Wen-Den Chen

An Approximate Likelihood Function for Panel Data with a Mixed Arma(P,Q) Remainder Disturbance Model

An approximate likelihood function for panel data with an autoregressive moving-average (ARMA)(p, q) model remainder disturbance is presented and Whittles approximate maximum likelihood estimator (MLE) is used to yield an asymptotic estimator. Although an asymptotic approach, the power test is quite successful for estimating and testing. In this approach, we do not need to calculate the transformation matrix in exact form. Through the Riemann sum approach, we can construct a simple approximate concentrated likelihood function. In addition, the model is also extended to the restricted maximum likelihood (REML) function, in which the package of Gilmour, Thompson and Cullis [Biometrics (1995) Vol. 51, pp. 1440-1450] is applied without difficulty. In the case study, we implement the model on the characteristic line for the investment analysis of Taiwanese computer motherboard makers. Copyright 2006 The Author Journal compilation 2006 Blackwell Publishing Ltd.

Detecting multiple factors in panel data: an application on the growth of local regions in China

ABSTRACT Due to unbalanced growth in China’s local regions, we construct a panel data model with multiple common factors to examine the differences among the growth factors in these areas. This article shows the various impacts from the supply and demand sides on economic growth. Different from the demand side, the supply-side impacts have permanent influences. This article focuses on these deep and profound impacts to explain the reasons behind China’s fast economic growing. By using data on 27 regions from 1958 to 2013, we summarize the main permanent influences along three lines. The first comes from the coastal regions, which have learned modern technology and systems from foreign companies, such as in Guangdong, Zhejiang, Fujian and Liaoning. The second comes from big cities, such as Beijing and Shanghai, in which a huge migration has given the companies opportunities to recruit excellent workers, making the resource allocation specialized and more efficient. The third is from the government’s major public works, which have improved areas’ infrastructure and assisted long-run economic growth, such as for Sichuan, Guangxi and Yunnan.

Testing for Spurious Regression in a Panel Data Model with the Individual Number and Time Length Growing

Abstract This article shows a test for the spurious regression problem in a panel data model with a growing individual number and time series length. In the estimation, tapers are used and the integrated order for the remainder disturbance is extended to a real number; at the same time, the spurious regression problem can be detected without prior knowledge. Through Monte Carlo experiments, we examine the consistent estimators by various sizes of time length and individual number, in which the remainder disturbance is assumed to be either stationary or non-stationary. In addition, the asymptotic normality properties are discussed with a quasi log-likelihood function. From the power tests we can see that the estimators are quite successful and powerful.

Detecting and identifying interventions with the Whittle spectral approach in a long memory panel data model

This article provides a procedure for the detection and identification of outliers in the spectral domain where the Whittle maximum likelihood estimator of the panel data model proposed by Chen [W.D. Chen, Testing for spurious regression in a panel data model with the individual number and time length growing, J. Appl. Stat. 33(88) (2006b), pp. 759–772] is implemented. We extend the approach of Chang and co-workers [I. Chang, G.C. Tiao, and C. Chen, Estimation of time series parameters in the presence of outliers, Technometrics 30 (2) (1988), pp. 193–204] to the spectral domain and through the Whittle approach we can quickly detect and identify the type of outliers. A fixed effects panel data model is used, in which the remainder disturbance is assumed to be a fractional autoregressive integrated moving-average (ARFIMA) process and the likelihood ratio criterion is obtained directly through the modified inverse Fourier transform. This saves much time, especially when the estimated model implements a huge data-set. Through Monte Carlo experiments, the consistency of the estimator is examined by growing the individual number N and time length T, in which the long memory remainder disturbances are contaminated with two types of outliers: additive outlier and innovation outlier. From the power tests, we see that the estimators are quite successful and powerful. In the empirical study, we apply the model on Taiwans computer motherboard industry. Weekly data from 1 January 2000 to 31 October 2006 of nine familiar companies are used. The proposed model has a smaller mean square error and shows more distinctive aggressive properties than the raw data model does.

Applying Kalman Filter on Solving Simultaneous Equations with Overidentifying Rank Restrictions: The Analysis of the Demand and Supply Model of Medium-size Scooter Market in Taiwan

Once the structure form of demand and supply is translated into areduced form, one can solve the reduced form with a state space modelof the Kalman filter method. This paper discusses an innovationrepresentation that links the structure form with the state space model.For the state space model, the recursive Expectation Maximization(EM) algorithm is used to estimate the parameters of a structure form.This research successfully applied the Kalman filter method to theestimation of the coefficients of simultaneous equations withoveridentifying rank restrictions. The empirical monthly data set camefrom the medium-size scooter market in Taiwan during 1987 to 1992period.

Detecting policy effects with wage rigidity and instability – evidence on the Taiwan labour market

ABSTRACT With stagnant wages and growing productivity, a widening gap is becoming prevalent in global labour markets. The relationship between wages and productivity has become indeterminate, especially after the 2008 financial crisis. This article presents the phenomenon for why salary rarely follows up with productivity after an economy recovers. By using the GMM method, this study shows the interaction among wage, productivity and tightness, in which we illustrate the Taiwan labour market as an example to show how hiring system changes press wages away from an efficient allocation, causing instability and market failure. Surveying 35 labour markets for different industries, we reveal that the situation in the labour markets has drastically changed since 2008. We find that this resulted in a severe problem when the Taiwan firms got used to policies like ‘22K’, ‘fix-term contract’ and ‘unpaid leave’ programmes. These plans negatively impacted the economy and raised market failure with instability.