Forecasting China's GDP growth using dynamic factors and mixed-frequency data

Forecasting GDP growth is important and necessary for Chinese government to set GDP growth target. To fully and efficiently utilize macroeconomic and financial information, this paper attempts to forecast China's GDP growth using dynamic predictors and mixed-frequency data. The dynamic factor model is first applied to select dynamic predictors among large amount of monthly macroeconomic and daily financial data and then the mixed data sampling regression is applied to forecast quarterly GDP growth based on the selected monthly and daily predictors. Empirical results show that forecasts using dynamic predictors and mixed-frequency data have better accuracy comparing to traditional forecasting methods. Moreover, forecasts with leads and forecast combination can further improve forecast performance.     

Retail channel price discrimination

This paper examines price differentials of identical items across retail channels. Many consumer packaged goods are sold through both grocery and drug stores. Liquor is unique in that in much of the country there is a third retail channel of distribution, liquor stores. If consumers in each retail channel differ in their willingness to pay for certain items, then sellers can exploit those differences and charge different prices for the same items in each channel. We examine a unique data set of pooled cross sectional retail scanner data on wine to test whether sellers use retail channel to identify heterogeneous consumer market segments and engage in price discrimination. We begin by presenting a model of price discrimination by retail channel along with behavioural assumptions regarding shoppers in each channel. Next we examine sales by retail channel and find persistent price differentials for the same item across retail channel after controlling for sample selection bias and seasonality. Lastly, we estimate the price elasticity of demand correcting for endogeneity and find differences across channel consistent with the price differentials. The extent of price differential, however, differs significantly with respect to price point.     

Quantifying land-use change impacts on the dynamic evolution of flood vulnerability

Recently, dramatic flood disasters have occurred incrementally in several regions of the world. Land-use change as one of the main affecting factors becomes a key component in flood risk management. This study strives to deal with quantifying how changes in land use to affect the dynamic evolution of flood vulnerability. The floodplains of Wuhan, which are located in the Yangtze River Basin, have been selected as an example. In this paper, we use GIS to gather different historical geometric data as sources of land-use information. By proposing the Simpsons-dominance index and location index to analyze the characteristics of land-use changes, and building a quantitative model to measure flood vulnerability, a series of flood vulnerability maps demonstrate differential flood vulnerability of floodplains of Wuhan in three inundation scenarios and four historical periods. Finally, the non-parametric correlation is used to reveal the interactive effect of land use and flood vulnerability. Based on this study, comprehensive flood disaster management strategies for land-use planning are proposed for government decision-makers to reduce the flood vulnerability of Wuhan in future.     

Wholly owned foreign subsidiary relation-based strategies in volatile environments

Drawing from dynamic capability, institutional, nonmarket strategy, and social-network literatures, we detail wholly owned subsidiary (WOFSs) relation-based strategies (RBSs). We explain how deploying RBSs with key nonmarket and market actors will create competitive advantages for WOFSs operating in volatile emerging market environments. We posit that dynamic capabilities will drive the deployment of RBSs by WOFSs, and argue that the positive relationship between dynamic capabilities and RBS deployment will strengthen as perceived institutional uncertainty increases. We further suggest that the greater the strength and frequency of RBS deployment, the more likely that a WOFS will establish a combination of nonmarket-based and market-based embedded assets. Also, our theory proposes that greater integration of nonmarket-based and market-based assets will enhance WOFS financial performance outcomes. Implications for future research are discussed.     

A new index of financial conditions

We use factor augmented vector autoregressive models with time-varying coefficients and stochastic volatility to construct a financial conditions index that can accurately track expectations about growth in key US macroeconomic variables. Time-variation in the models׳ parameters allows for the weights attached to each financial variable in the index to evolve over time. Furthermore, we develop methods for dynamic model averaging or selection which allow the financial variables entering into the financial conditions index to change over time. We discuss why such extensions of the existing literature are important and show them to be so in an empirical application involving a wide range of financial variables.     

Beauty contest,bounded rationality,and sentiment pricing dynamics

We present a dynamic asset pricing model that incorporates investor sentiment, bounded rationality and higher-order expectations to study how these factors affect asset pricing equilibrium. In the model, we utilize a two-period trading market and investors make decisions based on the heterogeneous expectations principle and the “sparsity-based bounded rational” sentiment. We find that bounded rationality results in mispricing and reduces it in next period. Investor sentiment produces more significant effects than private signals, optimistic investor sentiment increases hedging demand, thus causing prices to soar. Higher-order investors are more rational and attentive to the strategies of other participants rather than private signals. This model also derives the dampening effect of higher-order expectations to price volatility and the heterogeneity expectation depicts inconsistent investor behavior in financial markets. In the model, investors' expectations about future price is distorted by their sentiment and bounded rationality, so they obtain a biased mean from the signal extraction.     

Bibliometric analysis of absorptive capacity

This study contributes to our understanding of absorptive capacity (AC) by reviewing AC articles systematically using two types of blibliometric co-citation analysis – bibliometric co-citation and bibliometric cartography – for the last 25 years. In total, we analyzed 336 articles (using HistCite) and 2088 articles (using VOSviewer), respectively, finding five research streams in AC: (1) intra-organizational learning; (2) inter-organizational learning; (3) knowledge transfer; (4) dynamic capability; and (5) micro-foundations. This integrative literature review of AC adds to the categorization of the literature, links the international business research to AC, and provides promising future research directions. Our study gives detailed information about the development of each research stream by measuring the number of publications in each stream over 25 years using bibliometric cartography analysis. Based on the literature, we propose 26 future research questions for these five research streams.     

NONPARAMETRIC AND SEMIPARAMETRIC PANEL DATA MODELS: RECENT DEVELOPMENTS

In this paper, we provide an intensive review of the recent developments for semiparametric and fully nonparametric panel data models that are linearly separable in the innovation and the individual-specific term. We analyze these developments under two alternative model specifications: fixed and random effects panel data models. More precisely, in the random effects setting, we focus our attention in the analysis of some efficiency issues that have to do with the so-called working independence condition. This assumption is introduced when estimating the asymptotic variance–covariance matrix of nonparametric estimators. In the fixed effects setting, to cope with the so-called incidental parameters problem, we consider two different estimation approaches: profiling techniques and differencing methods. Furthermore, we are also interested in the endogeneity problem and how instrumental variables are used in this context. In addition, for practitioners, we also show different ways of avoiding the so-called curse of dimensionality problem in pure nonparametric models. In this way, semiparametric and additive models appear as a solution when the number of explanatory variables is large.     

A Bayesian approach to excess volatility,short-term underreaction and long-term overreaction during financial crises

In this paper, we introduce a new Bayesian approach to explain some market anomalies during financial crises and subsequent recovery. We assume that the earnings shock of an asset follows a random walk model with and without drift to incorporate the impact of financial crises. We further assume the earning shock follows an exponential family distribution to accommodate symmetric as well as asymmetric information. By using this model setting, we develop some properties on the expected earnings shock and its volatility, and establish properties of investor behavior on the stock price and its volatility during financial crises and the subsequent recovery. Thereafter, we develop properties to explain excess volatility, short-term underreaction, long-term overreaction, and their magnitude effects during financial crises and the subsequent recovery. We also explain why behavioral finance theory could be used to explain many of the asset pricing anomalies, but traditional asset pricing models cannot achieve this aim.     

The Role of Financial Ratios in the Variance of Stock Trading Volume in Emerging Stock Markets

The study used quarterly panel data of 6 years from 2010 to 2015 of all companies listed on both Vietnamese stock markets including the Ho Chi Minh City Stock Exchange and Ha Noi Stock Exchange, and on three leading industries consisting of insurance-banking, foodstuff, and real estate to explore the relationship among four key financial ratios and stock trading volume. Two models, fixed effects model (FEM) and random effects model (REM), with robust standard errors, were applied for this study. The key findings showed that earnings before tax on sales, debt on owner’s equity, and owner’s equity on total assets significantly influenced trading volume.