. We propose a Bayesian inferential framework for a multi-outcome endogenous three-stage model that accounts for incidental truncation in outcomes (intensive margin), selection into participation (extensive margin), and access restrictions. Simulation exercises assessing finite-sample properties under various misspecification settings suggest that incorporating access restrictions and unobserved correlations is crucial. In particular, access restrictions play a critical role, as failing to account for them may introduce measurement error when correcting for selection bias. This suggests a potential tension between attenuation and selection biases. We apply our framework to two novel datasets on credit and utility demand. We extend our parametric specification to a semi-parametric one in the latter application, modeling stochastic errors using a Dirichlet process mixture. The credit demand application suggests that better socioeconomic conditions increase the probability of using credit cards but decrease the likelihood of taking bank loans. In addition, women are more likely to use credit than men, but men tend to borrow larger amounts. The utility application highlights the importance of urban areas in increasing the probability of access to piped utilities, water and gas are inelastic goods, whereas electricity is elastic.

. In this article, we focus on the model specification problem in multivariate spatial econometric models when a candidate set for the spatial weights matrix is available. We propose a model selection method for the multivariate spatial autoregressive model when the true spatial weights matrix may not be in the candidates. We show that the selected estimator is asymptotically optimal in the sense of minimizing the squared loss. If the candidate set contains the true spatial weights matrix, the method has selection consistency. We further propose a model averaging estimator that combines a set of candidate models and show its asymptotic optimality. Monte Carlo simulation results indicate that the proposed model selection and model averaging estimators perform quite well in finite samples. The proposed methods are applied to a Sina Weibo data to reveal how the user’s posting behavior is influenced by the users that he follows. The analysis results indicate that the influence tends to be uniformly distributed among the user’s followee, or linearly correlated with the number of followers of the followee.

. This article is concerned with the significance testing of the effects of exogenous determinants upon the one-sided deviation term of a semiparametric stochastic frontier model. Two significance tests for all or a subset of the determinants of inefficiency are proposed. The tests are based on conditional moment restrictions and stochastic processes, with critical values simulated using the computationally fast multiplier bootstrap procedures. Our testing methodology addresses the omitted variable bias that arises naturally in stochastic frontier models when accommodating the determinants of inefficiency and accounts for the estimation effects that appear when using the estimated composite error in constructing the test statistics. We investigate the theoretical properties of the proposed tests and justify the validity of the multiplier bootstrap procedures. The tests are finally illustrated through simulation experiments and two empirical examples. In particular, the analysis from Taiwanese manufacturing supports the financing constraint hypothesis through the significant effect of cash flow, while the analysis from Indian rice production highlights the importance of schooling and farming experience in reducing farm inefficiency.

This article considers the estimation and inference of matrix-valued factor models under a fixed time horizon. We show that the 2dPCA method maintains consistency and asymptotic normality. However, the conventional Newey-West method becomes infeasible, posing challenges for making inferences. To address this limitation, we introduce an augmented CS-HAC estimator for computing the standard errors. Applying this method to a large set of county-industry-level economic indicators, we identify an aggregate factor, a public sector factor, and a leisure and hospitality factor and show how they are affected differently during the financial crisis and the COVID-19 pandemic.

We consider a two-step estimation procedure to estimate the panel sample selection models with interactive effects. In the first step, we follow the Robinson (1988) procedure to remove the sample selection factors. In the second step, we control the interactive effects. When the cross-section dimension N is large, we propose to use the Pesaran (2006) common correlated effects approach, and when the time series dimension T is large and N is finite we propose to follow the Hsiao, Shi, and Zhou (2022) transformed estimation procedure to eliminate the interactive effects. We show that the resulting estimators are consistent and asymptotically normally distributed. A limited Monte Carlo study is conducted, showing our methods appear to work well in a finite sample. An empirical illustration on female wage rate determination shows that an extra year of work experience could raise the expected log wage rate by 0.1507 under our maintained hypothesis, while neglecting sample selection or interactive effects could lead to seriously biased estimates.

I propose an alternative linear model for short panel data quantile regression. The model assumes a nonparametric correlated effect (CE) that is τ-quantile-specific and time-invariant. The resulting partially linear model provides inference robust to misspecification, and it is characterized as a best linear approximation to the truth under a generalized correlated random effect assumption. At the cost of modeling the individual heterogeneity, the model is free of incidental parameters, and it does not restrict within-group dependence of idiosyncratic errors at all. The modeled heterogeneity is still well-aligned with the fixed effect approach in the linear mean regression model. For estimation, sieve-approximated CE is regularized by nonconvex penalization which enjoys the oracle property against ultra-high dimensionality. Unpenalized sieve estimation is also available. As an empirical application, the proposed method is used to estimate the distributional effect of smoking on birth weights. Using a dataset where fixed effects quantile regression is computationally infeasible, the method yields more refined estimates compared to the one based on a linear CE.

In financial econometrics, it is empirically challenging to test the predictability of lagged predictors with varying levels of persistence in predictive quantile regression. A recent double-weighted method developed by Cai, Chen, and Liao (2023) has demonstrated desirable local power properties for both non stationary and stationary predictors. In this article, we propose a strategy to improve the construction of the auxiliary variables in the double-weighted method. This improvement makes it applicable to a broader range of persistent types in empirical analysis. Furthermore, we propose a random weighted bootstrap procedure to address the challenges involved in conditional density estimation. Simulation results demonstrate the effectiveness of the proposed test in correcting size distortion at the lower and upper quantiles. Finally, we apply the proposed test to reassess the predictability of macroeconomic and financial predictors on stock returns across different quantile levels, finding fewer significant predictors at the tails compared to Cai, Chen, and Liao (2023). Our results highlight that this test serves as a more conservative inference tool for practitioners evaluating the predictability of financial returns.

This article proposes using state-dependent local projections (SDLP) to forecast the dynamically asymmetrical effects of political regimes or policy transitions. By conditioning on the ex ante status, the SDLP method estimates first-differenced local projections that depend on the previous regime type, allowing for asymmetric regime transitions and reversals. Monte Carlo simulation results demonstrate that the SDLP estimator involves a smaller bias than existing local projections and performs well with small samples. We apply the proposed SDLP method to examine the effects of democratization and autocratization on economic growth. Our empirical results suggest that the accumulated impacts of democratization on economic growth are positive and increase over time, while the negative effects of autocratization are more substantial and increase over time. These findings suggest that democracy promotes economic growth, whereas reversals to autocracy have an even greater detrimental impact on growth.

We examine whether the employment effects of minimum wage depend on unknown tipping points in the labor market. We apply a continuous threshold regression model—regression kink with unknown thresholds—to U.S. state-level panel data in 1993–2016 to estimate the tipping point and quantile employment effects. Overall, we find that the marginal effect is near-zero or mildly negative below the tipping point, and it is considerably more negative above it. The tipping occurs at 50–55% of the state’s median wage among women and 40–45% among men. Simulations of minimum wage reforms reveal nonlinear and asymmetric employment effects.