[PDF] Top 20 Forecasting U S Macroeconomic and Financial Time Series with Noncausal and Causal AR Models: A Comparison
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Forecasting U S Macroeconomic and Financial Time Series with Noncausal and Causal AR Models: A Comparison
... As an example of the results in Table 2, consider the case where the BIC is used in model selection and the forecast horizon is three months h = 3: the mean relative MSFE is 0.9972 i.e.,[r] ... See full document
25
Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models
... The dependence between log-prices and temperatures is not as strong as the load-price relationship in California, nevertheless they are moderately anticorrelated, i.e. low tempera- tures in Scandinavia imply high ... See full document
23
Are Macroeconomic Variables Useful for Forecasting the Distribution of U S Inflation?
... for forecasting inflation has been the Phillips curve (PC) model that, in its expectation-augmented version, assumes a trade-off between unexpected inflation and unemployment, or more generally, indicators of real ... See full document
34
Modelling of crude oil prices using hybrid arima-garch model
... and forecasting volatility of a financial time series has become the area of ...and financial applications such as portfolio optimization, risk management and asset ...applied ... See full document
29
Causal Method and Time Series Forecasting model based on Artificial Neural Network
... times series models are preferred for they have been used in many applications such as: Economic Forecasting, Sales Forecasting, Budgetary Analysis, Stock Market Analysis, Process and Quality ... See full document
6
Do Seasonal Adjustments Induce Noncausal Dynamics in Inflation Rates?
... of financial asset returns are often negatively skewed and more leptokurtic than a normal distribu- ...modelling macroeconomic time ...a time series. In contrast to conventional ARMA ... See full document
34
Noncausal autoregressions for economic time series
... autoregressive models are commonly employed in characterizing the dy- namics of economic time ...include forecasting and the measurement of persistence (Andrews and Chen (1994)), but also in theoret- ... See full document
40
Forecasting bubbles with mixed causal noncausal autoregressive models
... for forecasting MAR(r,1) models, with unconstrained r number of lags, a unique lead and a positive lead ...in financial and economic time ...mixed causal- noncausal ... See full document
30
Dynamic staged trees for discrete multivariate time series : forecasting, model selection and causal analysis
... graphical models developed for event history analysis are local independence graphs (Didelez 2008) and graphical duration graphs (Gottard ...the time of events, usually allowed to occur within a continuous ... See full document
29
Bayesian Model Selection and Forecasting in Noncausal Autoregressive Models
... + s at 2 and calculate the marginal likelihoods and the maximum values of the approximate log likelihood function for the causal, purely noncausal and mixed ...a series of 150 observations ... See full document
32
Detecting Co Movements in Noncausal Time Series
... Mixed causal-noncausal models have recently become increasingly popular because of their appealing ...direct time into as process in reverse time with roots outside the unit ...mixed ... See full document
25
Improved models in fuzzy time series for forecasting
... fuzzy time series is highlighted when training dataset is ...fuzzy time series, this method work on trend data ...like financial time series that contain differently ... See full document
32
Search for Additive Nonlinear Time Series Causal Models
... non-linear causal inference algorithm presented in Section ...nonlinear time series causal inference. For comparison, we also apply a causal inference procedure designed for ... See full document
25
Does the Box Cox transformation help in forecasting macroeconomic time series?
... the series a transformation is ...the series forecast improvements are possible, this is too large a set of series to be ignored if one is seriously interested in improving forecast ...the ... See full document
23
A Neuro-wavelet Method for the Forecasting of Financial Time Series
... in time such as the ones found in nonstationary ...statistical models to data with significant precision. Examples of financial applications of NN can be found in [2], [14], and ... See full document
6
Performance evaluation of series and parallel strategies for financial time series forecasting
... in financial time series forecasting is ...on time series forecasting and modeling widely use a combination of different models and metaheuristic optimization ... See full document
24
Forecasting bubbles with mixed causal noncausal autoregressive models
... the series follows an explosive ...the series, and there is a point from which the SIR algorithm does not recover the whole density ...the series departs from central values and comprises both the ... See full document
33
A Comparison of Forecasting Procedures for Macroeconomic Series : The Contribution of Structural Break Models
... of models which address the issues which arise when forecasting subject to structural ...Such forecasting models can differ in three important ... See full document
47
Optimal Forecasting of Noncausal Autoregressive Time Series
... reestimated models at each step with the estimation sample always starting from the …rst quarter of ...forecast series are compared. First, forecasts from causal and purely noncausal ... See full document
31
Mixed Causal Noncausal AR Processes and the Modelling of Explosive Bubbles
... of financial assets such as stocks, it is common to observe phases of locally explosive behaviours, together with heavy-tailed marginal distributions and volatility ...linear models (namely the class of ... See full document
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