Exploring the motivations to use online meal delivery platforms: Before and during quarantine

Online meal delivery platforms (OMDPs), like UberEats, have received more attention since quarantine was established to prevent the spread of COVID-19. The research into antecedents of satisfaction and continuous usage intention for OMDPs, in general, is under researched. The current study compared the antecedents of satisfaction before quarantine to the antecedents during quarantine through the lens of Expectation-Confirmation Theory. Through multiple regression analyses, the researchers found that before quarantine, sharing economy ethos, price-value, food quality, ease of use and confirmation of beliefs had a significant impact on satisfaction while during quarantine, food quality, service speed, ease of use, and confirmation of beliefs were significant. Practical and theoretical implications are discussed.     

Factor extraction using Kalman filter and smoothing: This is not just another survey

Dynamic factor models have been the main “big data” tool used by empirical macroeconomists during the last 30 years. In this context, Kalman filter and smoothing (KFS) procedures can cope with missing data, mixed frequency data, time-varying parameters, non-linearities, non-stationarity, and many other characteristics often observed in real systems of economic variables. The main contribution of this paper is to provide a comprehensive updated summary of the literature on latent common factors extracted using KFS procedures in the context of dynamic factor models, pointing out their potential limitations. Signal extraction and parameter estimation issues are separately analyzed. Identification issues are also tackled in both stationary and non-stationary models. Finally, empirical applications are surveyed in both cases. This survey is relevant to researchers and practitioners interested not only in the theory of KFS procedures for factor extraction in dynamic factor models but also in their empirical application in macroeconomics and finance.     

Happiness before and after an election: An analysis based on a daily survey around Japan’s 2009 election

This paper investigates whether the Japanese voters became happy and/or unhappy due to the results of the General Election in 2009. We conducted a daily web survey for seven days before and after the election, obtaining 1068 responses. Estimating a fixed effects model, we found that among those whose expectation differed from the reality, supporters of the Democratic Party of Japan (DPJ), the winner, became significantly happier, and supporters of the Liberal Democratic Party of Japan (LDP) and New Komeito, the losers, became significantly unhappier on the day following the election. However, happiness returned to the previous level in one or two days, implying people adapted to the news very quickly. On the other hand, the happiness level of those whose expectation of the election results were realized did not change. In a word, only unexpected results matter. Dividing those who support the policies of DPJ into two groups, those who expect material benefits from the victory of DPJ and those who do not, the obtained results suggested that the reason why the supporters of the winner (DPJ) felt happy was not because they obtained material benefits from the change of government.     

金融周期、行业技术周期与经济结构优化

本文首先采用马尔科夫区制转移模型测算金融周期和12类行业的技术投入周期,进而采用交叉谱分析法和OLS法分析金融周期对行业技术周期的影响,结果发现:不同金融变量的周期具有异步性,各行业的技术投入周期具有明显差别;不同金融变量周期与行业技术投入周期之间存在不同的数量关联性,对不同的行业技术投入具有不同的影响作用;在繁荣阶段,金融市场和银行对各行业的技术投入都具有正向推动作用,但金融市场对高密度创新行业技术投入的推动作用更为显著,银行对低密度稳定行业技术投入的推动作用更为显著;在紧缩阶段,金融市场对高密度创新行业技术投入的紧缩效应更为剧烈,银行却对低密度稳定行业的技术投资下行起到缓解作用。根据这些事实和规律,可以引导利用各金融变量对具有发展前景的行业强化技术投资助推作用,提升经济结构优化效率。     

Corruption in space: A closer look at the world's subnations

Corruption levels differ not only between but also within countries. In this paper we analyze spatial interdependencies in corruption levels for a large sample of 1232 subnational regions from 81 countries. Based on a spatial autoregressive model, which controls for country-fixed effects and corrects for spatial autocorrelation in the error term, we find that a subnational region's corruption level is positively correlated with neighboring subnational regions' corruption levels. Extending the core model and allowing for heterogeneous spatial effects, we find that most spillovers among subnational regions occur within national borders. Moreover, in particular high income subnational regions and subnational regions with relative low corruption levels tend to spill in space. This is due to their high degree of connectivity in terms of economic, sociocultural and political exchange with other subnational regions. Our estimation results underline the importance to consider not only a subnational region's own characteristics, but also spatial interdependencies when implementing efficient anti-corruption policies at the local level.     

Macroeconomic implications of modeling the Internal Revenue Code in a heterogeneous-agent framework

Tax policy analysis in heterogeneous-agent models typically involves the use of smooth tax functions to approximate complex present tax law and proposed reforms. In this paper, we explore the extent to which the tax detail omitted under this conventional approach has macroeconomic implications relevant for policy analysis. To do this, we develop an alternative approach by embedding an internal tax calculator into a large-scale overlapping generations model that, while conditioning on idiosyncratic household characteristics, explicitly models key provisions in the Internal Revenue Code applied to labor income. We find that for a debt-constant steady state analysis of a given tax policy change, both approaches generate similar policy-induced patterns of macroeconomic activity despite variation in the underlying patterns of household tax-preferred consumption and labor supply behavior. However, this variation in underlying behavior is associated with significant quantitative and qualitative differences in macroeconomic aggregates along a debt-financed transition path immediately following a policy change. Consequentially, although the use of unconditional smooth tax functions may be a reasonable modeling simplification for steady state analysis of tax policy, caution should be taken for their use in transition path analysis within heterogeneous-agent models.     

Capital accumulation through studying abroad and return migration

This paper examines the interplays among studying abroad, return migration and capital accumulation, in a dynamic general equilibrium model featuring heterogeneous ability. Households invest in education and make two migration decisions: whether to study abroad and subsequently whether to return home. The model predicts that the highest, middle and lowest-ability people choose respectively permanent migration, return migration and no migration. More interestingly, we find a novel migration cycle: returnees bring back learned-knowledge and over time, capital accumulates, attracting more return migration. Further, the usual “brain drain” in the literature can be turned into “brain gain”, by providing a subsidy to studying abroad and returning home.     

Heterogeneous beliefs with herding behaviors and asset pricing in two goods world

We construct a incomplete information equilibrium model with heterogeneous beliefs and herding behaviors to identify their joint effects on the dynamics of asset prices. Herding behaviors make investors revise some of their estimations about expected growth rates of goods streams toward to the other one’s by a manner of weighted average of their own forecast and the other’s. As we expected, herding behaviors generate influences on the Radon Nikodym derivative, that is so-called “sentiment” as in Dumas et al. (2009), and in turn not only impact the dynamics of asset prices but also generate influences on investors’ survivals. We also show that introducing heterogeneous beliefs with herding behaviors permits to explain both the Backus–Smith puzzle and the mixed results about the influences of herding behaviors on asset prices. Moreover, we uncover that herding behaviors have positive influences on stocks’ risk premiums.     

Crime prediction by data-driven Green’s function method

We develop an algorithm that forecasts cascading events, by employing a Green’s function scheme on the basis of the self-exciting point process model. This method is applied to open data of 10 types of crimes happened in Chicago. It shows a good prediction accuracy superior to or comparable to the standard methods which are the expectation–maximization method and prospective hotspot maps method. We find a cascade influence of the crimes that has a long-time, logarithmic tail; this result is consistent with an earlier study on burglaries. This long-tail feature cannot be reproduced by the other standard methods. In addition, a merit of the Green’s function method is the low computational cost in the case of high density of events and/or large amount of the training data.     

Financial risk taking in the presence of correlated non-financial background risk

This paper characterizes the stochastic deterioration resulting from taking a zero-mean financial risk in the presence of correlated non-financial background risk. We show in particular that it has an equivalent stochastic order as well as a necessary and sufficient “integral condition” that implies and is implied by a particular sense in which the stochastic deterioration can be decomposed into a “correlation increase” and a “marginal risk increase”. We further characterize a measure of aversion to the stochastic deterioration. These characterizations provide for a more general framework for formulating concepts of increases in risk and correlation and for better understanding risk management decisions governed by individuals’ attitudes to them.