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1 – 10 of 301Simon Luechinger, Alois Stutzer and Rainer Winkelmann
We discuss a class of copula-based ordered probit models with endogenous switching. Such models can be useful for the analysis of self-selection in subjective well-being equations…
Abstract
We discuss a class of copula-based ordered probit models with endogenous switching. Such models can be useful for the analysis of self-selection in subjective well-being equations in general, and job satisfaction in particular, where assignment of regressors may be endogenous rather than random, resulting from individual maximization of well-being. In an application to public and private sector job satisfaction, and using data on male workers from the German Socio-Economic Panel for 2004, and using two alternative copula functions for dependence, we find consistent evidence for endogenous sector selection.
Panel data-based demand forecasting models have been widely adopted in various industrial settings over the past few decades. Despite being a highly versatile and intuitive…
Abstract
Purpose
Panel data-based demand forecasting models have been widely adopted in various industrial settings over the past few decades. Despite being a highly versatile and intuitive method, in the literature, there is a lack of comprehensive review examining the strengths, the weaknesses, and the industrial applications of panel data-based demand forecasting models. The purpose of this paper is to fill this gap by reviewing and exploring the features of various main stream panel data-based demand forecasting models. A novel process, in the form of a flowchart, which helps practitioners to select the right panel data models for real world industrial applications, is developed. Future research directions are proposed and discussed.
Design/methodology/approach
It is a review paper. A systematically searched and carefully selected number of panel data-based forecasting models are examined analytically. Their features are also explored and revealed.
Findings
This paper is the first one which reviews the analytical panel data models specifically for demand forecasting applications. A novel model selection process is developed to assist decision makers to select the right panel data models for their specific demand forecasting tasks. The strengths, weaknesses, and industrial applications of different panel data-based demand forecasting models are found. Future research agenda is proposed.
Research limitations/implications
This review covers most commonly used and important panel data-based models for demand forecasting. However, some hybrid models, which combine the panel data-based models with other models, are not covered.
Practical implications
The reviewed panel data-based demand forecasting models are applicable in the real world. The proposed model selection flowchart is implementable in practice and it helps practitioners to select the right panel data-based models for the respective industrial applications.
Originality/value
This paper is the first one which reviews the analytical panel data models specifically for demand forecasting applications. It is original.
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Douglas Miller, James Eales and Paul Preckel
We propose a quasi–maximum likelihood estimator for the location parameters of a linear regression model with bounded and symmetrically distributed errors. The error outcomes are…
Abstract
We propose a quasi–maximum likelihood estimator for the location parameters of a linear regression model with bounded and symmetrically distributed errors. The error outcomes are restated as the convex combination of the bounds, and we use the method of maximum entropy to derive the quasi–log likelihood function. Under the stated model assumptions, we show that the proposed estimator is unbiased, consistent, and asymptotically normal. We then conduct a series of Monte Carlo exercises designed to illustrate the sampling properties of the quasi–maximum likelihood estimator relative to the least squares estimator. Although the least squares estimator has smaller quadratic risk under normal and skewed error processes, the proposed QML estimator dominates least squares for the bounded and symmetric error distribution considered in this paper.
This study aims to revisit the stock price–volume relations, providing new evidence from the emerging market of Qatar. In particular, three main issues are examined using both…
Abstract
Purpose
This study aims to revisit the stock price–volume relations, providing new evidence from the emerging market of Qatar. In particular, three main issues are examined using both aggregate market- and sector-level data. First, the return–volume relation and whether or not this relation is asymmetric. Second, the common characteristics of return volatility; and third, the nature of the relation between trading volume and return volatility.
Design/methodology/approach
The study uses the OLS and VAR modeling approaches to examine the contemporaneous and dynamic (causal) relations between index returns and trading volume, respectively, while an EGARCH-X(1,1) model is used to analyze the volatility–volume relation. The data set comprises daily index observations and the corresponding trading volumes for the entire market and the individual seven sectors of the Qatar Exchange (i.e. banks and financial services, consumer goods and services, industrials, insurance, real estate, telecommunications and transportation).
Findings
The empirical analysis reports evidence of a positive contemporaneous return–volume relation in all sectors barring transportation and insurance. This relation appears to be asymmetric for all sectors. For the market and almost all sectors, there is no significant causality between returns and volume. By and large, these findings lend support for the implications of the mixture of distributions hypothesis (MDH). Lastly, the information content of lagged volume seems to have an important role in predicting the future dynamics of return volatility in all sectors, with the industrials being the exception.
Practical implications
The findings provide important implications for portfolio managers and investors, given that the volume of transactions is generally found to be informative about the price movement of sector indices. Specifically, tracking the behavior of trading volume over time can give a broad portrayal of the future direction of market prices and volatility of equity, thereby enriching the information set available to investors for decision-making.
Originality/value
Based on both market- and sector-level data from the emerging stock market of Qatar, this study attempts to fill an important void in the literature by examining the return–volume and volatility–volume linkages.
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Douglas Miller and Sang-Hak Lee
In this chapter, we use the minimum cross-entropy method to derive an approximate joint probability model for a multivariate economic process based on limited information about…
Abstract
In this chapter, we use the minimum cross-entropy method to derive an approximate joint probability model for a multivariate economic process based on limited information about the marginal quasi-density functions and the joint moment conditions. The modeling approach is related to joint probability models derived from copula functions, but we note that the entropy approach has some practical advantages over copula-based models. Under suitable regularity conditions, the quasi-maximum likelihood estimator (QMLE) of the model parameters is consistent and asymptotically normal. We demonstrate the procedure with an application to the joint probability model of trading volume and price variability for the Chicago Board of Trade soybean futures contract.
This paper aims to study the monetary transmission mechanism of China from January 1996 to December 2009 under endogenous structural breaks.
Abstract
Purpose
This paper aims to study the monetary transmission mechanism of China from January 1996 to December 2009 under endogenous structural breaks.
Design/methodology/approach
The study constructs a benchmark VAR model and then adds the proxy variables for four channels of monetary policy transmission as endogenous or exogenous variables in the model to study the transmission mechanism in China. Considering a number of reforms carried out in the economic and financial field in the past two decades and the possibility of structural changes in the monetary transmission mechanism, the methodology proposed by Qu and Perron is employed to allow for endogenous structural changes in the model.
Findings
By conducting a comparative analysis, conclusions can be drawn from this paper that bank lending is always the dominating channel for monetary policy to influence economy in China and the roles of the interest rate channel and the exchange rate channel have been improved in recent years. However, the role of the asset price channel in monetary policy transmission has weakened since late 2001.
Originality/value
This paper combines the quasi‐maximum likelihood procedure proposed by Qu and Perron in 2007 with a benchmark VAR model, thus providing a new approach to study monetary transmission mechanism and the conclusions can be more sensible.
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Carl Hope Korkpoe, Ferdinand Ahiakpor and Edward Nii Amar Amarteifio
The purpose of this paper is to emphasize the risks involved in modeling inflation volatility in the context of macroeconomic policy. For countries like Ghana that are always…
Abstract
Purpose
The purpose of this paper is to emphasize the risks involved in modeling inflation volatility in the context of macroeconomic policy. For countries like Ghana that are always battling economic problems, accurate models are necessary in any modeling endeavor. We estimate volatility taking into account the heteroscedasticity of the model parameters.
Design/methodology/approach
The estimations considered the quasi-maximum likelihood-based GARCH, stochastic and Bayesian inference models in estimating the parameters of the inflation volatility.
Findings
A comparison of the stochastic volatility and Bayesian inference models reveals that the latter is better at tracking the evolution of month-on-month inflation volatility, thus following closely the data during the period under review.
Research limitations/implications
The paper looks at the effect of parameter uncertainty of inflation volatility alone while considering the effects of other key variables like interest and exchange rates that affect inflation.
Practical implications
Economists have battled with accurate modeling and tracking of inflation volatility in Ghana. Where the data is not well-behaved, for example, in developing economies, the stochastic nature of the parameter estimates should be incorporated in the model estimation.
Social implications
Estimating the parameters of inflation volatility models is not enough in a perpetually gyrating economy. The risks of these parameters are needed to completely describe the evolution of volatility especially in developing economies like Ghana.
Originality/value
This work is one of the first to draw the attention of policymakers in Ghana towards the nature of inflation data generated in the economy and the appropriate model for capturing the uncertainty of the model parameters.
Igor Vaynman and Brendan K. Beare
The variance targeting estimator (VTE) for generalized autoregressive conditionally heteroskedastic (GARCH) processes has been proposed as a computationally simpler and…
Abstract
The variance targeting estimator (VTE) for generalized autoregressive conditionally heteroskedastic (GARCH) processes has been proposed as a computationally simpler and misspecification-robust alternative to the quasi-maximum likelihood estimator (QMLE). In this paper we investigate the asymptotic behavior of the VTE when the stationary distribution of the GARCH process has infinite fourth moment. Existing studies of historical asset returns indicate that this may be a case of empirical relevance. Under suitable technical conditions, we establish a stable limit theory for the VTE, with the rate of convergence determined by the tails of the stationary distribution. This rate is slower than that achieved by the QMLE. The limit distribution of the VTE is nondegenerate but singular. We investigate the use of subsampling techniques for inference, but find that finite sample performance is poor in empirically relevant scenarios.
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The authors consider the quasi maximum likelihood (MLE) estimation of dynamic panel models with interactive effects based on the Ahn et al. (2001, 2013) quasi-differencing methods…
Abstract
The authors consider the quasi maximum likelihood (MLE) estimation of dynamic panel models with interactive effects based on the Ahn et al. (2001, 2013) quasi-differencing methods to remove the interactive effects. The authors show that the quasi-difference MLE (QDMLE) over time is inconsistent when
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