Labor productivity forecasts based on a Beveridge–Nelson filter: Is there statistical evidence for a slowdown?

In this paper, I assess the evidence for a structural break in labor productivity growth in the years before the Great Recession with the use of out-of-sample forecasting exercises for the years 2010 to 2019 and the recently developed Beveridge–Nelson filter. Models based on a Beveridge–Nelson filter with no structural breaks outperform those allowing for a structural break, and there is statistically significant evidence that they outperform the random walk, though all models were too optimistic about labor productivity growth. Recently developed statistical tests do point to the presence of a structural break before the Great Recession, but uncertainty about the data-generating process for labor productivity growth or the timing and magnitude of the break may be too great to be helpful in forecast preparation.     

A fast and scalable ensemble of global models with long memory and data partitioning for the M5 forecasting competition

This work presents key insights on the model development strategies used in our cross-learning-based retail demand forecast framework. The proposed framework outperforms state-of-the-art univariate models in the time series forecasting literature. It has achieved 17th position in the accuracy track of the M5 forecasting competition, which is among the top 1% of solutions.     

Labor market analysis through transformations and robust multivariate models

The work presents a robust approach to labor share analysis. The estimate of labor share presents various complexities related to the nature of the data sets to be analyzed. Typically, labor share is evaluated by using discriminant analysis and linear or generalized linear models, that do not take into account the presence of possible outliers. Moreover, the variables to be considered are often characterized by a high dimensional structure. The proposed approach has the objective of improving the estimation of the model using robust multivariate regression techniques and data transformation.     

劳动力价格、信息不对称与农户精细管理技术投入——基于质量识别能力差异的视角

近年来,劳动力价格的快速上涨已然成为中国农业生产所必须面对的内在挑战。对于机械作业难度相对较大的劳动密集型作物而言,在缓解劳动力约束瓶颈的同时,更应通过精细化的果园管理,以提高产品质量,进而提高其市场价格。然而,由于农产品具有典型的经验品属性,在信息不对称的外部约束下,农户通常难以获得应有的"质量溢价"。基于此,本文通过构建理论分析框架,以果品质量关键环节的精细管理技术作为研究对象,系统分析在劳动力价格上涨背景下农户精细管理技术投入的决策机制,以及在不同农产品质量识别程度中,劳动力成本变化对其影响的异质性,并基于全国18个省份农户的调研数据进行实证研究。研究结果表明:农户交易对象质量识别能力的提高,能够缓解农户与购买者之间存在的信息不对称性,将农产品质量信息传递给购买者,使农户获得更高的销售价格,进而在有效提高农户精细管理技术投入水平的同时,能够弱化劳动力价格对其所产生的负面影响。     

水生态系统服务供需视角下的水网乡村 适应性规划策略——以吴江张鸭荡片区为例

苏南乡村地区一直是中国乡村建设的先行区域，特殊的纵横交错的水网结构构成了一个错综复杂的系统，呈现出其他地区不具有的复杂性和生态特殊性。随着城镇的扩张，乡村的发展建设使其水网空间的平衡发展面临极大的挑战。传统单一静态的规划方法逐渐显示出无法适应经济、社会等发展要求的缺陷。苏南乡村地区以水为核心，从水生态系统服务供需关系的视角下探究水网乡村的适应性规划策略更加适应当前的乡村现状和需求。以传统水网乡村空间形态转译为基础，建立水生态系统供需服务评估体系，在评估水生态系统服务供需能力的基础上，分析供需分异模式及供需矛盾。从构建乡村水域空间生态格局、乡村水域空间功能分区规划，以及乡村水域空间多情景预判3个方面，提出苏南水网乡村的适应性规划策略，并为水网乡村的生态实践提出新思路。     

工资对个人劳动供给的非线性影响：部门市场化程度差异视角

从市场经济体制的建立到经济社会高质量发展，我国不同部门市场化程度有所差异，劳动供给的部门异质性随之体现。基于中国劳动力动态调查数据，结合汉森门槛回归模型和劳动供给模型，依托部门市场化程度的差异性，文章探讨了工资对劳动力个人供给的非线性影响及其部门异质性。主要得出以下研究结论：第一，我国市场部门与公共部门劳动力的个人劳动供给有所差异。第二，工资与个人劳动供给呈现非线性关系并且存在部门异质性。实证分析结果显示，工资对个人劳动供给的影响存在三个"突变点"，工资门槛均使得工资"激励效应"加强并呈现逐步增加的趋势。第三，劳动供给的工资门槛存在性别、城乡和行业异质性。第四，随着年龄增长、人力资本积累，个人劳动时间供给趋于降低，签署书面劳工合同使得市场部门劳动力工作时间显著增加，而政治面貌显著影响公共部门劳动供给。总体来看，无论是市场部门还是公共部门，我国劳动力个人供给曲线均未"向后弯曲"，国民经济高质量发展、改善收入分配格局、提升居民幸福感等议题需持续关注。     

要素自由流动能实现区域协调发展吗——基于“协调性集聚”的理论假说与实证检验

经济活动的空间集聚是当代经济内涵式增长的源泉,现有文献多从要素流动视角来考察经济集聚过程中的区域差异。然而,若将要素流动引入区域生产函数进行推导,可以发现非区域性要素集中规模的扩大,既能提高要素流入区域的劳动生产率,也能提高要素流出区域的要素收益率,基于此,本文以“协调性集聚”为核心概念,通过拓展现代集聚模型来构建要素在自由流动中走向协调发展的理论假说。实证结果进一步显示,虽然我国发展要素在大规模流向东部发达地区,并呈现进一步向超大城市和城市群集聚的趋势,但要素流动在提高要素流入区域劳动生产率的同时,也显著提高了要素流出区域的要素收益率,并从需求侧激发了要素流出区域的市场潜能,要素流动在要素配置效率不断提高的过程中呈现“协调性集聚”的趋势。因此,发达的要素流入区域通过优化公共服务体系提高劳动生产率,欠发达的要素流出区域通过建立健全要素自由流动机制提高要素回报率和收益率,是新时代我国形成市场主导型区域协调发展机制的有效途径。     

Optimal targeting of latent tourism demand segments

This paper identifies for the first time the optimal target markets employing the latent tourism demand expenditure, a novel concept in tourism literature. The study quantifies latent tourism demand between each pair of origin-destination through distinguishing by type of tourism and seasonality. It works with market shares that are estimated via a fractional regression model. Moreover, latent demand is clustered using a market segmentation approach based on a latent class regression. Finally, the optimal target markets are chosen depending on the expected latent tourism expenditure. The result has clear policy implications in terms of which markets are promoted, the optimal channels of communication and the maximum budget for each marketing campaign.     

Is the system reliability profitable for retailing and consumer service of a dynamical system under cross-price elasticity of demand?

Retailing strategy is one of the most crucial factors for industries. A proper retailing strategy can help to enhance consumer service and increase the industry's profit. An improved approach to retailing is suggested in this research to deliver superior customer service while maximizing profits in a dynamic system. The study analyzes a retailing strategy for a demand with cross-price elasticity upon the retail price. A product's cross-price elasticity and the system reliability are critical factors in retailing. Understanding the cross-price elasticity of demand between products helps retailers to make pricing decisions that maximize profits by maintaining demand. Imperfect products are produced due to an imperfect production system. The imperfect ones must be adjusted with some costs to make them perfect for better retailing. The system failure rate is crucial for retailing under cross-price elasticity of demand patterns. Production system reliability, cross-price elasticity of demand, and consumer service are all essential factors that can impact a company's success in the market. The production rate is considered time- and system failure rate-dependent. Contradictory to the literature, a dynamical system is proposed for improved retail management, which is solved using the Euler-Lagrange theory. Finally, one can achieve the expected maximum profit for this retail system with optimum selling prices for different products by reducing the system failure rate. Some numerical illustrations with graphical representations are provided to validate the current study. Numerical examples show that applying cross-price elasticity of demand for more than two identical products provides 35% more profit for the retail industry than a single type of product.     

Cryptocurrencies and Cagan’s model of hyperinflation

The drivers of the prices of Bitcoin and Ethereum are studied within a framework based on Cagan’s model of hyperinflation. In the model, the prices of the cryptocurrencies are driven by stochastic adoption and velocity shocks as well as endogenous expectations of future prices. The model is estimated with data for prices, transaction volumes, and money supplies. A majority of price fluctuations in both currencies can be attributed to shocks in adoption, velocity shocks are much less important. The money demand sensitivity to expected price changes is estimated to be larger for Bitcoin than for Ethereum, and both have higher sensitivity than fiat currencies during episodes of hyperinflation.