Serial Correlation

Inflation means a persistent change in the price level of goods and services in an economy. It is generally measured in the consumer price index (CPI) or retail price index (RPI). Inflation reduces the purchasing power of a country's currency, as we need more units of currency over time to buy the same goods and services. The current empirical paper entitled “relationship between inflation and stock market evidence from selected global stock markets” have been undertaken with an intention to investigate the relationship between inflation and stock returns of the chosen economies. In order to realize the stated objectives, the researchers have collected the monthly data 2000 to 2017 for selected indices. In the first phase, log returns were computed and it has been tested for the existence of unit root in the distribution. In the second phase, we ran Pearson correlation coefficient for the collected data to find out the association between the inflation and stock returns. Majorit...

In this study we use the monthly excess holding period yields (EHPY), and their volatility for five government bond indices markets, in order to test the popular expectations theory of the term structure and to assess whether there are patterns in risk premia, which are common across these markets. For this reason, a GARCH-M model is used. The empirical results derived indicate that in most cases the Expectation Hypothesis is rejected and short medium-term government bond yields display serial correlation and co movement in the majority of the countries. Additionally, the term premia at the long end of the maturity structure are time varying in the USA, the UK and Canada. The risk factor is statistically significant as an explanatory variable for risk premia, only for 7 years maturity bonds in the USA and the UK.

This paper addresses an issue central to the estimation of discount rates for capital budgeting: should the geometric mean or arithmetic mean of past data be used when estimating the discount rate? the use of the arithmetic mean ignores estimation error and serial correlation in returns. Unbiased discount factors have been derived that correct for both these effects. In all cases, the corrected discount rates are closer to the arithmetic than the geometric mean.

A firm in the early stages of financial distress exhibits characteristics different from those of healthy firms. As the economic condition of a firm worsens, its financial characteristics shift toward those of failed firms. Practitioners in the financial sector have long been interested in the early detection of a firm's slide toward insolvency. Several models have been developed with this purpose in mind, but these older models are static in nature. Therefore, a need exists for the development of business failure prediction models that assess the financial condition of firms sequentially over time. This article addresses this need by presenting a sequential business failure prediction model.

Nonlinear mixed effects models for data in the form of continuous, repeated measurements on each of a number of individuals, also known as hierarchical nonlinear models, are a popular platform for analysis when interest focuses on individual-specific characteristics. This framework first enjoyed widespread attention within the statistical research community in the late 1980s, and the 1990s saw vigorous development of new methodological and computational techniques for these models, the emergence of general-purpose software, and broad application of the models in numerous substantive fields. This article presents an overview of the formulation, interpretation, and implementation of nonlinear mixed effects models and surveys recent advances and applications.

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levinlin estimates the panel unit root test developed by Levin, Lin and Chu (2002). The test assumes that each individual unit in the panel shares the same AR(1) coefficient, but allows for individual effects, time effects and possibly a time trend. Lags of the dependent variable may be introduced to allow for serial correlation in the errors. The test may

ipshin estimates the t-test for unit roots in heterogeneous panels developed by Im, Pesaran and Shin (IPS, 1997). It allows for individual effects, time trends, and common time effects. Based on the mean of the individual Dickey-Fuller t-statistics of each unit in the panel, the IPS test assumes that all series are non-stationary under the null hypothesis. Lags of the