Simultaneous Inference in General Parametric Models

Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the pre-specified significance level. Simultaneous inference procedures have to be used which adjust for multiplicity and thus control the overall type I error rate. In this paper we describe simultaneous inference procedures in general parametric models, where the experimental questions are specified through a linear combination of elemental model parameters. The framework described here is quite general and extends the canonical theory of multiple comparison procedures in ANOVA models to linear regression problems, generalized linear models, linear mixed effects models, the Cox model, robust linear models, etc. Several examples using a variety of different statistical models illustrate the breadth of the results. For the analyses we use the R add-on package multcomp, which provides a convenient interface to the general approach adopted here

A Factor-Adjusted Multiple Testing Procedure with Application to Mutual Fund Selection

In this article, we propose a factor-adjusted multiple testing (FAT) procedure based on factor-adjusted p-values in a linear factor model involving some observable and unobservable factors, for the purpose of selecting skilled funds in empirical finance. The factor-adjusted p-values were obtained after extracting the latent common factors by the principal component method. Under some mild conditions, the false discovery proportion can be consistently estimated even if the idiosyncratic errors are allowed to be weakly correlated across units. Furthermore, by appropriately setting a sequence of threshold values approaching zero, the proposed FAT procedure enjoys model selection consistency. Extensive simulation studies and a real data analysis for selecting skilled funds in the U.S. financial market are presented to illustrate the practical utility of the proposed method. Supplementary materials for this article are available online

Testing international parity hypothesis in a multivariate identified co-integrating system: the Turkish evidence

In this paper, a multivariate co-integrating model is constructed upon the Turkish economy to examine the validity of the purchasing power parity and the uncovered interest parity theories simultaneously. Estimation results obtained from the identified co-integrating vectors support a priori modelling expectations and yield evidence to the existence of both parities when integrated within each other. However, no evidence is obtained in favor of the two international exchange rate determination parity hypotheses when formulated in isolation. A policy inference derived from the paper can be summarized such that, since the market mechanisms seem to closely affect the long-run course of the nominal exchange rate, exchange rate based stabilization programs should be appreciated by economic agents in a cautious way.Purchasing Power Parity ; Uncovered Interest Parity ; Turkish Economy ;

Keynesian and new classical models of unemployment revisited

Economics;Unemployment

Are Errors in Official U.S. Budget Receipts Forecasts Just Noise?

Existing evidence suggests that U.S. Government budget receipts forecasts are unbiased and efficient. Our study is an attempt to examine the veracity of these findings. The time series framework employed in this study is distinguished from previous work in three ways. First, we build a model that explicitly admits serial correlation in the residuals by allowing for autoregressive, moving-average, serial correlation. Second, we employ the nonparametric Monte-Carlo bootstrap to free ourselves from reliance on asymptotic distribution theory which is suspect given the short data series available for this study. Third, we control for errors in the macroeconomic and financial assumptions used to produce the U.S. Government's budget forecasts. We find that the U.S. Government's annual, one-year ahead, budget receipts forecasts for fiscal years 1963 through 2003 are biased and inefficient. In addition, we find that these forecasts exhibit serial correlation in their errors and thus do not efficiently exploit all available information. Finally, we find evidence that is consistent with strategic bias that may reflect the political goals of the Administration in power. Working Paper 07-2

HE Plots for Repeated Measures Designs

Hypothesis error (HE) plots, introduced in Friendly (2007), provide graphical methods to visualize hypothesis tests in multivariate linear models, by displaying hypothesis and error covariation as ellipsoids and providing visual representations of effect size and significance. These methods are implemented in the heplots for R (Fox, Friendly, and Monette 2009a) and SAS (Friendly 2006), and apply generally to designs with fixed-effect factors (MANOVA), quantitative regressors (multivariate multiple regression) and combined cases (MANCOVA). This paper describes the extension of these methods to repeated measures designs in which the multivariate responses represent the outcomes on one or more âÂÂwithin-subjectâ factors. This extension is illustrated using the heplots for R. Examples describe one- sample profile analysis, designs with multiple between-S and within-S factors, and doubly- multivariate designs, with multivariate responses observed on multiple occasions.

On the linkages between stock prices and exchange rates: evidence from the banking crisis of 2007-2010

This study examines the nature of the linkages between stock market prices and exchange rates in six advanced economies, namely the US, the UK, Canada, Japan, the euro area, and Switzerland, using data on the banking crisis between 2007 and 2010. Bivariate UEDCC-GARCH models are estimated producing evidence of unidirectional Granger causality from stock returns to exchange rate changes in the US and the UK, in the opposite direction in Canada, and bidirectional causality in the euro area and Switzerland. Furthermore, causality-in-variance from stock returns to exchange rate changes is found in the US and in the opposite direction in the euro area and Japan, whilst there is evidence of bidirectional feedback in Switzerland and Canada. The results of the time-varying correlations also show that the dependence between the two variables has increased during the recent financial crisis. These findings imply limited opportunities for investors to diversify their assets during this period

Fractional integration and cointegration in US financial time series data

This paper examines several US monthly financial time series data using fractional integration and cointegration techniques. The univariate analysis based on fractional integration aims to determine whether the series are I(1) (in which case markets might be efficient) or alternatively I(d) with d < 1, which implies mean reversion. The multivariate framework exploiting recent developments in fractional cointegration allows to investigate in greater depth the relationships between financial series. We show that there exist many (fractionally) cointegrated bivariate relationships among the variables examined