Get Forecasting economic time series using locally stationary PDF

By Tina Loll

ISBN-10: 3631621876

ISBN-13: 9783631621875

ISBN-10: 3653017068

ISBN-13: 9783653017069

Stationarity has constantly performed an enormous half in forecasting idea. in spite of the fact that, a few fiscal time sequence express time-varying autocovariances. The query arises no matter if forecasts should be superior utilizing types that trap this type of time-varying second-order constitution. One risk is given by way of autoregressive versions with time-varying parameters. the writer makes a speciality of the improvement of a forecasting method for those methods and compares this method of classical forecasting tools through Monte Carlo simulations. An evaluate of the proposed approach is given through its program to futures costs and the Dow Jones index. The method seems to be more advantageous to the classical tools if the pattern sizes are huge and the forecasting horizons don't variety too a ways into the long run

Show description

Read Online or Download Forecasting economic time series using locally stationary processes : a new approach with applications PDF

Best nonfiction_12 books

Download e-book for kindle: Mathematical Results in Quantum Mechanics: Proceedings of by Radu Purice, Ingrid Beltita, Gheorghe Nenciu

The tenth Quantum arithmetic overseas convention (Qmath10) gave a chance to compile experts drawn to that a part of mathematical physics that is in shut reference to numerous points of quantum conception. It used to be additionally intended to introduce younger scientists and new trends within the box.

Read e-book online Anhedonia: A Comprehensive Handbook Volume I: Conceptual PDF

This is often the 1st accomplished two-volume assortment on anhedonia, a illness that performed a massive function in psychopathology theories at first of the 20th century. Anhedonia is a situation during which the skill of enjoyment is partly or thoroughly misplaced, and it refers to either a character trait, and a “state symptom” in numerous neuropsychiatric and actual problems.

Read e-book online Evaluation of Statistical Matching and Selected SAE Methods: PDF

Verena Puchner evaluates and compares statistical matching and chosen SAE equipment. considering the fact that poverty estimation at neighborhood point in line with EU-SILC samples isn't of sufficient accuracy, the standard of the estimations could be more advantageous via also incorporating micro census information. the purpose is to discover the easiest strategy for the estimation of poverty when it comes to small bias and small variance as a result of a simulated synthetic "close-to-reality" inhabitants.

Additional info for Forecasting economic time series using locally stationary processes : a new approach with applications

Example text

Dmp are chosen during the semiparametric estimation procedure. ˆ T −h (k), k = 1, . . 2 Approaches to forecast time series using TVAR processes 63 T −h+1 ˆ T −h (1) = −α XT −h − · · · X ˆ1 T T −h+1 XT −h−(p−1) −α ˆp T T −h+2 ˆ ˆ T −h (2) = −α XT −h (1) − · · · X ˆ1 T T −h+2 XT −h−(p−2) −α ˆp T .. ˆ T −h (h − 1) − · · · − α ˆ T −h−1 (h − p) ˆ T −h (h) = −α ˆ 1 (1) X ˆ p (1) X X with forecasting horizon h. 1. Approach 2 In the second approach we make use of constant coefficients for the different forecasting steps.

Et , et−1 , . . correspond to the prediction errors ˆ t−1,1 , Xt−1 − X ˆ t−2,1 , . . of the optimal 1-step-predictions. Xt − X 4. et+h , et+h−1 , . . , et+1 are replaced by zero, which is their expectation. For the proof see for example Schlittgen and Streitberg (2001), p. 215. 2 Approaches to forecast time series using TVAR processes Below we propose three procedures, which are natural generalizations of the above Theorem, for forecasting time series using TVAR(p) processes. In the style of Van Bellegem and von Sachs (2002)3 we make use of the following notation, to forecast the h values of an observed process: The observed variables are denoted by X1,T , .

P t T , t , λ exp(iλj) dλ = X T t t , 1 , . . , cT ,p T T t . ,p t+(j+1)/2 X t−(j−1)/2 , , The model selection procedure then consists of the following two steps: a) On each space Fm compute θˆm = arg min {LT (fθ , JT )} θ∈Fm for m ∈ ND,T . ˆ among θˆm : m ∈ ND,T b) Then choose m such that ˆ = arg min {LT (fθˆm , JT ) + pen(m)}. 2 Sieve estimation 55 We then get the sieve estimator θˆ = θˆm ˆ. A penalty function is necessary to ensure the choice of a parsimonious model in the model selection procedure.

Download PDF sample

Forecasting economic time series using locally stationary processes : a new approach with applications by Tina Loll

by Joseph

Rated 4.91 of 5 – based on 26 votes