Data for: Oil Price Shocks and Domestic Inflation in Thailand

Estimations 1. Perform unit root tests to determine the order of integration of each variable. 2. Estimate long-run equation using Gregory and Hansen (1996) algorithm. 3. Obtain the residual series from the estimated long-run equation. 4. When the ADF* of Gregory and Hansen (1996) indicates the absence of long-run relationship, non-linear cointegration tests (TAR and MTAR) of Enders and Siklos (2001) can be performed. 5. Use OLS to estimate short-run dynamic or error correction model to examine whether the long-run relationship is stable. 6. In the short-run analysis, a bivariate VECH-GARCH(1,1) model can be estimated for oil price shock and inflation series to obtain 2 volatility series. 7. Perform Granger causality tests of four variables and use unrestricted VAR model to analyze impulse response functions (IRFs) and variance decompositions (VDCs). 8. Separate oil price shock series to negative and positive components, and test for asymmetric causality using VAR and Granger causality/block exogeniet Wald tests. Then analyze IRFs for positive and negative oil price shocks and inflation rate

Uncovering investment management performance using SPIVA

Which performs better, passive or active funds management, a question that both fund managers and academics fiercely debate. Why does fund size matter? These are a number of typical questions that puzzle practitioners and academics alike. To date, the data has been shown to be somewhat problematic. This paper exploits the SPIVA and passive fund datasets with several novel methods in order to build a foundation for unbiased fund performance analysis and comparison. For this, we address a number of questions including: passive versus active management, fund size, time horizon and fund style on performance. We find that in general, passive funds outperform active funds due to lower management costs, larger funds tend to perform better and funds with longer (3+ years) records of accomplishment tend to perform better. Short termism tends to have a significant detrimental effect on performance. We introduce Dynamic Generalized Method of Moments to show that competition has a significant effect on fund performance. Furthermore, this demonstrates that SPIVA data has a significant dynamic panel time series that was largely ignored by prior research. This integrated dataset and associated methods that we illustrate here, provide both academic researchers and industry analysts alike with an environment to investigate and potentially draw conclusions about the fund factors that affect performance without the inherent limitations of the original sources