PITFALLS IN CROSS‐SECTION STUDIES WITH INTEGRATED REGRESSORS: A SURVEY AND NEW DEVELOPMENTS

In cross‐section studies, if the dependent variable is I(0) but the regressor is I(1), the true slope must be zero in the resulting “unbalanced regression.” A spuriously significant relationship may be found in large cross‐sections, however, if the integrated regressor is related to a stationary variable that enters the DGP but is omitted from the regression. The solution is to search for the related stationary variable, in some cases the first difference of the integrated regressor, in other cases, a categorical variable that can take on limited number of values which depend on the integrated variable. We present an extensive survey, new developments, and applications particularly in finance.     

PEDAKSI: Methodology for Collecting Data about Survey Non-Respondents

The effects of unit non-response on survey errors are of great concern to researchers.However, direct assessment of non-response bias in survey estimates is rarely possible.Attempts are often made to adjust for the effects of non-response by weighting, but thisusually relies on the use of frame data or external population data, which are at bestmodestly correlated with the survey variables. This paper reports the development ofa method to collect limited survey data from non-respondents to personal interviewsurveys and a large-scale field test of the method on the British Crime Survey (BCS).The method is shown to be acceptable and low cost, to provide valid data, and to haveno detrimental effect on the main survey. The use of the resultant data to estimatenon-response bias is illustrated and some substantive conclusions are drawn for the BCS.     

An objective Bayesian analysis of common stochastic trends in international stock prices and exchange rates

This paper uses Bayesian methods to analyze unit root and cointegration properties of two different finance data sets. Avoiding the use of subjective prior information, the paper surveys and utilizes several different objective Bayesian methodologies in an investigation of common stochastic trends in international stock markets and in spot and forward exchange rates for several different countries.     

UNEMPLOYMENT, HYSTERESIS AND TRANSITION

In this paper, we quantify the degree of persistence in the unemployment rates of transition countries using a variety of methods benchmarked against the EU. Initially, we work with the concept of linear ‘Hysteresis’ as described by the presence of unit roots in unemployment as in most empirical research on this area. Given that this is potentially a narrow definition, we also take into account the existence of structural breaks and nonlinear dynamics in unemployment. Finally, we examine whether CEECs' unemployment presents features of multiple equilibria, that is, if it remains locked into a new level whenever some structural change or sufficiently large shock occurs. Our findings show that, in general, we can reject the unit‐root hypothesis after controlling for structural changes and business‐cycle effects, but we can observe the presence of a high and low unemployment equilibria. The speed of adjustment is faster for CEECs than the EU, although CEECs tend to move more frequently between equilibria.     

Evidence on the existence of speculative bubbles in farmland prices

We conduct tests for the contribution of speculative bubbles to farmland prices. These tests are carried out under the hypothesis that farmland investors rationally form expectations. The outcome of tests reported here allows us to infer whether farmland prices are determined by market fundamentals-discounted returns from the highest economic land use-or whether rumors about farmland price movements are self-fulfilling. The tests are stationarity and cointegration tests relating farmland prices to rents. The tests are carried out using data from three farm production regions-the Corn Belt, the Northern Plains, and the Lake States. In each region, we find little evidence to reject the hypothesis that market fundamentals determine farmland prices.     

The adequacy of asymptotic approximations in the near-integrated autoregressive model with dependent errors

We consider the normalized least squares estimator of the parameter in a nearly integrated first-order autoregressive model with dependent errors. In a first step we consider its asymptotic distribution as well as asymptotic expansion up to order O_p(T⁻¹). We derive a limiting moment generating function which enables us to calculate various distributional quantities by numerical integration. A simulation study is performed to assess the adequacy of the asymptotic distribution when the errors are correlated. We focus our attention on two leading cases: MA(1) errors and AR(1) errors. The asymptotic approximations are shown to be inadequate as the MA root gets close to −1 and as the AR root approaches either −1 or 1. Our theoretical analysis helps to explain and understand the simulation results of Schwert (1989) and DeJong, Nankervis, Savin, and Whiteman (1992) concerning the size and power of Phillips and Perron's (1988) unit root test. A companion paper, Nabeya and Perron (1994), presents alternative asymptotic frameworks in the cases where the usual asymptotic distribution fails to provide an adequate approximation to the finite-sample distribution.     

CEV模型的单位根检验研究

CEV模型（Constant Elasticity of Variance Model）作为常用的利率模型，在实证分析中得到了广泛运用，但是其单位根检验一直被忽略或者被默认可以使用迪基一富勒检验。本文首次运用Box—Cox变换的技巧，针对CEV模型的单位根检验问题，找到了合适的统计量并且证明其渐进分布存在，然后通过蒙特卡罗方法求出了该统计量的分布表。得到了在大样本的情形下可以沿用迪基一富勒检验，但在小样本的情形下与迪基一富勒检验有所偏差的结论。     

Testing of unit roots and other fractionally integrated hypotheses in the presence of structural breaks

Tests of unit roots and other nonstationary hypotheses that were proposed by Robinson (1994) are applied in this article to the Nelson and Plosser's (1982) series. The tests can be expressed in a way allowing for structural breaks under both the null and the alternative hypotheses. When applying the tests to the same dataset as in Perron (1989), we observe that our results might be consistent with those in Perron (1989) when testing the nulls of trend-stationarity or a unit-root. However, we also observe that fractionally integrated hypotheses may be plausible alternatives in the context of structural breaks at a known period of time. Final version received: August 2000/Final version accepted: August 2001 RID="*" ID="*"  The author gratefully acknowledges the financial support from the European TMR grant No. ERBFMRX-CT-98-0213. Comments of two anonymous referees are also acknowledged.     

Nonlinear IV unit root tests in panels with cross-sectional dependency

We propose a unit root test for panels with cross-sectional dependency. We allow general dependency structure among the innovations that generate data for each of the cross-sectional units. Each unit may have different sample size, and therefore unbalanced panels are also permitted in our framework. Yet, the test is asymptotically normal, and does not require any tabulation of the critical values. Our test is based on nonlinear IV estimation of the usual augmented Dickey–Fuller type regression for each cross-sectional unit, using as instruments nonlinear transformations of the lagged levels. The actual test statistic is simply defined as a standardized sum of individual IV t-ratios. We show in the paper that such a standardized sum of individual IV t-ratios has limit normal distribution as long as the panels have large individual time series observations and are asymptotically balanced in a very weak sense. We may have the number of cross-sectional units arbitrarily small or large. In particular, the usual sequential asymptotics, upon which most of the available asymptotic theories for panel unit root models heavily rely, are not required. Finite sample performance of our test is examined via a set of simulations, and compared with those of other commonly used panel unit root tests. Our test generally performs better than the existing tests in terms of both finite sample sizes and powers. We apply our nonlinear IV method to test for the purchasing power parity hypothesis in panels.     

Testing for unit roots in time series models with non-stationary volatility

Many of the key macro-economic and financial variables in developed economies are characterized by permanent volatility shifts. It is known that conventional unit root tests are potentially unreliable in the presence of such behaviour, depending on a particular function (the variance profile) of the underlying volatility process. Somewhat surprisingly then, very little work has been undertaken to develop unit root tests which are robust to the presence of permanent volatility shifts. In this paper we fill this gap in the literature by proposing tests which are valid in the presence of a quite general class of permanent variance changes which includes single and multiple (abrupt and smooth-transition) volatility change processes as special cases. Our solution uses numerical methods to simulate the asymptotic null distribution of the statistics based on a consistent estimate of the variance profile which we also develop. The practitioner is not required to specify a parametric model for volatility. An empirical illustration using producer price inflation series from the Stock–Watson database is reported.