scholarly journals Fiscal Marksmanship in Pakistan

2010 ◽  
Vol 15 (2) ◽  
pp. 113-133 ◽  
Author(s):  
Muhammad Zakaria ◽  
Shujat Ali
Keyword(s):  
Prediction Error ◽  
Rational Expectations ◽  
Central Government ◽  
Mean Square ◽  
Government Budget ◽  
Exogenous Variables ◽  
Random Components ◽  
The Mean ◽  
Source Of Error ◽  
Over Time

Using Theil’s inequality coefficient based on the mean square prediction error, this paper evaluates the forecasting efficiency of the central government budget and revised budget estimates in Pakistan for the period 1987/88 to 2007/08 and decomposes the errors into biasedness, unequal variation and random components to analyze the source of error. The results reveal that budgetary forecasting is inefficient in Pakistan and the error is due mainly to exogenous variables (random factors). We also find that neither the budget nor revised budget estimates of revenue and expenditure satisfy the criteria of rational expectations of forecasting. Further, there is very little evidence of improvement in the efficiency of budgetary forecasts over time.

scholarly journals Fiscal Marksmanship Ex-ante to Fiscal Rules in India: An Empirical Investigation

2018 ◽  
Vol 9 (3) ◽  
pp. 53 ◽  
Author(s):  
Lekha S Chakraborty ◽  
Darshy Sinha
Keyword(s):  
Prediction Error ◽  
Empirical Investigation ◽  
Fiscal Rules ◽  
Budget Management ◽  
Fiscal Responsibility ◽  
Ex Ante ◽  
Threshold Ratio ◽  
Random Components ◽  
The Mean ◽  
Source Of Error

We analyse the fiscal marksmanship of the macro-fiscal variables, ex-ante to the formulation of fiscal rules in India. The fiscal marksmanship is the accuracy of budgetary forecasting. The fiscal rules have been legally mandated in India in the form of fiscal responsibility and budget management Act (FRBM Act) in 2003, with a criteria of fiscal-deficit threshold ratio of 3 per cent of GDP and also phasing out of revenue deficit. Using Theil’s inequality coefficient (U) based on the mean square prediction error, the paper estimates the magnitude of errors in the budgetary forecasts in India during the period ex-ante to fiscal rules, and also decomposed the errors into biasedness, unequal variation and random components to analyze the source of error. The proportion of error due to random variation has been significantly higher (which is beyond the control of the forecaster), while the errors due to bias has been negligible in the period prior to fiscal rules in India. The analysis related to efficiency of forecasts also showed that no significant improvement in forecasts over time prior to fiscal responsibility and budget management (FRBM) Act.

scholarly journals Adaptive recursive algorithm for optimal weighted suprathreshold stochastic resonance

2017 ◽  
Vol 4 (9) ◽  
pp. 160889 ◽  
Author(s):  
Liyan Xu ◽  
Fabing Duan ◽  
Xiao Gao ◽  
Derek Abbott ◽  
Mark D. McDonnell
Keyword(s):  
Stochastic Resonance ◽  
Recursive Algorithm ◽  
Signal Strength ◽  
Distinct Form ◽  
Mean Square ◽  
Least Mean Square ◽  
Complete Knowledge ◽  
The Mean ◽  
Suprathreshold Stochastic Resonance ◽  
Over Time

Suprathreshold stochastic resonance (SSR) is a distinct form of stochastic resonance, which occurs in multilevel parallel threshold arrays with no requirements on signal strength. In the generic SSR model, an optimal weighted decoding scheme shows its superiority in minimizing the mean square error (MSE). In this study, we extend the proposed optimal weighted decoding scheme to more general input characteristics by combining a Kalman filter and a least mean square (LMS) recursive algorithm, wherein the weighted coefficients can be adaptively adjusted so as to minimize the MSE without complete knowledge of input statistics. We demonstrate that the optimal weighted decoding scheme based on the Kalman–LMS recursive algorithm is able to robustly decode the outputs from the system in which SSR is observed, even for complex situations where the signal and noise vary over time.

scholarly journals Consumption of dry matter observed and predicted by the nutritional systems in senepol bulls kept in confinement

2020 ◽  
Vol 36 (6) ◽  
Author(s):  
Aline Maria Soares Ferreira ◽  
Simone Pedro da Silva ◽  
Carina Ubirajara de Faria ◽  
Egleu Diomedes Marinho Mendes ◽  
Ester Ferreira Felipe
Keyword(s):  
Prediction Error ◽  
Dry Matter ◽  
Bos Taurus ◽  
Electronic System ◽  
Mean Square ◽  
Simple Linear Regression ◽  
Initial Weight ◽  
Randomized Design ◽  
The Mean ◽  
Completely Randomized Design

The objective was to compare the dry matter consumption (CMS) observed, through the use of the GrowSafe® electronic system, with that predicted by the BR-Corte (2010 and 2016) and NRC (2000) nutritional systems in confined Senepol bulls. To this end, 24 Senepol Bulls were used in a completely randomized design, uncastrated with an average initial weight of 368 kg and 16 months of age. The evaluation of the accuracy and approximation of the CMS estimates by the nutritional systems was adjusted by the simple linear regression model and the decomposition of the mean square of the prediction error (QMEP). The mean CMS observed was 10.33 kg.day-1, higher than the values predicted by the nutritional systems, in which the values predicted by the NRC (2000) and BR-Corte 2010 and 2016 underestimated the CMS by 29.62, 6.19 and 2.03%, respectively. The verification of QMEP and its decomposition made it possible to infer the proximity of the values predicted by the BR-Corte 2010 and 2016 models and the values observed, which presented a better adjustment in relation to the NRC. Surprisingly the values predicted by the NRC, created from a database with Bos taurus animals, showed greater distance from the values predicted and observed, and it was expected greater accuracy of the NRC models for this category and animal breed. It is concluded that the BR-Corte 2016 was the most appropriate model to estimate the CMS of confined Senepol bulls.

scholarly journals The Prediction Error of the Chain Ladder Method (With Application to Real Data)

2020 ◽  
Vol 12 (12) ◽  
pp. 14
Author(s):  
Afaf Antar Zohry ◽  
Mostafa Abdelghany Ahmed
Keyword(s):  
Prediction Error ◽  
Real Data ◽  
Solvency Ii ◽  
Insurance Companies ◽  
Mean Square ◽  
Recursive Model ◽  
Chain Ladder Method ◽  
The Mean ◽  
Chain Ladder ◽  
S Model

The chain ladder method is the most widely used method of estimating claims reserves due to its simplicity and ease of application. It is very important to know the accuracy of the resulting estimates. Murphy presented a recursive model to estimate the standard error of claims reserves estimates, in line with the solvency ii requirements as a new regulatory framework adjusted according to risk, which requires the necessity to estimate the error and uncertainty of the claims reserving estimates. In Murphy's model, the mean square error (MSE) is analyzed into its components: variance and bias. In this paper, the recursive model of Murphy was used to estimate the prediction error in claims reserves estimates of General Accident & Miscellaneous Insurance in one of the Egyptian insurance companies.

scholarly journals Prediction Error of the Chain Ladder Reserving Method applied to Correlated Run-off Triangles

2007 ◽  
Vol 2 (1) ◽  
pp. 25-50 ◽  
Author(s):  
M. Merz ◽  
M. V. Wüthrich
Keyword(s):  
Time Series ◽  
Mean Square Error ◽  
Prediction Error ◽  
Time Series Model ◽  
Mean Square ◽  
Run Off ◽  
Chain Ladder Method ◽  
The Mean ◽  
Chain Ladder ◽  
Special Case

ABSTRACTIn Buchwalder et al. (2006) we revisited Mack's (1993) and Murphy's (1994) estimates for the mean square error of prediction (MSEP) of the chain ladder claims reserving method. This was done using a time series model for the chain ladder method. In this paper we extend the time series model to determine an estimate for the MSEP of a portfolio of N correlated run-off triangles. This estimate differs in the special case N = 2 from the estimate given by Braun (2004). We discuss the differences between the estimates.

Higher-Order Approximations for Testing Neglected Nonlinearity

2012 ◽  
Vol 24 (1) ◽  
pp. 273-287 ◽  
Author(s):  
Halbert White ◽  
Jin Seo Cho
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Asymptotic Distribution ◽  
Prediction Error ◽  
Activation Function ◽  
Higher Order ◽  
Mean Square ◽  
Test Statistic ◽  
The Mean ◽  
Zero Condition

We illustrate the need to use higher-order (specifically sixth-order) expansions in order to properly determine the asymptotic distribution of a standard artificial neural network test for neglected nonlinearity. The test statistic is a quasi-likelihood ratio (QLR) statistic designed to test whether the mean square prediction error improves by including an additional hidden unit with an activation function violating the no-zero condition in Cho, Ishida, and White ( 2011 ). This statistic is also shown to be asymptotically equivalent under the null to the Lagrange multiplier (LM) statistic of Luukkonen, Saikkonen, and Teräsvirta ( 1988 ) and Teräsvirta ( 1994 ). In addition, we compare the power properties of our QLR test to one satisfying the no-zero condition and find that the latter is not consistent for detecting a DGP with neglected nonlinearity violating an analogous no-zero condition, whereas our QLR test is consistent.

The Bordeaux Automatic Meridian Circle

1978 ◽  
Vol 48 ◽  
pp. 227-228
Author(s):  
Y. Requième
Keyword(s):  
Mean Square Error ◽  
Mean Square ◽  
Meridian Circle ◽  
The Mean ◽  
Full Automation

In spite of important delays in the initial planning, the full automation of the Bordeaux meridian circle is progressing well and will be ready for regular observations by the middle of the next year. It is expected that the mean square error for one observation will be about ±0.”10 in the two coordinates for declinations up to 87°.

The mean-square generalized delayed decision feedback sequence detector

2003 ◽  
Vol 14 (3) ◽  
pp. 265-268 ◽  
Author(s):  
Maurizio Magarini ◽  
Arnaldo Spalvieri ◽  
Guido Tartara
Keyword(s):  
Decision Feedback ◽  
Mean Square ◽  
The Mean

Limit tolerances for sums of measurement errors

2018 ◽  
Vol 934 (4) ◽  
pp. 59-62
Author(s):  
V.I. Salnikov
Keyword(s):  
Normal Distribution ◽  
Mean Square Error ◽  
Gaussian Distribution ◽  
Measurement Errors ◽  
Theory And Practice ◽  
Mean Square ◽  
The Mean ◽  
Limiting Values ◽  
Limiting Value

The question of calculating the limiting values of residuals in geodesic constructions is considered in the case when the limiting value for measurement errors is assumed equal to 3m, ie ∆рred = 3m, where m is the mean square error of the measurement. Larger errors are rejected. At present, the limiting value for the residual is calculated by the formula 3m√n, where n is the number of measurements. The article draws attention to two contradictions between theory and practice arising from the use of this formula. First, the formula is derived from the classical law of the normal Gaussian distribution, and it is applied to the truncated law of the normal distribution. And, secondly, as shown in [1], when ∆рred = 2m, the sums of errors naturally take the value equal to ?pred, after which the number of errors in the sum starts anew. This article establishes its validity for ∆рred = 3m. A table of comparative values of the tolerances valid and recommended for more stringent ones is given. The article gives a graph of applied and recommended tolerances for ∆рred = 3m.

Sign in / Sign up

Export Citation Format

Share Document