Devaluation expectations and the unit root hypothesis: The Italian Lira in the European monetary system

Summary In several studies the unit root hypothesis of EMS exchange rates is analysed within the context of devaluation expectations estimation. By means of bootstrap inference it is shown that these procedures are not compatible with standard Dickey-Fuller significance levels and may lead to a wrong rejection of the null hypothesis. In the case of the Italian Lira/Deutsche Mark exchange rate, the hypothesis of a unit root is not rejected and expectations can be modelled by means of a reflected Brownian motion. The estimated devaluation expectations are related with some macro variables which provide evidence for the structure of expectations. This research has been partially supported with 40% and 60% MURST grants. The author wishes to thank the Bank of Italy for the exchange rates and the interest rates data and Ulf S?derstr?m for providing macroeconomic indicators. Useful suggestions from Riccardo Cesari, Michele Costa and two anonymous referees are gratefully acknowledged.     

Using the Residual White Noise Autoregressive Order Determination Criterion to Identify Unit Roots in Arima Models

We present a simplified form of a univariate identification approach for time series models based on the residual white noise autoregressive order determination criterion and linear estimation methods. We also show how the procedure can be used to identify the degree of differencing necessary to induce stationarity in data. The performance of this approach is also contrasted with Portmanteau tests for detection of white noise residuals and with Dickey-Fuller and Bayesian procedures for detection of unit roots. Simulated and economic data are used to demonstrate the capabilities of the modified approach.     

The Message in Daily Exchange Rates: A Conditional-Variance Tale

Formal testing procedures confirm the presence of a unit root in the autoregressive polynomial of the univariate time series representation of daily exchange-rate data. The first differences of the logarithms of daily spot rates are approximately uncorrelated through time, and a generalized autoregressive conditional heteroscedasticity model with daily dummy variables and conditionally t-distributed errors is found to provide a good representation to the leptokurtosis and time-dependent conditional heteroscedasticity. The parameter estimates and characteristics of the models are found to be very similar for six different currencies. These apparent stylized facts carry over to weekly, fortnightly, and monthly data in which the degree of leptokurtosis and time-dependent heteroscedasticity is reduced as the length of the sampling interval increases.     

Powerful Trend Function Tests That Are Robust to Strong Serial Correlation,With an Application to the Prebisch–Singer Hypothesis

We propose tests for hypotheses on the parameters of the deterministic trend function of a univariate time series. The tests do not require knowledge of the form of serial correlation in the data, and they are robust to strong serial correlation. The data can contain a unit root and still have the correct size asymptotically. The tests that we analyze are standard heteroscedasticity autocorrelation robust tests based on nonparametric kernel variance estimators. We analyze these tests using the fixed-b asymptotic framework recently proposed by Kiefer and Vogelsang. This analysis allows us to analyze the power properties of the tests with regard to bandwidth and kernel choices. Our analysis shows that among popular kernels, specific kernel and bandwidth choices deliver tests with maximal power within a specific class of tests. Based on the theoretical results, we propose a data-dependent bandwidth rule that maximizes integrated power. Our recommended test is shown to have power that dominates a related test proposed by Vogelsang. We apply the recommended test to the logarithm of a net barter terms of trade series and we find that this series has a statistically significant negative slope. This finding is consistent with the well-known Prebisch–Singer hypothesis.     

ASYMPTOTIC DISTRIBUTIONS OF SEASONAL UNIT ROOT TESTS: A UNIFYING APPROACH

ABSTRACT

This paper adopts a unified approach to the derivation of the asymptotic distributions of various seasonal unit root tests. The procedures considered are those of Dickey et al. [DHF], Kunst, Hylleberg et al. [HEGY], Osborn et al. [OCSB], Ghysels et al. [GHL] and Franses. This unified approach shows that the asymptotic distributions of all these test statistics are functions of the same vector of Brownian motions. The Kunst test and the overall HEGY F-test are, indeed, equivalent both asymptotically and in finite samples, while the Franses and GHL tests are shown to have equivalent parameterizations. The OCSB and DHF test regressions are viewed as restricted forms of the Kunst-HEGY regressions, and these restrictions may have non-trivial asymptotic implications.     

A Bayesian View of Nominal Money and Real Output Through a New Classical Macroeconomic Window

This study investigates the empirical evidence on the effects of unanticipated changes in nominal money on real output in 47 countries when viewed through a window (i.e., likelihood function) that assumes the neutrality of anticipated changes. Using a Bayesian predictivist approach, it provides a pedagogical Bayesian analysis of generated regressor models in the face of specification uncertainty involving, among other things, multiple unit roots and trend stationary alternatives.     

The accuracy of normal approximation in a heterogeneous panel data unit root test

Tests for unit roots in panel data have become very popular. Two attractive features of panel data unit root tests are the increased power compared to time-series tests, and the often well-behaved limiting distributions of the tests. In this paper we apply Monte Carlo simulations to investigate how well the normal approximation works for a heterogeneous panel data unit root test when there are only a few cross sections in the sample. We find that the normal approximation, which should be valid for large numbers of cross-sectional units, works well, at conventional significance levels, even when the number of cross sections is as small as two. This finding is valuable for the applied researcher since critical values will be easy to obtain and p-values will be readily available.     

翻译单位研究综述与反思

翻译单位是翻译学理论的基本概念之一。本文从翻译单位的定义、性质和研究方法三个方面总结了国内外有关翻译单位的研究现状。分析认为,各种翻译单位的定义在内涵或外延上的差异,以及研究方法上存在的不足,是导致人们对翻译单位性质认识混乱的内因和外因。厘清翻译单位概念,并且将定量研究和定性研究结合,对翻译单位研究的发展至关重要。     

圆面探测器对圆面源(非同轴)的几何因子的加权蒙特卡罗计算

用椭圆观察因子加权Monte callo方法计算了圆面探测器对均匀平行圆面源(两圆面中心轴线间距离为C)的几何因子,并且与圆面探测器对均匀同轴圆面源的几何因子进行了比较,探讨了两圆面中心轴线间距离C对几何因子的影响;为了检验本文方法及所编程序的好坏,选取c=0时的数据与多边形近似法进行了比较.并根据讨论的结果,提出了对实验操作的要求.     

A small sample confidence interval for autoregressive parameters

This paper is concerned with interval estimation of an autoregressive parameter when the parameter space allows for magnitudes outside the unit interval. In this case, intervals based on the least-squares estimator tend to require a high level of numerical computation and can be unreliable for small sample sizes. Intervals based on the asymptotic distribution of instrumental variable estimators provide an alternative. If the instrument is taken to be the sign function, the interval is centered at the Cauchy estimator and a large sample interval can be created by estimating the standard error of this estimator. The interval proposed in this paper avoids estimating this standard error and results in a small sample improvement in coverage probability. In fact, small sample coverage is exact when the innovations come from a normal distribution.