Policy institutes such as central banks need accurate forecasts of key measures of economic activity to design stabilization policies that reduce the severity of economic fluctuations. Therefore, this paper develops a kernel ridge regression estimator in a mixed data sampling framework. Kernel ridge regression can handle many predictors with a nonlinear relationship to the target variable. Consequently, it has potential to improve the currently used principal component-based methods when the economic data follow a nonlinear factor structure. In a Monte Carlo study, we show that the kernel ridge regression approach is superior in terms of mean square error and is more robust than principal component-based methods to different nonlinear data generating processes. By using a dataset consisting of 24 economic indicators, we forecast Swedish gross domestic production. The results confirm the superiority of the kernel ridge regression approach. Therefore, we suggest that policy institutes consider the use of kernel-based approaches when forecasting key measures of economic activity.

Beta regression has gained significant attention for modeling outcome variables bounded withinthe open interval from zero to one. In this paper, we introduce a two-parameter Liu linear shrinkageestimator tailored for estimating the vector of parameters in a Beta regression model with a fixeddispersion parameter, under the assumption of linear restrictions on the parameter vector. Thisestimator is particularly applicable in various practical scenarios where the level of correlation amongthe regressors varies, and the coefficient vector is suspected to belong to a linear subspace. Thenecessary and sufficient conditions for establishing the superiority of the new estimator over both oneparameterLiu estimators and two-parameter Stein-type estimators are derived in this paper. Finally,we conclude this paper by presenting two empirical applications that demonstrate the advantages ofutilizing the new estimator for applied researchers.

BackgroundA growing body of evidence suggests that exposure to nature is beneficial for human health. However, the observed health effect of nature may be mediated by physical activity and that humans are physically active at a higher intensity outdoors compared to when they are physical active indoors.ObjectiveThis study examines the variation of heart rate and power output for a fixed rating of perceived exertion in a group of healthy older adults in three different environments representing three levels of exposure to nature.MethodsTo this randomized, 3-by-3 crossover design study, healthy older adults (≥65 years) were recruited from local gyms. All participants participated in three experimental conditions; indoors, simulated outdoors and outdoor environments, in a randomized order. The participants exercised for 20 minutes at an intensity equivalent to a rating of 11–13 on the Borg scale for perceived exertion (RPE). Measurements of heart rate, power output (Watt) and ratings of perceived exertion were taken at minutes 1 to 6 and at minute 20. To examine the effect of the environment on heart rate and power, linear mixed models were used.ResultsIn all, 48 participants (56% females) were included in the analysis. No significant main effects on the outcomes were observed for power output (p = 0.073, η2 = 0.04) or heart rate (p = 0.067, η2 = 0.04)ConclusionNo significant effect on the outcomes was observed. However, borderline significant outcomes for power output or heart rate outdoors in nature, along with previous studies in the field, indicates that such an effect cannot be completely ruled out, but any effect is likely to be small. Future research examining health benefits of the independent exposure to nature are encouraged to adjust for the dose of physical activity.

Abstract Beta regression has become a popular tool for performing regression analysis on chemical, environmental, or biological data in which the dependent variable is restricted to the interval [0, 1]. For the first time, in this paper, we propose a Liu estimator for the beta regression model with fixed dispersion parameter that may be used in several realistic situations when the degree of correlation among the regressors differs. First, we show analytically that the new estimator outperforms the maximum likelihood estimator (MLE) using the mean square error (MSE) criteria. Second, using a 'simulation study, we investigate the properties in finite samples of six different suggested estimators of the shrinkage parameter and compare it with the MLE. The simulation results indicate that in the presence of multicollinearity, the Liu estimator outperforms the MLE uniformly. Finally, using an empirical application on chemical data, we show the benefit of the new approach to applied researchers.

The problem of how to determine portfolio weights so that the variance of portfolio returns is minimized has been given considerable attention in the literature, and several methods have been proposed. Some properties of these estimators, however, remain unknown, and many of their relative strengths and weaknesses are therefore difficult to assess for users. This paper contributes to the field by comparing and contrasting the risk functions used to derive efficient portfolio weight estimators. It is argued that risk functions commonly used to derive and evaluate estimators may be inadequate and that alternative quality criteria should be considered instead. The theoretical discussions are supported by a Monte Carlo simulation and two empirical applications where particular focus is set on cases where the number of assets (p) is close to the number of observations (n).

Purpose: The purpose of this study was to test the hypothesis that the ”outdoors” has an added health effect on top of that of physical activity. This hypothesis is based on the fairly extensive volume of evidens suggesting that the outdoor environment is beneficial for overall health. We, however, consider most previous studies insufficiently designed in order to draw causal relationship between spending time outdoors and examined health parameters. In this study we state that our experimental design allows us to do just that. 

Methods:

In this randomized cross-over experimental design, 49 healthy subjects (+65 years, 27 female) performed a standardized submaximal bike ergometer test in three different environments (indoors, simulated outdoors and outdoors). The test consisted of 20 minutes cycling and subjects were instructed to stay between 11-13 at the Borg RPE scale (i.e. moderate intensity). Each test was followed by two hours of passive recovery. We measured power output, heart rate, lactate and counts of five fractions (lymphocytes, monocytes, neutrophiles, basophiles and eosinophiles) of white blood cells (leukocytes) in circulation.

Results:

Our results revealed that the outdoor treatment resulted in higher levels of lactate, higher heart rate and higher power output despite that there were no differences in Borg RPE-rating between the treatments. The white blood cell analysis showed an increase of circulating leukocytes in almost all fractions immediately post exercise in all treatments. Numbers decreased rapidly post exercise and the lymphocyte numbers stayed well under baseline for more than one hour during recovery. This is referred to as an “open window” where the immune function is temporarily suppressed and there is an elevated risk of infection of primarily URTI.

Conclusions:

Our results not only suggest that exercise is more efficient out of doors such that more exercise/effort is obtained but also that recovery outdoors may be a way to decrease the risk of infections due to exercise induced immune suppression. The risk of getting an infection is known to be generally lower out of doors. Taken together, our results support the notion that exercising and spending time outdoors indeed is beneficial for health in senior citizens.

Model selection criteria are often used to find a "proper" model for the data under investigation when building models in cases in which the dependent or explained variables are assumed to be functions of several independent or explanatory variables. For this purpose, researchers have suggested using a large number of such criteria. These criteria have been shown to act differently, under the same or different conditions, when trying to select the "correct" number of explanatory variables to be included in a given model; this, unfortunately, leads to severe problems and confusion for researchers. In this paper, using Monte Carlo methods, we investigate the properties of four of the most common criteria under a number of realistic situations. These criteria are the adjusted coefficient of determination (R²-adj), Akaike's information criterion (AIC), the Hannan–Quinn information criterion (HQC) and the Bayesian information criterion (BIC). The results from this investigation indicate that the HQC outperforms the BIC, the AIC and the R²-adj under specific circumstances. None of them perform satisfactorily, however, when the degree of multicollinearity is high, the sample sizes are small or when the fit of the model is poor (i.e., there is a low R²) . In the presence of all these factors, the criteria perform very badly and are not very useful. In these cases, the criteria are often not able to select the true model.

Purpose

In fast moving consumer goods sector (FMCGs), manufacturers’ access to demand related data (e.g. point of sales data, lost sales, end-customer orders) is often limited making demand forecasting impossible. Instead, to enhance production planning, the manufacturers might base their DC supply forecasts on limited information including shipments from DC to wholesalers and retail stores. This is not optimal. To enhance the situation, the purpose is: (i) to propose a method of how manufacturers can use Machine Learning (ML) approach to improve supply forecasting to a DC if demand information is lacking, and (ii) to identify the demand information needed for demand forecasting supported by ML approach.

Design/methodology/approach

A single case study methodology of a manufacturer from FMCG sector, principle component analysis, and simple linear regression in R programming language is used. Also, linear regression using stochastic gradient descent optimization technique in Amazon ML is applied.

Findings 

We propose a method of how FMCG manufacturers lacking demand data can utilize the ML approach to enhance supply forecasting, and identify needed demand related information to implement demand forecasting.

Research limitations/implications (if applicable)

The results are based on one case and need validation through more cases. 

Practical limitations/implications (if applicable)

The results can be used by managers lacking demand related data to improve DC supply forecasting in FMCGs sector.  Originality/valueThe results can contribute to the area of supply forecasting through ML.

This paper derives first-order sampling moments of individual Mahalanobis distances (MD) in cases when the dimension p of the variable is proportional to the sample size n. Asymptotic expected values when n, p → ∞ are derived under the assumption p/n → c, 0 ⩽ c < 1. It is shown that some types of standard estimators remain unbiased in this case, while others are asymptotically biased, a property that appears to be unnoticed in the literature. Second order moments are also supplied to give some additional insight to the matter.

In this article we investigate if the Swedish consumer prices for electricity are adjusted equally fast regardless of whether the NordPool power market prices are decreased or increased. Due to relatively moderate variations in the variables, we have applied quantile regression, since it is mainly the large changes (above the median) that essentially tend to have a considerable effect on the consumer prices. Moreover, in order to adjust for stochastic- and deterministic trends, autocorrelation, structural breaks as well as to measure APT effects in the short- and in the medium-run, we apply a wavelet decomposition approach. Our results show evidence that significantly positive asymmetric price transmission (APT) effects exist in this market. More specifically, in the short-run (based on the wavelet decomposition D1 for 1–2 months cycles), we find that that there is a higher propensity to rapidly and systematically increase the consumer prices subsequently to an increase in the NordPool market price, compared with the propensity to decrease their customers prices subsequently to a corresponding drop in the NordPool market prices. However, no significant APT effects were detected in the medium- or in the long-run (i.e. the asymmetric price transmission effects are observed only in the short-run). In summary, we could isolate significant APT effects in the short-run (1–2 months decomposition cycles), and for large changes in the dependent variable (percentiles = 0.9). Therefore, only large changes in the NordPool prices lead to feedback effects in the form of asymmetric price transmission effects. Our evidence supports the notion of firms’ downward stickiness of retail prices for maximizing profit, which are not expected to be found on a fully efficient market. Although our finding shows that the price inefficiency is short-lived, these large temporal inefficiencies are still costly for the consumers. It should be noted that blunt traditional powerless methods do not detect these APT effects, while our wavelet quantile methods are powerful and make a significant contribution in the literature by providing new empirical evidence.