scholarly journals Potential for pork production in the Czech Republic

2011 ◽  
Vol 57 (No. 11) ◽  
pp. 545-554
Author(s):  
M. Malý ◽  
Z. Kroupová ◽  
D. Žídková ◽  
J. Peterová ◽  
L. Šobrová ◽  
...  
Keyword(s):  
Czech Republic ◽  
Least Squares ◽  
Production Function ◽  
Least Squares Method ◽  
Final Analysis ◽  
Fixed Effect Model ◽  
Ordinary Least Squares ◽  
Coefficient Of Determination ◽  
The Czech Republic ◽  
Pork Production

The main aim of the paper was a partial analysis of the production potential for pig fattening in the Czech Republic. This aim was achieved by econometric modelling of the production function, which was specified as a Cobb-Douglas function, with the level of average daily increase as the dependent variable, and feed compounds, mortality and weight of new stock as independent variables. The model was specified as a fixed effect model, and the parameters of the function were estimated by the method of least squares dummy variable, based on the ordinary least squares method. Verification of the estimated model was based on a t-test, coefficient of determination, Wald test, autoregressive test, and test of normality distribution of residuals. Subsequently, the estimated function was analysed and significant determinants of production were identified. The behaviour of the production functions was analysed for the average and marginal productions. The functions were also illustrated in graphs of production surfaces, from which the maps of isoproduction functions were derived. The isoproduction functions were used for the final analysis of the potential for pork production. The analysis was based on panel data from 32 farms focused on pig fattening, collected by our own survey. The research indicated significant differences between the surveyed farms. It also declared the most important factor of final production to be, with 99% probability, the new stock weight. The second most important determinant of final production is the feed compound A3, which is used in the final stage of fattening. For maximized production, the farmer should focus on the weight of pigs coming into fattening, choose the biggest one, and introduce the use of the feed compound A3. The results in the submitted paper should also be used by farmers to evaluate their production activity, and to compare their actual output with the theoretical value enumerated by the production function.

scholarly journals The Technical Efficiency of Illinois Grain Farms: An Application of a Ray-Homothetic Production Function

1987 ◽  
Vol 19 (1) ◽  
pp. 69-78 ◽  
Author(s):  
Hassan Y. Aly ◽  
Krishna Belbase ◽  
Richard Grabowski ◽  
Steven Kraft
Keyword(s):  
Least Squares ◽  
Production Function ◽  
Linear Form ◽  
Least Squares Method ◽  
Ordinary Least Squares ◽  
Technical Inefficiency ◽  
Large Farms ◽  
Grain Farms ◽  
Ordinary Least Squares Method ◽  
Douglas Production Function

AbstractThe purpose of this paper is to measure the extent of technical inefficiency among a sample of Illinois grain farms using the corrected ordinary least squares method. Instead of assuming a Cobb-Douglas production function, a linear form of the ray-homothetic is used. The results show a significant amount of technical inefficiency among all the farms in the sample, but with large farms being less technically inefficient than small farms.

scholarly journals Comparing ols and rank-based estimation techniques for production analysis: An application to Ghanaian maize farms.

2016 ◽  
Vol 10 (4-5) ◽  
pp. 125-130
Author(s):  
Henry De-Graft Acquah
Keyword(s):  
Least Squares ◽  
Production Function ◽  
Least Squares Method ◽  
Estimation Method ◽  
Ordinary Least Squares ◽  
Least Square ◽  
Least Squares Regression ◽  
Estimation Techniques ◽  
Production Analysis ◽  
Douglas Production Function

This paper introduces the rank-based estimation method to modelling the Cobb-Douglas production function as an alternative to the least squares approach. The intent is to demonstrate how a nonparametric regression based on a rank-based estimator can be used to estimate a Cobb-Douglas production function using data on maize production from Ghana. The nonparametric results are compared to common parametric specification using the ordinary least squares regression. Results of the study indicate that the estimated coefficients of the CobbDouglas Model using the Least squares method and the rank-based regression analysis are similar. Findings indicated that in both estimation techniques, land and Equipment had a significant and positive influence on output whilst agrochemicals had a significantly negative effect on output. Additionally, seeds which also had a negative influence on output was found to be significant in the robust rank-based estimation, but insignificant in the ordinary least square estimation. Both the least squares and rank-based regression suggest that the farmers were operating at an increasing returns to scale. In effect this paper demonstrate the usefulness of the rank-based estimation in production analysis. JEL CODE: Q18, D24, Q12, C1 and C67

scholarly journals Least Squares Estimations for the General Linear Model Parameters with Epsilon Skew Normal Error Term

2020 ◽  
pp. 636-645
Author(s):  
Hussain Karim Nashoor ◽  
Ebtisam Karim Abdulah
Keyword(s):  
Least Squares ◽  
Linear Model ◽  
Least Squares Method ◽  
General Linear Model ◽  
Ordinary Least Squares ◽  
Skewed Distribution ◽  
General Linear ◽  
Average Error ◽  
Model Parameters ◽  
Skew Normal

Examination of skewness makes academics more aware of the importance of accurate statistical analysis. Undoubtedly, most phenomena contain a certain percentage of skewness which resulted to the appearance of what is -called "asymmetry" and, consequently, the importance of the skew normal family . The epsilon skew normal distribution ESN (μ, σ, ε) is one of the probability distributions which provide a more flexible model because the skewness parameter provides the possibility to fluctuate from normal to skewed distribution. Theoretically, the estimation of linear regression model parameters, with an average error value that is not zero, is considered a major challenge due to having difficulties, as no explicit formula to calculate these estimates can be obtained. Practically, values for these estimates can be obtained only by referring to numerical methods. This research paper is dedicated to estimate parameters of the Epsilon Skew Normal General Linear Model (ESNGLM) using an adaptive least squares method, as along with the employment of the ordinary least squares method for estimating parameters of the General Linear Model (GLM). In addition, the coefficient of determination was used as a criterion to compare the models’ preference. These methods were applied to real data represented by dollar exchange rates. The Matlab software was applied in this work and the results showed that the ESNGLM represents a satisfactory model. 

scholarly journals GM(1,1;λ) with Constrained Linear Least Squares

Axioms ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 278
Author(s):  
Ming-Feng Yeh ◽  
Ming-Hung Chang
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Model Fitting ◽  
Ordinary Least Squares ◽  
Linear Least Squares ◽  
Unknown Parameters ◽  
Constrained Problems ◽  
Linear Least Squares Problems ◽  
Ordinary Least Squares Method ◽  
Better Than

The only parameters of the original GM(1,1) that are generally estimated by the ordinary least squares method are the development coefficient a and the grey input b. However, the weight of the background value, denoted as λ, cannot be obtained simultaneously by such a method. This study, therefore, proposes two simple transformation formulations such that the unknown parameters, and can be simultaneously estimated by the least squares method. Therefore, such a grey model is termed the GM(1,1;λ). On the other hand, because the permission zone of the development coefficient is bounded, the parameter estimation of the GM(1,1) could be regarded as a bound-constrained least squares problem. Since constrained linear least squares problems generally can be solved by an iterative approach, this study applies the Matlab function lsqlin to solve such constrained problems. Numerical results show that the proposed GM(1,1;λ) performs better than the GM(1,1) in terms of its model fitting accuracy and its forecasting precision.

Creativity as a Tool of Tourism Development

2020 ◽  
pp. 504-530
Author(s):  
Alžbeta Kiráľová
Keyword(s):  
Czech Republic ◽  
Correlation Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Tourism Development ◽  
Multivariate Methods ◽  
The Czech Republic ◽  
Future Studies

This chapter shows how creativity is bounded with tourism development in the destination. It points out the influence of changes in visitors´ behavior on the destinations, defines creativity, and discusses the relation of culture and creativity in tourism. The chapter focuses on the relation between creativity and development of tourism in the Czech Republic´s regions in the pre-crisis, crisis and after-crisis period. The destinations were subjects to research using two multivariate methods i.e. canonical correlation analysis (CCA) and partial least squares (PLS). The chapter also makes suggestions for future studies.

Mandatory CSR expenditure and stock return

2020 ◽  
Vol 28 (6) ◽  
pp. 951-975
Author(s):  
Asit Bhattacharyya ◽  
Md Lutfur Rahman
Keyword(s):  
Least Squares ◽  
Stock Returns ◽  
Least Squares Method ◽  
Negative Impact ◽  
Generalized Method Of Moments ◽  
Ordinary Least Squares ◽  
Reverse Causality ◽  
Endogeneity Bias ◽  
Content Type ◽  
The Impact

Purpose India has mandated corporate social responsibility (CSR) expenditure under Section 135 of the Indian Companies Act, 2013 – the first national jurisdiction to do so. The purpose of this paper is to examine the impact of mandated CSR expenditure on firms’ stock returns by using actual CSR spending data, whereas the previous studies mostly focus on voluntary CSR proxied by CSR scores. Design/methodology/approach The authors estimate their baseline regression by using ordinary least squares(OLS) method. Although the baseline regression involving CSR expenditure and stock returns using ordinary least squares method are estimated, endogeneity and reverse causality biases are addressed by using two-stage least squares and generalized method of moments approaches. These approaches contribute mitigating endogeneity bias and biases associated with unobserved heterogeneity and simultaneity. Findings The findings document that mandatory CSR expenditure has a negative impact on firms’ stock returns which supports the “shareholders” expense’ view. This result remain robust after controlling for endogeneity bias and the use of both standard and robust test statistics. The authors however observe that this result holds for the firms with actual CSR expenditure equal to the mandated amount but does not hold for the firms with actual CSR expenditure greater than the mandated amount. Therefore, the authors provide evidence that CSR expenditure’s impact on stock returns depends on whether firms simply comply the regulation or voluntarily chose an amount of CSR expenditure above the mandated amount. Originality/value The primary contribution is to present a valid and robust evidence of negative effect of mandated CSR spending on firms’ stock returns when the mandatory CSR spending rule is already in place. This study contributes by examining the impact of mandated CSR spending on stock during post-implementation period (2015-2017), whereas other studies by Dharampala and Khanna (2018); Kapoor and Dhamija (2017); and Mukherjee et al. (2018) mainly examined the impact of legislation on Indian CSR. The authors use mandated actual CSR expenditure, whereas previous studies mostly focus on voluntary CSR proxied by CSR scores.

Harmonizing biomass tables by generalized least squares

1985 ◽  
Vol 15 (2) ◽  
pp. 331-340 ◽  
Author(s):  
T. Cunia ◽  
R. D. Briggs
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Weighted Least Squares ◽  
Regression Function ◽  
Generalized Least Squares ◽  
Ordinary Least Squares ◽  
Sample Tree ◽  
Tree Data ◽  
Regression Techniques ◽  
Ordinary Least Squares Method

To construct biomass tables for various tree components that are consistent with each other, one may use linear regression techniques with dummy variables. When the biomass of these components is measured on the same sample trees, one should also use the generalized rather than ordinary least squares method. A procedure is shown which allows the estimation of the covariance matrix of the sample biomass values and circumvents the problem of storing and inverting large covariance matrices. Applied to 20 sets of sample tree data, the generalized least squares regressions generated estimates which, on the average were slightly higher (about 1%) than the sample data. The confidence and prediction bands about the regression function were wider, sometimes considerably wider than those estimated by the ordinary weighted least squares.

scholarly journals HYPRESCZ - database of soil hydrophysical properties in the Czech Republic

2013 ◽  
Vol 8 (No. 1) ◽  
pp. 34-41 ◽  
Author(s):  
M. Miháliková ◽  
S. Matula ◽  
F. Doležal
Keyword(s):  
Czech Republic ◽  
Mean Squared Error ◽  
Arable Land ◽  
Pedotransfer Functions ◽  
Coefficient Of Determination ◽  
Quality Of Data ◽  
Soil Water Retention ◽  
The Czech Republic ◽  
European Database ◽  
S Model

The database of soil hydrophysical properties in the Czech Republic called HYPRESCZ was created. It is based on the European database HYPRES, HYdraulic PRoperties of European Soils, and follows its structure with few modifications. It collects the available data from the Czech Republic from which pedotransfer functions (PTFs) for the estimation of soil hydrophysical properties from easily available soil properties can be derived and 2101&nbsp;database entries were collected. The entries have different quality of data, out of the total number of entries 707 entries were applicable to PTFs derivation for the estimation of soil water retention curves (SWRCs). After elimination of replicates, finally 159 unique soil horizons (arable land only) were used for PTFs derivation. The parametric continuous pedotransfer functions for estimation of SWRCs in the Czech Republic were derived within this study and are based on W&ouml;sten&rsquo;s model. The retention curves were estimated using both these newly derived PTFs and W&ouml;sten&rsquo;s original model, which was derived for European soils in general. The uncertainty of estimation was evaluated, employing the root mean squared error (RMSE) and the coefficient of determination (R<sup>2</sup>) comparing the PTF-estimated and the directly fitted retention curves. The reliability of the newly derived PTFs for Czech soils was higher (RMSE = 0.059 cm<sup>3</sup>/cm<sup>3</sup> and R<sup>2</sup> = 71%) compared to W&ouml;sten&rsquo;s general PTFs (RMSE = 0.11 cm<sup>3</sup>/cm<sup>3</sup> and R<sup>2</sup> = 36%).

Extreme avalanche runout: a comparison of empirical models

2001 ◽  
Vol 38 (6) ◽  
pp. 1254-1265 ◽  
Author(s):  
D M McClung
Keyword(s):  
Least Squares ◽  
Dynamic Models ◽  
Least Squares Method ◽  
Gumbel Distribution ◽  
Ordinary Least Squares ◽  
Snow Avalanche ◽  
Statistical Prediction ◽  
Mountain Range ◽  
Empirical Methods ◽  
Slope Steepness

The prediction of snow avalanche runout distances and the probability of exceeding the predicted positions is the first and most important step required before making decisions about placement of facilities or control structures in snow avalanche prone terrain. There are two main prediction methods for calculating runout distances: (1) procedures linked to the selection of friction coefficients in avalanche dynamic models, and (2) empirical, statistical prediction based on terrain parameters for a set of extreme runout distances for the mountain range in question. Within the second method there are presently two competing empirical approaches to prediction: (i) ordinary least squares regression analysis related to angles measured for the path profile in question, and (ii) extreme value prediction of runout based on a Gumbel distribution of a dimensionless terrain parameter. In this paper a comparison of the two empirical methods with emphasis on the slope steepness in the runout zone is provided. The comparison is important, since the choice of method does affect the probability of the runout position being exceeded, particularly far into the runout zone where facilities are most likely to be located. The analysis shows that comparison of the models hinges on slope steepness in the runout zone and differences in calculating exceedance probabilities from the distributions used in the analysis (Gumbel distribution and Gaussian). The method based on the dimensionless Gumbel parameter provides more conservative predictions for flat terrain in the runout zone, and the regression - least squares method is more conservative for steep terrain in the runout zone. In addition, the Gumbel method is shown to be compatible with the characteristics of runout zone slope steepness shown by field data: there is very little dependence of runout distance on runout zone slope steepness.Key words: snow avalanche, runout, empirical methods, statistical methods.

Modelling of yields and soil nitrogen dynamics for crop rotations by HERMES under different climate and soil conditions in the Czech Republic

2013 ◽  
Vol 152 (2) ◽  
pp. 188-204 ◽  
Author(s):  
P. HLAVINKA ◽  
M. TRNKA ◽  
K. C. KERSEBAUM ◽  
P. ČERMÁK ◽  
E. POHANKOVÁ ◽  
...  
Keyword(s):  
Czech Republic ◽  
Soil Layer ◽  
Crop Rotations ◽  
Coefficient Of Determination ◽  
Soil Conditions ◽  
Good Precision ◽  
Soil Mineral ◽  
Organic C ◽  
The Czech Republic ◽  
Wide Range

SUMMARYThe crop growth model HERMES was used to model crop rotation cycles at 12 experimental sites in the Czech Republic. A wide range of crops (spring and winter barley, winter wheat, maize, potatoes, sugar beet, winter rape, oats, alfalfa and grass), cultivated between 1981 and 2009 under various soil and climatic conditions, were included. The model was able to estimate the yields of field crop rotations at a reasonable level, with an index of agreement (IA) ranging from 0·82 to 0·96 for the calibration database (the median coefficient of determination (R2) was 0·71), while IA for verification varied from 0·62 to 0·93 (median R2 was 0·78). Grass yields were also estimated at a reasonable level of accuracy. The estimates were less accurate for the above-ground biomass at harvest (the medians for IA were 0·76 and 0·72 for calibration and verification, respectively, and analogous medians of R2 were 0·50 and 0·49). The soil mineral nitrogen (N) content under the field crops was simulated with good precision, with the IA ranging from 0·49 to 0·74 for calibration and from 0·43 to 0·68 for verification. Generally, the soil mineral N was underestimated, and more accurate results were achieved at locations with intensive fertilization. Simulated yields, soil N, water and organic carbon (C) contents were compared with long-term field measurements at Němčice, located within the fertile Moravian lowland. At this station, all of the observed parameters were reproduced with a reasonable level of accuracy. In the case of the organic C content, HERMES reproduced a decrease ranging from c. 85 to 77 tonnes (t)/ha (for the 0–0·3 m soil layer) between the years 1980 and 2007. In spite of its relatively simple approach and restricted input data, HERMES was proven to be robust across various conditions, which is a precondition for its future use for both theoretical and practical purposes.

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