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Venture capital (VC) investment has long been conceptualized as a local business , in which the VC’s ability to source, syndicate, fund, monitor, and add value to portfolio firms critically depends on their access to knowledge obtained through their ties to the local (i.e., geographically proximate) network. Consistent with the view that local networks matter, existing research confirms that local and geographically distant portfolio firms are sourced, syndicated, funded, and monitored differently. Curiously, emerging research on VC investment practice within the United States finds that distant investments, as measured by “exits” (either initial public offering or merger & acquisition) out-perform local investments. These findings raise important questions about the assumed benefits of local network membership and proximity. To more deeply probe these questions, we contrast the deal structure of cross-border VC investment with domestic VC investment, and contrast the deal structure of cross-border VC investments that include a local
partner with those that do not. Evidence from 139,892 rounds of venture capital financing in the period 1980-2009 suggests that cross-border investment practice, in terms of deal sourcing, syndication, and performance indeed change with proximity, but that monitoring practices do not. Further, we find that the inclusion of a local partner in the investment syndicate yields surprisingly few benefits. This evidence, we argue, raises important questions about VC investment practice as well as the ability of firms to capture and lever the presumed benefits of network membership.
We examine the dynamics of assets under management (AUM) and management fees at the portfolio manager level in the closed-end fund industry. We find that managers capitalize on good past performance and favorable investor perception about future performance, as reflected in fund premiums, through AUM expansions and fee increases. However, the penalties for poor performance or unfavorable investor perception are either insignificant, or substantially mitigated by manager tenure. Long tenure is generally associated with poor performance and high discounts. Our findings suggest substantial managerial power in capturing CEF rents. We also document significant diseconomies of scale at the manager level.
This paper considers the desirability of the observed tendency of central banks to adjust interest rates only gradually in response to changes in economic conditions. It shows, in the context of a simple model of optimizing private-sector behavior, that such inertial behavior on the part of the central bank may indeed be optimal, in the sense of minimizing a loss function that penalizes inflation variations, deviations of output from potential, and interest-rate variability. Sluggish adjustment characterizes an optimal policy commitment, even though no such inertia would be present in the case of a reputationless (Markovian) equilibrium under discretion. Optimal interest-rate feedback rules are also characterized, and shown to involve substantial positive coefficients on lagged interest rates. This provides a theoretical explanation for the numerical results obtained by Rotemberg and Woodford (1998) in their quantitative model of the U.S. economy.
The paper considers optimal monetary stabilization policy in a forward-looking model, when the central bank recognizes that private-sector expectations need not be precisely model-consistent, and wishes to choose a policy that will be as good as possible in the case of any beliefs that are close enough to model-consistency. It is found that commitment continues to be important for optimal policy, that the optimal long-run inflation target is unaffected by the degree of potential distortion of beliefs, and that optimal policy is even more history-dependent than if rational expectations are assumed. JEL Classification: E52, E58, E42
The paper illustrates based on an example the importance of consistency between the empirical measurement and the concept of variables in estimated macroeconomic models. Since standard New Keynesian models do not account for demographic trends and sectoral shifts, the authors proposes adjusting hours worked per capita used to estimate such models accordingly to enhance the consistency between the data and the model. Without this adjustment, low frequency shifts in hours lead to unreasonable trends in the output gap, caused by the close link between hours and the output gap in such models.
The retirement wave of baby boomers, for example, lowers U.S. aggregate hours per capita, which leads to erroneous permanently negative output gap estimates following the Great Recession. After correcting hours for changes in the age composition, the estimated output gap closes gradually instead following the years after the Great Recession.
This paper studies the long-run effects of credit market disruptions on real firm outcomes and how these effects depend on nominal wage rigidities at the firm level. I trace out the long-run investment and growth trajectories of firms which are more adversely affected by a transitory shock to aggregate credit supply. Affected firms exhibit a temporary investment gap for two years following the shock, resulting in a persistent accumulated growth gap. I show that affected firms with a higher degree of wage rigidity exhibit a steeper drop in investment and grow more slowly than affected firms with more flexible wages.
This paper investigates the accuracy and heterogeneity of output growth and inflation forecasts during the current and the four preceding NBER-dated U.S. recessions. We generate forecasts from six different models of the U.S. economy and compare them to professional forecasts from the Federal Reserve’s Greenbook and the Survey of Professional Forecasters (SPF). The model parameters and model forecasts are derived from historical data vintages so as to ensure comparability to historical forecasts by professionals. The mean model forecast comes surprisingly close to the mean SPF and Greenbook forecasts in terms of accuracy even though the models only make use of a small number of data series. Model forecasts compare particularly well to professional forecasts at a horizon of three to four quarters and during recoveries. The extent of forecast heterogeneity is similar for model and professional forecasts but varies substantially over time. Thus, forecast heterogeneity constitutes a potentially important source of economic fluctuations. While the particular reasons for diversity in professional forecasts are not observable, the diversity in model forecasts can be traced to different modeling assumptions, information sets and parameter estimates. JEL Classification: C53, D84, E31, E32, E37 Keywords: Forecasting, Business Cycles, Heterogeneous Beliefs, Forecast Distribution, Model Uncertainty, Bayesian Estimation
This contribution draws on two recent publications in which the macroeconomic model data base (www.macromodelbase.com) is employed for model comparisons. The comparative approach is used to base policy analysis on a systematic evaluation of the different implications that a certain economic policy can have when submitted to different modeling approaches. In this manner, policy recommendations are more robust to modeling uncertainty. By extending the comparative approach to forecasting, the authors investigate the accuracy of different forecasting models and obtain more reliable mean forecasts.
In 2011 wurde der Preis für Wirtschaftswissenschaften der schwedischen Reichsbank im Gedenken an Alfred Nobel an die US-Ökonomen Thomas J. Sargent von der New York University und Chistopher A. Sims von Princeton University verliehen. Gerade deutsche Zeitungskommentare kritisierten die Forscher vielfach für die Verwendung „unrealistischer“ Annahmen wie Nutzenmaximierung und rationale Erwartungen. Diese Kritik verkennt den maßgeblichen Beitrag von Sargent und Sims zur Entwicklung der modernen Makroökonomik. Ihre empirischen Methoden sind heute Standardwerkzeuge der akademischen Forschung und werden auch von Ökonomen in Zentralbanken, Finanzministerien und internationalen Organisationen eingesetzt. Sie haben grundlegende neue Erkenntnisse ermöglicht, zum Beispiel über die Wirkungsweise der Geld- und Fiskalpolitik.
In the aftermath of the global financial crisis, the state of macroeconomic modeling and the use of macroeconomic models in policy analysis has come under heavy criticism. Macroeconomists in academia and policy institutions have been blamed for relying too much on a particular class of macroeconomic models. This paper proposes a comparative approach to macroeconomic policy analysis that is open to competing modeling paradigms. Macroeconomic model comparison projects have helped produce some very influential insights such as the Taylor rule. However, they have been infrequent and costly, because they require the input of many teams of researchers and multiple meetings to obtain a limited set of comparative findings. This paper provides a new approach that enables individual researchers to conduct model comparisons easily, frequently, at low cost and on a large scale. Using this approach a model archive is built that includes many well-known empirically estimated models that may be used for quantitative analysis of monetary and fiscal stabilization policies. A computational platform is created that allows straightforward comparisons of models’ implications. Its application is illustrated by comparing different monetary and fiscal policies across selected models. Researchers can easily include new models in the data base and compare the effects of novel extensions to established benchmarks thereby fostering a comparative instead of insular approach to model development.