A Comparison of Monthly Global Indicators for Forecasting Growth
Christiane Baumeister, Pierre Guérin
Abstract
This paper evaluates the predictive content of a set of alternative monthly indicators of global economic activity for nowcasting and forecasting quarterly world GDP using mixed-frequency models. We find that a recently proposed indicator that covers multiple dimensions of the global economy consistently produces substantial improvements in forecast accuracy, while other monthly measures have more mixed success. This global economic conditions indicator contains valuable information also for assessing the current and future state of the economy for a set of individual countries and groups of countries. We use this indicator to track the evolution of the nowcasts for the US, the OECD area, and the world economy during the coronavirus pandemic and quantify the main factors driving the nowcasts.
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14.10.2020 • 22/2020
Economic slump in East Germany not as severe as in Germany as a whole ‒ Implications of the Joint Economic Forecast and new data for East Germany
The German economy started recovering quickly after the drastic pandemic-related slump in spring 2020. The recovery, however, loses much of its momentum in the second half of the year. The Joint Economic Forecast predicts that production levels seen before the crisis will not be reached again until the second half of 2021. In principle, the East German economy is following this pattern, although the economic slump is likely to be somewhat milder.
Oliver Holtemöller
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Is there an Information Channel of Monetary Policy?
Oliver Holtemöller, Alexander Kriwoluzky, Boreum Kwak
IWH Discussion Papers,
No. 17,
2020
Abstract
Exploiting the heteroscedasticity of the changes in short-term and long-term interest rates and exchange rates around the FOMC announcement, we identify three structural monetary policy shocks. We eliminate the predictable part of the shocks and study their effects on financial variables and macro variables. The first shock resembles a conventional monetary policy shock, and the second resembles an unconventional monetary shock. The third shock leads to an increase in interest rates, stock prices, industrial production, consumer prices, and commodity prices. At the same time, the excess bond premium and uncertainty decrease, and the U.S. dollar depreciates. Therefore, this third shock combines all the characteristics of a central bank information shock.
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Epidemics in the Neoclassical and New Keynesian Models
Martin S. Eichenbaum, Sergio Rebelo, Mathias Trabandt
Abstract
We analyze the effects of an epidemic in three standard macroeconomic models. We find that the neoclassical model does not rationalize the positive comovement of consumption and investment observed in recessions associated with an epidemic. Introducing monopolistic competition into the neoclassical model remedies this shortcoming even when prices are completely flexible. Finally, sticky prices lead to a larger recession but do not fundamentally alter the predictions of the monopolistic competition model.
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Dynamic Equity Slope
Matthijs Breugem, Stefano Colonnello, Roberto Marfè, Francesca Zucchi
University of Venice Ca' Foscari Department of Economics Working Papers,
No. 21,
2020
Abstract
The term structure of equity and its cyclicality are key to understand the risks drivingequilibrium asset prices. We propose a general equilibrium model that jointly explainsfour important features of the term structure of equity: (i) a negative unconditionalterm premium, (ii) countercyclical term premia, (iii) procyclical equity yields, and (iv)premia to value and growth claims respectively increasing and decreasing with thehorizon. The economic mechanism hinges on the interaction between heteroskedasticlong-run growth — which helps price long-term cash flows and leads to countercyclicalrisk premia — and homoskedastic short-term shocks in the presence of limited marketparticipation — which produce sizeable risk premia to short-term cash flows. The slopedynamics hold irrespective of the sign of its unconditional average. We provide empirical support to our model assumptions and predictions.
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Corona Shutdown and Bankruptcy Risk
Oliver Holtemöller, Yaz Gulnur Muradoglu
IWH Online,
No. 3,
2020
Abstract
This paper investigates the consequences of shutdowns during the Corona crisis on the risk of bankruptcy for firms in Germany and United Kingdom. We use financial statements from the period 2014 to 2018 to predict how pervasive risk of bankruptcy becomes for micro, small, medium, and large firms due to shutdown measures. We estimate distress for firms using their capacity to service their debt. Our results indicate that under three months of shutdown almost all firms in shutdown industries face high risk of bankruptcy. In Germany, about 99% of firms in shutdown industries and in the UK about 98% of firms in shutdown industries are predicted to be under distress. The furlough schemes reduce the risk of bankruptcy only marginally to 97% of firms in shutdown industries in Germany and 95% of firms in shutdown industries in the United Kingdom in case of a three-month shutdown. In sectors that are not shutdown under conservative estimates of contagion of sales losses, our results indicate considerable risk of widespread bankruptcies ranging from 76% of firms in Germany to 69% of firms in the United Kingdom. These early findings suggest that the impact of corona crisis on corporate sector via shutdowns can be severe and subsequent policy should be designed accordingly.
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Why are some Chinese Firms Failing in the US Capital Markets? A Machine Learning Approach
Gonul Colak, Mengchuan Fu, Iftekhar Hasan
Pacific-Basin Finance Journal,
June
2020
Abstract
We study the market performance of Chinese companies listed in the U.S. stock exchanges using machine learning methods. Predicting the market performance of U.S. listed Chinese firms is a challenging task due to the scarcity of data and the large set of unknown predictors involved in the process. We examine the market performance from three different angles: the underpricing (or short-term market phenomena), the post-issuance stock underperformance (or long-term market phenomena), and the regulatory delistings (IPO failure risk). Using machine learning techniques that can better handle various data problems, we improve on the predictive power of traditional estimations, such as OLS and logit. Our predictive model highlights some novel findings: failed Chinese companies have chosen unreliable U.S. intermediaries when going public, and they tend to suffer from more severe owners-related agency problems.
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The Creation and Evolution of Entrepreneurial Public Markets
Shai B. Bernstein, Abhishek Dev, Josh Lerner
Journal of Financial Economics,
No. 2,
2020
Abstract
This paper explores the creation and evolution of new stock exchanges around the world geared toward entrepreneurial companies, known as second-tier exchanges. Using hand-collected novel data, we show the proliferation of these exchanges in many countries, their significant volume of Initial Public Offerings (IPOs), and lower listing requirements. Shareholder protection strongly predicted exchange success, even in countries with high levels of venture capital activity, patenting, and financial market development. Better shareholder protection allowed younger, less-profitable, but faster-growing, companies to raise more capital. These results highlight the importance of institutions in enabling the provision of entrepreneurial capital to young companies.
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Age and High-Growth Entrepreneurship
Pierre Azoulay, Benjamin Jones, J. Daniel Kim, Javier Miranda
American Economic Review: Insights,
No. 1,
2020
Abstract
Many observers, and many investors, believe that young people are especially likely to produce the most successful new firms. Integrating administrative data on firms, workers, and owners, we study start-ups systematically in the United States and find that successful entrepreneurs are middle-aged, not young. The mean age at founding for the 1-in-1,000 fastest growing new ventures is 45.0. The findings are similar when considering high-technology sectors, entrepreneurial hubs, and successful firm exits. Prior experience in the specific industry predicts much greater rates of entrepreneurial success. These findings strongly reject common hypotheses that emphasize youth as a key trait of successful entrepreneurs.
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Comparing Financial Transparency between For-profit and Nonprofit Suppliers of Public Goods: Evidence from Microfinance
John W. Goodell, Abhinav Goyal, Iftekhar Hasan
Journal of International Financial Markets, Institutions and Money,
January
2020
Abstract
Previous research finds market financing is favored over relationship financing in environments of better governance, since the transaction costs to investors of vetting asymmetric information are thereby reduced. For industries supplying public goods, for-profits rely on market financing, while nonprofits rely on relationships with donors. This suggests that for-profits will be more inclined than nonprofits to improve financial transparency. We examine the impact of for-profit versus nonprofit status on the financial transparency of firms engaged with supplying public goods. There are relatively few industries that have large number of both for-profit and nonprofit firms across countries. However, the microfinance industry provides the opportunity of a large number of both for-profit and nonprofit firms in relatively equal numbers, across a wide array of countries. Consistent with our prediction, we find that financial transparency is positively associated with a for-profit status. Results will be of broad interest both to scholars interested in the roles of transparency and transaction costs on market versus relational financing; as well as to policy makers interested in the impact of for-profit on the supply of public goods, and on the microfinance industry in particular.
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