The Impact of Institutional Credit on Agricultural Production in Pakistan

Three main factors that contribute to agricultural growth are the increased use of agricultural inputs, technological change and technical efficiency. Technological change is the result of research and development efforts, while technical efficiency with which new technology is adopted and used more rationally is affected by the flow of information, better infrastructure, availability of funds and farmers’ managerial capabilities. Higher use and better mix of inputs also requires funds at the disposal of farmers. These funds could come either from farmers’ own savings or through borrowings. In less developed countries like Pakistan where savings are negligible especially among the small farmers, agricultural credit appears to be an essential input along with modern technology for higher productivity.

MONETARY EXCHANGE RATE MODEL REVISITED: COINTEGRATION AND FORECASTING IN HETEROGENEOUS PANEL DATA

ABSTRACT This study re-examines the exchange rate-monetary fundamentals link with in a panel data framework. Pure time series and pooled time series-based tests fail to find empirical support for monetary exchange rate models (Sarantis (1994) and Groen (2000)). Using recently developed Panel Data Techniques; we would test the exchange rates and monetary fundamentals in a quarterly panel of 19 countries mostly from developed region extending from 1973.1 to 1997.1. Present analysis would be centered on three issues. First, we test whether exchange rates cointegrated with long run determinants predicted by economic theory. For this purpose, we would be employed Pedroni (1997) and Larsson et al (2001) panel cointegration tests for empirical validation of the study. Second, we will also test the short run implications of exchange rate model. These short run implications will be tested; through adapting the panel VEC model the short run identification schemes of Johansen and Juselius (1994). The last issue is to examine the ability for monetary fundamentals to forecast future exchange rate returns. The present endeavor will follow Mark and Sul (2001) approach for forecasting in the case of Panel Data Testing.Panel cointegration; Prediction; Exchange rates.

Correlation functions for extended mass galaxy clusters

The phenomenon of clustering of galaxies on the basis of correlation functions in an expanding Universe is studied by using equation of state, taking gravitational interaction between galaxies of extended nature into consideration. The partial differential equation for the extended mass structures of a two-point correlation function developed earlier by Iqbal, Ahmad and Khan is studied on the basis of assigned boundary conditions. The solution for the correlation function for extended structures satisfies the basic boundary conditions, which seem to be sufficient for understanding the phenomena, and provides a new insight into the gravitational clustering problem for extended mass structures.Comment: 3 pages, no figure

Can online trading algorithms beat the market? An experimental evaluation

From experimental evaluation, we reasonably infer that online trading algorithms can beat the market. We consider the scenario of trading in financial market and present an extensive experimental study to answer the question "Can online trading algorithms beat the market?". We evaluate the selected set of online trading algorithms on DAX30 and measure the performance against buy-and-hold strategy. In order to compute the experimentally achieved competitive ratio, we also compare the set of algorithms against an optimum offline algorithm. To add further dimensionality into experimental setup, we use trading periods of various lengths and apply a number of evaluation criteria (such as annualized geometric returns, average period returns and experimentally achieved competitive ratio) to measure the performance of algorithms in short vs. Long term investment decisions. We highlight the best and worst performing algorithms and discuss the possible reasons for the performance behavior of algorithms