On policymakers’ loss functions and the evaluation of early warning systems: Comment

Sarlin (2013) suggests that if a loss function approach is chosen to derive the optimal threshold for financial crisis early warning indicators, the loss function specification should explicitly take into account the unconditional sample crisis probability. In this comment we argue that this approach is not robust to small perturbations of the preference parameter and is not easy to use for policy purposes. We suggest therefore to continue using a simpler loss function specification.     

Can we reject linearity in an HAR-RV model for the S&P 500? Insights from a nonparametric HAR-RV

This article tests the linearity assumption underlying the popular heterogeneous autoregressive model for realized volatility (HAR-RV). We implement a consistent model specification test that is robust to both distributional and model misspecification. We find that, using a nonparametric HAR-RV (NPHAR-RV), we are unable to reject the null of linearity.     

Income and democracy: dynamic misspecification due to the presence of serial correlation

We address the issue of dynamic misspecification arising from the possible existence of serial correlation when examining the relationship between income and democracy by employing a panel count data estimation method. We find evidence that income per capita systematically predicts democracy, but it does not so in relation to the colonization experience of each country. Our results are robust across model specification and samples.     

Does corruption increase or decrease employment in firms?

We use representative data for firms for Latin American firms and show that corruption decreases employment in firms. This result is robust to changes in specification and also consistent with the use of an instrumental variables approach. Corruption appears to negatively impact the growth and wealth in a country, not by introducing labour distortion in firms, but by keeping them small.     

Calendar effects in Latin American stock markets

One of the most well-documented empirical regularities in international finance is the presence of calendar effects in historical stock returns. The literature focuses mainly on developed countries, and in general, emerging markets have not received much attention on this issue. We aim to bridge this gap by documenting the existence of significant and robust calendar effects for the main stock markets in Latin America. Upon performing an extreme bounds analysis that adjusts our estimations for model uncertainty, we find a significantly negative Monday effect, generally compensated by a significantly positive Friday effect. These effects are robust to model specification and are stable through time. Even though not as widespread, we also find evidence for a robust turn-of-the-month effect.     

Will elders provide for their grandchildren? Unconditional cash transfers and educational expenditures in Bolivia

We take advantage of repeated cross‐sectional household surveys and a sharp discontinuity created by the introduction of an unconditional cash transfer to elders in Bolivia, to evaluate its impact on educational expenditures on children within a household. We find positive and significant impacts of the program at the aggregate level. We also find that the program has stronger effects on indigenous populations as well as on female and rural populations. Our results are robust to a series of falsification tests, survey structure, model specification, and estimation methods.     

Venture capital availability and labour market performance around the world

This article studies the effects of venture capital on the performance of the labour market. Using data from a much larger sample of countries than previous papers, it finds more readily available venture capital to favourably affect both the unemployment and the employment rate. The magnitude of the estimated effects is substantial. We control for both endogeneity of venture capital availability and most major determinants of labour market performance. The results are robust to variations in specification.     

FISCAL DECENTRALIZATION AND ECONOMIC VOLATILITY: EVIDENCE FROM STATE‐LEVEL CROSS‐SECTION DATA OF THE USA*

This paper examines the implications of fiscal decentralization for economic stability. It has been discussed that fiscal decentralization reduces the variance of GDP growth, due to the greater diversification of performance across jurisdictions. We examine this theoretical result using a panel data set of the 50 states of the USA over the period of 1992–1997. We show that the theoretical specification of the production function is supported. We also show a negative significant relationship between fiscal decentralization and economic volatility. The results are robust when we take into account the endogeneity of fiscal decentralization.     

Specification and sensitivity analysis of cross-country growth regressions

We compare the sensitivity analysis of cross-country growth regressions based on extreme bounds analysis to a more direct specification testing approach using non-nested hypotheses tests. The results suggest that those specifications that are adequate are also those that include two of the only few conditioning variables that are found to be robust, namely the standard deviation of inflation and the standard deviation of domestic credit. First Version Received: November 2000/Final Version Received: May 2001     

Folk theorems with bounded recall under (almost) perfect monitoring

We prove the perfect-monitoring folk theorem continues to hold when attention is restricted to strategies with bounded recall and the equilibrium is essentially required to be strict. As a consequence, the perfect monitoring folk theorem is shown to be behaviorally robust under almost-perfect almost-public monitoring. That is, the same specification of behavior continues to be an equilibrium when the monitoring is perturbed from perfect to highly-correlated private.     

Using regression tree ensembles to model interaction effects: a graphical approach

Multiplicative interaction terms are widely used in economics to identify heterogeneous effects and to tailor policy recommendations. The execution of these models is often flawed due to specification and interpretation errors. This article introduces regression trees and regression tree ensembles to model and visualize interaction effects. Tree-based methods include interactions by construction and in a nonlinear manner. Visualizing nonlinear interaction effects in a way that can be easily read overcomes common interpretation errors. We apply the proposed approach to two different datasets to illustrate its usefulness.     

Housing Wealth Effects: Cross‐sectional Evidence from New Vehicle Registrations

We investigate the relationship between housing wealth and consumption using postcode‐level variation in house prices and administrative data on new passenger vehicle registrations as a proxy for consumption. We find a robust cross‐sectional relationship, and in our preferred specification estimate an elasticity of new passenger vehicle registrations with respect to gross housing wealth of 0.4–0.5, which based on our preferred calibration implies a marginal propensity to consume for total consumption of less than 0.5 cents. Notably, we find evidence that the relationship between house prices and new vehicle registrations is heterogeneous in income.     

地区放权与经济效率:以计划单列为例

中国的经济改革始于放权,尤其是对地区的放权。本文以计划单列为例,研究整体性行政放权对经济效率的影响并通过构造省内与省外城市的对比组来估计计划单列的因果效应。研究结果表明:从总体上,放权确实有助于计划单列市提高其经济效率。本文对此结论进行了一系列的稳健性检验。     

Large common value auctions with risk averse bidders

We analyze large symmetric auctions with conditionally i.i.d. common values and risk averse bidders. Our main result characterizes the asymptotic equilibrium price distribution for the first- and second-price auctions. As an implication, we show that with constant absolute risk aversion (CARA), the second-price auction raises significantly more revenue than the first-price auction. While this ranking seems robust in numerical analysis also outside the CARA specification, we show by counterexamples that the result does not generalize to all risk averse utility functions.     

Leading indicators of fiscal distress: evidence from extreme bounds analysis

Early warning systems (EWSs) are widely used to assess a country’s vulnerability to fiscal distress. A fiscal distress episode is identified as a period when government experiences extreme funding difficulties. Most EWSs employ a specific set of only fiscal leading indicators predetermined by the researchers, which casts doubt on their robustness. We revisit this issue using extreme bounds analysis, which allows identifying robust leading indicators of fiscal distress from a large set. A robust leading indicator’s effect does not strongly depend on the model specification. Consistent with the theoretical predictions of latest generation crisis models, we find that both fiscal and non-fiscal leading indicators are robust. In addition, we find that a fiscal vulnerability indicator based on fiscal and non-fiscal leading indicators offers a 29% gain in predictive power compared to a traditional one based only on fiscal leading indicators. This suggests that both fiscal and non-fiscal leading indicators should be taken into account when assessing country’s vulnerability to fiscal distress.     

TESTING THE STRUCTURE OF CONDITIONAL CORRELATIONS IN MULTIVARIATE GARCH MODELS: A GENERALIZED CROSS‐SPECTRUM APPROACH*

We introduce a class of generally applicable specification tests for constant and dynamic structures of conditional correlations in multivariate GARCH models. The tests are robust to the presence of time‐varying higher‐order conditional moments of unknown form and are pure significance tests. The tests can identify linear and nonlinear misspecifications in conditional correlations. Our approach does not necessitate a particular parameter estimation method and distributional assumption on the error process. The asymptotic distribution of the tests is invariant to the uncertainty in parameter estimation. We assess the finite sample performance of our tests using simulated and real data.     

Explaining Growth: A Contest Between Models

Recent contributions to the empirical growth literature show no tendency to convergence in specification, as researchers seek to identify new variables that can account for significant regional effects in earlier work. We conduct non-nested tests between the models of Barro (1997), Easterly and Levine (1997) and Sachs and Warner (1997). The data strongly prefer an encompassing model, but fail to reject any of the candidate models, implying that each model represents a partial truth. We identify a model that includes most (but not all) of the regressors in the candidate models and is robust to the inclusion of regional dummies.     

Engineering robust instruments for GMM estimation of panel data regression models with errors in variables: a note

Econometricians have long recognized the need to account in some way for measurement errors, specification errors and endogeneity to ensure that the ordinary least squares estimator is consistent. This article introduces a new generalized method of moments estimator that relies on robust instruments to estimate panel data regression models containing errors in variables. We show how this GMM approach can be generalized for the panel data framework using higher moments and cumulants as instruments. The new instruments, engineered for greater robustness, are proposed to tackle the pervasive problem of weak instruments.     

The impact of benefits on unemployment in Britain in the interwar period: Some further empirical evidence

Smyth's (1983) model of the labor market is modified to allow for real wage rather than money wage adjustment. The results reported by Smyth are robust with respect to this change in specification.     

Robust determinants of intergenerational mobility in the land of opportunity

This paper revisits the influential work by Chetty et al. (2014) who attempt to explain the variation in intergenerational mobility across commuter zones in the US (i.e., spatial mobility) using nine classes of variables. We employ Bayesian model averaging methods that allow for model uncertainty to identify robust predictors of spatial mobility. In doing so we pay special attention to the specification of model and parameter priors. We also investigate the heterogeneous effects of these predictors on spatial mobility across commuter zones in different average income quintiles. Our findings suggest a more nuance and complex characterization of the spatial mobility process than that proposed by Chetty, Hendren, Kline, and Saez.