2,475 research outputs found
Balancing lists: a proof pearl
Starting with an algorithm to turn lists into full trees which uses
non-obvious invariants and partial functions, we progressively encode the
invariants in the types of the data, removing most of the burden of a
correctness proof.
The invariants are encoded using non-uniform inductive types which parallel
numerical representations in a style advertised by Okasaki, and a small amount
of dependent types.Comment: To appear in proceedings of Interactive Theorem Proving (2014
A comparison of Forecasting Procedures for Macroeconomic Series: The Contribution of Structural Break Models
This paper compares the forecasting performance of different models which have been proposed for forecasting in the presence of structural breaks. These models differ in their treatment of the break process, the parameters defining the model which applies in each regime and the out-of-sample probability of a break occurring. In an extensive empirical evaluation involving many important macroeconomic time series, we demonstrate the presence of structural breaks and their importance for forecasting in the vast majority of cases. However, we find no single forecasting model consistently works best in the presence of structural breaks. In many cases, the formal modeling of the break process is important in achieving good forecast performance. However, there are also many cases where simple, rolling OLS forecasts perform well.Forecasting, change-points, Markov switching, Bayesian inference.
The contribution of structural break models to forecasting macroeconomic series
This paper compares the forecasting performance of different models which have been proposed for forecasting in the presence of structural breaks. These models differ in their treatment of the break process, the model which applies in each regime and the out-of-sample probability of a break occurring. In an extensive empirical evaluation involving 60 macroeconomic quarterly and monthly time series, we demonstrate the presence of structural breaks and their importance for forecasting in the vast majority of cases. We find no single forecasting model consistently works best in the presence of structural breaks. In many cases, the formal modeling of the break process is important in achieving good forecast performance.
However, there are also many cases where simple, rolling window based forecasts perform well
The Contribution of Structural Break Models to Forecasting Macroeconomic Series
This paper compares the forecasting performance of different models which have been proposed for forecasting in the presence of structural breaks. These models differ in their treatment of the break process, the model which applies in each regime and the out-of-sample probability of a break occurring. In an extensive empirical evaluation involving many important macroeconomic time series, we demonstrate the presence of structural breaks and their importance for forecasting in the vast majority of cases. We find no single forecasting model consistently works best in the presence of structural breaks. In many cases, the formal modeling of the break process is important in achieving good forecast performance. However, there are also many cases where simple, rolling window based forecasts perform well.Forecasting, change-points, Markov switching, Bayesian inference
A comparison of forecasting procedures for macroeconomic series: the contribution of structural break models
This paper compares the forecasting performance of different models which have been proposed for forecasting in the presence of structural breaks. These models differ in their treatment of the break process, the parameters defining the model which applies in each regime and the out-of-sample probability of a break occurring. In an extensive empirical evaluation involving many important macroeconomic time series, we demonstrate the presence of structural breaks and their importance for forecasting in the vast majority of cases. However, we find no single forecasting model consistently works best in the presence of structural breaks. In many cases, the formal modeling of the break process is important in achieving good forecast performance. However, there are also many cases where simple, rolling OLS forecasts perform well.forecasting, change-points, Markov switching, Bayesian inference
A Comparison of Forecasting Procedures for Macroeconomic Series: the Contribution of Structural Break Models
This paper compares the forecasting performance of different models which have been proposed for forecasting in the presence of structural breaks. These models differ in their treatment of the break process, the parameters defining the model which applies in each regime and the out-of-sample probability of a break occurring. In an extensive empirical evaluation involving many important macroeconomic time series, we demonstrate the presence of structural breaks and their importance for forecasting in the vast majority of cases. However, we find no single forecasting model consistently works best in the presence of structural breaks. In many cases, the formal modeling of the break process is important in achieving good forecast performance. However, there are also many cases where simple, rolling OLS forecasts perform well.Forecasting, change-points, Markov switching, Bayesian inference
Standard random walks and trapping on the Koch network with scale-free behavior and small-world effect
A vast variety of real-life networks display the ubiquitous presence of
scale-free phenomenon and small-world effect, both of which play a significant
role in the dynamical processes running on networks. Although various dynamical
processes have been investigated in scale-free small-world networks, analytical
research about random walks on such networks is much less. In this paper, we
will study analytically the scaling of the mean first-passage time (MFPT) for
random walks on scale-free small-world networks. To this end, we first map the
classical Koch fractal to a network, called Koch network. According to this
proposed mapping, we present an iterative algorithm for generating the Koch
network, based on which we derive closed-form expressions for the relevant
topological features, such as degree distribution, clustering coefficient,
average path length, and degree correlations. The obtained solutions show that
the Koch network exhibits scale-free behavior and small-world effect. Then, we
investigate the standard random walks and trapping issue on the Koch network.
Through the recurrence relations derived from the structure of the Koch
network, we obtain the exact scaling for the MFPT. We show that in the infinite
network order limit, the MFPT grows linearly with the number of all nodes in
the network. The obtained analytical results are corroborated by direct
extensive numerical calculations. In addition, we also determine the scaling
efficiency exponents characterizing random walks on the Koch network.Comment: 12 pages, 8 figures. Definitive version published in Physical Review
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