Explore the vast range of titles available.

In this paper, we consider forecast combination in a predictive regression. We construct the point forecast by combining predictions from all possible linear regression models, given a set of potentially relevant predictors. We derive the asymptotic risk of least-squares averaging estimators in a local asymptotic framework. We then develop a frequentist model averaging criterion, an asymptotically unbiased estimator of the asymptotic risk, to select forecast weights. Monte Carlo simulations show that our averaging estimator compares favourably with alternative methods, such as weighted AIC, weighted BIC, Mallows model averaging and jackknife model averaging. The proposed method is applied to stock return predictions.

Purpose a This study seeks to investigate the signals on which consumers may rely to reduce the problems of information asymmetry on an online auction site. The research aims to develop and test based on information signaling theory. It classifies signals from auction web pages into three types: seller reputation, product condition, and argument quality. To understand how the signals affect consumersE14 online buying decisions, the study intends to test the impacts of these signals on the auction outcome variables: number of bids, auction success, and willingness to pay. Design/methodology/approach a The paper employs an empirical test with real observation data comprised of 5,013 samples coded from the eBay auction site in the USA. Ordinary least squares (OLS) regression is used to predict the effect of web page signals on the number of bids, logistic regression to determine which web page signals contribute to auction success, and Tobit maximum likelihood estimation to estimate the impact of web page signals on willingness to pay. Findings a Results show that, in addition to the sellerE14s reputation, signals like product condition and the quality of the sellersE14 arguments on the web page are significantly related to the three auction outcomes. Buyers tend to rely on these signals to resolve information asymmetry in online auction transactions. Originality/value a Past studies have found that the sellerE14s feedback score is central to a positive online auction outcome. This paper is the first to classify web page signals comprehensively and to investigate their impacts on online auction outcomes using real transaction data. The findings provide substantial evidence and implications for both academic research and practitioners in online auctions. A dynamic strategy for success in online auctions is offered in the conclusion section.

Purpose - This study seeks to investigate the signals on which consumers may rely to reduce the problems of information asymmetry on an online auction site. The research aims to develop and test based on information signaling theory. It classifies signals from auction web pages into three types: seller reputation, product condition, and argument quality. To understand how the signals affect consumers' online buying decisions, the study intends to test the impacts of these signals on the auction outcome variables: number of bids, auction success, and willingness to pay.Design methodology approach - The paper employs an empirical test with real observation data comprised of 5,013 samples coded from the eBay auction site in the USA. Ordinary least squares (OLS) regression is used to predict the effect of web page signals on the number of bids, logistic regression to determine which web page signals contribute to auction success, and Tobit maximum likelihood estimation to estimate the impact of web page signals on willingness to pay.Findings - Results show that, in addition to the seller's reputation, signals like product condition and the quality of the sellers' arguments on the web page are significantly related to the three auction outcomes. Buyers tend to rely on these signals to resolve information asymmetry in online auction transactions.Originality value - Past studies have found that the seller's feedback score is central to a positive online auction outcome. This paper is the first to classify web page signals comprehensively and to investigate their impacts on online auction outcomes using real transaction data. The findings provide substantial evidence and implications for both academic research and practitioners in online auctions. A dynamic strategy for success in online auctions is offered in the conclusion section.

This article presents evidence on mean reversion in industrial countries' real exchange rates in a setup that accounts naturally for cross-sectional dependence, is invariant to the benchmark currency, and actually tests for the null of interest, that is, purchasing power parity. Our results are based on the Kwiatkowski et al. (1992) test for the stationarity null generalized in a multivariate random walk plus noise model by Nyblom and Harvey (2000).

By investigating the exam scores of introductory economics classes in the business school at National Chengchi University in Taiwan between 2008 and 2019, we find three sets of results: First, we find no significant difference between genders in the exam scores. Second, students' majors are significantly associated with their exam scores, which likely reflects their academic ability measured at college admission. Third, the exam scores are strong predictors of students' future academic performance.

This paper adapts the first-difference estimator of Phillip and Han (2008) to the estimation and inference in AR(1) models with trends. With a detrending procedure, the first-difference estimator remains applicable and is shown to retain the Gaussian asymptotics. A unit root test based on the estimator is more powerful than that based on the double-difference estimator. The proposed estimator is especially useful when applied to dynamic panels.

This paper investigates the asymptotic properties of the maximum marginal likelihood estimator for a regression model with a stochastic trend component when the signal-to-noise ratio is near zero. In particular, the local level model in Harvey (1989, Forecasting, Structural Time Series Models and the Kalman Filter, Cambridge: Cambridge University Press) and its variants where a time trend or an intercept is included are considered. A local-to-zero parameterization is adopted. Two sets of asymptotic properties are presented for the local maximizer: consistency and the limiting distribution. The estimator is found to be super-consistent. The limit distribution is derived and found to possess a long tail and a mass point at zero. It yields a good approximation for samples of moderate size. Simulation also documents that the empirical distribution converges less rapidly to the limit distribution as number of regression parameters increases. The results could be viewed as a transition step toward establishing new likelihood ratio–type or Wald-type tests for the stationarity null.

This paper develops a new approach to estimation in dynamic panel data models with fixed effects and incidental trends, based on second differencing. The proposed estimation method is immune to the weak instrument problem that is known to arise when the conventional GMM is applied to the cases where the autoregressive coefficient (ρ) is close to unity. Similar to the first-difference estimator introduced by Han and Phillips (2010, pp. 119-151), the new estimator has standard Gaussian asymptotics for all values of ρ ∈ (-1, 1] in both panel and time series cases. Given its smaller asymptotic variance when the series exhibit positive or moderate positive autocorrelation, the panel unit root test built on the second-difference estimator is more powerful than that built on Han and Phillips' counterpart. [PUBLICATION ABSTRACT]

Exporting producers, facing a trade-off between scale economies and trade costs, tend to cluster around the countries with larger demand, the so-called 'home market effect'. Hsu et al. (2012) cast doubt on estimates in previous empirical studies on the effect using gravity regression. The potential bias comes from ignoring the important interactive effect of relative demand and trade cost on locational choice and thus on trade patterns. Using a dataset on the trade flow of creative goods from APEC countries, we find that after controlling for the interactive term, the estimated coefficients on the elasticity of demand are much corrected toward those predicted by theory. This constitutes more credible evidence for the existence of the home market effect.