Explosive Roots in Level Vector Autoregressive Models

Level vector autoregressive (VAR) models are used extensively in empirical macroeconomic research. However, estimated level VAR models may contain explosive roots, which is at odds with the widespread consensus among macroeconomists that roots are at most unity. This paper investigates the frequency of explosive roots in estimated level VAR models in the presence of stationary and nonstationary variables. Monte Carlo simulations based on datasets from the macroeconomic literature reveal that the frequency of explosive roots exceeds 40% in the presence of unit roots. Even when all the variables are stationary, the frequency of explosive roots is substantial. Furthermore, explosion increases significantly, to as much as 100% when the estimated level VAR coefficients are corrected for small-sample bias. These results suggest that researchers estimating level VAR models on macroeconomic datasets encounter explosive roots, a phenomenon that is contrary to common macroeconomic belief, with a very high frequency. Monte Carlo simulations in the paper reveal that imposing unit roots in the estimation can substantially reduce the frequency of explosion. Hence one way to mitigate explosive roots is to estimate vector error correction models.Level VAR Models, Explosive Roots, Bias Correction

News Shocks and Learning-by-doing

The idea that expectations about future economic fundamentals can drive business cycles dates back to the early twentieth century. However, the standard real business cycle (RBC) model fails to generate positive comovement in output, consumption, labor-hours and investment in response to news shocks. This paper proposes a simple and intuitive solution to this puzzling feature of the RBC model, based on a mechanism that has strong empirical support: learning-by-doing (LBD). First, we show that the one-sector RBC model augmented by LBD can generate aggregate comovement in response to news shock about technology. Second, we show that in the two-sector RBC model, LBD along with an intratemporal adjustment cost can generate sectoral comovement in response to news about three types of shocks: i) neutral technology shock, ii) consumption technology shock, and iii) investment technology shock. We show that these results hold for contemporaneous technology shocks and for different specifications of LBD.News Shocks, Learning-by-Doing, Pigou Cycles

Home Away From Home

Home Away From Home (HAFH) is a web-based application provides a solution for owners of property and people seeking short-term accommodation. When the marketplace offers Services or Rentals it requires an availability management system so that providers can reliably specify their availability, and Customers can easily book on the dates that fit them. In addition, it has single sign in feature available for customer to check history. Furthermore, it will have different functionality such as search engine to sort out the best deal for the customer. There are three types of user accounts admin, owner and renter. Each of these roles can manage its own profile and settings and has its specific authority and restrictions. The operating system that to use this Web-application is Windows 7 and the user interface of the application is JSP, CSS and we use JavaScript, jQuery as a client-side scripting. Our IDE is Visual Eclipse Neon and the database is MYSQL Database. We will be including major functionalities in our project. A customer Registration and Login screen. Users can look for desired accommodation by filtering their travel like Dates, room type, price range etc. Payment Feature user can select desired payment method and also they can check history of the payment. User can share Experience by rate and review

Comparative analysis of spatial and transform domain methods for meningioma subtype classification

Pattern recognition in histopathological image analysis requires new techniques and methods. Various techniques have been presented and some state of the art techniques have been applied to complex textural data in histological images. In this paper, we compare the novel Adaptive Discriminant Wavelet Packet Transform (ADWPT) with a few prominent techniques in texture analysis namely Local Binary Patterns (LBP), Grey Level Co-occurrence Matrices (GLCMs) and Gabor Transforms. We show that ADWPT is a better technique for Meningioma subtype classification and produces classification accuracies of as high as 90%

Landlessness and Rural Poverty in Pakistan

Although reducing rural poverty has been the key agenda of economic reforms in Pakistan, the rural poverty continued to rise during the 1990s. The causes of rural poverty are complex and multidimensional. The rural poor are quite diverse both in the problems they face, and the possible solutions to these problems are also different. The paper uses the most recent household data set available—PIHS 2001-02—to examine the causes of rural poverty, as to what accounts for its persistence and what policy measures should be taken to alleviate it. Poverty estimates using official poverty line suggest the high prevalence of rural poverty ranging from 39 percent to 48 percent in all provinces. Rural poverty is found to be strongly correlated with lack of asset in rural areas. The unequal land ownership in the country is found to be one of the major causes of rural poverty, as poverty level was the highest among the landless households followed by non-agriculture households. The incidence of landlessness is common in rural areas. About 67 percent households own no land in the country. Unusually, just 0.3 percent households own 55 and above acres of land across the country, suggesting a highly skewed landownership pattern. Gini Coefficient of landholding suggests that Punjab has the most unequal landownership pattern, followed by the NWFP, Sindh, and Balochistan. The highly unequal land distribution seems to have resulted in tenancy arrangements such as sharecropping, resulting in high prevalence of absolute poverty particularly in Sindh. A broad-based land reform programme, including land redistribution and fair and enforceable tenancy contracts together with rural public works programmes and access to credit, is critical to reducing rural poverty in Pakistan.Poverty, Pakistan

Meningioma classification using an adaptive discriminant wavelet packet transform

Meningioma subtypes classification is a real world problem from the domain of histological image analysis that requires new methods for its resolution. Computerised histopathology presents a whole new set of problems and introduces new challenges in image classification. High intra-class variation and low inter-class differences in textures is often an issue in histological image analysis problems such as Meningioma subtypes classification. In this thesis, we present an adaptive wavelets based technique that adapts to the variation in the texture of meningioma samples and provides high classification accuracy results. The technique provides a mechanism for attaining an image representation consisting of various spatial frequency resolutions that represent the image and are referred to as subbands. Each subband provides different information pertaining to the texture in the image sample. Our novel method, the Adaptive Discriminant Wavelet Packet Transform (ADWPT), provides a means for selecting the most useful subbands and hence, achieves feature selection. It also provides a mechanism for ranking features based upon the discrimination power of a subband. The more discriminant a subband, the better it is for classification. The results show that high classification accuracies are obtained by selecting subbands with high discrimination power. Moreover, subbands that are more stable i.e. have a higher probability of being selected provide better classification accuracies. Stability and discrimination power have been shown to have a direct relationship with classification accuracy. Hence, ADWPT acquires a subset of subbands that provide a highly discriminant and robust set of features for Meningioma subtype classification. Classification accuracies obtained are greater than 90% for most Meningioma subtypes. Consequently, ADWPT is a robust and adaptive technique which enables it to overcome the issue of high intra-class variation by statistically selecting the most useful subbands for meningioma subtype classification. It overcomes the issue of low inter-class variation by adapting to texture samples and extracting the subbands that are best for differentiating between the various meningioma subtype textures

Associating Perinatal Mortality With Diet By Adapting Robust Clustering Using Links For Categorical Variables

Perinatal Mortality (perinatal death), is death of a neonate within 6 days (early neonatal mortality) or from 7 – 27 days of birth (late neonatal mortality). Food consumed by an expectant mother is said to have an impact on the pregnancy outcome apart from other factors. For the past few years, perinatal mortality rate has been increasing in developing and under-developed parts of the world. Two-thirds of the world’s perinatal deaths occur in only 10 countries, and Pakistan is ranked third amongst these countries. These deaths have not been studied widely, in fact they have been under-reported and these reports have not even been considered in any attempts made to improve birth outcomes in developing nations [1]. Nutritional, socioeconomic, demographic and health advice seeking behavior factors are responsible for higher mortality rates in countries such as Pakistan. Data mining and machine learning can be used to identify factors that are responsible for such high infant mortality rates as it is an important factor indicating progress on Millennium Development Goals. In this paper, we discuss how using ROCK we can cluster expectant mothers as per the food intake and identify major food items causing perinatal mortality

A robust adaptive wavelet-based method for classification of meningioma histology images

Intra-class variability in the texture of samples is an important problem in the domain of histological image classification. This issue is inherent to the field due to the high complexity of histology image data. A technique that provides good results in one trial may fail in another when the test and training data are changed and therefore, the technique needs to be adapted for intra-class texture variation. In this paper, we present a novel wavelet based multiresolution analysis approach to meningioma subtype classification in response to the challenge of data variation.We analyze the stability of Adaptive Discriminant Wavelet Packet Transform (ADWPT) and present a solution to the issue of variation in the ADWPT decomposition when texture in data changes. A feature selection approach is proposed that provides high classification accuracy

Landlessness and Rural Poverty in Pakistan

Poverty imposes a repressive weight on Pakistan particularly in rural areas where almost one third of population and majority of the poor live. Although poverty has declined during the 1970s and 1980s, the absolute number of poor has increased substantially since the 1960s. Despite a number of policy initiatives and programmes undertaken for poverty alleviation by various governments, absolute poverty particularly in rural areas continued to rise in Pakistan during the 1990s. Much has been written about poverty in Pakistan so far. A number of attempts have been made by various authors/institutions to estimate the rural poverty in Pakistan in the 1990s. Discussions have remained limited to estimating the regional and provincial trends for rural poverty in Pakistan. Although landlessness and rural poverty in Pakistan received significant attention in the 1970 and 1980, discussions on this issue remained limited in the 1990s. Landlessness and rural poverty are closely linked since land is a principal asset in a rural economy like Pakistan. Landlessness to agricultural land is considered to be the most important contributor to rural poverty. A high concentration of landownership is a major constraint to agricultural growth and alleviation of poverty. There is a general perception that highly skewed distribution of land in Pakistan is one of the important causes of widespread poverty particularly in rural areas