基于时间距离的河南城市经济联系及其空间结构

采用GIS网络分析获得城市间公路交通的OD时间距离对引力模型的距离进行修正,对河南各地市间1997年和2007年的经济联系进行测算和比较,继而采用分形模型对各城市对外经济联系总量及联系强度的分形维数进行测算,探讨了地市对外经济联系的空间结构特征.研究表明,河南各城市经济联系总量等级特征明显,地市间经济联系初步形成了“十...     

农村集体建设用地流转地权的激励模式

农村集体建设用地流转中存在严重的资金时差悖论,导致难以利用集体土地本身进行融资,土地整理融资难反过来又成为阻碍集体建设用地流转的瓶颈性因素。现有流转模式难以协调各种利益,不能对流转各方产生足够激励,变相征地的方式削弱了农民的土地持续收益能力,未能真正实现城乡建设用地"同质、同价、同权"。因此,应根据利益协调的原则创新制度设计,以地权激励引导社会资源投入,促进集体建设用地顺畅流转。     

The M4 Competition: Results,findings, conclusion and way forward

The M4 competition is the continuation of three previous competitions started more than 45 years ago whose purpose was to learn how to improve forecasting accuracy, and how such learning can be applied to advance the theory and practice of forecasting. The purpose of M4 was to replicate the results of the previous ones and extend them into three directions: First significantly increase the number of series, second include Machine Learning (ML) forecasting methods, and third evaluate both point forecasts and prediction intervals. The five major findings of the M4 Competitions are: 1. Out Of the 17 most accurate methods, 12 were “combinations” of mostly statistical approaches. 2. The biggest surprise was a “hybrid” approach that utilized both statistical and ML features. This method’s average sMAPE was close to 10% more accurate than the combination benchmark used to compare the submitted methods. 3. The second most accurate method was a combination of seven statistical methods and one ML one, with the weights for the averaging being calculated by a ML algorithm that was trained to minimize the forecasting. 4. The two most accurate methods also achieved an amazing success in specifying the 95% prediction intervals correctly. 5. The six pure ML methods performed poorly, with none of them being more accurate than the combination benchmark and only one being more accurate than Naïve2. This paper presents some initial results of M4, its major findings and a logical conclusion. Finally, it outlines what the authors consider to be the way forward for the field of forecasting.     

Time inconsistency and delayed retirement decision: the French pension bonus

With the increase in life expectancy and demographic shocks, several public policies in the last decades aim to encourage individuals to postpone retirement. One of them, the pension bonus, gives an increased pension if individuals retire beyond their Full Retirement Age. Previous ex post analyses found that the responsiveness to this type of financial incentives, which encourage to postpone retirement, is heterogeneous among agents and that the global effect is rather limited. Deriving from previous research in Behavioural Economics, this article analyses the impact of time inconsistency in the decision to delay retirement to get the bonus. Using public national survey data, short-term and long-term impatience are measured with questions on retiring motivations. After controlling for the endogeneity of the bonus knowledge, econometric results show that time-inconsistent agents are less likely to retire with the bonus.     

Forecasting UK house prices: A time varying coefficient approach

Previous studies of UK house prices, developed from the demand and supply ofhousing or from the asset market approach have been poor in terms of robustness and ex-post forecasting ability. The UK housing market has suffered a number of structural changes, particularly since the early 1980s with substantial house price increases, financial market deregulation and the removal of mortgage market constraints through competition. Consequently, models which assume that the underlying data-generating process is stable and apply constant parameter techniques tend to suffer in terms of parameter instability. This article uses the Time Varying Coefficient (TVC) methodology where the underlying data-generating process in the UK housing market is treated as unstable. The estimation results of the TVC regression of UK house prices is compared with those obtained from three alternative constant parameter regressions. Comparisons of forecasting performance suggest the TVC regression out-performs forecasts from an Error Correction Mechanism (ECM), Vector Autoregressive (VAR) and an Autoregressive Time Series regression.     

房地产价格波动与投机行为--对中国14城市的实证研究

本文首先构建了适合中国的房地产投机理论模型,然后利用该模型对中国14个城市房地产价格波动与投机行为的关系进行实证研究.对14城市的时间序列数据研究结果表明各城市房地产投机水平都很高,个别城市更加突出.通过对14城市的截面数据分析,可以看出可支配收入对房地产价格没有显著影响,这些城市房地产价格的上升,主要是由投机来推动的,而且整体投机度非常高,说明房地产价格极大的偏离长期均衡值,市场出现了非理性繁荣,政府和产业部门应该采取适当的措施,积极引导消费者和投资者,为建立均衡的房地产市场提供良好的宏观和微观环境.     

人民币实际有效汇率的变化对我国进出口的影响

本文从空间和时间两个角度全面考察了人民币实际有效汇率的变化对我国进出口的影响。结果表明,人民币汇率的变化会显著影响我国的进出口。但是,这种影响程度在1994年以后明显地出现了下降趋势。同时,人民币汇率变化对我国进出口的调整存在明显的J曲线效应,其中进口变动大致滞后于汇率变动2个季度,而出口则滞后1个季度,滞后期限均短于西方发达国家。本文分析了其中的原因并提出了相关建议。     

Impatience and equilibrium indeterminacy

This paper extends the neoclassical one-sector optimal growth model by postulating that individual agents’ time preference (impatience) depends on the economy-wide average consumption and average income, which are social factors taken as external by individual agents. The paper shows that with the socially determined individual time preference local equilibrium indeterminacy can arise. Moreover, local indeterminacy can also be associated with global indeterminacy in many cases. The results hold in models with bounded and unbounded growth.     

Macroeconomic forecasting using structural factor analysis

The use of a small number of underlying factors to summarize the information from a much larger set of information variables is one of the new frontiers in forecasting. In prior work, the estimated factors have not usually had a structural interpretation and the factors have not been chosen on a theoretical basis. In this paper we propose several variants of a general structural factor forecasting model, and use these to forecast certain key macroeconomic variables. We make the choice of factors more structurally meaningful by estimating factors from subsets of information variables, where these variables can be assigned to subsets on the basis of economic theory. We compare the forecasting performance of the structural factor forecasting model with that of a univariate AR model, a standard VAR model, and some non-structural factor forecasting models. The results suggest that our structural factor forecasting model performs significantly better in forecasting real activity variables, especially at short horizons.     

Destabilizing role of futures markets on North American hard red spring wheat spot prices

The objective of the paper is to determine if the futures prices of hard red spring wheat (HRSW) have stabilizing or destabilizing impact on spot HRSW price in North America. Several important results emerge from thorough empirical analysis. First, both Granger causality tests and directed acyclic graph algorithms (DAGs) point to two-way causality between futures and spot HRSW prices and thus endogeneity in both prices formation. To the contrary, both procedures suggest that ending stocks are exogenous to spot and futures HRSW prices. Both vector error correction model and impulse response functions point to a large and long-lasting impact of a shock to futures price on spot price level. Finally, variance decomposition analysis indicates that futures prices are responsible for the bulk of spot price volatility in both short and long run. Our result is consistent with those of theoretical models suggesting that when production (supply side) is the dominant disturbance, spot price is destabilized in both the short and the long run by futures prices. An important implication of this research is the need for alternative market mechanisms or alternative farm policy measures that would mitigate price risk and ensure sustainable farming of American HRSW farmers.