Covariance matrix

3. Data and Methodology Present paper utilizes the annual data of GDP, Indian FDI, level of Investment and Export in real terms from the period 1989/90 to 2013/14. The concerned variables are transformed into logarithm and hereafter these are denoted by 〖LnGDP〗_t,〖LnFDI〗_t 〖LnI〗_t and 〖LnX〗_t . Fully Modified Ordinary Least Squares (FMOLS) is the main econometric methodology used in this paper to examine the role and impact of Indian FDI on Nepalese economic growth. The FMOLS of economic growth

The appropriateness of a Mean-Variance Simulation Model for a Commonwealth public sector defined pension benefit fund Kevin Lian October 20, 2014 In this section, we will examine the use of a mean-variance simulation model on a commonwealth public section defined pension benefit fund. Firstly, we will examine the expected medium term returns using the reverse optimisation approach and discuss whether any adjustments are necessary. Secondly, we will examine how appropriate the model is for the fund

SIG Interview Questions 1. Torpedo question: 2 torpedoes, each with 1/3 probability of hitting/ sinking a ship 2. I have 20% chance to have cavity gene. If I do have the gene, there is 51% chance that I will have at least one cavity over 1 year. If I don’t have the gene, there is 19% chance that I will have at least one cavity over 1 year. Given that I have a cavity in 6 months, what’s the probability that I have at least a cavity over 1 year? 3. What is the probability of 5 people with different

To assess the sensitiveness of subjects to the homogeneity of the covariance matrices of the random effects, Nobre and Singer develop the method of influence methods from Cook (1986). The idea is to put some weights to the var(b), i.e. var(b) = WG and then calculate |dmax|, which is the normalized eigenvector associated with the direction of largest normal curvature of the influence graph under a perturbation of the covariance matrix of the random effects (for detail, see appendix or Cook (1986)).

tools and approach as the main analytical methods. It includes OLS regression after testing stationarity, co-integration and causal relationships among the variables for time series data-set. Present study employs secondary data on the concerned variables such as Indian FDI flows, structure of Indian FDI, Nepalese GDP, investment and export from FY 1988/89 to 2013/14. The necessary data are taken from Economic Survey 2013/14 and Department of Industry, Nepal. The data are transformed into real terms

entered the selection process. First, knowing the signal definition, and the binning for the truth and reconstructed variable distribution, we process the microtree file to build the truth and reconstructed signal histograms, as well as the smearing matrix. Then, we

Selected Explanatory Variables A micro approach was used to analyze which factors influence the adoption behavior of coffee producers with regard to SMPs. We considered farm and management characteristics, the socioeconomic profile of producers as well social capital indicators. Table 1 presents the descriptive statistics of the data collected. Several factors that were considered are accepted as common predictors of agricultural adoption in developing countries. However, three questions related

Based on the objectives of the experiment, it is important to describe the credit datasets, the classiers, the combination techniques of the classiers, and lastly the software used in carrying out the experiments.. 3.1 Datasets To access the prediction accuracy of the four classiers and their combinations in the two class classication problem analysis, two real life datasets taken from the University of California, Irvine(UCI) repository were used. These datasets are described below [28]. 3.1

Several factors that were considered are accepted as common predictors of agricultural adoption in developing countries. However, three questions related to explanatory variables were not included in the final questionnaire. Firstly, we did not consider access to bank loans because coffee farmers in Mexico are mostly unable to obtain investment credit from lending institutions. Coffee producers usually have the option of obtaining short term financing (cash advances) either from local traders or

Selected explanatory variables A micro-level approach was used in order to test which factors influence the adoption behavior of coffee producers with regard to SMPs. The model incorporates farm and management characteristics, the socioeconomic profile of producers and social capital indicators. Table 1 presents the descriptive statistics of the data collected. Several factors that were considered are accepted as common predictors of adoption in developing countries. However, three questions related