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更高功效的面板单位根检验及其实际汇率均值回复应用AI 解读

Abstract Unit root tests, seeking mean or trend reversion, are frequently applied to panel data. We show that more powerful variants of commonly applied tests are readily available. Moreover, power gains persist when the modifications are applied to bootstrap procedures that may be employed when cross‐correlation of a rather general sort among individual panel members is suspected. Copyright © 2004 John Wiley & Sons, Ltd.

以差分平稳为原假设的持久性变化检验AI 解读

Economists have recognized the possibility that a time series may change structure from trend-stationarity to difference-stationarity, or vice versa. Taking difference-stationarity as the null hypothesis, we develop tests for this possibility, where neither the location nor direction of any possible change under the alternative hypothesis need be specified. Copyright Royal Economic Society, 2003

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英国宏观经济预测面板分析AI 解读

This paper looks at unobserved components models and examines the implied weighting patterns for signal extraction. There are four main themes. The first concerns the implications of correlated disturbances driving the components, especially those cases in which the correlation is perfect. The second is about the way in which ARIMA-based methods for trend extraction relate to those based on unobserved components. The third explores the impact of heteroscedasticity and…

备择假设下存在断点时 Dickey–Fuller t 检验的行为AI 解读

This paper analyzes the limiting behavior of Dickey–Fuller t -tests when the true generating model is stationary around a broken linear trend. The cases of a break in level and a break in slope are considered separately and found to generate qualitatively different outcomes. In the asymptotic analysis, appropriate normalizations are applied to the break sizes. This leads to theoretical results that generate interesting predictions for sample sizes and break amounts of…

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多重预测涵盖检验AI 解读

In the evaluation of economic forecasts, it is frequently the case that comparisons are made between a number of competing predictors. A natural question to ask in such contexts is whether one forecast encompasses its competitors, in the sense that they contain no useful information not present in the superior forecast. We develop tests for this notion of multiple forecast encompassing which are robust to properties expected in the forecast errors, and apply the tests to…

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预测涵盖检验AI 解读

The authors consider the situation in which two forecasts of the same variable are available. The possibility exists of forming a combined forecast as a weighted average of the individual ones and estimation the weights that should be optimally attached to each forecast. If the entire weight should optimally be associated with one forecast, that forecast is said to encompass the other. A natural test for forecast encompassing is based on least squares regression. The authors…

经济冲击持续性的不确定性AI 解读

The behavior of time series of economic output has been characterized in terms of the persistence of shocks. For a series generated by an autoregressive integrated moving average model, persistence is a function of the model parameters. This paper demonstrates that a careful analysis, based on the likelihood, can lead to considerably more uncertainty about persistence than is implied by the confidence intervals that are usually calculated. The estimation of persistence is…

伪结构突变AI 解读

A quasi-maximum likelihood estimator of the break date is analyzed. Consistency of the estimator is demonstrated under very general conditions, provided that the data-generating process is not integrated. However, the asymptotic distribution of the estimator is quite different for time series that are integrated of order one. In that case, when there is no break, the analyst can be spuriously led to the estimation of a break near the middle of the time series.

分数噪声样本自相关的偏差AI 解读

The autocorrelation function of fractional noise, when the difference parameter is positive, is quite distinctive. However, for time series of moderate length, this pattern is unlikely to be seen in the sample autocorrelations, which are severely biased when the process mean is unknown.

分数噪声样本自相关的偏差AI 解读

The autocorrelation function of fractional noise, when the difference parameter is positive, is quite distinctive. However, for time series of moderate length, this pattern is unlikely to be seen in the sample autocorrelations, which are severely biased when the process mean is unknown.

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季节时间序列的趋势与增长率估计AI 解读

We consider the problem of trend estimation from an ARIMA-model-based perspective. Given that the time series of interest is well represented by a multiplicative seasonal ARIMA model (Box and Jenkins 1970), a method is presented for estimating the trend of this series as a component of the model's forecast function. This method is applied to the “airline model,” a commonly occurring model form for economic and social time series. For those numerous series that are rendered…

季节性时间序列的趋势与增长率估计AI 解读

We consider the problem of trend estimation from an ARIMA-model-based perspective. Given that the time series of interest is well represented by a multiplicative seasonal ARIMA model (Box and Jenkins 1970), a method is presented for estimating the trend of this series as a component of the model's forecast function. This method is applied to the “airline model,” a commonly occurring model form for economic and social time series. For those numerous series that are rendered…

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预测均方误差估计中的偏差AI 解读

We examine the forecasting problem for a finite series of observations from either a nonseasonal or a seasonal autoregressive-moving average process. Four sources of bias in the usual estimator of forecast mean squared error are identified and analyzed for particular models using maximum likelihood and least squares parameter estimates. The usual estimator is found to be biased downwards, especially near the boundary of a stationary or invertible region, and the bias is…

预测均方误差估计中的偏差AI 解读

We examine the forecasting problem for a finite series of observations from either a nonseasonal or a seasonal autoregressive-moving average process. Four sources of bias in the usual estimator of forecast mean squared error are identified and analyzed for particular models using maximum likelihood and least squares parameter estimates. The usual estimator is found to be biased downwards, especially near the boundary of a stationary or invertible region, and the bias is…

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It is shown that two tests for the adequacy of a fitted ARMA ( p, q, ) model, based respectively on comparison through the Lagrange multiplier test with an ARMA ( p + k, q ) model and on analysis of the first k residual autocorrelations from the estimated model, are equivalent.