Unemployment insurance benefits and income smoothing

Labor unemployment insurance reduces unemployment concerns. We argue that these benefits moderate incentives to smooth earnings to reduce employees’ concerns about unemployment risk. Using exogenous variations in unemployment insurance benefits, we find evidence consistent with this argument. We also find that the link between unemployment insurance benefits and income smoothing is stronger when there is higher unemployment risk and when the firm is likely to employ more low-wage workers, who find unemployment insurance benefits especially useful. Our paper contributes to the literature by showing that public policy decisions such as unemployment insurance have significant, albeit probably unintended, externalities on corporate financial reporting.     

Nonparametric estimation of distribution functions

Summary Using Silverman and Young’s (1987) idea of rescaling a rescaled smoothed empirical distribution function is defined and investigated when the smoothing parameter depends on the data. The rescaled smoothed estimator is shown to be often better than the commonly used ordinary smoothed estimator.     

The Tax‐smoothing Hypothesis: Evidence from Sweden, 1952–1999

This paper tests Barro's (1979) tax‐smoothing hypothesis using Swedish central government data for the period 1952–1999. According to the tax‐smoothing hypothesis, the government sets the budget surplus equal to expected changes in government expenditure. When expenditure is expected to increase, the government runs a budget surplus, and when expenditure is expected to fall, the government runs a budget deficit. The empirical evidence suggests that the model provides a useful benchmark and that tax‐smoothing behavior can explain about 60 percent of the variability in the Swedish central government budget surplus.     

A Semiparametric Method for Valuing Residential Locations: Application to Automated Valuation

This paper is motivated by automated valuation systems, which would benefit from an ability to estimate spatial variation in location value. It develops theory for the local regression model (LRM), a semiparametric approach to estimating a location value surface. There are two parts to the LRM: (1) an ordinary least square (OLS) model to hold constant for interior square footage, land area, bathrooms, and other structural characteristics; and (2) a non-parametric smoother (local polynomial regression, LPR) which calculates location value as a function of latitude and longitude. Several methods are used to consistently estimate both parts of the model. The LRM was fit to geocoded hedonic sales data for six towns in the suburbs of Boston, MA. The estimates yield substantial, significant and plausible spatial patterns in location values. Using the LRM as an exploratory tool, local peaks and valleys in location value identified by the model are close to points identified by the tax assessor, and they are shown to add to the explanatory power of an OLS model. Out-of-sample MSE shows that the LRM with a first-degree polynomial (local linear smoothing) is somewhat better than polynomials of degree zero or degree two. Future applications might use degree zero (the well-known NW estimator) because this is available in popular commercial software. The optimized LRM reduces MSE from the OLS model by between 5 percent and 11 percent while adding information on statistically significant variations in location value.     

The Impact of SFAS No. 114 on the Linear Information Dynamic for Commercial Banks

Increase (decrease) in loan loss provisions would decrease (increases) bank earnings, but increase (decreases) regulatory capital. Previous studies have separately documented earnings and capital management behavior via loan loss provisions by commercial banks. However, it is difficult to isolate a bank's demand for increasing earnings from its demand for regulatory capital because earnings is a source of capital. Based on the objective bank function, this study investigates the impact of SFAS No. 114 on the information content of loan loss provisions in relation to both earnings quality and capital adequacy in a linear information dynamic framework. Test results show that the association between market value with loan loss provisions became significantly stronger for commercial banks in the post- than in the pre-adoption period. As a result, SFAS No. 114 is also found to positively affect the association of market value with both bank earnings and regulatory capital through the clean surplus relation because of the higher value relevance of loan loss provisions. The findings thus provide empirical evidence that SFAS No. 114 has significantly complemented banking regulations in enhancing (reducing) the (dispersion from the) accounting measurement construct of loan loss provisions.     

Modified efficient importance sampling for partially non‐Gaussian state space models

The construction of an importance density for partially non‐Gaussian state space models is crucial when simulation methods are used for likelihood evaluation, signal extraction, and forecasting. The method of efficient importance sampling is successful in this respect, but we show that it can be implemented in a computationally more efficient manner using standard Kalman filter and smoothing methods. Efficient importance sampling is generally applicable for a wide range of models, but it is typically a custom‐built procedure. For the class of partially non‐Gaussian state space models, we present a general method for efficient importance sampling. Our novel method makes the efficient importance sampling methodology more accessible because it does not require the computation of a (possibly) complicated density kernel that needs to be tracked for each time period. The new method is illustrated for a stochastic volatility model with a Student's t distribution.     

Properties of Census Dual System Population Size Estimators

We study parametric and non‐parametric approaches for assessing the accuracy and coverage of a population census based on dual system surveys. The two parametric approaches being considered are post‐stratification and logistic regression, which have been or will be implemented for the US Census dual system surveys. We show that the parametric model‐based approaches are generally biased unless the model is correctly specified. We then study a local post‐stratification approach based on a non‐parametric kernel estimate of the Census enumeration functions. We illustrate that the non‐parametric approach avoids the risk of model mis‐specification and is consistent under relatively weak conditions. The performances of these estimators are evaluated numerically via simulation studies and an empirical analysis based on the 2000 US Census post‐enumeration survey data.     

Market structure and rating strategies in credit rating markets – A dynamic model with matching of heterogeneous bond issuers and rating agencies

This paper investigates the impact of rating agencies in a market with asymmetric information. In particular, the role of credit rating agencies as an intermediary between investors and bond issuers is discussed. We model this setting in a dynamic framework in which both rating agencies and bond issuers are of heterogeneous quality. Rating agencies can apply costly research technology to reveal the fundamental nature of bond issuers and engage in rating smoothing. We show that rating smoothing can compensate for low research quality, even though it is accompanied by a quality deterioration in the rating market and market clustering. Moreover, low-quality bond issuers have a general tendency to match with low-quality rating agencies. If investors place a strong emphasis on the reputation of rating agencies, rating markets also tend to be strongly clustered.     

Varying Coefficient Regression Models: A Review and New Developments

Varying coefficient regression models are known to be very useful tools for analysing the relation between a response and a group of covariates. Their structure and interpretability are similar to those for the traditional linear regression model, but they are more flexible because of the infinite dimensionality of the corresponding parameter spaces. The aims of this paper are to give an overview on the existing methodological and theoretical developments for varying coefficient models and to discuss their extensions with some new developments. The new developments enable us to use different amount of smoothing for estimating different component functions in the models. They are for a flexible form of varying coefficient models that requires smoothing across different covariates' spaces and are based on the smooth backfitting technique that is admitted as a powerful technique for fitting structural regression models and is also known to free us from the curse of dimensionality.