On Testing Sample Selection Bias under the Multicollinearity Problem

This paper examines and compares the finite sample performance of the existing tests for sample selection bias, especially under the multi-collinearity problem pointed out by Nawata (1993). The results show that under such multicollinearity problem, (i) the t-test for sample selection bias based on the Heckman and Greene variance estimator can be unreliable; (ii) the standard t-test (Heckman 1979) and the asymptotically efficient Lagrange multiplier test (Melino 1982) have correct size but very little power; (iii) however, the likelihood ratio test following the maximum likelihood estimation remains powerful

Firm Level Volatility-Return Analysis using Dynamic Panels

This paper examines "leverage" and volatility feedback effects at the firm level by considering both market effects and firm level effects, using 242 individual firm stock data in the US market. We adopt a panel vector autoregressive framework which allows us to control simultaneously for common business cycle effects, unobserved cross correlation effects in return and volatility via industry effects, and heterogeneity across firms. Our results suggest that volatility feedback effects at the firm level are present due to both market effects and firm effects, though the market volatility feedback effect is stronger than the corresponding firm level effect. We also find that the leverage effect at the firm level is persistent, significant and negative, while the effect of market return on firm volatility is persistent, significant and positive. The presence of these effects is further explored through the responses of the model's variables to market-wide return and volatility shocks.Volatility Feedback; Stock Return; Leverage Effects; Panel Vector Autoregression

Testing CAPM with a Large Number of Assets

This paper is concerned with testing the time series implications of the capital asset pricing model (CAPM) due to Sharpe (1964) and Lintner (1965), when the number of securities, N, is large relative to the time dimension, T, of the return series. Two new tests of CAPM are proposed that exploit recent advances on the analysis of large panel data models, and are valid even if N>T. When the errors are Gaussian and cross sectionally independent, a test, denoted by J_{α,1}, is proposed which is N(0,1) as N→∞, with T fixed. Even when the errors are non-Gaussian we are still able to show that J_{α,1} tends to N(0,1) so long as the errors are cross-sectionally independent and N/T³→0, with N and T→∞, jointly. In the case of cross sectionally correlated errors, using a threshold estimator of the average squares of pair-wise error correlations, a modified version of J_{α,1}, denoted by J_{α,2}, is proposed. Small sample properties of the tests are compared using Monte Carlo experiments designed specifically to match the correlations, volatilities, and other distributional features of the residuals of Fama-French three factor regressions of individual securities in the Standard & Poor 500 index. Overall, the proposed tests perform best in terms of power, with empirical sizes very close to the chosen nominal value even in cases where N is much larger than T. The J_{α,2} test (which allows for non-Gaussian and weakly cross correlated errors) is applied to all securities in the S&P 500 index with 60 months of return data at the end of each month over the period September 1989-September 2011. Statistically significant evidence against Sharpe-Lintner CAPM is found mainly during the recent financial crisis. Furthermore, a strong negative correlation is found between a twelve-month moving average p-values of the J_{α,2} test and the returns of long/short equity strategies relative to the return on S&P 500 over the period December 2006 to September 2011, suggesting that abnormal profits are earned during episodes of market inefficiencies.CAPM, Testing for alpha, Market e¢ ciency, Long/short equity returns, Large panels, Weak and strong cross-sectional dependence.

A Spatio-Temporal Model of House Prices in the US

In this paper we model the dynamic adjustment of real house prices using data at the level of US States. We consider interactions between housing markets by examining the extent to which real house prices at the State level are driven by fundamentals such as real income, as well as by common shocks, and determine the speed of adjustment of house prices to macroeconomic and local disturbances. We take explicit account of both cross sectional dependence and heterogeneity. This allows us to find a cointegrating relationship between house prices and incomes and to identify a small role for real interest rates. Using this model we examine the role of spatial factors, in particular the effect of contiguous states by use of a weighting matrix. We are able to identify a significant spatial effect, even after controlling for State specific real incomes, and allowing for a number of unobserved common factors

Panels with Nonstationary Multifactor Error Structures

The presence of cross-sectionally correlated error terms invalidates much inferential theory of panel data models. Recent work by Pesaran (2006) suggests a method which makes use of cross-sectional averages to provide valid inference for stationary panel regressions with multifactor error structure. This paper extends this work and examines the important case where the unobserved common factors follow unit root processes and could be cointegrated. It is found that the presence of unit roots does not affect most theoretical results which continue to hold irrespective of the integration and the cointegration properties of the unobserved factors. This finding is further supported for small samples via an extensive Monte Carlo study. In particular, the results of the Monte Carlo study suggest that the cross-sectional average based method is robust to a wide variety of data generation processes and has lower biases than all of the alternative estimation methods considered in the paper

A Bias-Adjusted LM Test of Error Cross Section Independence

This paper proposes bias-adjusted normal approximation versions of Lagrange multiplier (NLM) test of error cross section independence of Breusch and Pagan (1980) in the case of panel models with strictly exogenous regressors and normal errors. The exact mean and variance of the Lagrange multiplier (LM) test statistic are provided for the purpose of the bias-adjustments, and it is shown that the proposed tests have a standard normal distribution for the fixed time series dimension (T) as the cross section dimension (N) tends to infinity. Importantly, the proposed bias-adjusted NLM tests are consistent even when the Pesaran’s (2004) CD test is inconsistent. The finite sample evidence shows that the bias adjusted NLM tests successfully control the size, maintaining satisfactory power. However, it is also shown that the bias-adjusted NLM tests are not as robust as the CD test to non-normal errors and/or in the presence of weakly exogenous regressors

Spatial and Temporal Diffusion of House Prices in the UK

This paper provides a method for the analysis of the spatial and temporal diffusion of shocks in a dynamic system. We use changes in real house prices within the UK economy at the level of regions to illustrate its use. Adjustment to shocks involves both a region specific and a spatial effect. Shocks to a dominant region – London – are propagated contemporaneously and spatially to other regions. They in turn impact on other regions with a delay. We allow for lagged effects to echo back to the dominant region. London in turn is influenced by international developments through its link to New York and other financial centers. It is shown that New York house prices have a direct effect on London house prices. We analyse the effect of shocks using generalised spatio-temporal impulse responses. These highlight the diffusion of shocks both over time (as with the conventional impulse responses) and over space.spatial dependence, cross sectional dependence, house prices

Panels with Nonstationary Multifactor Error Structures

The presence of cross-sectionally correlated error terms invalidates much inferential theory of panel data models. Recently work by Pesaran (2006) has suggested a method which makes use of cross-sectional averages to provide valid inference for stationary panel regressions with multifactor error structure. This paper extends this work and examines the important case where the unobserved common factors follow unit root processes and could be cointegrated. It is found that the presence of unit roots does not affect most theoretical results which continue to hold irrespective of the integration and the cointegration properties of the unobserved factors. This finding is further supported for small samples via an extensive Monte Carlo study. In particular, the results of the Monte Carlo study suggest that the cross-sectional average based method is robust to a wide variety of data generation processes and has lower biases than all of the alternative estimation methods considered in the paper.cross section dependence, large panels, unit roots, principal components, common correlated effects