Journal of Applied Econometrics最新文献

This paper evaluates the impact of quantitative easing on income and wealth of individual euro area households. We first estimate the aggregate effects of a quantitative easing (QE) shock, identified by means of external instruments, in a multi-country vector autoregression (VAR) model with unemployment, wages, gross operating surplus, interest rates, house prices, and stock prices. We then distribute the aggregate effects across households using a reduced-form simulation on micro-data, which captures the portfolio composition, the income composition, and the earnings heterogeneity channels of transmission. The earnings heterogeneity channel is important: QE compresses the income distribution because many households with lower incomes become employed. In contrast, monetary policy has only negligible effects on the Gini coefficient for wealth: While high-wealth households benefit from higher stock prices, middle-wealth households benefit from higher house prices.

This paper extends the discussion on the effects of the two distinctive monetary surprises in the literature. First, we show that the proxy of conventional monetary shock Granger causes the endogenous variables in the vector autoregressive model. Second, we provide evidence that the existing model can be exposed to a weak instrument problem. With our alternative model mitigating these concerns, the second monetary shock can be interpreted as unconventional monetary news in general. The estimation results show contractionary effect of the unconventional policy. We find increases in output after a positive conventional monetary surprise, suggesting an important Fed's information effect.

Bull and bear market identification generally focuses on a broad index of returns through a univariate analysis. This paper proposes a new approach to identify and forecast bull and bear markets through multivariate returns. The model assumes that all assets are directed by a common discrete state variable from a hierarchical Markov switching model. The hierarchical specification allows the cross-section of state-specific means and variances to differ over bull and bear markets. We investigate several empirically realistic specifications that permit feasible estimation even with 100 assets. Our results show that the multivariate framework provides competitive bull and bear regime identification and improves portfolio performance and density prediction compared with several benchmark models including univariate Markov switching models.

Location choices of differently skilled workers are analyzed in previous work on labor mobility, which proposes a model that suggests thicker tails in the skill distributions of large cities. This paper replicates the empirical findings of this work by using quantile regression and density plots as employed in the existing study, while also suggesting an alternative testing method for thick tails in the form of an initial stochastic dominance test. The test reveals clear evidence of a thicker lower tail, but the results are less clear for the upper tail, which raises some questions on how to best handle extreme upper tails of skill distributions.

We propose a per-cluster instrumental variable (PCIV) approach for estimating linear correlated random coefficient models in the presence of contemporaneous endogeneity and two-way fixed effects. This approach estimates heterogeneous effects and aggregates them to population averages. We demonstrate consistency, showing robustness over standard estimators, and provide analytic standard errors for robust inference. In Monte Carlo simulation, PCIV performs relatively well in finite samples in either dimension. We apply PCIV in estimating the price elasticity of gasoline demand using state fuel taxes as instrumental variables. We find significant elasticity heterogeneity and more elastic gasoline demand on average than with standard estimators.

We examine the impact of annual hours worked on annual earnings by decomposing changes in the real annual earnings distribution into composition, structural, and hours effects. We do so via a nonseparable simultaneous model of hours, wages, and earnings. Using the Current Population Survey for the survey years 1976–2019, we find that changes in the female distribution of annual hours of work are important in explaining movements in inequality in female annual earnings. This captures the substantial changes in their employment behavior over this period. Movements in the male hours' distribution only affect the lower part of their earnings distribution and reflect the sensitivity of these workers' annual hours of work to cyclical factors.

We provide a novel analytic procedure to construct best linear and quadratic moments of the generalized method of moments estimation for a large class of cross-sectional network and spatial econometric models. These moments generate an estimator that is asymptotically more efficient than the quasi-maximum likelihood estimator when the disturbances follow a non-normal and unknown distribution. We apply this procedure to a high-order spatial autoregressive model with spatial errors, where the disturbances are heteroskedastic. Two normality tests of disturbances are developed. We apply the model to employment data in US counties, which demonstrates spatial interdependence patterns of regional employment growth.