Trend smoothness achieved by penalized least squares with the smoothing parameter chosen by optimality criteria

This work presents a study about the smoothness attained by the methods more frequently used to choose the smoothing parameter in the context of splines: Cross Validation, Generalized Cross Validation, and corrected Akaike and Bayesian Information Criteria, implemented with Penalized Least Squares. It is concluded that the amount of smoothness strongly depends on the length of the series and on the type of underlying trend, while the presence of seasonality even though statistically significant is less relevant. The intrinsic variability of the series is not statistically significant and its effect is taken into account only through the smoothing parameter.     

LS estimation of periodic autoregressive models with non-Gaussian errors: a simulation study

In this article, the least squares (LS) estimates of the parameters of periodic autoregressive (PAR) models are investigated for various distributions of error terms via Monte-Carlo simulation. Beside the Gaussian distribution, this study covers the exponential, gamma, student-t, and Cauchy distributions. The estimates are compared for various distributions via bias and MSE criterion. The effect of other factors are also examined as the non-constancy of model orders, the non-constancy of the variances of seasonal white noise, the period length, and the length of the time series. The simulation results indicate that this method is in general robust for the estimation of AR parameters with respect to the distribution of error terms and other factors. However, the estimates of those parameters were, in some cases, noticeably poor for Cauchy distribution. It is also noticed that the variances of estimates of white noise variances are highly affected by the degree of skewness of the distribution of error terms.     

Comparing diagnostic tests: test of hypothesis for likelihood ratios

Likelihood ratios (LRs) are used to characterize the efficiency of diagnostic tests. In this paper, we use the classical weighted least squares (CWLS) test procedure, which was originally used for testing the homogeneity of relative risks, for comparing the LRs of two or more binary diagnostic tests. We compare the performance of this method with the relative diagnostic likelihood ratio (rDLR) method and the diagnostic likelihood ratio regression (DLRReg) approach in terms of size and power, and we observe that the performances of CWLS and rDLR are the same when used to compare two diagnostic tests, while DLRReg method has higher type I error rates and powers. We also examine the performances of the CWLS and DLRReg methods for comparing three diagnostic tests in various sample size and prevalence combinations. On the basis of Monte Carlo simulations, we conclude that all of the tests are generally conservative and have low power, especially in settings of small sample size and low prevalence.     

Local estimation for varying-coefficient models with longitudinal data

Varying-coefficient models are very useful for longitudinal data analysis. In this paper, we focus on varying-coefficient models for longitudinal data. We develop a new estimation procedure using Cholesky decomposition and profile least squares techniques. Asymptotic normality for the proposed estimators of varying-coefficient functions has been established. Monte Carlo simulation studies show excellent finite-sample performance. We illustrate our methods with a real data example.     

Robust sparse regression and tuning parameter selection via the efficient bootstrap information criteria

There is currently much discussion about lasso-type regularized regression which is a useful tool for simultaneous estimation and variable selection. Although the lasso-type regularization has several advantages in regression modelling, owing to its sparsity, it suffers from outliers because of using penalized least-squares methods. To overcome this issue, we propose a robust lasso-type estimation procedure that uses the robust criteria as the loss function, imposing L₁-type penalty called the elastic net. We also introduce to use the efficient bootstrap information criteria for choosing optimal regularization parameters and a constant in outlier detection. Simulation studies and real data analysis are given to examine the efficiency of the proposed robust sparse regression modelling. We observe that our modelling strategy performs well in the presence of outliers.     

Several least-squares problems related to the Hodrick–Prescott filtering

The Hodrick–Prescott (HP) filtering is frequently used in macroeconometrics to decompose time series, such as real gross domestic product, into their trend and cyclical components. Because the HP filtering is a basic econometric tool, it is necessary to have a precise understanding of the nature of it. This article contributes to the literature by listing several (penalized) least-squares problems that are related to the HP filtering, three of which are newly introduced in the article, and showing their properties. We also remark on their generalization.     

土地流转中为什么会形成大量小农复制

土地流转是实现规模化经营的前提之一,然而土地流转中却出现大量小农复制,从而影响土地流转的质量和效果。运用12省(自治区)2 553份农户问卷,详细分析土地流转中小农复制的现状及其形成原因。研究发现：(1)农户流转耕地面积不大,转入户流转面积20亩及以下的约占总转入户数的60.3％,转出户流转面积5亩及以下的约占总转出户数的74.38％,转出户中仍然经营土地的约占总转出户数的74％。(2)农业分工发达使生产更加方便、文化传统形成路径依赖、禀赋效应产生“价值幻觉”以及公共服务不健全使未来存在不确定性导致转出户流转部分土地。乡土社会限制土地流转范围和经营风险增加风险感知导致转入户流转小规模土地。     

Information and communication technology adoption in micro and small firms: Can internet access improve labour productivity?

The rapid spread of information and communication technologies (ICT) may increase firms’ productivity with important consequences for job creation and for economic growth. This article contributes to this discussion by analysing the impact of internet adoption on labour productivity and the mechanisms shaping this relationship in Peruvian micro and small manufacturing firms over the period 2011–2013. The article estimates a reduced form where labour productivity is a function of internet adoption and other explanatory factors. Internet adoption is instrumented using a measure of the availability of financial opportunities for micro and small firms in Peru. Findings indicate that internet adoption: (a) increases firms’ labour productivity; (b) reallocates employment away from temporary administrative workers and non‐remunerated workers and expands employment of permanent production workers; (c) leads to the formalization of labour relationships, to the implementation of new organizational practices and to the improvement of training measures. While changes in employment and formalization of workers are linked to labour productivity gains, increases in training measures and organizational changes do not generate any additional productivity increase. Policies oriented to promote the adoption of ICT in micro and small firms can be beneficial to close the productivity gap with larger firms in Peru. Moreover, policies directed to the formalization of the workforce can provide an extra benefit, i.e. additional labour productivity gains in firms adopting the internet. Finally, policies oriented to the development of digital skills are also important to ease the re‐employment of those workers losing their jobs and the achievement of additional productivity gains that new organizational practices can provide.     

Supply chain agility as an acclimatisation process to environmental uncertainty and organisational vulnerabilities: insights from British SMEs

Abstract

Even though supply chain agility (SCA) has been considered an essential concept in supply chain management (SCM) research, the way it is experienced and manifested, especially by small and medium-sized enterprises (SMEs), has received much less attention. Our purpose is to focus on SME organisational vulnerabilities in the context of increased environmental uncertainty, and explore how SCA is developed and applied by SMEs amid their vulnerabilities. By relying on insights from comparative case studies of three British SMEs, we examine SME SCA as an acclimatisation process and delve into SMEs’ experiences of facing environmental uncertainty while developing and applying SCA. Our findings highlight that organisational attitudes underlie how SMEs perceive environmental uncertainty, tackle organisational vulnerabilities and develop SCA as an acclimatisation process. Our findings also reveal that resource constraints, supply chain relationships, interorganisational power dynamics, and access to information play important roles in developing SCA.