Quantitative Economics November 2021, Volume 12, Issue 4 is now online

TABLE OF CONTENTS, November 2021, Volume 12, Issue 4
Full Issue

Articles
Abstracts follow the listing of articles.

Fixed‐k inference for volatility
Tim Bollerslev, Jia Li, Zhipeng Liao

A new approach to measuring economic policy shocks, with an application to conventional and unconventional monetary policy
Atsushi Inoue, Barbara Rossi

Regime‐dependent effects of uncertainty shocks: A structural interpretation
Stéphane Lhuissier, Fabien Tripier

Panel experiments and dynamic causal effects: A finite population perspective
Iavor Bojinov, Ashesh Rambachan, Neil Shephard

Synthetic controls with imperfect pretreatment fit
Bruno Ferman, Cristine Pinto

Imposing equilibrium restrictions in the estimation of dynamic discrete games
Victor Aguirregabiria, Mathieu Marcoux

Blurred boundaries: A flexible approach for segmentation applied to the car market
Laura Grigolon

Neighborhood effects and housing vouchers
Morris A. Davis, Jesse Gregory, Daniel A. Hartley, Kegon T. K. Tan

Do elite colleges matter? The impact on entrepreneurship decisions and career dynamics
Naijia Guo, Charles Ka Yui Leung

A job ladder model with stochastic employment opportunities
Jake Bradley, Axel Gottfries

Fixed‐k inference for volatility
Tim Bollerslev, Jia Li, Zhipeng Liao

Abstract

We present a new theory for the conduct of nonparametric inference about the latent spot volatility of a semimartingale asset price process. In contrast to existing theories based on the asymptotic notion of an increasing number of observations in local estimation blocks, our theory treats the estimation block size k as fixed. While the resulting spot volatility estimator is no longer consistent, the new theory permits the construction of asymptotically valid and easy‐to‐calculate pointwise confidence intervals for the volatility at any given point in time. Extending the theory to a high‐dimensional inference setting with a growing number of estimation blocks further permits the construction of uniform confidence bands for the volatility path. An empirically realistically calibrated simulation study underscores the practical reliability of the new inference procedures. An empirical application based on intraday data for the S&P 500 equity index reveals highly significant abrupt changes, or jumps, in the market volatility at FOMC news announcement times, validating recent uses of various high‐frequency‐based identification schemes in asset pricing finance and monetary economics.

Spot volatility high‐frequency identification semimartingale uniform inference C14 C22 C32
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A new approach to measuring economic policy shocks, with an application to conventional and unconventional monetary policy
Atsushi Inoue, Barbara Rossi

Abstract

We propose a new approach to analyze economic shocks. Our new procedure identifies economic shocks as exogenous shifts in a function; hence, we call them “functional shocks.” We show how to identify such shocks and how to trace their effects in the economy via VARs using “VARs with functional shocks” and “functional local projections.” Using our new procedure, we address the crucial question of studying the effects of monetary policy by identifying monetary policy shocks as shifts in the whole term structure of government bond yields in a narrow window of time around monetary policy announcements. Our approach sheds new light on the effects of monetary policy shocks, both in conventional and unconventional periods, and shows that traditional identification procedures may miss important effects. Our new procedure has the advantage of identifying monetary policy shocks during both conventional and unconventional monetary policy periods in a unified manner and can be applied more generally to other economic shocks.

Identification VARs zero lower bound unconventional monetary policy D1 E21 E4 E52 H31 I3
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Regime‐dependent effects of uncertainty shocks: A structural interpretation
Stéphane Lhuissier, Fabien Tripier

Abstract

Using a Markov‐switching VAR, we show that the effects of uncertainty shocks on output are four times higher in a regime of economic distress than in a tranquil regime. We then provide a structural interpretation of these facts. To do so, we develop a business cycle model in which agents are aware of the possibility of regime changes when forming expectations. The model is estimated using a Bayesian minimum distance estimator that minimizes, over the set of structural parameters, the distance between the regime‐switching VAR‐based impulse response functions and those implied by the model. Our results point to worsening credit‐market conditions that amplify shocks during distress periods. Finally, we show that the expectation effect of regime switching in financial conditions is an important component of the financial accelerator mechanism. If agents are more pessimistic about future financial conditions, then the output effects of shocks are amplified.

Uncertainty shocks regime switching financial frictions expectation effects C32 E32 E44
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Panel experiments and dynamic causal effects: A finite population perspective
Iavor Bojinov, Ashesh Rambachan, Neil Shephard

Abstract

In panel experiments, we randomly assign units to different interventions, measuring their outcomes, and repeating the procedure in several periods. Using the potential outcomes framework, we define finite population dynamic causal effects that capture the relative effectiveness of alternative treatment paths. For a rich class of dynamic causal effects, we provide a nonparametric estimator that is unbiased over the randomization distribution and derive its finite population limiting distribution as either the sample size or the duration of the experiment increases. We develop two methods for inference: a conservative test for weak null hypotheses and an exact randomization test for sharp null hypotheses. We further analyze the finite population probability limit of linear fixed effects estimators. These commonly‐used estimators do not recover a causally interpretable estimand if there are dynamic causal effects and serial correlation in the assignments, highlighting the value of our proposed estimator.

Panel data dynamic causal effects potential outcomes finite population nonparametric C14 C21 C23
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Synthetic controls with imperfect pretreatment fit
Bruno Ferman, Cristine Pinto

Abstract

We analyze the properties of the Synthetic Control (SC) and related estimators when the pre‐treatment fit is imperfect. In this framework, we show that these estimators are generally biased if treatment assignment is correlated with unobserved confounders, even when the number of pre‐treatment periods goes to infinity. Still, we show that a demeaned version of the SC method can improve in terms of bias and variance relative to the difference‐in‐difference estimator. We also derive a specification test for the demeaned SC estimator in this setting with imperfect pre‐treatment fit. Given our theoretical results, we provide practical guidance for applied researchers on how to justify the use of such estimators in empirical applications.

Synthetic control difference‐in‐differences policy evaluation linear factor model C13 C21 C23
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Imposing equilibrium restrictions in the estimation of dynamic discrete games
Victor Aguirregabiria, Mathieu Marcoux

Abstract

Imposing equilibrium restrictions provides substantial gains in the estimation of dynamic discrete games. Estimation algorithms imposing these restrictions have different merits and limitations. Algorithms that guarantee local convergence typically require the approximation of high‐dimensional Jacobians. Alternatively, the Nested Pseudo‐Likelihood (NPL) algorithm is a fixed‐point iterative procedure, which avoids the computation of these matrices, but—in games—may fail to converge to the consistent NPL estimator. In order to better capture the effect of iterating the NPL algorithm in finite samples, we study the asymptotic properties of this algorithm for data generating processes that are in a neighborhood of the NPL fixed‐point stability threshold. We find that there are always samples for which the algorithm fails to converge, and this introduces a selection bias. We also propose a spectral algorithm to compute the NPL estimator. This algorithm satisfies local convergence and avoids the approximation of Jacobian matrices. We present simulation evidence and an empirical application illustrating our theoretical results and the good properties of the spectral algorithm.

Dynamic discrete games nested pseudo‐likelihood fixed‐point algorithms spectral algorithms convergence convergence selection bias C13 C57 C61 C73
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Blurred boundaries: A flexible approach for segmentation applied to the car market
Laura Grigolon

Abstract

Prominent features of differentiated product markets are segmentation and product proliferation blurring the boundaries between segments. I develop a tractable demand model, the Ordered Nested Logit, which allows for asymmetric substitution between segments. I apply the model to the automobile market where segments are ordered from small to luxury. I find that consumers, when substituting outside their vehicle segment, are more likely to switch to a neighboring segment. Accounting for such asymmetric substitution matters when evaluating the impact of new product introduction or the effect of subsidies on fuel‐efficient cars.

Discrete choice model Generalized Extreme Value Ordered Nested Logit D11 D12 L62 M3
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Neighborhood effects and housing vouchers
Morris A. Davis, Jesse Gregory, Daniel A. Hartley, Kegon T. K. Tan

Abstract

Researchers and policy makers have explored the possibility of restricting the use of housing vouchers to neighborhoods that may positively affect the outcomes of children. Using the framework of a dynamic model of optimal location choice, we estimate preferences over neighborhoods of likely recipients of housing vouchers in Los Angeles. We combine simulations of the model with estimates of how locations affect adult earnings of children to understand how a voucher policy that restricts neighborhoods in which voucher‐recipients may live affects both the location decisions of households and the adult earnings of children. We show the model can nearly replicate the impact of the Moving to Opportunity experiment on the adult wages of children. Simulations suggest a policy that restricts housing vouchers to the top 20% of neighborhoods maximizes expected aggregate adult earnings of children of households offered these vouchers.

Neighborhood choice housing vouchers I240 I31 I38 J13 R23 R38
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Do elite colleges matter? The impact on entrepreneurship decisions and career dynamics
Naijia Guo, Charles Ka Yui Leung

Abstract

Elite college attendance significantly impacts students' entrepreneurship decisions and career dynamics. We find that an elite college degree is positively correlated with entrepreneurship (i.e., owning an incorporated business) but not with other self‐employment forms. Our overlapping generations model captures self‐selection in education and career choices based on heterogeneous ability and family wealth endowments over the life cycle. Our estimates show that (1) entrepreneurs and other self‐employed individuals require different types of human capital, and (2) elite colleges generate considerably more human capital gain than ordinary colleges, particularly for entrepreneurs. Distinguishing between elite and ordinary colleges improves our prediction of entrepreneurship decisions. Providing subsidies for elite colleges is more efficient than subsidizing their ordinary counterparts to encourage entrepreneurship, enhance intergenerational mobility, and enhance welfare. In contrast, although start‐up subsidy increases entrepreneurship, it does not improve their performance, and it is inferior to education subsidy in generating efficiency, equality, and intergenerational mobility.

Entrepreneurship elite college intergenerational transfer D15 I20 J24
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A job ladder model with stochastic employment opportunities
Jake Bradley, Axel Gottfries

Abstract

We set up a model with on‐the‐job search in which firms infrequently post vacancies for which workers occasionally apply. The model nests the standard job ladder and stock‐flow models as special cases, while remaining analytically tractable and easy to estimate from standard panel data sets. The parameters from a structurally estimated model on US data are significantly different from either the restrictions imposed by a stock‐flow or job ladder model. Imposing these restrictions significantly understates the search option associated with employment and are, unlike our model, inconsistent with recent survey evidence and declining job finding rates and starting wage with duration of unemployment, both of which are present in the data.

On‐the‐job search wage dispersion wage posting stock‐flow J31 J64

Publication Date: 
Monday, November 8, 2021

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