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Modelling and forecasting interval-valued time series (ITS) have received increasing attention in statistics and econometrics. An interval-valued observation contains more information than a point-valued observation in the same time period. The previous literature has mainly considered modelling and forecasting a univariate ITS. However, few works attempt to model a vector process of ITS. In this paper, we propose an interval-valued vector autoregressive moving average (IVARMA) model to capture the cross-dependence dynamics within an ITS vector system. A minimum-distance estimation method is developed to estimate the parameters of an IVARMA model, and consistency, asymptotic normality and asymptotic efficiency of the proposed estimator are established. A two-stage minimum-distance estimator is shown to be asymptotically most efficient among the class of minimum-distance estimators. Simulation studies show that the two-stage estimator indeed outperforms other minimum-distance estimators for various data-generating processes considered.

Contrary to the implications of economic theory, consumption inequality in the United States did not react to the increases in income inequality during the last three decades. This paper investigates if a change in the type of income inequality – from permanent to transitory – or a change in the ability to insure income shocks is responsible for this. A measure of household consumption is imputed into the Panel Study of Income Dynamics to create panel data on income and consumption for the period 1980–2010. The minimum distance investigation of covariance relationships shows that both explanations work together: the share of transitory shocks increases over time, but the capability to insure against permanent and transitory income shocks also improves. Together, these phenomena can explain the lack of an increase in consumption inequality.

Identification in a regression discontinuity (RD) design hinges on the discontinuity in the probability of treatment when a covariate (assignment variable) exceeds a known threshold. If the assignment variable is measured with error, however, the discontinuity in the relationship between the probability of treatment and the observed mismeasured assignment variable may disappear. Therefore, the presence of measurement error in the assignment variable poses a challenge to treatment effect identification. This chapter provides sufficient conditions to identify the RD treatment effect using the mismeasured assignment variable, the treatment status and the outcome variable. We prove identification separately for discrete and continuous assignment variables and study the properties of various estimation procedures. We illustrate the proposed methods in an empirical application, where we estimate Medicaid takeup and its crowdout effect on private health insurance coverage.

This paper aims to propose several factors which can explain the negative relationship between financial constraints and investment-cash flow sensitivity.

The author uses traditional fixed effects model and minimum distance panel estimation by Erickson and Whited (2000) to estimate investment-cash flow sensitivity in the cash flow-augmented investment equation. In addition, principal component analysis is used to construct a financial constraints measure.

First, it was found that substitutability between cash holdings and free cash flow can partially explain why financially constrained firms do not depend on cash flow as heavily as we expect. Second, it was confirmed that the level of net external financing can also partially explain the investment-cash flow sensitivity puzzle. Furthermore, it was argued that the influence of cash holdings and external financing on investment-cash flow sensitivity is caused by the low level of internal cash flow for financially constrained firms. This argument is supported by our findings from an examination of investment-cash flow sensitivity for bank-dependent firms during the recession periods.

This paper contributes to the literature by suggesting possible partial explanations for the contradictory relationship between investment-cash flow sensitivity and financial constraints.

Japan has been suffering from a decline in the rate of young adults homeownership for a long time. The reduction of the homeownership rate for young adults suggests a delay of tenure transition from renting to owning a home. Such delays further imply that there is insufficient wealth accumulation and a low level of welfare. This paper examines these influences of the credit rationing and the credit rationing impact on the reduction in the young adults’ homeownership rate.

Credit rationing impacts the timing of house purchases and the value of the houses at the same time. This paper estimates these impacts jointly using a simultaneous equation system (minimum distance estimation) and the micro data on Japan.

This paper divides the effect of credit rationing on the timing into direct and indirect effects. The former is the rationing effect on timing, keeping the other variables constant, while the latter is the effect via changes in house values. This paper finds that the indirect effect reduces the rationing effect on the timing by decreasing house values. Furthermore, the results show that credit rationing delays home acquisition by prospective young owners (direct effect) and necessarily lowers the quality of houses they purchase.

In the previous papers, the endogeneity among the variables related to the housing purchase was not addressed. To separate the endogeneity of the timing from the house value, this paper applies the simultaneous equation model. Furthermore, this paper exhibits that there are direct and indirect effects of credit rationing on the timing of housing purchase made by young households. None of the previous papers recognize these two effects.

This paper analyzes the roles of idiosyncratic risk and firm-level conditional skewness in determining cross-sectional returns. It is shown that the traditional EGARCH estimates of conditional idiosyncratic volatility may bring significant finite sample estimation bias in the presence of non-Gaussianity. We propose a new estimator that has more robust sampling performance than the EGARCH MLE in the presence of heavy-tail or skewed innovations. Our cross-sectional portfolio analysis demonstrates that the idiosyncratic volatility puzzle documented by Ang, Hodrick, Xiang, and Zhang (2006) exists intertemporally. We conduct further analysis to solve the puzzle. We show that two factors idiosyncratic variance and individual conditional skewness play important roles in determining cross-sectional returns. A new concept, the “expected windfall,” is introduced as an alternate measure of conditional return skewness. After controlling for these two additional factors, we solve the major piece of this puzzle: Our cross-sectional regression tests identify a positive relationship between conditional idiosyncratic volatility and expected returns for over 99% of the total market capitalization of the NYSE, NASDAQ, and AMEX stock exchanges.

The authors examine whether or not applicants and recipients of federal disability insurance (DI) inflate their self-assessed health (SAH) problems relative to others. To do this, the authors employ a technique which uses anchoring vignettes. This approach allows them to examine how various cohorts of the population interpret survey questions associated with subjective self-assessments of health. The results of the analysis suggest that DI participants do inflate the severity of a given health problem, but by a small but significant degree. This tendency to exaggerate the severity of disability problems is much more apparent among those with more education (especially those with a college degree). In contrast, racial minorities tend to underestimate severity ratings for a given disability vignette when compared to their white peers.

This paper investigates the relationship between plot size and fertilizer use efficiency (FE) in Chinese large-scale farming and searches for the underlying mechanisms that explain this relationship.

Based on a household- and plot-level data set of large-scale production units (LSPUs) from Jiangsu and Jiangxi Provinces, the technical and fertilizer use efficiency of large-scale rice production is estimated by applying a translog stochastic frontier production function. The authors impose a monotonicity condition on the translog frontier using a three-step procedure to get theoretically consistent efficiency estimates. A beta regression model is then used to explore the association between plot size and LSPUs' efficiency in fertilizer application.

The average FE for the sampled plots is around 30%, which shows a large potential for LSPUs to reduce fertilizer use. A U-shaped relationship is observed between plot size and FE. The authors relate this non-linear pattern to the substitution of labour with capital-intensive technology and the efficiency differences in terms of farming performance between family and hired workers.

First, according to the authors’ knowledge, this paper is a first attempt to study the size–efficiency relationship focussing on fertilization practices of large-scale farming. The second contribution lies in the large-scale ranges of the plot-level data set. Third, efforts are made to reveal the mechanisms determining the plot size–FE relationship. Fourth, the authors provide guiding evidence for policymaking, as they show that the size of individual plots deserves equal attention in land consolidation decisions. Methodologically, this paper improves existing estimates of single-factor technical efficiency issued from a restricted production frontier model.