Results 1  10
of
336
Have Individual Stocks Become More Volatile? An Empirical Exploration of Idiosyncratic Risk
 THE JOURNAL OF FINANCE • VOL. LVI
, 2001
"... This paper uses a disaggregated approach to study the volatility of common stocks at the market, industry, and firm levels. Over the period 1962–1997 there has been a noticeable increase in firmlevel volatility relative to market volatility. Accordingly, correlations among individual stocks and the ..."
Abstract

Cited by 509 (17 self)
 Add to MetaCart
This paper uses a disaggregated approach to study the volatility of common stocks at the market, industry, and firm levels. Over the period 1962–1997 there has been a noticeable increase in firmlevel volatility relative to market volatility. Accordingly, correlations among individual stocks and the explanatory power of the market model for a typical stock have declined, whereas the number of stocks needed to achieve a given level of diversification has increased. All the volatility measures move together countercyclically and help to predict GDP growth. Market volatility tends to lead the other volatility series. Factors that may be responsible for these findings are suggested.
The bootstrap
 In Handbook of Econometrics
, 2001
"... The bootstrap is a method for estimating the distribution of an estimator or test statistic by resampling one’s data. It amounts to treating the data as if they were the population for the purpose of evaluating the distribution of interest. Under mild regularity conditions, the bootstrap yields an a ..."
Abstract

Cited by 175 (2 self)
 Add to MetaCart
The bootstrap is a method for estimating the distribution of an estimator or test statistic by resampling one’s data. It amounts to treating the data as if they were the population for the purpose of evaluating the distribution of interest. Under mild regularity conditions, the bootstrap yields an approximation to the distribution of an estimator or test statistic that is at least as accurate as the
Relative labor productivity and the real exchange rate in the long run: evidence for a panel of OECD countries. NBER Working Paper no
, 1996
"... The BalassaSamuelson model, which explains real exchange rate movements in terms of sectoral productivities, rests on two components. First, it implies that the relative price of nontraded goods in each country should reflect the relative productivity of labor in the traded and nontraded goods se ..."
Abstract

Cited by 149 (1 self)
 Add to MetaCart
The BalassaSamuelson model, which explains real exchange rate movements in terms of sectoral productivities, rests on two components. First, it implies that the relative price of nontraded goods in each country should reflect the relative productivity of labor in the traded and nontraded goods sectors. Second, it assumes purchasing power parity holds for traded goods. We test both of these using a panel of OECD countries. Our results suggest that relative prices generally reflect relative labor productivities in the long run. The evidence on purchasing power parity in traded goods is less favorable, at least when we
Is the Short Rate Drift Actually Nonlinear?
, 1999
"... AitSahalia (1996) and Stanton (1997) use nonparametric estimators applied to short term interest rate data to conclude that the drift function contains important nonlinearities. We study the finitesample properties of their estimators by applying them to simulated sample paths of a squareroot dif ..."
Abstract

Cited by 81 (1 self)
 Add to MetaCart
AitSahalia (1996) and Stanton (1997) use nonparametric estimators applied to short term interest rate data to conclude that the drift function contains important nonlinearities. We study the finitesample properties of their estimators by applying them to simulated sample paths of a squareroot diffusion. Although the drift function is linear, both estimators suggest nonlinearities of the type and magnitude reported in AitSahalia (1996) and Stanton (1997). Combined with the results of a weighted least squares estimator, this evidence implies that nonlinearity of the short rate drift is not a robust stylized fact.
Bootstrap Methods in Econometrics: Theory and Numerical Performance
 Eds.), Advances in Economics and Econometrics: Theory and Applications, Seventh World Congress, Vol. III
, 1997
"... 1. ..."
OneStep Estimators for OverIdentified Generalized Method of Moments Models
 Review of Economic Studies
, 1997
"... In this paper I discuss alternatives to the GMM estimators proposed by Hansen (1982) and others. These estimators are shown to have a number of advantages. First of all, there is no need to estimate in an initial step a weight matrix as required in the conventional estimation procedure. Second, it i ..."
Abstract

Cited by 74 (2 self)
 Add to MetaCart
In this paper I discuss alternatives to the GMM estimators proposed by Hansen (1982) and others. These estimators are shown to have a number of advantages. First of all, there is no need to estimate in an initial step a weight matrix as required in the conventional estimation procedure. Second, it is straightforward to derive the distribution of the estimator under general misspecification. Third, some of the alternative estimators have appealing informationtheoretic interpretations. In particular, one of the estimators is an empirical likelihood estimator with an interpretation as a discrete support maximum likelihood estimator. Fourth, in an empirical example one of the new estimators is shown to perform better than the conventional estimators. Finally, the new estimators make it easier for the researcher to get better approximations to their distributions using saddlepoint approximations. The main cost is computational: the system of equations that has to be solved is of greater dimension than the number of parameters of interest. In practice this mayor may not be a problem in particular applications. 1.
RegressionBased Tests of Predictive Ability
 International Economic Review
, 1998
"... helpful comments, and the National Science Foundation and the Graduate School We develop regressionbased tests of hypotheses about out of sample prediction errors. Representative tests include ones for zero mean and zero correlation between a prediction error and a vector of predictors. The relevan ..."
Abstract

Cited by 72 (9 self)
 Add to MetaCart
helpful comments, and the National Science Foundation and the Graduate School We develop regressionbased tests of hypotheses about out of sample prediction errors. Representative tests include ones for zero mean and zero correlation between a prediction error and a vector of predictors. The relevant environments are ones in which predictions depend on estimated parameters. We show that standard regression statistics generally fail to account for error introduced by estimation of these parameters. We propose computationally convenient test statistics that properly account for such error. Simulations indicate that the procedures can work well in samples of size typically available, although there sometimes are substantial size distortions.
Rethinking the univariate approach to unit root testing: Using covariates to increase power. Econometric Theory
, 1995
"... In the context of testing for a unit root in a univariate time series, the convention is to ignore information in related time series. This paper shows that this convention is quite costly, as large power gains can be achieved by including correlated stationary covariates in the regression equation. ..."
Abstract

Cited by 68 (2 self)
 Add to MetaCart
In the context of testing for a unit root in a univariate time series, the convention is to ignore information in related time series. This paper shows that this convention is quite costly, as large power gains can be achieved by including correlated stationary covariates in the regression equation. The paper derives the asymptotic distribution of ordinary leastsquares estimates of the largest autoregressive root and its tstatistic. The asymptotic distribution is not the conventional DickeyFuller distribution, but a convex combination of the DickeyFuller distribution and the standard normal, the mixture depending on the correlation between the equation error and the regression covariates. The local asymptotic power functions associated with these test statistics suggest enormous gains over the conventional unit root tests. A simulation study and empirical application illustrate the potential of the new approach. 1.
Bootstrap methods for time series
 International Statist. Review
, 2003
"... The bootstrap is a method for estimating the distribution of an estimator or test statistic by resampling one’s data or a model estimated from the data. The methods that are available for implementing the bootstrap and the accuracy of bootstrap estimates depend on whether the data are a random sampl ..."
Abstract

Cited by 44 (0 self)
 Add to MetaCart
The bootstrap is a method for estimating the distribution of an estimator or test statistic by resampling one’s data or a model estimated from the data. The methods that are available for implementing the bootstrap and the accuracy of bootstrap estimates depend on whether the data are a random sample from a distribution or a time series. This paper is concerned with the application of the bootstrap to timeseries data when one does not have a finitedimensional parametric model that reduces the data generation process to independent random sampling. We review the methods that have been proposed for implementing the bootstrap in this situation and discuss the accuracy of these methods relative to that of firstorder asymptotic approximations. We argue that methods for implementing the bootstrap with timeseries data are not as well understood as methods for data that are sampled randomly from a distribution. Moreover, the performance of the bootstrap as measured by the rate of convergence of estimation errors tends to be poorer with time series than with random samples. This is an important problem for applied research because firstorder asymptotic approximations are often inaccurate and misleading with timeseries data and samples of the sizes encountered in applications. We conclude that there is a need for further research in the application of the bootstrap to time series, and we describe some of the important unsolved problems.