stock (1996); b) optimal selection of lags used by ng and perron (2001); c) the non parametric correction for terms of perturbation non i.i.d., from the kernel smoothing. the empirical results show, for some series, a characterization of the dgp from the local persistence.
Model Averaging by Stacking  [PDF]
Claudio Morana
Open Journal of Statistics (OJS) , 2015, DOI: 10.4236/ojs.2015.57079
Abstract:

The paper introduces a new Frequentist model averaging estimation procedure, based on a stacked OLS estimator across models, implementable on cross-sectional, panel, as well as time series data. The proposed estimator shows the same optimal properties of the OLS estimator under the usual set of assumptions concerning the population regression model. Relatively to available alternative approaches, it has the advantage of performing model averaging exante in a single step, optimally selecting models’ weight according to the MSE metric, i.e. by minimizing the squared Euclidean distance between actual and predicted value vectors. Moreover, it is straightforward to implement, only requiring the estimation of a single OLS augmented regression. By exploiting exante a broader information set and benefiting of more degrees of freedom, the proposed approach yields more accurate and (relatively) more efficient estimation than available expost methods.

Towards Automatic Transformation from UML Model to FSM Model for Web Applications  [PDF]
Xi Wang, Huaikou Miao, Liang Guo
Journal of Software Engineering and Applications (JSEA) , 2008, DOI: 10.4236/jsea.2008.11010
Abstract: The need for automatic testing of large-scale web applications suggests the use of model-based testing technology. Among various modeling languages, UML is widely spread and used for its simplicity, understandability and ease of use. But rigorous analysis for UML model is difficult due to its lack of precise semantics. On the other hand, as a formal notation, FSM provides an avenue for automatic generation of test cases, but the requirement for mathematical basis makes itself academic inventions divorced from real applications. This paper proposes an approach to transforming UML model to FSM model, taking advantage of both languages. As our work focuses on the transformation of UML state diagrams to FSM models, a specific transformation mechanism is presented, which deals with different elements with different mapping rules. To illustrate the mechanism we proposed, an example of a web application for software download is presented. Finally, we give a method for implementation of the mechanism and a tool prototype to support the method.
The Models of Investing Schools  [PDF]
June Liu, Lei Chai, Zina Xu
Journal of Applied Mathematics and Physics (JAMP) , 2016, DOI: 10.4236/jamp.2016.46113
Abstract: In this paper, we build the Linear Programming (LP) model, factor analysis model and return on investment model to measure the investment amount and which year to invest of each selected schools. We firstly analyze the indicators from attached files, and select effective indexes to choose schools donated. Then we select 17 indexes out after preprocessing all the indices. Secondly, we extract 1064 schools by MATLAB which is the Potential Candidate Schools from the table of attached files; we extract 10 common factors of these schools by factor analysis. After calculation, we rank the universities and select the top 100. We calculate the Return on Investment (ROI) based on these 17 indexes. Thirdly, we figure out the investment amount by conducting LP model through MATLAB. According to the property of schools, we calculate the annual limit investment and the mount of investment of each school. Fourthly, we determine which year to invest by ROI model which is operated by LINGO. In order to achieve optimal investment strategy and not duplication of investment, for five years, starting July 2016, we assume that the time duration that the organization’s money should be provided is one year, and the school return to the Good grant Foundation only one year. Then we can get the investment amount per school, the return on that investment, and which years to invest. Fifthly, by changing parameter, the sensitivity analysis is conducted for our models. The result indicates that our models are feasible and robust. Finally, we evaluate our models, and point out the strengths and weakness. Through previous analysis, we can find that our models can be applied to many fields, which have a relatively high generalization.
A Small-Size Macroeconometric Model for Pakistan Economy  [PDF]
Muhammad Nadim Hanif, Zulfiqar Hyder, Muhammad Amin Khan Lodhi, Mahmood ul Hassan Khan, Irem Batool
Technology and Investment (TI) , 2011, DOI: 10.4236/ti.2011.22008
Abstract: This paper attempts to develop a small size macro-econometric model for Pakistan to analyze the effects of monetary policy on key macro variables through forecasting and simulations. The model comprises of 17 equations, out of which 11 are behavioral equations while the rest are either identities or definitional equations. OLS method is used to estimate the behavioral equations by using annual data from FY73-FY06. The paper analyzes results of policy simulations to quantify the impact of shocks to various exogenous variables.
Solving Basic Inventory Models Using Excel  [PDF]
Sarbjit Singh
Theoretical Economics Letters (TEL) , 2018, DOI: 10.4236/tel.2018.811137
Abstract: In this note, I have introduced a simple way to solve the four basic inventory models using Microsoft excel. This note can be used in courses like economics, operations management, operations research, supply chain management. This note can be used in teaching basic inventory models to avoid the lengthy manual calculation involved in solving them. It can also be used as an interesting example for an advanced class in Excel. The user just needs to enter the data in the white cells and all the results are automatically calculated. We recommend showing students how to first solve the models by hand (not necessarily the example problem), so that they understand the procedure, and then show them how to do it using Excel. The four models considered here are EOQ model, Basic production model, Discount Model and Shortage Model. Using the excel managers would be able to compare the various scenarios provided by the organization. They would find it very convenient to use these models.
NARCCAP Model Skill and Bias for the Southeast United States  [PDF]
Erik D. Kabela, Gregory J. Carbone
American Journal of Climate Change (AJCC) , 2015, DOI: 10.4236/ajcc.2015.41009
Abstract: This paper investigates dynamically downscaled regional climate model (RCM) output from the North American Regional Climate Change Assessment Program (NARCCAP) for two sub-regions of the Southeast United States. A suite of four statistical measures were used to assess model skill and biases were presented in hindcasting daily minimum and maximum temperature and mean precipitation during a historical reference period, 1970-1999. Most models demonstrated high skill for temperature during the historical period. Two outliers included two RCMs run using the Geophysical Fluids Dynamics Lab (GFDL) model as their lateral boundary conditions; these models suffered from a cold maximum temperature bias. Improvement with GFDL-based projections of maximum temperature was noted from May through November when they ran with observed seasurface conditions (GFDL-timeslice), particularly for the east sub-region. Precipitation skill proved mixed-relatively high when measured using a probability density function overlap measurement or the index of agreement, but relatively low when measured with root-mean square error or mean absolute error, because several models overestimated the frequency of extreme precipitation events.
Model Interpretation Development: Analysis Design of Automatic Control System  [PDF]
Guohua Wu, Wenning Liu, Qiuhua Zheng, Zhen Zhang
Journal of Software Engineering and Applications (JSEA) , 2009, DOI: 10.4236/jsea.2009.22017
Abstract: Currently the development of automatic control system is mainly based on manual design. This has made the develop-ment process complicated and has made it difficult to guarantee system requirement. This paper presents a Model in-terpretation development architecture built on meta-models and model interpretation. In this modeling and developing process, different meta-models or domain models may be constructed in terms of various system requirements. Inter-preters are used to transform the meta-model into relevant domain model and generate some other formats from do-main models, typically with different semantic domains. An interpretation extension interface is introduced, which can be accelerated to develop the model interpreter. This development architecture can improve system reusability and en-hance development efficiency. Finally, an example is introduced to explain the advantage of method.
Comparison of Stochastic Models in Forecasting Monthly Streamflow in Rivers: A Case Study of River Nile and Its Tributaries  [PDF]
Mohammed A. Elganiny, Alaa Esmaeil Eldwer
Journal of Water Resource and Protection (JWARP) , 2016, DOI: 10.4236/jwarp.2016.82012
Abstract: The dynamic and accurate forecasting of monthly streamflow processes of a river are important in the management of extreme events such as floods and drought, optimal design of water storage structures and drainage network. Many Rivers are selected in this study: White Nile, Blue Nile, Atbara River and main Nile. This paper aims to recommend the best linear stochastic model in forecasting monthly streamflow in rivers. Two commonly hydrologic models: the deseasonalized autoregressive moving average (DARMA) models and seasonal autoregressive integrated moving average (SARIMA) models are selected for modeling monthly streamflow in all Rivers in the study area. Two different types of monthly streamflow data (deseasonalized data and differenced data) were used to develop time series model using previous flow conditions as predictors. The one month ahead forecasting performances of all models for predicted period were compared. The comparison of model forecasting performance was conducted based upon graphical and numerical criteria. The result indicates that deasonalized autoregressive moving average (DARMA) models perform better than seasonal autoregressive integrated moving average (SARIMA) models for monthly streamflow in Rivers.
A Mixed Model Analysis of a Fertilizer Experiment on Oil Palm in Nigeria  [PDF]
Edwin A. Iguodala, Eghwerido Joseph Thomas, Austin Edokpayi, Obilade Titilola
Agricultural Sciences (AS) , 2016, DOI: 10.4236/as.2016.78052
Abstract:
Mixed model analysis procedure was used to analyze the effect of fertilizer application on the Fresh Fruit Bunch (FFB) yield of oil palm. This was with a view to achieve the most appropriate and a robust model for analyzing yield response for fertilizer application in oil palm. In this study, a mixed model analysis procedure was used to analyze yield data obtained from a fertilizer trial conducted between 1997 and 2005. In mixed effect model, replicates and years were used as block. In contrast the fixed effect ANOVA model usually lumped up replicates and years as a random error. In the model replicates were used as block with no block interaction, replicates as block with allowance for block-fertilizer interaction, years as block with allowance for block-fertilizer interaction, and years and replicates as block with allowance for year fertilizer and replicate-fertilizer interaction. Mixed model theory was also used to provide the explicit description of the design matrices in the models. Also, hypotheses relevant to each model were formulated and used to test for specific effects in the models such as, fixed part, random part and interacting parts using appropriate error terms as determined by the derived Expected Mean Squares (EMS). The results revealed that at 5% significant level (p < 0.05), the combination of Potassium (K) at 3.5 kg and magnesium (Mg) at 1.7 kg was sufficient for bunch yield of oil palm as the effect of fertilizer application was significant in the interactions of K and Mg due to treatment.
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