Showing papers by "Serena Ng published in 2004"

A PANIC Attack on Unit Roots and Cointegration

Abstract: This paper develops a new methodology that makes use of the factor structure of large dimensional panels to understand the nature of nonstationarity in the data. We refer to it as PANIC—Panel Analysis of Nonstationarity in Idiosyncratic and Common components. PANIC can detect whether the nonstationarity in a series is pervasive, or variable-specific, or both. It can determine the number of independent stochastic trends driving the common factors. PANIC also permits valid pooling of individual statistics and thus panel tests can be constructed. A distinctive feature of PANIC is that it tests the unobserved components of the data instead of the observed series. The key to PANIC is consistent estimation of the space spanned by the unobserved common factors and the idiosyncratic errors without knowing a priori whether these are stationary or integrated processes. We provide a rigorous theory for estimation and inference and show that the tests have good finite sample properties.

Intergenerational Linkages in Consumption Behavior

Abstract: Consumption is partly a social activity, yet most studies of consumer behavior treat households in isolation. We investigate familial relationships in consumption patterns using a sample of parents and their children from the Panel Study of Income Dynamics. We Þnd a positive and statistically signiÞcant parent-speciÞc eect on children's consumption even after controlling for the eect of parental income, and we Þnd similar eects for sibling pairs. Child consumption responds negatively to large post-retirement shortfalls in consumption of the parents. This behavior holds up even after allowing for the possibility of smaller parent-to-child transfers made necessary by the parental consumption shortfalls. These results suggest that although income is an important source of the intergenerational correlation, parental choices and experiences also aect consumption behavior of the children.

Confidence Intervals for Diffusion Index Forecasts with a Large Number of Predictor

Abstract: We consider the situation when there is a large number of series, $N$, each with $T$ observations, and each series has some predictive ability for the variable of interest, $y$. A methodology of growing interest is to first estimate common factors from the panel of data by the method of principal components, and then augment an otherwise standard regression or forecasting equation with the estimated factors. In this paper, we show that the least squares estimates obtained from these factor augmented regressions are $\sqrt{T}$ consistent if $\sqrt{T}/N\rightarrow 0$. The factor forecasts for the conditional mean are $\min[\sqrt{T},\sqrt{N}]$ consistent, but the effect of ``estimated regressors' is asymptotically negligible when $T/N$ goes to zero. We present analytical formulas for predication intervals that take into account the sampling variability of the factor estimates. These formulas are valid regardless of the magnitude of $N/T$, and can also be used when the factors are non-stationary. The generality of these results is made possible by a covariance matrix estimator that is robust to weak cross-section correlation and heteroskedasticity in the idiosyncratic errors. We provide a consistency proof for this CS-HAC estimator.

Evaluating Latent and Observed Factors in Macroeconomics and Financ

Abstract: Common factors play an important role in many disciplines of social science. In economics, the factors are the common shocks that underlie the co-movements of the large number of economic time series. The question of interest is whether some observable economic variables are in fact the underlying unobserved factors. We consider statistics to determine if the observed and the latent factors are exactly the same. We also provide simple to construct statistics that indicate the extent to which the two sets of factors differ. The key to the analysis is that the space spanned by the latent factors can be consistently estimated when the sample size is large in both the cross-section and the time series dimensions. The tests are used to assess how well the so-called Fama and French factors as well as several business cycle indicators approximate the factors in portfolio and individual stock returns. Data from a large panel of macroeconomic are also analyzed.