نتایج جستجو برای: ardl method jel classification c12
تعداد نتایج: 2044390 فیلتر نتایج به سال:
We propose a new test for superior predictive ability. The new test compares favorable to the reality check for data snooping (RC), because the former is more powerful and less sensitive to poor and irrelevant alternatives. The improvements are achieved by two modifications of the RC. We employ a studentized test statistic that reduces the influence of erratic forecasts and we invoke a sample d...
We prove that a subtle but substantial bias exists in a common measure of the conditional dependence of present outcomes on streaks of past outcomes in sequential data. The magnitude of this novel form of selection bias generally decreases as the sequence gets longer, but increases in streak length, and remains substantial for a range of sequence lengths often used in empirical work. We observe...
We examine the joint predictability of return and cash flow within a present value framework, by imposing the implications from a long-run risk model that allow for both time-varying volatility and volatility uncertainty. We provide new evidence that the expected return variation and the variance risk premium positively forecast both short-horizon returns and dividend growth rates. We also conf...
In this paper we suggest a number of statistical tests based on neural network models, that are designed to be powerful against structural breaks in otherwise stationary time series processes while allowing for a variety of nonlinear specifications for the dynamic model underlying them. It is clear that in the presence of nonlinearity standard tests of structural breaks for linear models may no...
This paper considers a simple test for the correct specification of linear spatial autoregressive models, assuming that the choice of the weight matrix is true. We derive the limiting distributions of the test under the null hypothesis of correct specification and a sequence of local alternatives. We show that the test is free of nuisance parameters asymptotically under the null and prove th...
In this paper, we propose a minimum LM unit root test that endogenously determines a structural break in intercept and trend. Critical values are provided, and size and power properties are compared to the endogenous one-break unit root test of Zivot and Andrews (1992). Nunes, Newbold, and Kuan (1997) and Lee and Strazicich (2001) previously demonstrated that the Zivot and Andrews test exhibits...
I propose two simple variable addition test statistics for three tests of the specification of high-frequency predictors in a model to forecast a series observed at a lower frequency. The first is similar to existing test statistics and I show that it is robust to biased forecasts, integrated and cointegrated predictors, and deterministic trends, while it is feasible and consistent even if esti...
This paper provides a review of the literature on unit roots and cointegration in panels where the time dimension (T ), and the cross section dimension (N) are relatively large. It distinguishes between the first generation tests developed on the assumption of the cross section independence, and the second generation tests that allow, in a variety of forms and degrees, the dependence that might...
Analyzing Cost Efficient Production Behavior Under Economies of Scope: A Nonparametric Methodology In designing a production model for firms that generate multiple outputs, we take as a starting point that such multi-output production refers to economies of scope, which in turn originate from joint input use and input externalities. We provide a nonparametric characterization of cost efficient ...
Using a standard factor decomposition of a panel of forecasts, we have shown that the forecast uncertainty from the standpoint of a policy maker can be expressed as the disagreement among forecasters plus the perceived variability of common aggregate shocks. Thus, the uncertainty of the average forecast is not the variance of the average forecast but rather the average of the variances of the i...
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