Venda door-to-door na L'Oréal Brasil: por que a maior empresa de cosméticos do mundo não entra no canal da maior empresa de cosméticos no Brasil?

Por que a maior empresa de cosmético do mundo não entra no canal de vendas da maior empresa de cosméticos do Brasil? O trabalho a seguir tem o objetivo de analisar a L’Oréal Brasil e entender os motivos de ela não atuar no canal de vendas door-to-door, principal canal de vendas da Natura, a maior empresa de cosméticos do Brasil. Com base no método do Estudo do Caso, foram feitas 23 entrevistas tendo como amostra o corpo executivo da L’Oréal Brasil, bem como seu CEO. O estudo abrangeu diversas áreas da organização: marketing, suprimentos, fabricação, recursos humanos, vendas e logística. Com base na teoria da Lógica Dominante, notou-se que apesar de o canal de vendas door-to-door ser atrativo, existem diversas razões estratégicas/crenças que impedem que a L’Oréal entre no canal, são elas: a desconfiança no futuro do canal, a operação logística e o paradigma histórico da L’Oréal.

A critical value function approach, with an application to persistent time-series

Researchers often rely on the t-statistic to make inference on parameters in statistical models. It is common practice to obtain critical values by simulation techniques. This paper proposes a novel numerical method to obtain an approximately similar test. This test rejects the null hypothesis when the test statistic islarger than a critical value function (CVF) of the data. We illustrate this procedure when regressors are highly persistent, a case in which commonly-used simulation methods encounter dificulties controlling size uniformly. Our approach works satisfactorily, controls size, and yields a test which outperforms the two other known similar tests.

Forecasting Multivariate Time Series under Present-Value-Model Short- and Long-run Co-movement Restrictions

This paper has two original contributions. First, we show that the present value model (PVM hereafter), which has a wide application in macroeconomics and fi nance, entails common cyclical feature restrictions in the dynamics of the vector error-correction representation (Vahid and Engle, 1993); something that has been already investigated in that VECM context by Johansen and Swensen (1999, 2011) but has not been discussed before with this new emphasis. We also provide the present value reduced rank constraints to be tested within the log-linear model. Our second contribution relates to forecasting time series that are subject to those long and short-run reduced rank restrictions. The reason why appropriate common cyclical feature restrictions might improve forecasting is because it finds natural exclusion restrictions preventing the estimation of useless parameters, which would otherwise contribute to the increase of forecast variance with no expected reduction in bias. We applied the techniques discussed in this paper to data known to be subject to present value restrictions, i.e. the online series maintained and up-dated by Shiller. We focus on three different data sets. The fi rst includes the levels of interest rates with long and short maturities, the second includes the level of real price and dividend for the S&P composite index, and the third includes the logarithmic transformation of prices and dividends. Our exhaustive investigation of several different multivariate models reveals that better forecasts can be achieved when restrictions are applied to them. Moreover, imposing short-run restrictions produce forecast winners 70% of the time for target variables of PVMs and 63.33% of the time when all variables in the system are considered.