A Nonlinear Regression Model with Integrated Time Series
نویسندگان
چکیده
Introduction In this paper we consider a model related in form to that treated in Park and Phillips (2001). To introduce the model, let f (y; ) be a given function of y and (such that the conditions stated in Section 2 are satis ed; in particular it is assumed that R @f(y; ) @ 2 dy <1 for all ). Let "j ; j , 1 < j <1, be iid such that E ["1] = 0 = E [ 1], 0 < E "1 < 1 and 0 < E 1 <1. Consider the model Xt = f (Yt 1; ) + t, t = 1; :::; n; Yt = Yt 1 + t; t = 1; :::; n; for the observations (Xt; Yt), t = 1; :::; n, where Yt, t = 1; :::; n, form observable exogenous variables. The unknown parameter 2 , where is a subset of the r-dimensional Euclidean space. Further, t is a linear process t = 1 X
منابع مشابه
Which Methodology is Better for Combining Linear and Nonlinear Models for Time Series Forecasting?
Both theoretical and empirical findings have suggested that combining different models can be an effective way to improve the predictive performance of each individual model. It is especially occurred when the models in the ensemble are quite different. Hybrid techniques that decompose a time series into its linear and nonlinear components are one of the most important kinds of the hybrid model...
متن کاملAN EXTENDED FUZZY ARTIFICIAL NEURAL NETWORKS MODEL FOR TIME SERIES FORECASTING
Improving time series forecastingaccuracy is an important yet often difficult task.Both theoretical and empirical findings haveindicated that integration of several models is an effectiveway to improve predictive performance, especiallywhen the models in combination are quite different. In this paper,a model of the hybrid artificial neural networks andfuzzy model is proposed for time series for...
متن کاملFunctional-Coefficient Autoregressive Model and its Application for Prediction of the Iranian Heavy Crude Oil Price
Time series and their methods of analysis are important subjects in statistics. Most of time series have a linear behavior and can be modelled by linear ARIMA models. However, some of realized time series have a nonlinear behavior and for modelling them one needs nonlinear models. For this, many good parametric nonlinear models such as bilinear model, exponential autoregressive model, threshold...
متن کاملA Three-phase Hybrid Times Series Modeling Framework for Improved Hospital Inventory Demand Forecast
Background and Objectives: Efficient cost management in hospitals’ pharmaceutical inventories have the potential to remarkably contribute to optimization of overall hospital expenditures. To this end, reliable forecasting models for accurate prediction of future pharmaceutical demands are instrumental. While the linear methods are frequently used for forecasting purposes chiefly due to their si...
متن کاملA New Nonlinear Specification of Structural Breaks for Money Demand in Iran
In a structural time series regression model, binary variables have been used to quantify qualitative or categorical quantitative events such as politic and economic structural breaks, regions, age groups and etc. The use of the binary dummy variables is not reasonable because the effect of an event decreases (increases) gradually over time not at once. The simple and basic idea in this paper i...
متن کاملبرآورد منحنی فیلیپس با استفاده از مدلهای رگرسیونی انتقال ملایم
The Phillips curve usually has been estimated in a linear framework which implies a stable constant relationship between inflation and unemployment. Some of the studies claim that the slope of the Phillips curve is a function of macroeconomic conditions and also the relationship is asymmetric. This article deals with a smooth transition regression model for relationship between inflation and un...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2007