Using a unique set of banking data containing both originally-reported and subsequently-revised financial variables, we study accounting restatements. Our results indicate the worse a bank's financial condition, the more likely it is for originally-reported data to understate financial losses. Also, we find supervisory exams have an important role in uncovering financial problems and prompting accounting restatements to correct loss underreporting. While revisions are directly related to financial difficulties, exam-based restatements are evident at even the earliest stages of deterioration, indicating substantial accounting misstatements—at both banks and other types of companies—can occur well outside severe business circumstances.

Abstract Fama and French (1992) show that size and book-to-price dominate CAPM beta and other variables such as the price-earnings ratio and dividend yield in explaining the cross-section of US stock returns. Comparable evidence for the UK points to a book-to-price effect, but not a size effect (Chan and Chui, 1996; Strong and Xu, 1997). In this paper, our first contribution is to show that a measure of research and development (RD) helps explain cross-sectional variation in UK stock returns. Our cross-sectional results on the association between stock returns and RD are consistent with recent US evidence reported by Lev and Sougiannis (1996, 1999) and Chan, Lakonishok and Sougiannis (2001).Fama and French (1993, 1995, 1996) also show that a three-factor model captures a high proportion of the time series variation in portfolio returns, again for the US. Our second contribution is to show, for the UK, that a modification to the three-factor model to take account of RD activity can significantly enhance the explanatory power of the three-factor model. We show that, as a practical matter, estimated risk premia based on the modified three-factor model can differ considerably from risk premia estimated using the CAPM or the three-factor model. In particular, risk premia for industries in which few firms undertake RD activities tend to be over-estimated.

This paper considers tests for structural instability of short duration, such as at the end of the sample. The key feature of the testing problem is that the number, m, of observations in the period of potential change is relatively small—possibly as small as one. The well-known F test of Chow (1960) for this problem only applies in a linear regression model with normally distributed iid errors and strictly exogenous regressors, even when the total number of observations, n+m, is large. We generalize the F test to cover regression models with much more general error processes, regressors that are not strictly exogenous, and estimation by instrumental variables as well as least squares. In addition, we extend the F test to nonlinear models estimated by generalized method of moments and maximum likelihood. Asymptotic critical values that are valid as n→∞ with m fixed are provided using a subsampling-like method. The results apply quite generally to processes that are strictly stationary and ergodic under the null hypothesis of no structural instability.

This paper examines old and new evidence on the predictive performance of asset prices for inflation and real output growth. We first review the large literature on this topic, focusing on the past dozen years. We then undertake an empirical analysis of quarterly date on up to 38 candidate indicators (mainly asset prices) for seven OECD countries for a span of up to 41 years (1959 - 1999). The conclusions from the literature review and the empirical analysis are the same. Some asset prices predict either inflation or output growth in some countries in some periods. Which series predicts what, when and where is, however, itself difficult to predict: good forecasting performance by an indicator in one period seems to be unrelated to whether it is a useful predictor in a later period. Intriguingly, forecasts produced by combining these unstable individual forecasts appear to improve reliably upon univariate benchmarks.

In this paper, we propose a simple bias–reduced log–periodogram regression estimator, ^dr, of the long–memory parameter, d, that eliminates the first– and higher–order biases of the Geweke and Porter–Hudak (1983) (GPH) estimator. The bias–reduced estimator is the same as the GPH estimator except that one includes frequencies to the power 2k for k=1,…,r, for some positive integer r, as additional regressors in the pseudo–regression model that yields the GPH estimator. The reduction in bias is obtained using assumptions on the spectrum only in a neighborhood of the zero frequency. Following the work of Robinson (1995b) and Hurvich, Deo, and Brodsky (1998), we establish the asymptotic bias, variance, and mean–squared error (MSE) of ^dr, determine the asymptotic MSE optimal choice of the number of frequencies, m, to include in the regression, and establish the asymptotic normality of ^dr. These results show that the bias of ^dr goes to zero at a faster rate than that of the GPH estimator when the normalized spectrum at zero is sufficiently smooth, but that its variance only is increased by a multiplicative constant. We show that the bias–reduced estimator ^dr attains the optimal rate of convergence for a class of spectral densities that includes those that are smooth of order s≥1 at zero when r≥(s−2)/2 and m is chosen appropriately. For s>2, the GPH estimator does not attain this rate. The proof uses results of Giraitis, Robinson, and Samarov (1997). We specify a data–dependent plug–in method for selecting the number of frequencies m to minimize asymptotic MSE for a given value of r. Some Monte Carlo simulation results for stationary Gaussian ARFIMA (1, d, 1) and (2, d, 0) models show that the bias–reduced estimators perform well relative to the standard log–periodogram regression estimator.

We present a model of mergers and acquisitions based on stock market misvaluations of the combining firms. The key ingredients of the model are the relative valuations of the merging firms and the market's perception of the synergies from the combination. The model explains who acquires whom, the choice of the medium of payment, the valuation consequences of mergers, and merger waves. The model is consistent with available empirical findings about characteristics and returns of merging firms, and yields new predictions as well.

The consequences of bank distress for the economy during the Depression remain an area of unresolved controversy. Since John M. Keynes (1931) and Irving Fisher (1933), macroeconomists have argued that bank distress magnified the extent of the economic decline during the Depression. As the intermediaries controlling money and credit, banks were in a special position to transmit their distress to other sectors. But the mechanism through which banking distress mattered for the economy has been hotly contested.

This study examines the effects of air quality regulation on economic activity. Anecdotal evidence and some recent empirical studies suggest that an inverse relationship exists between the stringency of environmental regulations and new plant formations. Using a unique county-level data set for New York State from 1980 to 1990, we revisit this conjecture using a seminonparametric method based on propensity score matching. Our empirical estimates suggest that pollution-intensive plants are responding to environmental regulations; more importantly, we find that traditional parametric methods used in previous studies may dramatically understate the impact of more stringent regulations.