Showing papers in "Journal of Applied Econometrics in 2018"

Estimating global bank network connectedness

Abstract: Summary We use LASSO methods to shrink, select, and estimate the high-dimensional network linking the publicly traded subset of the world's top 150 banks, 2003–2014. We characterize static network connectedness using full-sample estimation and dynamic network connectedness using rolling-window estimation. Statically, we find that global bank equity connectedness has a strong geographic component, whereas country sovereign bond connectedness does not. Dynamically, we find that equity connectedness increases during crises, with clear peaks during the Great Financial Crisis and each wave of the subsequent European Debt Crisis, and with movements coming mostly from changes in cross-country as opposed to within-country bank linkages.

Policy uncertainty and aggregate fluctuations

Abstract: This paper estimates the impact on the US economy of four types of uncertainty about (i) government spending, (ii) tax changes, (iii) public debt sustainability and (iv) monetary policy. Following a one standard deviation shock, uncertainty about debt sustainability has the largest and most significant impact on real activity, with negative effects on output, consumption and investment after two years around 0.5%, 0.2% and 1.5% respectively. Uncertainty on the other economic policies has also detrimental consequences but these tend to be smaller and short-lived, especially for taxes and monetary policy. About 30% of output fluctuations are explained by policy uncertainty at most frequencies, with the lion's share accounted for by debt sustainability. Our results are based on a new empirical framework that allows the volatility of identified shocks to have a direct impact on the endogenous variables of an otherwise standard structural VAR.

Ancestry and development: New evidence

Abstract: We revisit the relation between ancestral distance and barriers to the diffusion of development using a new genomic dataset on human microsatellite variation. With these new data we find a statistically and economic significant effect of ancestral distance from the technological frontier on income per capita, controlling for geographic factors, climatic differences, continental fixed effects and genetic diversity within populations. The historical pattern of the effect is hump shaped, peaking between 1870 and 1913, and declining steeply afterwards. These findings are consistent with the hypothesis that ancestral distance acts as a temporary barrier to the diffusion of innovations and development.

Bayesian model comparison for time-varying parameter VARs with stochastic volatility

Abstract: We develop importance sampling methods for computing two popular Bayesian model comparison criteria, namely, the marginal likelihood and the deviance information criterion (DIC) for time-varying parameter vector autoregressions (TVP-VARs), where both the regression coefficients and volatilities are drifting over time. The proposed estimators are based on the integrated likelihood, which are substantially more reliable than alternatives. Using US data, we find overwhelming support for the TVP-VAR with stochastic volatility compared to a conventional constant coefficients VAR with homoskedastic innovations. Most of the gains, however, appear to have come from allowing for stochastic volatility rather than time variation in the VAR coefficients or contemporaneous relationships. Indeed, according to both criteria, a constant coefficients VAR with stochastic volatility outperforms the more general model with time-varying parameters.

Private Debt Overhang and the Government Spending Multiplier: Evidence for the United States

Abstract: Using state‐dependent local projections and historical US data, we find that government spending multipliers are considerably larger in periods of private debt overhang. In particular, while multipliers are below or close to one in low private debt states, we find significant crowding‐in of private spending in periods of debt overhang, resulting in multipliers that are much larger than one. In high private debt episodes, more government purchases even reduce the ratio of government debt to gross domestic product. These results are robust for the type of shocks, and when we control for the business cycle, financial crises, deleveraging episodes, government debt overhang, and the zero‐lower‐bound.

Business, housing, and credit cycles

Abstract: Summary We use multivariate unobserved components models to estimate trend and cyclical components in gross domestic product (GDP), credit volumes, and house prices for the USA and the five largest European economies. With the exception of Germany, we find large and long cycles in credit and house prices, which are highly correlated with a medium-term component in GDP cycles. Differences across countries in the length and size of cycles appear to be related to the properties of national housing markets. The precision of pseudo real-time estimates of credit and house price cycles is roughly comparable to that of GDP cycles.

A multilevel factor model: Identification, asymptotic theory and applications

Abstract: This paper studies a multilevel factor model with global and country factors. The global factors affect all individuals while the country factors affect only those within each specific country. A sequential procedure to identify the global and country factors separately is proposed. In the initial step, the global factors are estimated by canonical correlation analysis. Using this initial estimator, the principal component estimators (PCEs) of the global and country factors are constructed. It is shown that the PCEs estimate the spaces of the global and country factors consistently and are normally distributed in the limit. Several information criteria that can estimate the numbers of the country factors are proposed. The number of the global factors is assumed to be known. Extensive simulation results demonstrate that the sequential procedure and the information criteria work well in finite samples. The method of this paper is applied to 25 OECD countries to identify international business cycle. It is reported that the method extracts a global factor reasonably well.

Do contractionary monetary policy shocks expand shadow banking

Abstract: Summary Using VAR models for the USA, we find that a contractionary monetary policy shock has a persistent negative impact on the level of commercial bank assets, but increases the assets of shadow banks and securitization activity. To explain this “waterbed” effect, we propose a standard New Keynesian model featuring both commercial and shadow banks, and we show that the model comes close to explaining the empirical results. Our findings cast doubt on the idea that monetary policy can usefully “get in all the cracks” of the financial sector in a uniform way.

Homogeneity pursuit in panel data models: Theory and application

Abstract: This paper studies the estimation of a panel data model with latent structures where individuals can be classified into different groups with the slope parameters being homogeneous within the same group but heterogeneous across groups. To identify the unknown group structure of vector parameters, we design an algorithm called Panel‐CARDS. We show that it can identify the true group structure asymptotically and estimate the model parameters consistently at the same time. Simulations evaluate the performance and corroborate the asymptotic theory in several practical design settings. The empirical application reveals the heterogeneous grouping effect of income on democracy.

Dynamic Factor model with infinite dimensional factor space: forecasting

Abstract: . The paper compares the pseudo real-time forecasting performance of threeDynamic Factor Models: (i) The standard principal-component model, Stock and Watson(2002a), (ii) The model based on generalized principal components, Forni et al. (2005),(iii) The model recently proposed in Forni et al. (2015) and Forni et al. (2016). We employa large monthly dataset of macroeconomic and financial time series for the US economy,which includes the Great Moderation, the Great Recession and the subsequent recovery.Using a rolling window for estimation and prediction, we find that (iii) neatly outperforms(i) and (ii) in the Great Moderation period for both Industrial Production and Inflation,and for Inflation over the full sample. However, (iii) is outperfomed by (i) and (ii) over thefull sample for Industrial Production.

What Are the Macroeconomic Effects of High-Frequency Uncertainty Shocks

Abstract: This paper evaluates the effects of high-frequency uncertainty shocks on a set of low-frequency macroeconomic variables that are representative of the U.S. economy. Rather than estimating models at the same common low-frequency, we use recently developed econometric models, which allows us to deal with data of different sampling frequencies.

Time series copulas for heteroskedastic data

Abstract: We propose parametric copulas that capture serial dependence in stationary heteroskedastic time series. We suggest copulas for first-order Markov series, and then extend them to higher orders and multivariate series. We derive the copula of a volatility proxy, based on which we propose new measures of volatility dependence, including co-movement and spillover in multivariate series. In general, these depend upon the marginal distributions of the series. Using exchange rate returns, we show that the resulting copula models can capture their marginal distributions more accurately than univariate and multivariate generalized autoregressive conditional heteroskedasticity models, and produce more accurate value-at-risk forecasts.

Binary response panel data models with sample selection and self-selection

Abstract: Summary We consider estimating binary response models on an unbalanced panel, where the outcome of the dependent variable may be missing due to nonrandom selection, or there is self-selection into a treatment. In the present paper, we first consider estimation of sample selection models and treatment effects using a fully parametric approach, where the error distribution is assumed to be normal in both primary and selection equations. Arbitrary time dependence in errors is permitted. Estimation of both coefficients and partial effects, as well as tests for selection bias, are discussed. Furthermore, we consider a semiparametric estimator of binary response panel data models with sample selection that is robust to a variety of error distributions. The estimator employs a control function approach to account for endogenous selection and permits consistent estimation of scaled coefficients and relative effects.

Assessing international commonality in macroeconomic uncertainty and its effects

Abstract: This paper uses a large vector autoregression (VAR) to measure international macroeconomic uncertainty and its effects on major economies, using two datasets, one with GDP growth rates for 19 industrialized countries and the other with a larger set of macroeconomic indicators for the U.S., euro area, and U.K. Using basic factor model diagnostics, we first provide evidence of significant commonality in international macroeconomic volatility, with one common factor accounting for strong comovement across economies and variables. We then turn to measuring uncertainty and its effects with a large VAR in which the error volatilities evolve over time according to a factor structure. The volatility of each variable in the system reflects time-varying common (global) components and idiosyncratic components. In this model, global uncertainty is allowed to contemporaneously affect the macroeconomies of the included nations—both the levels and volatilities of the included variables. In this setup, uncertainty and its effects are estimated in a single step within the same model. Our estimates yield new measures of international macroeconomic uncertainty, and indicate that uncertainty shocks (surprise increases) lower GDP and many of its components, adversely affect labor market conditions, lower stock prices, and in some economies lead to an easing of monetary policy.

Multivariate choices and identification of social interactions

Abstract: Summary This paper considers the identification of social interaction effects in the context of multivariate choices. First, we generalize the theoretical social interaction model to allow individuals to make interdependent choices in different activities. Based on the theoretical model, we propose a simultaneous equation network model and discuss the identification of social interaction effects in the econometric model. We also provide an empirical example to show the empirical salience of this model. Using the Add Health data, we find that a student's academic performance is not only affected by academic performance of his peers but also affected by screen-related activities of his peers.

A kink that makes you sick: The effect of sick pay on absence

Abstract: We exploit a regression kink design to estimate the elasticity of the duration of sickness absence with respect to replacement rate. Elasticity is a central parameter in defining the optimal social insurance scheme compensating for lost earnings due to sickness. We use comprehensive administrative data and a kink in the policy rule near the median earnings. We find a statistically significant estimate of the elasticity of the order of one.

Understanding the economic determinants of the severity of operational losses: A regularized generalized Pareto regression approach

Abstract: We investigate a novel database of 10,217 extreme operational losses from the Italian bank UniCredit. Our goal is to shed light on the dependence between the severity distribution of these losses and a set of macroeconomic, financial, and firm‐specific factors. To do so, we use generalized Pareto regression techniques, where both the scale and shape parameters are assumed to be functions of these explanatory variables. We perform the selection of the relevant covariates with a state‐of‐the‐art penalized‐likelihood estimation procedure relying on L1‐penalty terms. A simulation study indicates that this approach efficiently selects covariates of interest and tackles spurious regression issues encountered when dealing with integrated time series. Lastly, we illustrate the impact of different economic scenarios on the requested capital for operational risk. Our results have important implications in terms of risk management and regulatory policy.

Increasing the credibility of the twin birth instrument

Abstract: Twin births are an important instrumental variable for the endogenous fertility decision. However, in many economic settings, twins are not exogenous as dizygotic twinning is known to be correlated with maternal characteristics and fertility treatments. Following the medical literature, we assume that monozygotic twins are exogenous, and construct a new instrument, which corrects for the selection bias although monozygotic twinning is usually unobserved. We use longitudinal administrative data from Sweden and US census data and show that the usual twin instrument is not only related to observed but also to unobserved determinants of economic outcomes, while our new instrument is not. We demonstrate the relevance of our new instrument in two labor market applications and find that the classical twin instrument underestimates the true negative effect of fertility on labor force participation and earnings.

Difference‐in‐differences when the treatment status is observed in only one period

Abstract: Summary This paper considers the difference-in-differences (DID) method when the data come from repeated cross-sections and the treatment status is observed either before or after the implementation of a program. We propose a new method that point-identifies the average treatment effect on the treated (ATT) via a DID method when there is at least one proxy variable for the latent treatment. Key assumptions are the stationarity of the propensity score conditional on the proxy and an exclusion restriction that the proxy must satisfy with respect to the change in average outcomes over time conditional on the true treatment status. We propose a generalized method of moments estimator for the ATT and we show that the associated overidentification test can be used to test our key assumptions. The method is used to evaluate JUNTOS, a Peruvian conditional cash transfer program. We find that the program significantly increased the demand for health inputs among children and women of reproductive age.

Dynamic discrete copula models for high‐frequency stock price changes

Abstract: We develop a dynamic model for the intraday dependence between discrete stock price changes. The conditional copula mass function for the integer tick-size price changes has time-varying parameters that are driven by the score of the predictive likelihood function. The marginal distributions are Skellam and also have score-driven time-varying parameters. We show that the integration steps in the copula mass function for large dimensions can be accurately approximated via numerical integration. The resulting computational gains lead to a methodology that can treat high-dimensional applications. Its accuracy is shown by an extensive simulation study. In our empirical application of 10 US bank stocks, we reveal strong evidence of time-varying intraday dependence patterns: Dependence starts at a low level but generally rises during the day. Based on one-step-ahead out-of-sample density forecasting, we find that our new model outperforms benchmarks for intraday dependence such as the cubic spline model, the fixed correlation model, or the rolling average realized correlation.

Information shocks and the empirical evaluation of training programs during unemployment spells

Abstract: We study the role of notifications in the evaluation of training programs for unemployed workers. Using a unique administrative data set containing the dates when information is exchanged between job seekers and caseworkers, we address three questions: Do information shocks, such as notification of future training, have an effect on unemployment duration? What is the joint effect of notification and training programs on unemployment? Can ignoring information shocks lead to a large bias in the estimation of the effect of training programs? We discuss these issues through the lens of a job search model and then conduct an empirical analysis following a “random effects” approach to deal with selectivity. We find that notification has a strong positive effect on the training probability but a negative one on the probability of leaving unemployment. This “attraction” effect highlights the importance of accounting for notifications in the evaluation of active labor market policies.

Estimating the Effects of the Minimum Wage in a Developing Country: A Density Discontinuity Design Approach

Abstract: Summary This paper proposes a framework to identify the effects of the minimum wage on the joint distribution of sector and wage in a developing country. I show how the discontinuity of the wage distribution around the minimum wage identifies the extent of noncompliance with the minimum wage policy, and how the conditional probability of sector given wage recovers the relationship between latent sector and wages. I apply the method in the “PNAD,” a nationwide representative Brazilian cross-sectional dataset for the years 2001–2009. The results indicate that the size of the informal sector is increased by around 39% compared to what would prevail in the absence of the minimum wage, an effect attributable to (i) unemployment effects of the minimum wage on the formal sector and (ii) movements of workers from the formal to the informal sector as a response to the policy.

Spillovers among sovereign debt markets: Identification through absolute magnitude restrictions

Abstract: This paper studies spillovers among US and European sovereign yields. We employ absolute magnitude restrictions on the impact matrix to identify the countries that were the main sources of spillovers. Despite the large size of shocks from euro area stressed countries, connectedness among sovereign yields declined between 2008 and 2012 due to financial fragmentation, particularly between countries with more divergent business and fiscal cycles. We show that none of the sovereign yields were insulated from foreign shocks and that shocks to the Greek bond market in 2010 explained 20–30% of the variance of sovereign yields in stressed countries, while in 2011–2012 Italy (not Spain) was the source of systemic risk.

Estimating the Distribution of Welfare Effects Using Quantiles

Abstract: This paper proposes a framework to model empirically welfare effects that are associated with a price change in a population of heterogeneous consumers which is similar to Hausman and Newey (1995), but allows for more general forms of heterogeneity. Individual demands are characterized by a general model which is nonparametric in the regressors, as well as monotonic in unobserved heterogeneity. In this setup, we first provide and discuss conditions under which the heterogeneous welfare effects are identified, and establish constructive identification. We then propose a sample counterpart estimator, and analyze its large sample properties. For both identification and estimation, we distinguish between the cases when regressors are exogenous and when they are endogenous. Finally, we apply all concepts to measuring the heterogeneous effect of a chance of gasoline price using US consumer data and find very substantial differences in individual effects across quantiles.

Genetic distance, trade, and the diffusion of development

Abstract: The determinants of countries' long‐term income differences feature prominently in the literature. Spolaore and Wacziarg (The diffusion of development, Quarterly Journal of Economics, 2009, 124, 469–529) argue that cultural differences, measured by countries' genetic distance, are an important barrier to the diffusion of development from the world's technological frontier. We revisit their findings in three ways. First, we successfully reproduce their results and confirm the robustness of their baseline findings. Second, we estimate their models for different time periods and find that the impact of genetic distance on income differences did not significantly change over time. Finally, we explore one of the underlying mechanisms of technology adoption and show that bilateral trade is one channel through which cultural differences retard the diffusion of development. (Less)

How important are fixed effects and time trends in estimating returns to schooling? Evidence from a replication of Jacobson, Lalonde, and Sullivan, 2005

Abstract: A substantial and rapidly growing literature has developed around estimating earnings gains from 2‐year college degrees using administrative data. These papers almost universally employ a person‐level fixed‐effects strategy to estimate earnings premia net of fixed attributes. We note that the seminal piece on which these papers build—Jacobson, Lalonde, & Sullivan, Journal of Econometrics, 2005, 125(1–2), 271–304—provides theoretical and empirical evidence for the importance of additionally differencing out individual time trends. The subsequent literature has not followed suit. Through replication we ask whether this matters. We show that it does, and further that these person‐level time trends need not be computationally burdensome in large administrative data. We recommend them as a unifying econometric standard for future work.

Weak‐instrument robust inference for two‐sample instrumental variables regression

Abstract: Summary Instrumental variable (IV) methods for regression are well established. More recently, methods have been developed for statistical inference when the instruments are weakly correlated with the endogenous regressor, so that estimators are biased and no longer asymptotically normally distributed. This paper extends such inference to the case where two separate samples are used to implement instrumental variables estimation. We also relax the restrictive assumptions of homoskedastic error structure and equal moments of exogenous covariates across two samples commonly employed in the two-sample IV literature for strong IV inference. Monte Carlo experiments show good size properties of the proposed tests regardless of the strength of the instruments. We apply the proposed methods to two seminal empirical studies that adopt the two-sample IV framework.

Self-employment among women: Do children matter more than we previously thought?

Abstract: Summary This paper presents an estimation approach that addresses the problems of sample selection and endogeneity of fertility decisions when estimating the effect of young children on women's self-employment. Using data from the National Longitudinal Survey of Youth 1979, 1982–2006, we find that ignoring self-selection and endogeneity leads to underestimating the effect of young children. Once both sources of biases are accounted for, the estimated effect of young children roughly triples when compared to uncorrected results. This finding is robust to several changes in the specification and to the use of a different dataset.

Sequentially testing polynomial model hypotheses using power transforms of regressors

Abstract: Summary We provide a methodology for testing a polynomial model hypothesis by generalizing the approach and results of Baek, Cho, and Phillips (Journal of Econometrics, 2015, 187, 376–384; BCP), which test for neglected nonlinearity using power transforms of regressors against arbitrary nonlinearity. We use the BCP quasi-likelihood ratio test and deal with the new multifold identification problem that arises under the null of the polynomial model. The approach leads to convenient asymptotic theory for inference, has omnibus power against general nonlinear alternatives, and allows estimation of an unknown polynomial degree in a model by way of sequential testing, a technique that is useful in the application of sieve approximations. Simulations show good performance in the sequential test procedure in both identifying and estimating unknown polynomial order. The approach, which can be used empirically to test for misspecification, is applied to a Mincer (Journal of Political Economy, 1958, 66, 281–302; Schooling, Experience and Earnings, Columbia University Press, 1974) equation using data from Card (in Christofides, Grant, and Swidinsky (Eds.), Aspects of Labour Market Behaviour: Essays in Honour of John Vanderkamp, University of Toronto Press, 1995, 201-222) and Bierens and Ginther (Empirical Economics, 2001, 26, 307–324). The results confirm that the standard Mincer log earnings equation is readily shown to be misspecified. The applications consider different datasets and examine the impact of nonlinear effects of experience and schooling on earnings, allowing for flexibility in the respective polynomial representations.

Risk-Neutral Moment-Based Estimation of Affine Option Pricing Models

Abstract: This paper provides a novel methodology for estimating option pricing models based on risk-neutral moments. We synthesize the distribution extracted from a panel of option prices and exploit linear relationships between risk-neutral cumulants and latent factors within the continuous time affine stochastic volatility framework.