COINTEGRATING REGRESSIONS WITH TIME VARYING COEFFICIENTS

1999 ◽  
Vol 15 (5) ◽  
pp. 664-703 ◽  
Author(s):  
Joon Y. Park ◽  
Sang B. Hahn
Keyword(s):  
Efficient Method ◽  
Demand Function ◽  
Model Specification ◽  
Time Varying ◽  
Smooth Functions ◽  
Varying Coefficients ◽  
Automobile Demand ◽  
Time Varying Coefficients ◽  
The U.S ◽  
Over Time

This paper considers cointegrating regressions with time varying coefficients. The coefficients are modeled as smooth functions evolving over time. It is shown that they can be estimated nonparametrically, using suitably modified series estimators. Presented is the efficient method of estimation, which relies on simple prefiltering of the data and preestimation of the model. The test for the adequacy of model specification is also developed. Our model and statistical methods are applied to analyze the U.S. automobile demand function.

RMB Bloc in East Asia: Too Early to Talk About It?

2018 ◽  
Vol 17 (3) ◽  
pp. 31-48 ◽  
Author(s):  
Chang Sik Kim ◽  
Sunghyun Kim ◽  
Yunjong Wang
Keyword(s):  
East Asia ◽  
East Asian ◽  
Positive Influence ◽  
The Philippines ◽  
Varying Coefficients ◽  
Time Varying Coefficients ◽  
Trade Share ◽  
The U.S ◽  
Over Time

This paper investigates whether the Chinese RMB has become more influential (than the U.S. dollar) in determining the exchange rates of East Asian currencies in recent years. We use a regression method with time-varying coefficients to trace changes in coefficients over time. The empirical results show that the RMB's effects on East Asian currencies were near zero before 2008, but since then have significantly increased and taken over the role of the U.S. dollar in some countries (Indonesia, Malaysia, and the Philippines). In Singapore and Thailand, the RMB is still a non-factor. South Korea shows an interesting pattern, in that the role of the RMB swings over time, with an increase in the past couple of years. We conjecture that the trade share with China has a positive influence on the role of the RMB. In conclusion, given the small absolute value of the regression coefficient on RMB, although the RMB has attained a more significant status in the currency market, it is too early to talk about the creation of an RMB bloc in East Asia.

Quantifying Change Over Time: Interpreting Time-varying Effects In Duration Analyses

2018 ◽  
Vol 26 (1) ◽  
pp. 90-111 ◽  
Author(s):  
Constantin Ruhe
Keyword(s):  
Interstate Conflict ◽  
Event History ◽  
Time Varying ◽  
Correct Interpretation ◽  
Nonproportional Hazards ◽  
Varying Coefficients ◽  
Hazard Ratios ◽  
Time Varying Coefficients ◽  
Over Time ◽  
Survivor Functions

Duration analyses in political science often model nonproportional hazards through interactions with analysis time. To facilitate their interpretation, methodologists have proposed methods to visualize time-varying coefficients or hazard ratios. While these techniques are a useful, initial postestimation step, I argue that they are insufficient to identify the overall impact of a time-varying effect and may lead to faulty inference when a coefficient changes its sign. I show how even significant changes of a coefficient’s sign do not imply that the overall effect is reversed over time. In order to enable a correct interpretation of time-varying effects in this context, researchers should visualize their results with survivor functions. I outline how survivor functions are calculated for models with time-varying effects and demonstrate the need for such a nuanced interpretation using the prominent finding of a time-varying effect of mediation on interstate conflict. The reanalysis of the data using the proposed visualization methods indicates that the conclusions of earlier mediation research are misleading. The example highlights how survivor functions are an essential tool to clarify the ambiguity inherent in time-varying coefficients in event history models.

scholarly journals An efficient method for vibration equations with time varying coefficients and nonlinearities

2021 ◽  
pp. 146134842110251
Author(s):  
Jinsong Yang ◽  
Jingsong Xie ◽  
Gaige Chen ◽  
Jinglong Chen
Keyword(s):  
Nonlinear Vibration ◽  
Efficient Method ◽  
Stiffness Matrix ◽  
Time Varying ◽  
Rotor Systems ◽  
Varying Coefficients ◽  
Multistage System ◽  
Shaft System ◽  
Time Varying Coefficients ◽  
Time Required

An efficient method for solving vibration equations is the basis of the vibration analysis of a cracked multistage blade–disk–shaft system. However, dynamic equations are usually time varying and nonlinear, and the time required for solving is greatly increased accompanied by an increase in the model order caused by the multistage system and the nonlinearity caused by cracks. In this article, an efficient method for solving the time varying and nonlinear vibration equation is investigated. In the proposed method, the time varying terms are transformed into constant terms, while the local nonlinear matrix of the cracked blade is separated from the assembly stiffness matrix under the constraint of proper orthogonal decomposition (POD) transformation rules. Furthermore, the POD transformations of the constant terms and the linear assembled stiffness matrix can be implemented in the pretreatment steps to achieve a more efficient POD reduction operation. This research provides a method for efficiently performing the comprehensive and rapid analysis of nonlinear vibration characteristics of rotor systems.

scholarly journals The VIT Transform Approach to Discrete-Time Signals and Linear Time-Varying Systems

Eng ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 99-125
Author(s):  
Edward W. Kamen
Keyword(s):  
Discrete Time ◽  
Initial Conditions ◽  
Linear Time ◽  
Order Term ◽  
Initial Time ◽  
Time Varying ◽  
Varying Coefficients ◽  
Time Signals ◽  
Time Varying Systems ◽  
Time Varying Coefficients

A transform approach based on a variable initial time (VIT) formulation is developed for discrete-time signals and linear time-varying discrete-time systems or digital filters. The VIT transform is a formal power series in z−1, which converts functions given by linear time-varying difference equations into left polynomial fractions with variable coefficients, and with initial conditions incorporated into the framework. It is shown that the transform satisfies a number of properties that are analogous to those of the ordinary z-transform, and that it is possible to do scaling of z−i by time functions, which results in left-fraction forms for the transform of a large class of functions including sinusoids with general time-varying amplitudes and frequencies. Using the extended right Euclidean algorithm in a skew polynomial ring with time-varying coefficients, it is shown that a sum of left polynomial fractions can be written as a single fraction, which results in linear time-varying recursions for the inverse transform of the combined fraction. The extraction of a first-order term from a given polynomial fraction is carried out in terms of the evaluation of zi at time functions. In the application to linear time-varying systems, it is proved that the VIT transform of the system output is equal to the product of the VIT transform of the input and the VIT transform of the unit-pulse response function. For systems given by a time-varying moving average or an autoregressive model, the transform framework is used to determine the steady-state output response resulting from various signal inputs such as the step and cosine functions.

scholarly journals Estimating latent group structure in time-varying coefficient panel data models

2019 ◽  
Author(s):  
Jia Chen
Keyword(s):  
Panel Data ◽  
Data Models ◽  
Panel Data Models ◽  
Time Varying ◽  
Clustering Method ◽  
Varying Coefficient ◽  
Varying Coefficients ◽  
Time Varying Coefficients ◽  
Latent Group ◽  
Group Structures

Summary This paper studies the estimation of latent group structures in heterogeneous time-varying coefficient panel data models. While allowing the coefficient functions to vary over cross-sections provides a good way to model cross-sectional heterogeneity, it reduces the degree of freedom and leads to poor estimation accuracy when the time-series length is short. On the other hand, in a lot of empirical studies, it is not uncommon to find that heterogeneous coefficients exhibit group structures where coefficients belonging to the same group are similar or identical. This paper aims to provide an easy and straightforward approach for estimating the underlying latent groups. This approach is based on the hierarchical agglomerative clustering (HAC) of kernel estimates of the heterogeneous time-varying coefficients when the number of groups is known. We establish the consistency of this clustering method and also propose a generalised information criterion for estimating the number of groups when it is unknown. Simulation studies are carried out to examine the finite-sample properties of the proposed clustering method as well as the post-clustering estimation of the group-specific time-varying coefficients. The simulation results show that our methods give comparable performance to the penalised-sieve-estimation-based classifier-LASSO approach by Su et al. (2018), but are computationally easier. An application to a panel study of economic growth is also provided.

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