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.     

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.     

Value at risk forecasting for volatility index

Forecasts of values at risk (VaRs) are made for volatility indices such as the VIX for the US S&P 500 index, the VKOSPI for the KOSPI (Korea Stock Price Index) and the OVX (oil volatility index) for crude oil funds, which is the first in the literature. In the forecasts, dominant features of the volatility indices are addressed: long memory, conditional heteroscedasticity, asymmetry and fat-tails. An out-of-sample comparison of the VaR forecasts is made in terms of violation probabilities, showing better performance of the proposed method than several competing methods which consider the features differently from ours. The proposed method is composed of heterogeneous autoregressive model for the mean, GARCH model for the volatility and skew-t distribution for the error.     

The promise and peril of dynamic targeted pricing

Dynamic pricing is widely adopted in many industries, such as travel and insurance. These industries are also gaining extensive capabilities in identifying and segmenting customers, partly fueled by the increasing availability of data. It is natural to ask whether firms should take advantage of such developments by charging different prices to different customer segments. If so, under what conditions? We seek answers to these highly managerially relevant questions.We consider a market with two customer segments served by a monopolist. The monopolist can choose among a set of pricing strategies to exploit consumers’ inter-temporal preferences and/or inter-segment variations. At one end of the spectrum, the firm can charge a constant price to all customers, which is called static pricing. At the other end of the spectrum, the firm can charge different prices to different customer segments and vary these prices over time, which is referred to as dynamic targeted pricing. We systematically compare these alternative pricing strategies. We show that dynamic pricing without targeting can be more effective than static targeted pricing when customers are not very forward looking, which corroborates the findings in the empirical literature. Interestingly, we find that the monopolist can be worse off when she adopts targeting in addition to dynamic pricing. We conduct laboratory experiments to test several key model predictions. The studies show that individuals behave in a manner consistent with the predictions of our model.     

Financial stress,sovereign debt and economic activity in industrialized countries: Evidence from dynamic threshold regressions

We analyze how the impact of a change in the sovereign debt-to-GDP ratio on economic growth depends on the level of debt, the stress level on the financial market and the membership in a monetary union. A dynamic growth model is put forward demonstrating that debt affects macroeconomic activity in a non-linear manner due to amplifications from the financial sector. Employing dynamic country-specific and dynamic panel threshold regression methods, we study the non-linear relation between the growth rate and the debt-to-GDP ratio using quarterly data for sixteen industrialized countries for the period 1981Q1-2013Q2. We find that the debt-to-GDP ratio has impaired economic growth primarily during times of high financial stress and only for countries of the European Monetary Union and not for the stand-alone countries in our sample. A high debt-to-GDP ratio by itself does not seem to necessarily negatively affect growth if financial markets are calm.     

Dynamic DEA models with network structure: An application for Iranian airlines

Efficiency estimation of interdependent divisions within a company or assessing the interrelated processes in a production system provides insights for improving the operational performance. Recent developments in network data envelopment analysis (NDEA) models enable decision making units (DMUs) to be informed of inefficient processes within the system. The NDEA model assesses the processes of the system in a specific moment and ignores the dynamic effects within the production processes. Thus, without considering the temporal dimension of production processes, biased efficiency measurement will be obtained that provides misleading information to DMUs. For evaluating the performance of a DMU with interrelated processes during specified multiple periods, this paper proposes a relational dynamic NDEA (DNDEA) model which measures the efficiencies of the system and its internal processes over the time, simultaneously. To illustrate the capability of the proposed model, this study for the first time measures the efficiency of eight Iranian airlines in several periods connected to each other by carry over flows. The actual data is gathered in three periods from 2010 to 2012 and the results are compared with the dynamic DEA and network DEA models in the same time span.