Misclassification errors in labor force statuses and the early identification of economic recessions

Accurate identification of economic recessions in a timely fashion is a major macroeconomic challenge. The so-called Sahm recession indicator (Sahm, 2019), one of the most reliable early detectors of the U.S. recessions, relies on changes in unemployment rates, and is thus subject to misclassification errors in labor force statuses based on survey data. We propose a novel misclassification-errors correction to improve the predictive timeliness and provide a proper threshold value. Using historical data, we show that the adjusted unemployment-based recession indicator offers earlier identification of economic recessions.     

How justified is abandoning money in the conduct of monetary policy in Australia on the grounds of instability in the money‐demand function?

The role of money in the design and conduct of monetary policy has reemerged as an important issue in both advanced and developing economies, especially since the 2007 global financial crisis. A growing body of recent literature suggests that the causal relationship between money supply growth and inflation remains intact across countries and over time and that this relation is not conditional on the stability of the money‐demand function or whether money is endogenous or exogenous. Moreover, critical for a rule‐based monetary policy is the presence of a long‐run stable money‐demand function, rather than a short‐run money‐demand model that may exhibit instability for many reasons, including problems with estimating a money‐demand model with high‐frequency data. Provided that a stable money‐demand function exists, it could be useful to establish long‐run equilibrium relations among money, output, prices, and exchange rates, as the classical monetary theory suggests. Within this analytical framework, this paper addresses the question of whether money has any role in the conduct of monetary policy in Australia. The conventional wisdom is that the money‐demand function in Australia has been unstable since the mid‐1980s due to financial deregulation and reforms; this led to a change in the strategy of monetary policy for price stability in the form of inflation targeting that ignores money insofar as inflation and its control are concerned. This paper reports empirical findings for Australia, obtained from a longer quarterly data series over the period 1960Q1–2015Q1, which suggest that instability in the narrow‐money‐demand function in Australia was primarily due to the exclusion of variables which have become important in the deregulated environment since the 1980s. These findings are confirmed by an expanded form of the narrow‐money‐demand function that was found stable over the past two decades, although it experienced multiple structural breaks over the study period. The paper draws the conclusion that abandoning the monetary aggregate as an instrument of monetary policy in Australia, under a rule‐based monetary policy such as inflation targeting, cannot be justified by instability in the money‐demand function or even by lack of a causal link between money supply growth and inflation.     

The puzzle of greenhouse gas footprints of oil abundance

The present inquiry lays a groundwork for the analysis of the net greenhouse gas (GHG) footprint of oil in the oil-abundant settings. To address the research question, the study puts forward a three-sector decision model, which provides a common ground for the assessment of the interaction of the structuralist and institutional factors influencing environmental pollution in the oil-reliant economies. The study shows that fossil-fuel abundance triggers forces, which induce diametrically opposed effects concerning atmospheric pollution. These are the rising carbon-intensive oil extraction and processing and fossil-fueled power generation versus shrinkage of the carbon-intensive manufacturing and growth of the low-carbon tertiarization. The theoretical analysis enables compartmentalization of the essential factors, which determine GHG emissions in the respective countries. To assess the significance of the proposed theoretical framework, the study employs multivariate panel co-integration techniques and two-stage fixed effects estimations for a dataset of 38 oil-producing countries for the time period between 1960 and 2018. In contrast to the existing literature, this study drives apart from the black box approaches that employ just one omnibus variable, per capita income.     

Combining wavelet decomposition with machine learning to forecast gold returns

This paper combines the discrete wavelet transform with support vector regression for forecasting gold-price dynamics. The advantages of this approach are investigated using a relatively small set of economic and financial predictors. I measure model performance by differentiating between a statistically-motivated out-of-sample forecasting exercise and an economically-motivated trading strategy. Disentangling the predictors with respect to their time and frequency domains leads to improved forecasting performance. The results are robust compared to alternative forecasting approaches. My findings on the relative importances of such wavelet decompositions suggest that the influences of short-term and long-term trends are not stable over the full evaluation period.     

Optimal granularity for portfolio choice

Many optimization-based portfolio rules fail to beat the simple 1/N rule out-of-sample because of parameter uncertainty. In this paper we suggest a grouping strategy in which we first form groups of equally weighted stocks and then optimize over the resulting groups only. This strategy aims at balancing the trade-off between the benefits from optimization and the losses from estimation risk. We rely on Monte-Carlo simulations to illustrate the performance of the strategy, and we derive the optimal group size for a simplified setup. Furthermore, we show that estimation risk also has an impact via the criterion by which the assets are sorted into groups (like the expected excess returns or betas), but does not negate the grouping approach. We relate our work to linear asset pricing models, and we conduct out of sample back-tests in order to confirm the validity of our grouping strategy empirically.     

Macroeconomic adjustment with managed exchange rates and capital controls: Some lessons from China

This paper examines the performance of capital controls and exchange-rate management when the economy finds itself in dark corners. These are times when the real sector experiences a sequence of prolonged negative shocks from world demand, while the central bank faces low world interest rates on its foreign-exchange reserve holdings. We examine two regimes, one of a fixed exchange rate with strong capital controls and another with a more open capital account with a managed exchange rate. We show how this model replicates recent experiences of China as it moved from a relatively fixed exchange rate regime with strong capital controls to a more flexible exchange rate regime with a more open capital account. Our results show that capital-account liberalization should be accompanied by domestic price liberalization to avoid large losses in foreign exchange reserve and jumps in unemployment during dark corners in the more open regime.     

现代治理视域下行政审批机构改革研究

推进行政审批机构改革是新时代完善现代国家治理体系和提升现代国家治理能力的必然要求。行政审批局改革模式的现代治理意涵在于优化配置制度建设主体要素以及重构革新治理主体组织结构,并在制度依据、机制运行和治理效能三个层面建构起改革运行机理。尽管通过集中审批权限、实现"一枚印章管审批"大力提升了审批效率,但行政审批局模式依然面临有悖"职权法定"、纵横协调不足、制度建设不充分和资源配置不合理的四重困境。现代治理视域下,应从树立科学合理的审批改革理念、完善行政审批机构改革的顶层设计以及创新行政审批局具体工作模式等方面不断完善和深化行政审批机构改革。     

Asymmetry,Complementarities, and State Dependence in Federal Reserve Forecasts

Forecasts are a central component of policymaking; the Federal Reserve's forecasts are published in a document called the Greenbook. Previous studies of the Greenbook's inflation forecasts have found them to be rationalizable but asymmetric if considering particular subperiods, for example, before and after the Volcker appointment. In these papers, forecasts are analyzed in isolation, assuming policymakers value them independently. We analyze the Greenbook forecasts in a framework in which the forecast errors for different variables are allowed to interact. We find that allowing the losses to interact makes the unemployment forecasts virtually symmetric, the output forecasts symmetric prior to the Volcker appointment, and the inflation forecasts symmetric after the onset of the Great Moderation.     

不完备信息系统辐射源威胁等级判定

针对不完备信息系统条件下的辐射源威胁等级判定问题，提出了一种基于改进容差关系粗糙集（ITR-RS）的不完备信息系统辐射源威胁等级判定方法。该方法将粗糙集中的不完备信息系统理论引入辐射源威胁等级判定中，并构建一套完备的决策规则提取模型。此外，利用辨识矩阵实现对属性的约简，在降低系统所需处理数据量的同时，提高了算法的实时性；在容差关系粗糙集的基础上，提出一种改进的容差关系，在属性主、客权重相结合的基础上引入阈值来划分加权阈值容差类，得到更为合理、准确的决策规则。仿真试验及分析表明，与经典RS算法相比，所提算法的威胁等级判定正确率提高了23%，可用于信息系统不完备条件下的辐射源威胁等级判定。     

A product-centric data mining algorithm for targeted promotions

Targeted promotions in retail are becoming increasingly popular, particularly in UK grocery retail sector, where competition is stiff and consumers remain price sensitive. Given this, a targeted promotion algorithm is proposed to enhance the effectiveness of promotions by retailers. The algorithm leverages a mathematical model for optimising items to target and fuzzy c-means clustering for finding the best customers to target. Tests using simulations with real life consumer scanner panel data from the UK grocery retailer sector show that the algorithm performs well in finding the best items and customers to target whilst eliminating “false positives” (targeting customers who do not buy a product) and reducing “false negatives” (not targeting customers who could buy a product). The algorithm also shows better performance when compared to a similar published framework, particularly in handling “false positives” and “false negatives”. The paper concludes by discussing managerial and research implications, and highlights applications of the model to other fields.