Is the Hybrid New Keynesian Phillips Curve Stable? Evidence from Some Emerging Economies

This paper primarily examines whether the ‘hybrid new Keynesian Phillips curve’ (HNKPC) holds for four important emerging economics viz., Brazil, Russia, India and South Africa. This has been done after testing for the structural stability of this relationship. Econometric issues like the test of unit roots in presence of a structural break and estimation of output gap have also been done appropriately. Our findings suggest that the HNKPC is not stable for all the four countries. However, the analysis based on the two sub-periods thus formed clearly shows mixed evidence in respect of holding of this relationship.

     

Nowcasting India’s Quarterly GDP Growth: A Factor-Augmented Time-Varying Coefficient Regression Model (FA-TVCRM)

Governments, central banks, private firms and others need high frequency information on the state of the economy for their decision making. However, a key indicator like GDP is only available quarterly and that too with a lag. Hence decision makers use high frequency daily, weekly or monthly information to project GDP growth in a given quarter. This method, known as nowcasting, started out in advanced country central banks using bridge models. Nowcasting is now based on more advanced techniques, mostly dynamic factor models. In this paper we use a novel approach, a Factor Augmented Time Varying Coefficient Regression (FA-TVCR) model, which allows us to extract information from a large number of high frequency indicators and at the same time inherently addresses the issue of frequent structural breaks encountered in Indian GDP growth. One specification of the FA-TVCR model is estimated using 19 variables available for a long period starting in 2007–08:Q1. Another specification estimates the model using a larger set of 28 indicators available for a shorter period starting in 2015–16:Q1. Comparing our model with two alternative models, we find that the FA-TVCR model outperforms a Dynamic Factor Model (DFM) model and a univariate Autoregressive Integrated Moving Average (ARIMA) model in terms of both in-sample and out-of-sample Root Mean Square Error (RMSE). Further, comparing the predictive power of the three models using the Diebold-Mariano test, we find that FA-TVCR model outperforms DFM consistently. In terms of out-of-sample forecast accuracy both the FA-TVCR model and the ARIMA model have the same predictive accuracy under normal conditions. However, the FA-TVCR model outperforms the ARIMA model when applied for nowcasting in periods of major shocks like the Covid–19 shock of 2020–21.

     

A reexamination of corporate sell-offs of real estate assets

This article reexamines the now generally accepted notion that sell-offs of real estate assets provide positive returns for sellers but not for buyers. Following previous research, we use event study methods, but we modify the conventional market model to permit its residuals (unexpected returns) to be described by a time-varying conditional variance. We also differ from previous work in that our sample contains only sell-offs that can be precisely dated. Although we find substantial evidence of time-varying volatility in the unexpected return series, our economic results confirm the conventional viewpoint.     

Assessing the long‐run economic impact of labour law systems: a theoretical reappraisal and analysis of new time series data

Standard economic theory sees labour law as an exogenous interference with market relations and predicts mostly negative impacts on employment and productivity. We argue for a more nuanced theoretical position: labour law is, at least in part, endogenous, with both the production and the application of labour law norms influenced by national and sectoral contexts, and by complementarities between the institutions of the labour market and those of corporate governance and financial markets. Legal origin may also operate as a force shaping the content of the law and its economic impact. Time‐series analysis using a new data set on legal change from the 1970s to the mid‐2000s shows evidence of positive correlations between regulation and growth in employment and productivity, at least for France and Germany. No relationship, either positive or negative, is found for the UK and, although the United States shows a weak negative relationship between regulation and employment growth, this is offset by productivity gains.     

Alliance portfolios and shareholder value in post-IPO firms: The moderating roles of portfolio structure and firm-level uncertainty

Using longitudinal data for initial public offering (IPO) firms, we examine the role played by structural differences between different types of alliance portfolios in the relationship between IPO firm alliance portfolios and shareholder returns. We show that because of the different signals they send to the capital market, different types of alliance portfolios affect IPO firm performance differently. Namely, financial markets seem to reward firms whose alliance portfolio is diversified across different types of alliances (a portfolio high in functional diversity), but not those who align their alliance partners into multiple functional points in the value chain (a portfolio high in vertical scope). We also examine the signaling role of alliance portfolios under different IPO firm uncertainty conditions. We note that uncertainty about the IPO firm is not limited to pre-IPO quality uncertainty. Investors also face transition uncertainty, post-IPO uncertainty about the ability of the firm to adapt to the new managerial challenges it faces and succeed post-IPO. We find that these two types of uncertainties moderate alliance portfolio effects in different ways. The beneficial effects of alliance portfolios in mitigating liabilities of newness is of greater importance for firms associated with higher quality uncertainty and for those associated with lower transition uncertainty.     

Time variation in the correlation structure of exchange rates: high‐frequency analyses

The correlation structure of asset returns is a crucial parameter in risk management as well as in theoretical finance. In practice, however, the true correlation structure between the returns of assets can easily become obscured by time variation in the observed correlation structure and in the liquidity of the assets. We employed a time‐stamped high‐frequency data set of exchange rates, namely, the US$–deutsche mark and the US$–yen exchange rates, to calibrate the observed time variation in the correlation structure between their returns. We also documented time variation in the liquidity structure of these rates. We then attempted to link the observed correlations with the liquidity via an application of an illiquid trading model first developed by Scholes and Williams (1976). We show that the observed correlation structure is strongly biased by the liquidity and that it is possible to effect at least a partial rectification of the otherwise downward‐biased observed correlation. The rectified sample correlation is, therefore, more appropriate for input into models used for forecasting, option pricing, and other risk management applications. © 2001 John Wiley & Sons, Inc. Jrl Fut Mark 21:127–144, 2001     

Knowledge as a public good: efficient sharing and incentives for development effort

We consider Research-Development joint ventures where adverse selection arises in knowledge sharing, while there is moral hazard involved in the choice of private development efforts aimed at translating privately acquired and/or shared knowledge into valuable marketable innovations. We extend earlier work by Bhattacharya et al. [Bhattacharya, S., Glazer, J., Sappington, D., 1992. Licensing and the Sharing of Knowledge in Research Joint Ventures, J. Econ. Theory, Vol. 56, pp. 43–69.] to situations where one cannot identify a ‘most knowledgeable' partner, by giving conditions under which there exist transfers implementing both efficient first best knowledge sharing and subsequent development efforts.     

An Internet Access Solution: MANET Routing and a Gateway Selection Approach for Disaster Scenarios

After natural and man-made disasters, the telecommunication infrastructure is usually severely damaged, thus hampering communication and rescue services. It is impossible for disaster victims to make use of communication devices such as cellular phones, iPads, or their laptops to make a connection with the outside world (Internet). With infrastructure less and decentralized features, the mobile ad hoc network (MANET) can play an important role in improving communication in post-disaster affected areas. Therefore, the important functionalities of a MANET that allow users to create dynamically configurable wireless networks without fixed infrastructure using common devices such as mobile phones is necessary. This paper reports on the development of new techniques for routing selection and gateway load balancing in MANETs. Network fairness, throughput, and packet delays are measured empirically. The proposed mechanisms can reduce network congestion and consequently improve communication in affected areas.