Improved Output Gap Estimates and Forecasts Using a Local Linear Regression

The output gap, the difference between potential and actual output, has a direct impact on policy decisions, e.g., monetary policy. Estimating this gap and its further analysis remain the subject of controversial debates due to methodological problems. We propose a local polynomial regression combined with a Self-Exciting Threshold AutoRegressive (SETAR) model and its forecasting extension for a systematic output gap estimation. Furthermore, local polynomial regression is proposed for the (multivariate) OECD production function approach and its reliability is demonstrated in forecasting output growth. A comparison of the proposed gap to the Hodrick–Prescott filter as well as to estimations by experts from the FED and OECD shows a higher correlation of our output gap with those from those economic institutions. Furthermore, sometimes gaps with different magnitude and different positions above or below the trend are observed between different methods. This may cause competing policy implications which can be improved with our results.

RELAÇÃO ENTRE DISTRIBUIÇÃO DE RENDIMENTOS DO TRABALHO E INDUSTRIALIZAÇÃO: UMA ANÁLISE PARA OS MUNICÍPIOS BRASILEIROS

O presente estudo investiga, empiricamente, a relação entre industrializaçãoe distribuição dos rendimentos do trabalho nos municípios brasileiros. Com base emdebates históricos sobre distribuição de renda da academia brasileira e em hipótesesassociadas à curva de Kuznets, especialmente, a que se refere à economia dual, o estudo encontra uma relação não linear entre a desigualdade de rendimentos do trabalhoe a industrialização, medida pelas participações industriais no produto e no emprego.A partir de dados municipais referentes a 2000 e 2010, são realizadas regressões paramétricas em painel (efeitos fixos, efeitos aleatórios e tobit) e não paramétricas (kernel--weighted local polynomial regression). As evidências, relativamente robustas, sugeremque a curva derivada da relação entre a desigualdade de renda e a industrialização temum formato próximo a um U invertido. Ou seja, a distribuição dos rendimentos do trabalho piora com a industrialização até certo nível de participação industrial (no produto e no emprego), porém, atingido determinado nível, a distribuição passa a melhorar.

To the Issue of Determining Agricultural Tractor Slipping

The well-known methods for determining the slipping of the propulsion devices of agricultural tractors are described. A regression analysis of the traction characteristics of a wheeled tractor is performed. The equivalence of the method for determining the current slippage through the actual tractor speed and engine crankshaft speed is assessed. A regression model is proposed to determine the amount of slippage based on the method of local polynomial regression (locally estimated scatterplot smoothing or LOESS)

The kernel-weighted local polynomial regression (KwLPR) approach: an efficient, novel tool for development of QSAR/QSAAR toxicity extrapolation models

The ability of accurate predictions of biological response (biological activity/property/toxicity) of a given chemical makes the quantitative structure‐activity/property/toxicity relationship (QSAR/QSPR/QSTR) models unique among the in silico tools. In addition, experimental data of selected species can also be used as an independent variable along with other structural as well as physicochemical variables to predict the response for different species formulating quantitative activity–activity relationship (QAAR)/quantitative structure–activity–activity relationship (QSAAR) approach. Irrespective of the models' type, the developed model's quality, and reliability need to be checked through multiple classical stringent validation metrics. Among the validation metrics, error-based metrics are more significant as the basic idea of a good predictive model is to improve the predictions' quality by lowering the predicted residuals for new query compounds. Following the concept, we have checked the predictive quality of the QSAR and QSAAR models employing kernel-weighted local polynomial regression (KwLPR) approach over the traditional linear and non-linear regression-based approaches tools such as multiple linear regression (MLR) and k nearest neighbors (kNN). Five datasets which were previously modeled using linear and non-linear regression method were considered to implement the KwPLR approach, followed by comparison of their validation metrics outcomes. For all five cases, the KwLPR based models reported better results over the traditional approaches. The present study's focus is not to develop a better or improved QSAR/QSAAR model over the previous ones, but to demonstrate the advantage, prediction power, and reliability of the KwLPR algorithm and establishing it as a novel, powerful cheminformatic tool. To facilitate the use of the KwLPR algorithm for QSAR/QSPR/QSTR/QSAAR modeling, the authors provide an in-house developed KwLPR.RMD script under the open-source R programming language.