A STUDY OF INDONESIA’S STOCK MARKET

Using monthly data from January 1995 to December 2017, this paper tests whetherIndonesian stock index returns are predictable. In particular, we use eight macrovariables to predict the Indonesian composite and six sectoral index returns using thefeasible generalized least squares estimator. Our results suggest that the Indonesianstock index returns are predictable. However, the predictability depends not only onthe macro predictor used but also on the indexes examined. Second, we find that themost popular predictor is the exchange rate, followed by the interest rate. Finally, ourmain findings hold for a number of robustness tests.

Panel Data Estimation for Correlated Random Coefficients Models

This paper considers methods of estimating a static correlated random coefficient model with panel data. We mainly focus on comparing two approaches of estimating unconditional mean of the coefficients for the correlated random coefficients models, the group mean estimator and the generalized least squares estimator. For the group mean estimator, we show that it achieves Chamberlain (1992) semi-parametric efficiency bound asymptotically. For the generalized least squares estimator, we show that when T is large, a generalized least squares estimator that ignores the correlation between the individual coefficients and regressors is asymptotically equivalent to the group mean estimator. In addition, we give conditions where the standard within estimator of the mean of the coefficients is consistent. Moreover, with additional assumptions on the known correlation pattern, we derive the asymptotic properties of panel least squares estimators. Simulations are used to examine the finite sample performances of different estimators.

Exchange Rate Exposure and Firm Value: An Assessment of Domestic Versus Multinational Firms

This paper examines the impact of exchange rate changes on the stock returns of 232 nonfinancial firms listed on the Pakistan Stock Exchange, for the period January 2000 to June 2014. To mitigate the problem of heteroskedasticity, we use a generalized least squares estimator. The estimated regression models indicate that exchange rate variations have a significant effect on firm value and that firms are exposed significantly to one-period lagged variation in the exchange rate. Our results suggest that, in addition to exchange rate dynamics, increased exchange rate volatility appears to have significant and negative effects on firms’ stock returns. Compared to domestic firms, multinational firms experience greater exchange rate exposure. Finally, we show that exchange rate depreciation and appreciation have significant differential effects on firms’ stock returns. These effects vary significantly across domestic and multinational firms.

Modern Trends in Capital Flows in Emerging Markets

This chapter provides an evaluation of the influence of the most significant external and internal factors on international capital flows in the form of direct and portfolio investments for 24 developing countries during the period 1990–2015. The authors have adopted the partial adjustment model and the feasible generalized least squares estimator for panel data. Results show that the determinants of capital flow for foreign direct and portfolio investments differ. The impact of political risks on cross-border capital flows has been identified.

Analysis of MIMO Receiver Using Generalized Least Squares Method in Colored Environments

The classical detection techniques for multiple-input multiple-output (MIMO) systems are usually designed with the assumption that the additive complex Gaussian noise is uncorrelated. However, for closely spaced antennas, the additive noise is correlated due to the mutual antenna coupling. This letter analyzes an improved zero-forcing (ZF) technique for MIMO channels in colored environments. The additive noise is assumed to be correlated and the Rayleigh MIMO channel is considered doubly correlated. The improved ZF detector, based on the generalized least squares estimator (GLS), takes into account the noise covariance matrix and provides an unbiased estimator of the transmitted symbol vectors. We introduce some novel bounds on the achievable sum rate, on the normalized mean square error at the receiver output, and on the outage probability. The derived expressions are compared to Monte Carlo simulations.

ADDING REGRESSORS TO OBTAIN EFFICIENCY

It is well known that in standard linear regression models with independent and identically distributed data and homoskedasticity, adding “irrelevant regressors” hurts (asymptotic) efficiency unless such irrelevant regressors are orthogonal to the remaining regressors. But we have found that under (conditional) heteroskedasticity “irrelevant regressors” can always be found such that one can achieve the asymptotic variance of the generalized least squares estimator by adding the “irrelevant regressors” to the model.