Semiparametric EGARCH model with the case study of China stock market

In this paper, we propose a new semiparametric method for GARCH model by combining the EGARCH (1,1) model and local polynomial regression. Based on the idea of two-stage estimate, a link function is estimated by the local polynomial and then the parameters are obtained via the weighted least square method. Finally we apply this method to the Shanghai Composite Index in the China stock market and compared the results with these of EGARCH.     

Real-time data revisions and the PCE measure of inflation

This paper tracks data revisions in the Personal Consumption Expenditure using the exclusions-from-core inflation persistence model. Keeping the number of observations the same, the regression parameters of earlier vintages of real-time data, beginning with vintage 1996:Q1, are tested for coincidence against the regression parameters of the last vintage of real-time data, used in this paper, which is vintage 2008:Q2 in a parametric and two nonparametric frameworks. The effects of data revisions are not detectable in the vast majority of cases in the parametric model, but the flexibility of the two nonparametric models is able to utilize the data revisions.     

South African stock return predictability in the context data mining: The role of financial variables and international stock returns

In this paper, we examine the predictive ability, both in-sample and the out-of-sample, for South African stock returns using a number of financial variables, based on monthly data with an in-sample period covering 1990:01 to 1996:12 and the out-of-sample period of 1997:01 to 2010:04. We use the t-statistic corresponding to the slope coefficient in a predictive regression model for in-sample predictions, while for the out-of-sample, the MSE-F and the ENC-NEW tests statistics with good power properties were utilised. To guard against data mining, a bootstrap procedure was employed for calculating the critical values of both the in-sample and out-of-sample test statistics. Furthermore, we use a procedure that combines in-sample general-to-specific model selection with out-of-sample tests of predictive ability to further analyse the predictive power of each financial variable. Our results show that, for the in-sample test statistic, only the stock returns for our major trading partners have predictive power at certain short and long run horizons. For the out-of-sample tests, the Treasury bill rate and the term spread together with the stock returns for our major trading partners show predictive power both at short and long run horizons. When accounting for data mining, the maximal out-of-sample test statistics become insignificant from 6-months onward suggesting that the evidence of the out-of-sample predictability at longer horizons is due to data mining. The general-to-specific model shows that valuation ratios contain very useful information that explains the behaviour of stock returns, despite their inability to predict stock return at any horizon. The model also highlights the role of multiple variables in predicting stock returns at medium- to long run horizons.     

Innovation and duration of exports

This paper uses survival analysis to investigate the effect of innovation on export duration of 105 countries at the product level. The estimation shows that the duration of exports increases with innovation. The effect is stronger for differentiated products than for homogeneous products. The estimates are consistent with the quality ladder model.     

A waste management model for optimal recycling-landfilling policies under macroeconomic conditions

This research provides a waste management model in the presence of macroeconomic conditions. An optimal control problem which integrates the recycling-landfilling decision with industry supply-demand dynamics is designed to achieve the value-maximizing objective. The model simultaneously considers idiosyncratic technical and aggregate demand shocks, while the stoppage time for landfill use is assumed to follow a Cox process with the intensity function of a firm's total waste collection. The closed-form solution of the optimal recycling ratio is also derived. The results suggest that when macroeconomic effects are neglected, optimal recycling ratios tend to be underestimated.     

Efficient estimation of partially linear varying coefficient models

In this paper, we consider the problem of estimating a semiparametric partially linear varying coefficient model. We derive the semiparametric efficiency bound for the asymptotic variance of the finite-dimensional parameter estimator. We also propose an efficient estimator for estimating the finite-dimensional parameter of the model. Simulation results show substantial efficiency gain of our proposed estimator over a conventional estimator as considered in Ahmad et al. (2005).     

Nonparametric lag selection for nonlinear additive autoregressive models

The lag selection procedure based on the final prediction error (FPE) is investigated when the additive structure is a priori known in the nonparametric autoregression. The consistency of the lag selection is proved, followed by the finite sample simulation results.     

An MPEC estimator for misclassification models

In this paper, we propose a constrained maximum likelihood estimator for misclassification models, by formulating the estimation as an MPEC (Mathematical Programming with Equilibrium Constraints) problem. Our approach improves the numerical accuracy and avoids the singularity problem. Monte Carlo simulations confirm that the proposed estimator reduces bias and standard deviation of the estimator, especially when the sample is small/medium and/or the dimension of latent variable is large.     

Lagged duration dependence in mixed proportional hazard models

We study the identification of a mixed proportional hazard model with lagged duration dependence when data provide multiple outcomes per stratum. Within strata variation is exploited to non-parametrically identify lagged duration dependence in more general models than in the literature.     

Iterative algorithm for non parametric estimation of the instrumental variables quantiles

This paper proposes a simple algorithm for the numerical computation of the non parametric IV quantile estimation. This algorithm is based on the Landweber iterations for solving a nonlinear integral equation. The paper is illustrated by numerical simulations.     

Testing for structural breaks with local smoothers: A simulation study

By means of an extensive Monte Carlo simulation study based on the design of Chen and Hong (2012) we compare the performance of the tests they proposed for parameter stability with the linearity test of Li et al. (2002) and the functional form test of Li and Wang (1998). We find that the test of Li et al. (2002) test adapted to testing for parameter stability performs favorably well in terms of size and equally well in terms of power compared with the others, whereas the test by Li and Wang has no power.     

Small-sample inference with spatial HAC estimators

This paper examines the small-sample performance of spatial HAC (SHAC) estimators of the standard errors on parameters. We find that, in small to moderately-sized datasets, the use of HAC estimators may be recommended only with a relatively large degree of cross-sectional interdependence.     

A Bayesian method of combining judgmental and model-based density forecasts

This paper introduces a formal method of combining expert and model density forecasts when the sample of past forecasts is unavailable. It works directly with the expert forecast density and endogenously delivers weights for forecast combination, relying on probability rules only. The empirical part of the paper illustrates how the framework can be applied in forecasting US inflation by mixing density forecasts from an autoregressive model and the Survey of Professional Forecasters.     

MEAN‐PLUS‐NOISE FACTOR MODELS: AN EMPIRICAL EXPLORATION

In this paper I propose an alternative factor model whose common factor follows a mean‐plus‐noise process. While the existing methods for factor models should be valid for this model, their performance may be misleading in small samples. Because the likelihood function of this model is not tractable, I estimate this model by Bayesian methods. When applied to US macroeconomic time series, we find that the estimated mean‐plus‐noise factor is useful for predicting real personal income, industrial production and unemployment at long prediction horizons.     

When are adaptive expectations rational? A generalization

This note presents a simple generalization of the adaptive expectations mechanism in which the learning parameter is time variant. Expectations generated in this way minimize mean squared forecast errors for any linear state space model.     

Stock exchange mergers and weak form of market efficiency: The case of Euronext Lisbon

This exploratory paper is among the first to examine the impact of stock exchange mergers on informational market efficiency. We focus on the merger of Bolsa de Valores de Lisboa e Porto (Portuguese Stock Exchange) with Euronext in 2002 (that created Euronext Lisbon). To investigate this question we perform numerous statistical tests: serial correlation test (ACF test), runs test, unit root test (Kwiatkowski, Philips, Schmidt, & Shin, 1992), multiple variance ratio test (Chow & Denning, 1993) and ranks and signs test (Wright, 2000).The results indicate that the Portuguese Equity Market is inefficient in weak form during pre-merger period implying that investors possessed an opportunity to earn abnormal returns though small in magnitude. The results, sensitive to the methodology used, indicate a mixed evidence of improvement in market efficiency during the post-merger period. Although the findings are mixed, yet most tests show a tendency of improved efficiency.