Donggyu Sul

Dynamic Panel Estimation and Homogeneity Testing Under Cross Section Dependence

This paper deals with cross section dependence, homogeneity restrictions and small sample bias issues in dynamic panel regressions. To address the bias problem we develop a panel approach to median unbiased estimation that takes account of cross section dependence. The new estimators given here considerably reduce the effects of bias and gain precision from estimating cross section error correlation. The paper also develops an asymptotic theory for tests of coefficient homogeneity under cross section dependence, and proposes a modified Hausman test to test for the presence of homogeneous unit roots. An orthogonalization procedure is developed to remove cross section dependence and permit the use of conventional and meta unit root tests with panel data. Some simulations investigating the finite sample performance of the estimation and test procedures are reported.

Nominal exchange rates and monetary fundamentals Evidence from a small post-Bretton woods panel

We study the long-run relationship between nominal exchange rates and monetary fundamentals in a quarterly panel of 18 countries extending from 1973.1 to 1997.1. Our analysis is centered on two issues. First, we test whether exchange rates are cointegrated with long-run determinants predicted by economic theory. These results generally support the hypothesis of cointegration. The second issue is to re-examine the ability for monetary fundamentals to forecast future exchange rate returns. Panel regression estimates and forecasts confirm that this forecasting power is significant.

Spatial Market Efficiency and Policy Regime Change: Seemingly Unrelated Error Correction Model Estimation

Now published as a Journal Article in American Journal of Agricultural Economics Volume 84 Issue 4 Page 1042 - November 2002 doi:10.1111/1467-8276.00366

Bias Reduction in Dynamic Panel Data Models by Common Recursive Mean Adjustment

The within-group estimator (same as the least squares dummy variable estimator) of the dominant root in dynamic panel regression is known to be biased downwards. This article studies recursive mean adjustment (RMA) as a strategy to reduce this bias for AR(p) processes that may exhibit cross-sectional dependence. Asymptotic properties for N,T→∞ jointly are developed. When ( log 2T)(N/T)→ζ, where ζ is a non-zero constant, the estimator exhibits nearly negligible inconsistency. Simulation experiments demonstrate that the RMA estimator performs well in terms of reducing bias, variance and mean square error both when error terms are cross-sectionally independent and when they are not. RMA dominates comparable estimators when T is small and/or when the underlying process is persistent.

Uniform Asymptotic Normality in Stationary and Unit Root Autoregression

While differencing transformations can eliminate nonstationarity, they typically reduce signal strength and correspondingly reduce rates of convergence in unit root autoregressions. The present paper shows that aggregating moment conditions that are formulated in differences provides an orderly mechanism for preserving information and signal strength in autoregressions with some very desirable properties. In first order autoregression, a partially aggregated estimator based on moment conditions in differences is shown to have a limiting normal distribution which holds uniformly in the autoregressive coefficient rho including stationary and unit root cases. The rate of convergence is root of n when |rho|

Exchange Rates as Exchange Rate Common Factors

Factor analysis performed on a panel of 23 nominal exchange rates from January 1999 to December 2010 yields three common factors. This paper identifies the euro/dollar, Swiss-franc/dollar and yen/dollar exchange rates as empirical counterparts to these common factors. These empirical factors explain a large proportion of exchange rate variation over time and have significant in-sample and out-of-sample predictive power.

Lag length selection in panel autoregression

ABSTRACT Model selection by BIC is well known to be inconsistent in the presence of incidental parameters. This article shows that, somewhat surprisingly, even without fixed effects in dynamic panels BIC is inconsistent and overestimates the true lag length with considerable probability. The reason for the inconsistency is explained, and the probability of overestimation is found to be 50% asymptotically. Three alternative consistent lag selection methods are considered. Two of these modify BIC, and the third involves sequential testing. Simulations evaluate the performance of these alternative lag selection methods in finite samples.

Asymptotic Power Advantages of Long-Horizon Regressions

Local asymptotic power advantages are available for testing the hypothesis that the slope coefficient is zero in regressions of yt+k- yton xtfor k > 1, when { yt} ~ I(0) and {xt} ~ I(0). The advantages of these long-horizon regression tests accrue in empirically relevant regions of the admissible parameter space. In Monte Carlo experiments, small sample power advantages to long-horizon regression tests accrue in a region of the parameter space that is larger than that predicted by the asymptotic analysis.

Weak s- Convergence: Theory and Applications

The concept of relative convergence, which requires the ratio of two time series to converge to unity in the long run, explains convergent behavior when series share commonly divergent stochastic or deterministic trend components. Relative convergence of this type does not necessarily hold when series share common time decay patterns measured by evaporating rather than divergent trend behavior. To capture convergent behavior in panel data that do not involve stochastic or divergent deterministic trends, we introduce the notion of weak σ-convergence, whereby cross section variation in the panel decreases over time. The paper formalizes this concept and proposes a simple-to-implement linear trend regression test of the null of no σ-convergence. Asymptotic properties for the test are developed under general regularity conditions and various data generating processes. Simulations show that the test has good size control and discriminatory power. The method is applied to examine whether the idiosyncratic components of 90 disaggregate personal consumption expenditure (PCE) price index items σ-converge over time. We find strong evidence of weak σ-convergence in the period after 1992, which implies that cross sectional dependence has strenthened over the last two decades. In a second application, the method is used to test whether experimental data in ultimatum games converge over successive rounds, again finding evidence in favor of weak σ-convergence. A third application studies convergence and divergence in US States unemployment data over the period 2001-2016.

Identification of Unknown Common Factors: Leaders and Followers

This article has the following contributions. First, this article develops a new criterion for identifying whether or not a particular time series variable is a common factor in the conventional approximate factor model. Second, by modeling observed factors as a set of potential factors to be identified, this article reveals how to easily pin down the factor without performing a large number of estimations. This allows the researcher to check whether or not each individual in the panel is the underlying common factor and, from there, identify which individuals best represent the factor space by using a new clustering mechanism. Asymptotically, the developed procedure correctly identifies the factor when N and T jointly approach infinity. The procedure is shown to be quite effective in the finite sample by means of Monte Carlo simulation. The procedure is then applied to an empirical example, demonstrating that the newly developed method identifies the unknown common factors accurately.