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Anandamayee Majumdar - One of the best experts on this subject based on the ideXlab platform.
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reconsidering the welfare cost of inflation in the us a nonparametric estimation of the nonlinear long run money demand equation using projection pursuit regressions
Empirical Economics, 2014Co-Authors: Rangan Gupta, Anandamayee MajumdarAbstract:This paper, first, estimates the appropriate, Log–Log or semi-Log, linear long-run money-demand relationship capturing the behavior US money demand over the period of 1980:Q1–2010:Q4, using the standard linear cointegration procedures found in the literature, and the corresponding nonparametric version of the same based on projection pursuit regression (PPR) methods. We then, compare the resulting welfare costs of inflation obtained from the linear and nonlinear money-demand cointegrating equations. We make the following observations: (i) the appropriate money-demand relationship for the period of 1980:Q1–2010:Q4 is captured by a semi-Log Function; (ii) based on the estimation of semi-Log cointegrating equations, the welfare cost of inflation was found to at the most lie between 0.0131 % of GDP and 0.2186 % of GDP for inflation rates between 0 and 10 %, and; (iii) in comparison, the welfare cost of inflation obtained from the semi-Log non-linear long-run money-demand Function, derived using the PPR method, for 0–10 % of inflation ranges between 0.4930 and 1.9468 % of GDP. However, the standard errors associated with the welfare cost estimates obtained from PPR relative to the linear models tend to indicate that the nonlinear money demand provides more precise estimates of the welfare costs primarily for higher rates of inflation.
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reconsidering the welfare cost of inflation in the us a nonparametric estimation of the nonlinear long run money demand equation using projection pursuit regressions
Research Papers in Economics, 2011Co-Authors: Rangan Gupta, Anandamayee MajumdarAbstract:This paper, first, estimates the appropriate, Log-Log or semi-Log, linear long-run money demand relationship capturing the behavior US money demand over the period of 1980:Q1 to 2010:Q4, using the standard linear cointegration procedures found in the literature, and the corresponding nonparametric version of the same based on Projection Pursuit Regression (PPR) methods. We then, compare the resulting welfare costs of inflation obtained from the linear and nonlinear money demand cointegrating equations. We make the following observations: (i) The appropriate money demand relationship for the period of 1980:Q1 to 2010:Q4 is captured by a semi-Log Function, since no cointegrating relationship could be obtained for the Log-Log model; (ii) The semi-elasticity of interest rate obtained from the PPR method is found to be more than double the corresponding estimate obtained under the linear case; (iii) Based on the estimation of semi-Log cointegrating equations, the welfare cost of inflation was found to at the most lie between 0.0131 percent of GDP to 0.2186 percent of GDP for inflation rates between 0 percent and 10 percent, and; (iv) In comparison, the welfare cost of inflation obtained from the semi-Log non-linear long-run money demand Function, obtained using the PPR method, for 0 to 10 percent of inflation ranges between 0.4929 to 1.9468 percent of GDP. These results suggest that the Federal Reserve’s current policy, which generates low but still positive rates of inflation, might not be an adequate approximation in terms of the welfare cost of inflation. Perhaps, moving all the way to a Friedman-type deflationary rule for a zero nominal interest is a more desired policy given the size of welfare loss.
Rangan Gupta - One of the best experts on this subject based on the ideXlab platform.
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reconsidering the welfare cost of inflation in the us a nonparametric estimation of the nonlinear long run money demand equation using projection pursuit regressions
Empirical Economics, 2014Co-Authors: Rangan Gupta, Anandamayee MajumdarAbstract:This paper, first, estimates the appropriate, Log–Log or semi-Log, linear long-run money-demand relationship capturing the behavior US money demand over the period of 1980:Q1–2010:Q4, using the standard linear cointegration procedures found in the literature, and the corresponding nonparametric version of the same based on projection pursuit regression (PPR) methods. We then, compare the resulting welfare costs of inflation obtained from the linear and nonlinear money-demand cointegrating equations. We make the following observations: (i) the appropriate money-demand relationship for the period of 1980:Q1–2010:Q4 is captured by a semi-Log Function; (ii) based on the estimation of semi-Log cointegrating equations, the welfare cost of inflation was found to at the most lie between 0.0131 % of GDP and 0.2186 % of GDP for inflation rates between 0 and 10 %, and; (iii) in comparison, the welfare cost of inflation obtained from the semi-Log non-linear long-run money-demand Function, derived using the PPR method, for 0–10 % of inflation ranges between 0.4930 and 1.9468 % of GDP. However, the standard errors associated with the welfare cost estimates obtained from PPR relative to the linear models tend to indicate that the nonlinear money demand provides more precise estimates of the welfare costs primarily for higher rates of inflation.
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reconsidering the welfare cost of inflation in the us a nonparametric estimation of the nonlinear long run money demand equation using projection pursuit regressions
Research Papers in Economics, 2011Co-Authors: Rangan Gupta, Anandamayee MajumdarAbstract:This paper, first, estimates the appropriate, Log-Log or semi-Log, linear long-run money demand relationship capturing the behavior US money demand over the period of 1980:Q1 to 2010:Q4, using the standard linear cointegration procedures found in the literature, and the corresponding nonparametric version of the same based on Projection Pursuit Regression (PPR) methods. We then, compare the resulting welfare costs of inflation obtained from the linear and nonlinear money demand cointegrating equations. We make the following observations: (i) The appropriate money demand relationship for the period of 1980:Q1 to 2010:Q4 is captured by a semi-Log Function, since no cointegrating relationship could be obtained for the Log-Log model; (ii) The semi-elasticity of interest rate obtained from the PPR method is found to be more than double the corresponding estimate obtained under the linear case; (iii) Based on the estimation of semi-Log cointegrating equations, the welfare cost of inflation was found to at the most lie between 0.0131 percent of GDP to 0.2186 percent of GDP for inflation rates between 0 percent and 10 percent, and; (iv) In comparison, the welfare cost of inflation obtained from the semi-Log non-linear long-run money demand Function, obtained using the PPR method, for 0 to 10 percent of inflation ranges between 0.4929 to 1.9468 percent of GDP. These results suggest that the Federal Reserve’s current policy, which generates low but still positive rates of inflation, might not be an adequate approximation in terms of the welfare cost of inflation. Perhaps, moving all the way to a Friedman-type deflationary rule for a zero nominal interest is a more desired policy given the size of welfare loss.
Pushp Raj Shivahre - One of the best experts on this subject based on the ideXlab platform.
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comparative evaluation of different lactation curve models in prediction of monthly test day milk yields in murrah buffaloes
Journal of Animal Research, 2015Co-Authors: Manvendra Singh, Avtar Singh, Saroj Kumar Sahoo, Shakti Kant Dash, Ashok Kumar Gupta, Atul Gupta, Soumya Dash, Pushp Raj ShivahreAbstract:Present investigation was undertaken to compare the different lactation curve models for describing the shape of the lactation curve in Murrah buffaloes. Data for the present study included 9071 monthly test-day milk yield (MTDMY) from 965 Murrah buffaloes calved during 1977 to 2012 at the National Dairy Research Institute, Karnal. A total of 10 monthly test-day milk yield records were taken at an interval of 30 days. The data were used to estimate lactation curve parameters for four lactation curve models viz. Gamma type Function (GF), Exponential Function (EF), Mixed Log Function (MLF) and Polynomial regression Function (MLF). The mean monthly test day milk yields (MTDMY) increased from 5.91±0.13 kg on TD1 to a peak yield of 7.41±0.12 kg on TD3. The estimates of coefficient of determination (R2) and root mean square error (RMSE) for GF, EF, MLF, and PRF were 96.42%, 98.65%, 98.48%, 99.86% and 0.077, 0.049, 0.052, 0.015, respectively. PRF fitted best to the test day data followed by EF on the basis of higher R2 and lower RMSE estimates, whereas GF fitted least.
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prediction of fortnightly test day milk yields using four different lactation curve models in indian murrah buffalo
Advances in Animal and Veterinary Sciences, 2014Co-Authors: Saroj Kumar Sahoo, Avtar Singh, Manvendra Singh, Soumya Dash, Pushp Raj Shivahre, Shakti Kant DashAbstract:The present investigation was carried out using data on 18871 fortnightly test day milk yield (FTDMY) records during first lactation of 961 Murrah buffaloes calved during 1977-2012 maintained in an organized farm at National Dairy Research Institute, Karnal. The least squares means of the FTDMY ranged from 2.35kg to 7.92kg. The relative efficiency of four lactation curve models via. Gamma-type Function (GF), Exponential Function (EF), Mixed Log Function (MLF) and polynomial regression Function (PRF) were compared. PRF described the highest coefficient of determination (R2) (99.78%) and with least value (0.02kg) of root mean squares error (RMSE), whereas, least coefficient of determination (93.13%) was observed in Gamma-type Function having maximum (0.1kg) RMSE value suggesting PRF being best mathematical model for prediction of FTDMYs in Murrah buffaloes.
Shakti Kant Dash - One of the best experts on this subject based on the ideXlab platform.
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comparative evaluation of different lactation curve models in prediction of monthly test day milk yields in murrah buffaloes
Journal of Animal Research, 2015Co-Authors: Manvendra Singh, Avtar Singh, Saroj Kumar Sahoo, Shakti Kant Dash, Ashok Kumar Gupta, Atul Gupta, Soumya Dash, Pushp Raj ShivahreAbstract:Present investigation was undertaken to compare the different lactation curve models for describing the shape of the lactation curve in Murrah buffaloes. Data for the present study included 9071 monthly test-day milk yield (MTDMY) from 965 Murrah buffaloes calved during 1977 to 2012 at the National Dairy Research Institute, Karnal. A total of 10 monthly test-day milk yield records were taken at an interval of 30 days. The data were used to estimate lactation curve parameters for four lactation curve models viz. Gamma type Function (GF), Exponential Function (EF), Mixed Log Function (MLF) and Polynomial regression Function (MLF). The mean monthly test day milk yields (MTDMY) increased from 5.91±0.13 kg on TD1 to a peak yield of 7.41±0.12 kg on TD3. The estimates of coefficient of determination (R2) and root mean square error (RMSE) for GF, EF, MLF, and PRF were 96.42%, 98.65%, 98.48%, 99.86% and 0.077, 0.049, 0.052, 0.015, respectively. PRF fitted best to the test day data followed by EF on the basis of higher R2 and lower RMSE estimates, whereas GF fitted least.
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prediction of fortnightly test day milk yields using four different lactation curve models in indian murrah buffalo
Advances in Animal and Veterinary Sciences, 2014Co-Authors: Saroj Kumar Sahoo, Avtar Singh, Manvendra Singh, Soumya Dash, Pushp Raj Shivahre, Shakti Kant DashAbstract:The present investigation was carried out using data on 18871 fortnightly test day milk yield (FTDMY) records during first lactation of 961 Murrah buffaloes calved during 1977-2012 maintained in an organized farm at National Dairy Research Institute, Karnal. The least squares means of the FTDMY ranged from 2.35kg to 7.92kg. The relative efficiency of four lactation curve models via. Gamma-type Function (GF), Exponential Function (EF), Mixed Log Function (MLF) and polynomial regression Function (PRF) were compared. PRF described the highest coefficient of determination (R2) (99.78%) and with least value (0.02kg) of root mean squares error (RMSE), whereas, least coefficient of determination (93.13%) was observed in Gamma-type Function having maximum (0.1kg) RMSE value suggesting PRF being best mathematical model for prediction of FTDMYs in Murrah buffaloes.
Manvendra Singh - One of the best experts on this subject based on the ideXlab platform.
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comparative evaluation of different lactation curve models in prediction of monthly test day milk yields in murrah buffaloes
Journal of Animal Research, 2015Co-Authors: Manvendra Singh, Avtar Singh, Saroj Kumar Sahoo, Shakti Kant Dash, Ashok Kumar Gupta, Atul Gupta, Soumya Dash, Pushp Raj ShivahreAbstract:Present investigation was undertaken to compare the different lactation curve models for describing the shape of the lactation curve in Murrah buffaloes. Data for the present study included 9071 monthly test-day milk yield (MTDMY) from 965 Murrah buffaloes calved during 1977 to 2012 at the National Dairy Research Institute, Karnal. A total of 10 monthly test-day milk yield records were taken at an interval of 30 days. The data were used to estimate lactation curve parameters for four lactation curve models viz. Gamma type Function (GF), Exponential Function (EF), Mixed Log Function (MLF) and Polynomial regression Function (MLF). The mean monthly test day milk yields (MTDMY) increased from 5.91±0.13 kg on TD1 to a peak yield of 7.41±0.12 kg on TD3. The estimates of coefficient of determination (R2) and root mean square error (RMSE) for GF, EF, MLF, and PRF were 96.42%, 98.65%, 98.48%, 99.86% and 0.077, 0.049, 0.052, 0.015, respectively. PRF fitted best to the test day data followed by EF on the basis of higher R2 and lower RMSE estimates, whereas GF fitted least.
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prediction of fortnightly test day milk yields using four different lactation curve models in indian murrah buffalo
Advances in Animal and Veterinary Sciences, 2014Co-Authors: Saroj Kumar Sahoo, Avtar Singh, Manvendra Singh, Soumya Dash, Pushp Raj Shivahre, Shakti Kant DashAbstract:The present investigation was carried out using data on 18871 fortnightly test day milk yield (FTDMY) records during first lactation of 961 Murrah buffaloes calved during 1977-2012 maintained in an organized farm at National Dairy Research Institute, Karnal. The least squares means of the FTDMY ranged from 2.35kg to 7.92kg. The relative efficiency of four lactation curve models via. Gamma-type Function (GF), Exponential Function (EF), Mixed Log Function (MLF) and polynomial regression Function (PRF) were compared. PRF described the highest coefficient of determination (R2) (99.78%) and with least value (0.02kg) of root mean squares error (RMSE), whereas, least coefficient of determination (93.13%) was observed in Gamma-type Function having maximum (0.1kg) RMSE value suggesting PRF being best mathematical model for prediction of FTDMYs in Murrah buffaloes.
