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Article

Score-Driven Models: Methodology and Theory  

Mariia Artemova, Francisco Blasques, Janneke van Brummelen, and Siem Jan Koopman

Score-driven models belong to a wider class of observation-driven time series models that are used intensively in empirical studies in economics and finance. A defining feature of the score-driven model is its mechanism of updating time-varying parameters by means of the score function of the predictive likelihood function. The class of score-driven models contains many other well-known observation-driven models as special cases, and many new models have been developed based on the score-driven principle. Score-driven models provide a general way of parameter updating, or filtering, in which all relevant features of the observation density function are considered. In models with fat-tailed observation densities, the score-driven updates are robust to large observations in time series. This kind of robustness is a convenient feature of score-driven models and makes them suitable for applications in finance and economics, where noisy data sets are regularly encountered. Parameter estimation for score-driven models is straightforward when the method of maximum likelihood is used. In many cases, theoretical results are available under rather general conditions.

Article

Score-Driven Models: Methods and Applications  

Mariia Artemova, Francisco Blasques, Janneke van Brummelen, and Siem Jan Koopman

The flexibility, generality, and feasibility of score-driven models have contributed much to the impact of score-driven models in both research and policy. Score-driven models provide a unified framework for modeling the time-varying features in parametric models for time series. The predictive likelihood function is used as the driving mechanism for updating the time-varying parameters. It leads to a flexible, general, and intuitive way of modeling the dynamic features in the time series while the estimation and inference remain relatively simple. These properties remain valid when models rely on non-Gaussian densities and nonlinear dynamic structures. The class of score-driven models has become even more appealing since the developments in theory and methodology have progressed rapidly. Furthermore, new formulations of empirical dynamic models in this class have shown their relevance in economics and finance. In the context of macroeconomic studies, the key examples are nonlinear autoregressive, dynamic factor, dynamic spatial, and Markov-switching models. In the context of finance studies, the major examples are models for integer-valued time series, multivariate scale, and dynamic copula models. In finance applications, score-driven models are especially important because they provide particular updating mechanisms for time-varying parameters that limit the effect of the influential observations and outliers that are often present in financial time series.

Article

The Costs of Bankruptcy Restructuring  

Wei Wang

Financially distressed and insolvent firms file for bankruptcy to either reorganize or liquidate under court supervision. Fundamentally, bankruptcy law is designed to resolve creditor coordination and holdout problems. It not only sets up rules and guidelines to allow firms to restructure their debt claims but also provides means for firms to reallocate their assets to other users. Although an efficient bankruptcy system can help mitigate bargaining frictions and maximize asset value and thus creditor recovery by avoiding inefficient liquidation or excess continuation, the bankruptcy process itself can be costly. Understanding and quantifying the costs of bankruptcy restructuring are important not only to financially distressed firms but also to the capital structure decisions and the pricing of securities of healthy firms. More broadly, efficient bankruptcy mechanisms are important for economic growth, the productivity of firms in an economy, and the resiliency of the economy to adverse shocks. From the 1990s through the 2020s, the literature has flourished, with a growing number of empirical studies investigating the efficiency of the bankruptcy system and different aspects of bankruptcy costs. Bankruptcy costs are typically classified as either direct or indirect costs. The former refers to out-of-pocket expenses associated with the retention of professionals, while the latter refers to opportunity costs incurred as a result of the adverse effect of a bankruptcy filing on business operations, human capital, and investments. Indirect costs are typically larger and more difficult to measure and quantify than direct costs, which studies show to be a small fraction of a bankrupt firm’s assets. Because of significant economic frictions such as conflicts of interest, information asymmetry, and judicial biases presented in the system, bankruptcy can be a lengthy process. Since delay allows both direct and indirect costs to accumulate, a number of studies show that shortening the bargaining process can effectively help preserve firm value. Besides delay, bankruptcy costs can be manifested in inefficient liquidation, excess continuation, fire sales, loss of human capital, and managerial turnover, which impose real costs on bankrupt firms. How to mitigate frictions and minimize costs has been the central theme of bankruptcy research from the 1990s through the 2020s, a time that has also witnessed several notable changes to the U.S. bankruptcy system, including the rise of specialized distressed investors, the strengthening of secured creditor control rights, and the increasing intensity of asset sales. These changes have important implications for the restructuring landscape.

Article

Applications of Web Scraping in Economics and Finance  

Piotr Śpiewanowski, Oleksandr Talavera, and Linh Vi

The 21st-century economy is increasingly built around data. Firms and individuals upload and store enormous amount of data. Most of the produced data is stored on private servers, but a considerable part is made publicly available across the 1.83 billion websites available online. These data can be accessed by researchers using web-scraping techniques. Web scraping refers to the process of collecting data from web pages either manually or using automation tools or specialized software. Web scraping is possible and relatively simple thanks to the regular structure of the code used for websites designed to be displayed in web browsers. Websites built with HTML can be scraped using standard text-mining tools, either scripts in popular (statistical) programming languages such as Python, Stata, R, or stand-alone dedicated web-scraping tools. Some of those tools do not even require any prior programming skills. Since about 2010, with the omnipresence of social and economic activities on the Internet, web scraping has become increasingly more popular among academic researchers. In contrast to proprietary data, which might not be feasible due to substantial costs, web scraping can make interesting data sources accessible to everyone. Thanks to web scraping, the data are now available in real time and with significantly more details than what has been traditionally offered by statistical offices or commercial data vendors. In fact, many statistical offices have started using web-scraped data, for example, for calculating price indices. Data collected through web scraping has been used in numerous economic and finance projects and can easily complement traditional data sources.

Article

Gene–Environment Interplay in the Social Sciences  

Rita Dias Pereira, Pietro Biroli, Titus Galama, Stephanie von Hinke, Hans van Kippersluis, Cornelius A. Rietveld, and Kevin Thom

Nature (one’s genes) and nurture (one’s environment) jointly contribute to the formation and evolution of health and human capital over the life cycle. This complex interplay between genes and environment can be estimated and quantified using genetic information readily available in a growing number of social science data sets. Using genetic data to improve our understanding of individual decision making, inequality, and to guide public policy is possible and promising, but requires a grounding in essential genetic terminology, knowledge of the literature in economics and social-science genetics, and a careful discussion of the policy implications and prospects of the use of genetic data in the social sciences and economics.

Article

Maternity Leave and Paternity Leave: Evidence on the Economic Impact of Legislative Changes in High-Income Countries  

Serena Canaan, Anne Sophie Lassen, Philip Rosenbaum, and Herdis Steingrimsdottir

Labor market policies for expecting and new mothers emerged at the turn of the 19th century. The main motivation for these policies was to ensure the health of mothers and their newborn children. With increased female labor market participation, the focus has gradually shifted to the effects that parental leave policies have on women’s labor market outcomes and gender equality. Proponents of extending parental leave rights for mothers in terms of duration, benefits, and job protection have argued that this will support mothers’ labor market attachment and allow them to take time off from work after childbirth and then safely return to their pre-birth jobs. Others have noted that extended maternity leave can work as a double-edged sword for mothers: If young women are likely to spend months, or even years, on leave, employers are likely to take that into consideration when hiring and promoting their employees. These policies may therefore end up adversely affecting women’s labor market outcomes. This has led to an increased focus on activating fathers to take parental leave, and in 2019, the European Parliament approved a directive requiring member states to ensure at least 2 months of earmarked paternity leave. The literature on parental leave has proliferated during the past two decades. The increased number of studies on the topic has brought forth some consistent findings. First, the introduction of short maternity leave is beneficial for both maternal and child health and for mothers’ labor market outcomes. Second, there appear to be negligible benefits from a leave extending beyond 6 months in terms of health outcomes and children’s long-term outcomes. Furthermore, longer leaves have little, or even adverse, influence on mothers’ labor market outcomes. However, evidence suggests that there may be underlying heterogeneous effects from extended leave among different socioeconomic groups. The literature on the effect of earmarked paternity leave indicates that these policies are effective in increasing fathers’ leave-taking and involvement in child care. However, the evidence on the influence of paternity leave on gender equality in the labor market remains scarce and is somewhat mixed. Finally, recent studies that focus on the effect of parental leave policies for firms find that in general, firms are able to compensate for lost labor when their employees go on leave. However, if firms face constraints when replacing employees, it could negatively influence their performance.

Article

Trade Shocks and Labor-Market Adjustment  

John McLaren

When international trade increases, either because of a country’s lowering its trade barriers, a trade agreement, or productivity surges in a trade partner, the surge of imports can cause dislocation and lowered incomes for workers in the import-competing industry or the surrounding local economy. Trade economists long used static approaches to analyze these effects on workers, assuming either that workers can adjust instantly and costlessly, or (less often) that they cannot adjust at all. In practice, however, workers incur costs to adjust, and the adjustment takes time. An explosion of research, mostly since about 2008, has explored dynamic worker adjustment through change of industry, change of occupation, change of location, change of labor-force participation, adjustment to change in income, and change in marital status or family structure. Some of these studies estimate rich structural models of worker behavior, allowing for such factors as sector-specific or occupation-specific human capital to accrue over time, which can be imperfectly transferable across industries or occupations. Some allow for unobserved heterogeneity across workers, which creates substantial technical challenges. Some allow for life-cycle effects, where adjustment costs vary with age, and others allow adjustment costs to vary by gender. Others simplify the worker’s problem to embed it in a rich general equilibrium framework. Some key results include: (a) Switching either industry or occupation tends to be very costly; usually more than a year’s average wages on average. (b) Given that moving costs change over time and workers are able to time their moves, realized costs are much lower, but the result is gradual adjustment, with a move to a new steady state that typically takes several years. (c) Idiosyncratic shocks to moving costs are quantitatively important, so that otherwise-identical workers often are seen moving in opposite directions at the same time. These shocks create a large role for option value, so that even if real wages in an industry are permanently lowered by a trade shock, a worker initially in that industry can benefit. This softens or reverses estimates of worker losses from, for example, the China shock. (d) Switching costs vary greatly by occupation, and can be very different for blue-collar and white-collar workers, for young and old workers, and for men and women. (e) Simple theories suggest that a shock results in wage overshooting, where the gap in wages between highly affected industries and others opens up and then shrinks over time, but evidence from Brazil shows that at least in some cases the wage differentials widen over time. (f) Some workers adjust through family changes. Evidence from Denmark shows that some women workers hit by import shocks withdraw from the labor market at least temporarily to marry and have children, unlike men. Promising directions at the frontier include more work on longitudinal data; the role of capital adjustment; savings, risk aversion and the adjustment of trade deficits; responses in educational attainment; and much more exploration of the effects on family.

Article

Asset Pricing: Time-Series Predictability  

David E. Rapach and Guofu Zhou

Asset returns change with fundamentals and other factors, such as technical information and sentiment over time. In modeling time-varying expected returns, this article focuses on the out-of-sample predictability of the aggregate stock market return via extensions of the conventional predictive regression approach. The extensions are designed to improve out-of-sample performance in realistic environments characterized by large information sets and noisy data. Large information sets are relevant because there are a plethora of plausible stock return predictors. The information sets include variables typically associated with a rational time-varying market risk premium, as well as variables more likely to reflect market inefficiencies resulting from behavioral influences and information frictions. Noisy data stem from the intrinsically large unpredictable component in stock returns. When forecasting with large information sets and noisy data, it is vital to employ methods that incorporate the relevant information in the large set of predictors in a manner that guards against overfitting the data. Methods that improve out-of-sample market return prediction include forecast combination, principal component regression, partial least squares, the LASSO and elastic net from machine learning, and a newly developed C-ENet approach that relies on the elastic net to refine the simple combination forecast. Employing these methods, a number of studies provide statistically and economically significant evidence that the aggregate market return is predictable on an out-of-sample basis. Out-of-sample market return predictability based on a rich set of predictors thus appears to be a well-established empirical result in asset pricing.

Article

Missing Women: A Review of Underlying Causes and Policy Responses  

Aparajita Dasgupta and Anisha Sharma

One of the most egregious manifestations of gender bias is the phenomenon of “missing women.” The number of missing women is projected to increase to 150 million by 2035, as a result of prenatal sex selection and excess female mortality relative to men, and is reflected in male-biased sex ratios at all ages. The economics literature identifies several proximate causes of the deficit of females, including the widespread use of prenatal sex selection in many Asian countries, which has been fueled by the diffusion of ultrasound and other fetal sex-detection technology. The use of prenatal sex selection has become even more expansive with a decline in fertility, as parents with a preference for sons are less likely to achieve their desired sex composition of children at lower levels of fertility. Gender discrimination in investments in health and nutrition also leads to excess female mortality among children through multiple channels. The deeper causes of son preference lie in the socioeconomic and cultural norms embedded in patriarchal societies, and recent literature in economics seeks to quantify the impact of these norms and customs on the sex ratio. Particularly important are the norms of patrilineality, in which property and assets are passed through the male line, and patrilocality, in which elderly parents coreside with their sons, whereas their daughters move to live with their husbands’ families after marriage. Another strand of the literature explores the hypothesis that the devaluing of women has roots in historical agricultural systems: Societies that have made little use of women’s labor are today the ones with the largest female deficits. Finally, economic development is often associated with a decline in son preference, but, in practice, many correlates of development, such as women’s education, income, and work status, have little impact on the sex ratio unless accompanied by more extensive social transformations. A number of policies have been implemented by governments throughout the world to tackle this issue, including legislative bans on different forms of gender discrimination, financial incentives for families to compensate them for the perceived additional costs of having a daughter, and media and advocacy campaigns that seek to increase the inherent demand for daughters by shifting the norm of son preference. Quantitative evaluations of some of these policies find mixed results. Where policies are unable to address the root causes of son preference, they often simply deflect discrimination from the targeted margin to another margin, and in some cases, they even fail in their core objectives. On the other hand, the expansion of social safety nets has had a considerable impact in reducing the reliance of parents on their sons. Similarly, media and advocacy campaigns that aim to increase the perceived value of women have also shown promise, even if their progress appears slow. Analysis of the welfare consequences of such interventions suggests that governments must pay close attention to underlying sociocultural norms when designing policy.

Article

Modern Swedish Economic History  

Svante Prado and Jakob Molinder

The Swedish growth trajectory began in the mid-19th century as external demand for its staples added an important impetus to industrialization and structural transformation. Since then, GDP per capita has increased by a factor of 21, which means that GDP per capital has doubled 4.4 times. At the same time, the population has increased from about 3.5 million to 9.5 million. The manufacturing industry has been the outstanding force propelling the economy forward since the 1870s. It was early on based on the exploitation of the domestic supply of raw materials. From the 1890s, it was gradually producing products higher up in the value-added chain, manifested by the growth of the mechanical engineering industry and the emergence of the electro-mechanical industry. The share of manufacturing in employment terms peaked at about 35% in the 1960s but then declined to about 18% in the 2010s. Yet, the importance of it as a locomotive for economy-wide growth has not declined by nearly as much. Another principal characteristic of Swedish development is large public sector-spending, implying high taxes and ambitions welfare state arrangements. Much of the expansion in social spending occurred in the post-World War II decades by the emergence of the welfare state based on universal principles and income-related benefits. A third attribute of the Swedish economic history is far-reaching compression of incomes. Thanks to wide-spread unionization and centralized agreements between the major organizations in the labor markets, the road was paved for far-reaching compression of the wage structure, which occurred in brief episodes during the 1940s, the late 1960s, and the 1970s. The joint force of these compressions and the welfare state produced a remarkable flat income distribution by the early 1980s, testified by a Gini of about 0.2, perhaps unparalleled among developed countries. As in many other similar countries, the income distribution has widened since the early 1980s, and the other Nordic countries had lower Gini coefficients than Sweden by the mid-2010s. Migration has set a deep mark in Swedish society. Whereas the latter half of the 19th century witnessed a massive outflow of Swedes going to the United States, two different waves of immigration dominated population movements after World War II. The first wave comprised workers from Finland, former Yugoslavia, and South Europe seeking employment in the prospering labor markets of the post-World War II period. This wave ebbed out in the 1970s. The second one comprised mostly asylum seekers from conflict-ridden countries. It began in the 1980s, and it continues. Combined, these waves of immigrations have transformed the Swedish population from being very homogenous into a blend of different origins.