RIDGE ESTIMATORS FOR PROBIT REGRESSION: WITH AN APPLICATION TO LABOUR MARKET DATA

In this paper we propose ridge regression estimators for probit models since the commonly applied maximum likelihood (ML) method is sensitive to multicollinearity. An extensive Monte Carlo study is conducted where the performance of the ML method and the probit ridge regression (PRR) is investigated when the data are collinear. In the simulation study we evaluate a number of methods of estimating the ridge parameter k that have recently been developed for use in linear regression analysis. The results from the simulation study show that there is at least one group of the estimators of k that regularly has a lower mean squared error than the ML method for all different situations that have been evaluated. Finally, we show the benefit of the new method using the classical Dehejia and Wahba dataset which is based on a labour market experiment.     

Forecasting China's foreign exchange reserves using dynamic model averaging: The roles of macroeconomic fundamentals,financial stress and economic uncertainty

We develop models for examining possible predictors of growth of China's foreign exchange reserves that embrace Chinese and global trade, financial and risk (uncertainty) factors. Specifically, by comparing with other alternative models, we show that the dynamic model averaging (DMA) and dynamic model selection (DMS) models outperform not only linear models (such as random walk, recursive OLS-AR(1) models, recursive OLS with all predictive variables models) but also the Bayesian model averaging (BMA) model for examining possible predictors of growth of those reserves. The DMS is the best overall across all forecast horizons. While some predictors matter more than others over the forecast horizons, there are few that stand the test of time. The US–China interest rate differential has a superior predictive power among the 13 predictors considered, followed by the nominal effective exchange rate and the interest rate spread for most of the forecast horizons. The relative predictive prowess of the oil and copper prices alternates, depending on the commodity cycles. Policy implications are also provided.     

Robust influence diagnostics for generalized linear models with continuous responses

This article introduces two parametric robust diagnostic methods for detecting influential observations in the setting of generalized linear models with continuous responses. The legitimacy of the two proposed methods requires no knowledge of the true underlying distributions so long as their second moments exist. The performance of the two proposed influence diagnostic tools is investigated through limited simulation studies and the analyses of an illustration.     

Forecasting macroeconomic variables in emerging economies

Forecasting macroeconomic variables in rapidly changing emerging economies presents a number of challenges. In addition to structural changes, the time-series data are usually available only for a short number of periods, and predictors are available in different lengths and frequencies. Dynamic model averaging (DMA), by allowing the forecasting model to change dynamically over time, permits the use of predictors with different lengths and frequencies for the purpose of forecasting in a rapidly changing economy. This study uses DMA to forecast inflation and growth in Vietnam, Thailand, Philippines, Sri Lanka and Ghana. We compare its forecasting performance with a wide range of other time-series methods. We find that the size and composition of the optimal predictor set changed, indicating changes in the economic relationships over time. We also find that DMA frequently produces more accurate forecasts than other forecasting methods for both inflation and economic growth in the countries studied.     

How do “gatekeepers” affect credit risk?

This study investigates the relationship between auditor tenure and credit default swap (CDS) spreads of U.S. firms based on quantile regression. After allowing for common determinants of CDS spreads, auditor tenure exerts both statistically and economically significant additional impacts on the CDS market. Furthermore, there are differential effects of common CDS spread determinants and auditor tenure. While common determinants of CDS spreads (e.g., leverage, volatility, risk free rate, credit ratings, and earnings) have monotonically increasing impacts when CDS spreads (and their changes) are increasingly higher, auditor tenure primarily has the impact when CDS spreads are of low or median levels for less risky firms.     

对江苏省电力负荷进行回归预测

本文主要根据2001-2011年江苏省用电量样本数据,建立了江苏省电力负荷与人均GDP、工业化以及人口数之间的多元回归预测方程,并预测了江苏省2014-2020年总用电量数据,在此基础上提出了相应的建议。     

How can investors build a better portfolio in small open economies? Evidence from Asia’s Four Little Dragons

This study investigates the role of hedging and portfolio design among stocks, exchange rates, and gold in small open economies (SOEs) from 4 January 2000 to 31 March 2020. We adopt the trivariate dynamic conditional correlation-fractionally integrated asymmetric power ARCH model and unconditional quantile regression model, and our findings show that the hedging role of the U.S. dollar (USD) and gold against stocks differs under regular and extreme market conditions. The USD can act as a powerful hedge asset for stocks in regular market periods. Moreover, during the global financial crisis and COVID-19 outbreak, the safe-haven effect of gold becomes stronger for almost all stocks, whereas the USD can serve as a strong safe haven against stock markets of Korea, Taiwan, and Singapore when stock returns are extremely low. In terms of portfolio designing, we find that adding the USD and gold to portfolios improves their hedging effectiveness, and the optimally weighted stock-USD-gold portfolio is the best portfolio strategy, irrespective of referring to return or risk.     

Effects of Firm Size,Financial Leverage and R&D Expenditures on Firm Earnings: An Analysis Using Quantile Regression Approach

As documented in the literature, the effects of firm size, financial leverage, and R&D expenditures on firm earnings are inclusive. Our hypothesis is that the inconsistent empirical results of such effects may be driven by the regression models implemented in data analysis. Using the quantile regression (QR) approach developed by Koenker and Basset (1978), this study analyses S&P 500 firms from 1996 to 2005. We find that the effects of firm size, financial leverage and R&D expenditures on firm earnings differ considerably across earnings quantiles. Comparing the results from the QR approach with those from the ordinary least squares (OLS) and least absolute deviation (LAD) methods, this study further explains the puzzling relationship between firm size, financial leverage, R&D expenditures and firm earnings.     

Real exchange rates: are they dominated by fundamental factors?

Using Bayesian model averaging, we determine which fundamental pair-wise differences suggested by the literature on optimum currency areas give the best explanation of medium-term variability of bilateral real exchange rates. The intercept in the best specification is statistically insignificant, implying that for a hypothetical pair of economies for which the differences were zero, the bilateral real exchange rate would not move. Thus, the ‘non-fundamental’ element of the medium-term real exchange rate variability is, in our sample at least, negligible on average. In other words, floating exchange rate does not in itself imply, on average, more real exchange rate variability in the medium term than an exchange rate peg.     

How credible is inflation targeting in Asia? A quantile unit root perspective

This article examines the dynamic behavior of the inflation rate for eight Asian countries using a quantile unit root test. We advocate a three-way definition of inflation targeting based on perfect, imperfect and zero credibility and advance the analysis by incorporating a fully-fledged adoption of inflation targeting. In doing so, we offer new insights by showing that the credibility of inflation targeting and the alternative monetary policy frameworks in Asia are imperfect, except for Malaysia and South Korea under a fully-fledged adoption of inflation targeting. In contrast to past studies that focus on the mean-reversion in inflation rates, we also consider trend-reversion and find that Asian inflation targeting countries have been building up their monetary policy credibility more than the non- inflation targeting countries in terms of a faster rate of decline in inflation rate changes. Our results generally indicate the presence of mean reversion at the lower quantiles only. Where stationarity is present, we find evidence of a varied speed of adjustment process across the quantiles. Finally, we determine the threshold levels whereby inflation becomes stationary and demonstrate that Asian inflation rates generally display stationary behavior during periods of inflation declining or slowing down.