There is a vast literature on the compilation and use of Input Output Transactions Table (IOTT). The System of National Accounts (SNA?2008), the most recent international Standard presents the Input Output framework explaining how a pair of supply and use tables may be transformed into a single symmetric input output matrix. Each of the supply and use tables shows disaggregation by products and industries. In an IOTT one of these dimensions is eliminated. Thus a single table may show the relationship between the supply and use of products or alternatively the output of industries and the demand for the output of various products among the producing industry sectors and final users (households, non-profit institutions serving households, general government, gross fixed capital formation, change in stocks, valuables and net exports) during a year for an economy/ region. The uses of IOTT are well known.well known. At national level, the first official IOTT, consistent with the National Accounts Statistics related to the year 1968-69 and was published by the Central Statistical Organisation (CSO) in 1978. Subsequently the CSO has been undertaking preparation and publication of IOTT for the economy on regular basis once in every five years. At regional level, however, official regional IOTT based on survey data and following SNA guidelines have not been constructed in the country by the States. Survey based methods give high accuracy but they have not been undertaken so far, mainly due to lack of resources and non-availability of required disaggregated data at regional level. Research Institutions and researchers have prepared regional IOTT for some States using non- survey, semi-survey techniques and hybrid methods under all kind of assumptions. The limitations of such efforts include borrowing input structures from national level, assuming ratios of specific final use category to GDP as invariant for economy and region, not making IOTT aggregates? and State accounts aggregate?s consistent, not making any efforts to prepare the required matrices (Absorption and Make matrices, Trade and Transport matrices and the Taxes on products and Subsidies on products matrices) and not following the SNA recommendations for preparing symmetric matrices and IOTT. This presentation considers all relevant issues for compiling IOTT at regional level and suggests a feasible methodology for constructing regional IOTT. The proposed methodology takes care of balancing of supply and uses of most services products which form a major share of total products in the economy and more importantly makes IOTT consistent with official State macro- aggregates based on survey data. well known.

  Randomization of run sequences in factorial experiments may result in large number of changes in factor levels which will make the experimentation expensive, time-consuming and difficult. Experiments, in which it is difficult to change the levels of factor(s), use of run sequences with minimum changes may often be preferable to a random run sequence. Here, we have developed a general method to obtain symmetric factorial with minimum changes in run sequence along with general expression of factor-wise number of level changes. Further, minimally changed run sequences to develop half replicate of 2-level factorial experiments and 2-level factorial in which only highest order factorial effect get confounded, have also been obtained. For providing readymade solution to the end users, SAS macro have been developed for generating symmetric factorial and half replicate of 2-level factorial with minimum changes in run sequence along with its parameters. Keywords: Confounding, Factorial, Fractional factorial, Macro, Minimal change, Randomization, Run sequence.

  The present paper deals with the problem of estimating the correlation coefficient for finite population in case of Probability Proportional to Size without Replacement Sampling. Asymptotic expressions for the bias, an upper limit of the bias, variance and an estimate of variance of the proposed estimator for finite population correlation ? have been obtained. An empirical investigation has also been made. Keywords: Correlation coefficient, Probability proportional to size without replacement sampling, Midzuno's scheme of sampling, mean square error.

  In this article the problem of finding designs insensitive to the presence of an outlier in a block design for estimating a complete set of orthonormal treatment contrasts has been considered when the block effects are random. Also for a treatment- control block design robustness has been studied for the estimation of the set of elementary contrasts between the effects of each test treatment and a control treatment under the same as sumption on the block effects. The criterion of robustness, suggested by Mandal (1989) in the block design setup for estimating a full set of orthonormal treatment contrasts, is adapted here. It is shown that a randomized block design (RBD) in complete blocks, a balanced incomplete block design (BIBD) and a partially balanced incomplete block design (PBIBD), under certain conditions, in incomplete blocks are robust in the above sense. In the treatment- control setup, a balanced treatment incomplete block design (BTIBD) and a partially balanced treatment incomplete block design (PBTIBD), under certain conditions, are also proved to be robust in the above sense. Keywords: Robust designs, PBIBD, BTIBD, PBTIBD, Outlier, Mixed effects model.

  The assumption of normality in data has been considered in the field of statistical analysis for a long time. However, in many practical situations, this assumption is clearly unrealistic. It has recently been suggested to study the performance of various statistical techniques like classification by using the data from distributions indexed by skewness/ shape parameters. In this study, four different classification techniques, namely linear discriminant analysis, quadratic discriminant analysis, k-th nearest neighbor and oblique axes method are considered for classification of observations. To assess the performance of the above techniques under non-normality caused by skewness, which is introduced in the ricebean data by using multivariate skew-normal distribution through simulation. Apparent error rate is used to study the classification performance of these techniques. The result of this study can be used for choosing the best method of classification for skewed-normal situation. The results indicate that k-th nearest neighbour followed by oblique axes methodand linear discriminant analysisperform better in skew-normal situations than normal condition and quadratic discriminant analysis performed better in normal data. For maximum consistency and accuracy of classification of skew-normal data, k-th nearest neighbor is best among the four classification techniques. Keywords: Classification, Linear discriminant analysis, Quadratic discriminant analysis, k-th nearest neighbor, Oblique axes method, Apparent error rate, Multivariate skew normal distribution.

  The price signals of agricultural commodities from markets located in different locations play a very important role in the economy. The price signals guide and regulate production, consumption and marketing decisions over time. Therefore, if markets are not well integrated, the price signals are distorted, which will lead to inefficient resource allocation and hamper sustainable agricultural development. This paper employs an econometric modeling for estimating a vector error correction model (VECM) to investigate the degree of spatial market integration and price transmission between the important coffee consuming centers in India (viz. Bangalore, Chennai and Hyderabad) using month-wise wholesale prices of coffee seeds. The cointegration analysis reveals long run equilibrium subject to price transmission among the markets. The out of sample forecasting performance of VECM model is also computed for cointegrated markets. The degree of integration and price adjustment to deviations from long run equilibrium ranges between 12 to 52%. The results obtained are expected to contribute in the field of planning and forecasting. Keywords: Cointegration, Error correction model, Forecasting, Price transmission.

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  The presence of genotype-environment interactions necessitates the development of varieties or breeds suited or tailored to different agro-ecological environments based on their stability and adaptability characteristics. In many situations, the assumption about the normality and independence of observations as well as homogeneity of error variances is not fulfilled. This investigation aims to determine and compare, in terms of statistical power, the performance of different parametric and non-parametric methods for stability measures when the basic data are not normally distributed. The presence of genotype-environment interactions necessitates the development of varieties or breeds suited or tailored to different agro-ecological environments based on their stability and adaptability characteristics. In many situations, the assumption about the normality and independence of observations as well as homogeneity of error variances is not fulfilled. This investigation aims to determine and compare, in terms of statistical power, the performance of different parametric and non-parametric methods for stability measures when the basic data are not normally distributed. Keywords: Genotype environment interaction, Statistical power, Simulation, Stability measures.

  The purpose of present study is to develop the econometric simulation model of Madhya Pradesh dal industry. The annual production data of pulse was taken from Directorate of Economics and Statistics, Govt. of Madhya Pradesh and Govt. of India. The data of population was taken from the Directorate of Census, Govt. of India. Four models were developed for domestic consumption of pulse, price of pulse, supply of pulse and price of milled pulse dal. Regression analysis and two stage least square analysis techniques were used to estimate the parameters. The estimated models were validated with the help of Theil inequality, root mean square error, mean absolute error and coefficient of determination (R2). The result indicated that all the four models fits very well to the data sets. In all the four models, Theil inequality coefficient was almost zero and coefficient of determination (R2) was almost equal to one, this indicates perfect prediction. The purpose of present study is to develop the econometric simulation model of Madhya Pradesh dal industry. The annual production data of pulse was taken from Directorate of Economics and Statistics, Govt. of Madhya Pradesh and Govt. of India. The data of population was taken from the Directorate of Census, Govt. of India. Four models were developed for domestic consumption of pulse, price of pulse, supply of pulse and price of milled pulse dal. Regression analysis and two stage least square analysis techniques were used to estimate the parameters. The estimated models were validated with the help of Theil inequality, root mean square error, mean absolute error and coefficient of determination (R2). The result indicated that all the four models fits very well to the data sets. In all the four models, Theil inequality coefficient was almost zero and coefficient of determination (R2) was almost equal to one, this indicates perfect prediction.

  Cluster analysis is used extensively to organize data into groups based on similarities among the individual data items, leading to a crisp or fuzzy partition of sample space. Fuzzy c-means (FCM) is a clustering algorithm which all owsone data point to be long to two or more clusters. In this paper, principal component based fuzzy c-means clustering is applied for classifying 518 lentil genotypes based on their numeric agronomic and morphological traits. The appropriate number of clusters is obtained with the help of validity measures. Results of the study revealed that the genetic divergence is not highly related to geographical origins as exotic and indigenous lentil genotypes are distributed in all the four clusters. Keywords: FCM algorithm , Fuzzy clustering, Lentil and Validity measures.

  Crop production statistics for a small area like Community Development Block (generally referred to as Block) or Panchayat are now essential in view of decentralized planning process at micro-level in India. Generally, estimates of crop production or yield through crop cutting experiments (CCEs) are being reported at district level and these estimates are aggregated at state and country level. If reasonably precise estimates are required for further smaller geographical levels such as Block or Gram Panchayat the number of CCEs is expected to increase enormously. However, conducting requisite number of area specific CCEs is neither operationally feasible nor is economically viable. Sisodia and Singh (2001) proposed a scale down approach using multiple regression model to obtain the block level estimate from the district level crop-production estimate. Singh et al. (2012) proposed a predictive approach of estimation of crop-production at block level using the multiple regression model fitted at district level. In the present paper, an attempt has been made to examine the various options of using explanatory variables in the regression model for better prediction of block level estimate of crop-production. Three options are considered (i) using the auxiliary variables as such in the model, (ii) application of principal component analysis and (iii) step-wise regression analysis. It is assumed that the auxiliary information available at district level is also available at block level. An empirical study with wheat production data of Sultanpur district of the State of Uttar Pradesh, India shows that approach works well and provides reliable estimates of wheat production at block level by applying technique of principal component analysis. The estimator based on least squares adjustment has performed better in most of the cases than other estimators in terms of percent standard error. Keywords: Block level estimate, Principal component analysis, Regression model.