Mean Group Tests for Stationarity in Heterogenous Panels

This paper proposes the panel-based mean group tests for the null of stationarity against the alternative of unit roots in the presence of both heterogeneity across crosssection units and serial correlation across time periods. Using both sequential and joint asymptotic analyses the proposed test statistic is shown to be distributed as standard normal under the null for large N (number of groups) and large T (number of time periods). Monte Carlo results support the use of join asymptotic limits (under further condition that N/T ? 0) as a guide to finite sample performance, but also clearly indicate that the power of our suggested panel-based test is substantially higher than that of the single time series-based test.Mean Group Tests, Heterogeneous Panels, Joint Asymptotic Theory, Stationarity, Unit Roots, Monte Carlo Simulation, Finite Sample Adjustment.

Gravity Models of the Intra-EU Trade: Application of the Hausman-Taylor Estimation in Heterogeneous Panels with Common Time-specific Factors

In this paper we follow recent developments of panel data studies and explicitly allow for the existence of unobserved common time-specific factors where their individual responses are also allowed to be heterogeneous across cross section units. In the context of this extended panel data framework we generalize the Hausman-Taylor estimation methodology and develop the associated econometric theory. We apply our proposed estimation technique along with the conventional panel data approaches to a comprehensive analysis of the gravity equation of bilateral trade flows amongst the 15 European countries over 1960-2001. Empirical results clearly demonstrate that our proposed approach fits the data reasonably well and provides much more sensible results than the conventional approach based on the fixed time dummies. These findings may highlight the importance of allowing for a certain degree of cross section dependence through unobserved heterogeneous time specific common effects, otherwise the resulting estimates would be severely biased.Gravity Models of Trade, Heterogeneous Panel Data, Hausman-Taylor Estimation, Time-specific Common Factors, Intra-EU Trade.

GLS Detrending-Based Unit Root Tests in Nonlinear STAR and SETAR Frameworks.

This paper consider the GLS detrending procedure advanced by Elliott et al. (1996) for unit root tests against alternative hypotheses where the time series data under investigation follow either globally stationary SETAR or STAR processes with deterministic components being present. It is found that the proposed testing procedures have considerable power gains against both the standard Dickey-Fuller unit root tests and existing nonlinear unit root tests recently proposed by Kapetanios and Shin (2002) and Kapetanios et al. (2003). The empirical application to DM and Yen bilateral real exchange rates against a number of other currencies also confirms that nonlinear unit root tests based on GLS detrending will be more powerful than linear ones. Interestingly, we find that the DM dataset seems to produce more rejections of the null using the GLS detrending-based SETAR tests than using the GLS detrending-based STAR tests, whereas the number of rejections of both tests are similar for the Yen dataset. The different results may arise from the respective liquidity of the DM and Yen Forex markets.Unit Root Tests, Nonlinear STAR and SETAR Models, GLS Detrending, Real Exchange Rates.

Unit Root Tests in Three-Regime SETAR Models.

This paper proposes a simple testing procedure to distinguish a unit root process from a globally stationary three-regime self-exciting threshold autoregressive process. Following the threshold cointegration literature we assume that the process follows the random walk in the corridor regime, and therefore we propose that the null of a unit root be tested by the Wald statistic for the joint significance of autoregressive parameters in both lower and upper regimes. We establish that when threshold parameters are known, the suggested Wald test has a well-defined asymptotic null distribution free of nuisance parameters. In the general case where threshold parameters are unknown a priori, we consider the three most commonly used summary statistics - average, exponential average and supremum. Assuming that the grid set for thresholds can be selected such that the corridor regime be of finite width both under the null and under the alternative, we can establish both stochastic equicontinuity and uniform convergence of the aforementioned summary statistics. Monte Carlo evidence clearly indicates that the proposed tests are more powerful than the Dickey-Fuller test that ignores the threshold nature under the alternative. We illustrate the usefulness of our proposed tests by examining stationarity of bilateral real exchange rates for the G7 countries.Self-exciting Threshold Autoregressive Models, Unit Roots, Globally Stationary Processes, Threshold Cointegration, Monte Carlo Simulations, Real Exchange Rates, Transactions Costs, Dread of Depreciation

Unit Root Tests in Three-Regime SETAR Models

This paper proposes a simple direct testing procedure to distinguish a linear unit root process from a globally stationary three-regime self-exciting threshold autoregressive process. We derive the asymptotic null distribution of the Wald statistic, and show that it does not depend on unknown fixed threshold values. Monte Carlo evidence clearly indicates that the exponential average of the Wald statistic is more powerful than the Dickey-Fuller test that ignores the threshold nature under the alternative.Self-exciting threshold autoregressive models, Unit roots, Globally stationary processes, Threshold cointegration, Wald tests, Monte Carlo simulations, Real exchange rates

Trade, Technology and Wage Inequality in the South African Manufacturing Sectors

This paper advances on previous work on the effects of trade and technical change on labour markets within the framework of Heckscher-Ohlin trade theory. First, we employ dynamic heterogeneous panel estimation techniques not previously used in this context, which separate Heckscher-Ohlin-based long run relationships from short run dynamics that are heterogeneous across sectors. Second, we provide evidence for an unskilled labor abundant developing country that allows comparison of the results against developed country evidence. Third, we consider the appropriateness of alternative approaches and examine endogeneity issues in the impact of technology and price changes on factor returns. For South African manufacturing we find that output prices increase most strongly in sectors that are labor intensive. Our results further suggest that trade-mandated earnings increases are positive for labor, and negative for capital. By contrast technology has mandated negative earnings increases for both factors. We also find that separation of different demand side factors collectively constituting globalization is useful in understanding the impact of trade, and taking account of endogeneity is important in isolating factor and sector bias of technological change.Trade, Total Factor Productivity, Stolper-Samuelson Theorem, Mandated Factor Earnings Changes, Dynamic Heterogeneous Panel Data, Pooled Mean Group Estimation.

Testing for Nonstationary Long Memory against Nonlinear Ergodic Models

Interest in the interface of nonstationarity and nonlinearity has been increasing in the econometric literature. This paper provides a formal method of testing for nonstationary long memory against the alternative of particular forms of nonlinerarity. The nonlinear models we consider are ESTAR and SETAR models. We provide analysis on the asymptotic properties of the tests and carry out a detailed Monte Carlo study. We find that the tests are in most cases able to dinstinguish between the competing models but in a few cases they are unable to do so raising the prospect that long memory and nonlinear processes may have similar characteristics in small samples.Nonlinearity, Long memory, ESTAR models, SETAR models

Asymmetric Price Impacts of Order Flow on Exchange Rate Dynamics

We generalize the portfolio shifts model advanced by Evans and Lyons (2002a; b), and develop the dynamic asymmetric portfolio shifts (DAPS) model by explicitly allowing for possible market under- and overreactions and for asymmetric pricing impacts of order flows. Using the Reuters D2000-1 daily trading data for eight currency markets over a four-month period from 1 May to 31 August 1996, we find strong evidence of a nonlinear cointegrating relationship between exchange rates and (cumulative) order flows: The price impact of negative order flows (selling pressure) is overwhelmingly stronger than that of the positive ones (buying pressure). Through the dynamic multiplier analysis, we find two typical patterns of the price discovery process. The markets following overreactions tend to display a delayed overshooting and a volatile but faster adjustment towards equilibrium whereas the markets following underreactions are generally characterized by a gradual but persistent adjustment. In our model, these heterogeneous adjustment patterns reflect different liquidity provisions associated with different market conditions following under- and overreactions. In addition, the larger is the mispricing, the faster is the overall adjustment speed, a finding consistent with Abreu and Brunnermeier (2002) and Cai et al. (2011). We also find that underreactions are followed mostly by positive feedback trading while overreactions are characterized by delayed overshooting in the short run but corrected by negative feedback trading at longer horizons, the finding is consistent with Barberis et al. (1998) who show that positive short-run autocorrelations (momentum) signal underreaction while negative long-run autocorrelations (reversal) signal overreaction.Exchange rate, order flow, under- and overreaction, asymmetric pricing impacts, asymmetric cointegrating relationship and dynamic multipliers

Shifting Preferences at the Fed: Evidence from Rolling Dynamic Multipliers and Impulse Response Analysis

The existing empirical literature on Taylor-type interest rate rules has failed to achieve a robust consensus. Indeed, the relatively common finding that the Taylor principle does not hold has fueled a degree of controversy in the field. We attribute these mixed estimation results to a raft of empirical issues from which many existing studies suffer, including bias, inconsistency, endogeneity and a failure to adequately account for the combination of persistent and stationary variables. We propose a new method of combining I(0) and I(1) series in a system setting based on the long-run structural approach of Garratt, Lee, Pesaran and Shin (2006). The application of this method to a long sample of US data provides modest support for the operation of a Taylor-type rule, albeit with considerable inertia. We argue that estimation across rolling windows may better reflect shifts in the underlying preferences of the monetary policymakers at the Federal Reserve. Such rolling estimation provides substantial evidence that the inflation and output preferences of the Fed have varied through time, presumably reflecting the prevailing economic and political conditions, its chairmanship, and the composition of the Federal Open Market Committee. Our most significant finding is that the Taylor Principle was robustly upheld under Volcker, often upheld pre-Volcker but rarely observed post-Volcker over any horizon.System Estimation with Mixed I(0) and I(1) Variables, Long-Run Structural Modelling, Rolling Estimation, Taylor Rule

A Nonlinear Panel Model of Cross-sectional Dependence

This paper proposes a new panel model of cross-sectional dependence. The model has a number of potential structural interpretations that relate to economic phenomena such as herding in financial markets. On an econometric level it provides a flexible approach to the modelling of interactions across panel units and can generate endogenous cross-sectional dependence that can resemble such dependence arising in a variety of existing models such as factor or spatial models. We discuss the theoretical properties of the model and ways in which inference can be carried out. We supplement this analysis with a detailed Monte Carlo study and two empirical illustrations.Cross-sectional dependence, Nonlinearity, Factor models, Panel models, Fixed effects