Econometric Causality

This paper presents the econometric approach to causal modelling. It is motivated by policy problems. New causal parameters are defined and identified to address specific policy problems. Economists embrace a scientific approach to causality and model the preferences and choices of agents to infer subjective (agent) evaluations as well as objective outcomes. Anticipated and realized subjective and objective outcomes are distinguished. Models for simultaneous causality are developed. The paper contrasts the Neyman–Rubin model of causality with the econometric approach.     

The local impact of typhoons on economic activity in China: A view from outer space

We examine the impact of typhoons on local economic activity in coastal China. To capture potential damages from an individual typhoon we use historical typhoon track data in conjunction with a detailed wind-field model. We then combine our damage proxy with satellite derived nightlight intensity data to construct a panel data set that allows us to estimate the impact of typhoons at a spatially highly disaggregated level (approx. 1 km). Our results show that typhoons have a negative and significant, but short-term, impact on local activity – a typhoon that is estimated to destroy 50% of the property reduces local economic activity by 20% for that year. Over our period of analysis (1992–2010) total net economic losses are estimated to be in the region of $US 28.34 billion. To assess the damage risk from future typhoons we use simulated probability distributions of typhoon occurrence and intensity and combine these with our estimated effects. Results suggest that expected annual losses are likely to be around $US 0.54 billion.     

Three Corrected Score Tests for Generalized Linear Models with Dispersion Covariates

We develop three corrected score tests for generalized linear models with dispersion covariates, thus generalizing the results of Cordeiro , Ferrari and Paula (1993) and Cribari-Neto and Ferrari (1995) . We present, in matrix notation, general formulae for the coefficients which define the corrected statistics. The formulae only require simple operations on matrices and can be used to obtain analytically closed-form corrections for score test statistics in a variety of special generalized linear models with dispersion covariates. They also have advantages for numerical purposes since our formulae are readily computable using a language supporting numerical linear algebra. Two examples, namely, iid sampling without covariates on the mean or dispersion parameter oand one-way classification models, are given. We also present some simulations where the three corrected tests perform better than the usual score test, the likelihood ratio test and its Bartlett corrected version. Finally, we present a numerical example for a data set discussed by Simonoff and Tsai (1994) .     

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.     

Forecasting government support in Irish general elections: Opinion polls and structural models

Election forecasting is a cottage industry among pollsters, the media, political scientists, and political anoraks. Here, we plow a fresh field in providing a systematic exploration of election forecasting in Ireland. We develop a structural forecast model for predicting incumbent government support in Irish general elections between 1977 and 2020 (the Iowa model). We contrast this structural model with forecasts from opinion polls, the dominant means of predicting Ireland’s elections to date. Our results show that with appropriate lead-in time, structural models perform similarly to opinion polls in predicting government support when the dependent variable is vote share. Most importantly, however, the Iowa model is superior to opinion polls in predicting government seat share, the ultimate decider of government fate in parliamentary systems, and especially significant in single transferable vote (STV) systems where vote and seat shares are not always in sync. Our results provide cumulative evidence of the potency of structural electoral forecast models globally, with the takeaway that the Iowa model estimating seat share outpaces other prediction approaches in anticipating government performance in Irish general elections.     

Optimal lockdown in altruistic economies

The recent COVID-19 crisis has revealed the urgent need to study the impact of an infectious disease on market economies and provide adequate policy recommendations. The present paper studies the optimal lockdown policy in a dynamic general equilibrium model where households are altruistic and they care about the share of infected individuals. The spread of the disease is modeled here using SIS dynamics, which implies that recovery does not confer immunity. To avoid non-convexity issues, we assume that the lockdown is constant in time. This strong assumption allows us to provide analytical solutions. We find that the zero lockdown is efficient when agents do not care about the share of infected, while a positive lockdown is recommended beyond a critical level of altruism. Moreover, the lockdown intensity increases in the degree of altruism. Our robust analytical results are illustrated by numerical simulations, which show, in particular, that the optimal lockdown never trespasses 60% and that eradication is not always optimal.     

Systemic circular business model application at the company,supply chain and society levels—A view into circular economy native and adopter companies

This study contributes to the conceptualization of a sustainable circular business model (CBM), the application of the circular economy (CE) by companies. The study utilized a three-level framework adapted from the CE literature with company (micro), supply chain (meso) and society (macro) levels. This multiple-case study in the textile industry included two types of companies operating in the CE: companies founded on the sustainable principles of a CE (natives) and companies transitioning to a CE from the linear economy (adopters). The findings show that the adopters emphasized long-term economic sustainability on a company level and implemented CE elements to varying degrees on all three levels. The natives pursued business decisions from environmental and social sustainability standpoints, and the three levels were integral in their systemic approach to a CE. The study highlights two key claims: established operational structures and economic volition hindered adopters in their systemic CE implementation, and an integral part of the CBM for natives was a proactive approach towards the society level.     

基于VAR模型探究BDI和燃油价格的相互影响

燃油价格近期受疫情影响出现剧烈波动,国际干散货航运市场也受到巨大影响。基于Clarksons官方数据库1992年1月至2020年4月相关数据,进行实证研究发现,国际燃油价格的暴跌对波罗的海干散货运价指数的下降有着显著的正向影响;燃油价格虽然也在一定程度上受到BDI的影响,但存在一定的滞后性。这有利于干散货运输公司根据燃油价格波动预测运价指数的变化趋势,从而采取多样的方式应对冲击。也对保障干散货运输市场的稳定发展有重要意义。     

基于EGARCH模型的我国向美国出口变动杠杆效应分析

本文运用EGARCH计量模型,对我国向美国出口增速变动进行杠杆效应分析。模型结果显示,出口增速在负向冲击下比受到正向冲击时的波动更加剧烈。次贷危机的恶化,也将会对我国向美国出口增速产生较大的负面影响,从而影响我国的整体出口。对此,文章给出了相关建议。     

Revisiting the relationship between suicide and unemployment: Evidence from linear and nonlinear cointegration

This study employs the linear and nonlinear ARDL cointegration methodologies to examine the potential symmetric and asymmetric responses of suicide rates to unemployment rates in the US from 1928 to 2013. Our results suggest that suicide rates are pro-cyclical with respect to the business cycle (measured by changes in the unemployment rate) after extensively controlling for divorce and fertility rates. Unemployment has symmetric long-run effects on the age-adjusted suicide rate and four age-specific (from ages 25–34 to 55–64) suicide rates, while the effect of an economic expansion on suicide rates for those aged over 45 is greater than the effect of an economic recession. These findings imply that the effect of an economic expansion on the decrease of the suicide rate is higher than the effect of an economic recession on the increase of the suicide rate for individuals aged over 45. Therefore, intervention designed to reduce suicidal behaviors should emphasize periods of economic recession more than periods of economic expansion for those of middle age and beyond.