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Conditional Mean (conditional + mean)
Selected AbstractsCross Section and Panel Data Estimators for Nonseparable Models with Endogenous RegressorsECONOMETRICA, Issue 4 2005Joseph G. Altonji We propose two new methods for estimating models with nonseparable errors and endogenous regressors. The first method estimates a local average response. One estimates the response of the conditional mean of the dependent variable to a change in the explanatory variable while conditioning on an external variable and then undoes the conditioning. The second method estimates the nonseparable function and the joint distribution of the observable and unobservable explanatory variables. An external variable is used to impose an equality restriction, at two points of support, on the conditional distribution of the unobservable random term given the regressor and the external variable. Our methods apply to cross sections, but our lead examples involve panel data cases in which the choice of the external variable is guided by the assumption that the distribution of the unobservable variables is exchangeable in the values of the endogenous variable for members of a group. [source] The geography of hospital admission in a national health service with patient choiceHEALTH ECONOMICS, Issue 9 2010Daniele Fabbri Abstract Each year about 20% of the 10 million hospital inpatients in Italy get admitted to hospitals outside the Local Health Authority of residence. In this paper we carefully explore this phenomenon and estimate gravity equations for ,trade' in hospital care using a Poisson pseudo-maximum likelihood method. Consistency of the PPML estimator is guaranteed under the null of independence provided that the conditional mean is correctly specified. In our case we find that patients' flows are affected by network autocorrelation. We correct for it by relying upon spatial filtering. Our results suggest that the gravity model is a good framework for explaining patient mobility in most of the examined diagnostic groups. We find that the ability to restrain patients' outflows increases with the size of the pool of enrollees. Moreover, the ability to attract patients' inflows is reduced by the size of pool of enroless for all LHAs except for the very big LHAs. For LHAs in the top quintile of size of enrollees, the ability to attract inflows increases with the size of the pool. Copyright © 2010 John Wiley & Sons, Ltd. [source] Forecasting and Finite Sample Performance of Short Rate Models: International Evidence,INTERNATIONAL REVIEW OF FINANCE, Issue 3-4 2005SIRIMON TREEPONGKARUNA ABSTRACT This paper evaluates the forecasting and finite sample performance of short-term interest rate models in a number of countries. Specifically, we run a series of in-sample and out-of-sample tests for both the conditional mean and volatility of one-factor short rate models, and compare the results to the random walk model. Overall, we find that the out-of-sample forecasting performance of one-factor short rate models is poor, stemming from the inability of the models to accommodate jumps and discontinuities in the time series data. In addition, we perform a series of Monte Carlo analyses similar to Chapman and Pearson to document the finite sample performance of the short rate models when ,3 is not restricted to be equal to one. Our results indicate the potential dangers of over-parameterization and highlight the limitations of short-term interest rate models. [source] Bootstrapping Financial Time SeriesJOURNAL OF ECONOMIC SURVEYS, Issue 3 2002Esther Ruiz It is well known that time series of returns are characterized by volatility clustering and excess kurtosis. Therefore, when modelling the dynamic behavior of returns, inference and prediction methods, based on independent and/or Gaussian observations may be inadequate. As bootstrap methods are not, in general, based on any particular assumption on the distribution of the data, they are well suited for the analysis of returns. This paper reviews the application of bootstrap procedures for inference and prediction of financial time series. In relation to inference, bootstrap techniques have been applied to obtain the sample distribution of statistics for testing, for example, autoregressive dynamics in the conditional mean and variance, unit roots in the mean, fractional integration in volatility and the predictive ability of technical trading rules. On the other hand, bootstrap procedures have been used to estimate the distribution of returns which is of interest, for example, for Value at Risk (VaR) models or for prediction purposes. Although the application of bootstrap techniques to the empirical analysis of financial time series is very broad, there are few analytical results on the statistical properties of these techniques when applied to heteroscedastic time series. Furthermore, there are quite a few papers where the bootstrap procedures used are not adequate. [source] Volatility forecasting with double Markov switching GARCH modelsJOURNAL OF FORECASTING, Issue 8 2009Cathy W. S. Chen Abstract This paper investigates inference and volatility forecasting using a Markov switching heteroscedastic model with a fat-tailed error distribution to analyze asymmetric effects on both the conditional mean and conditional volatility of financial time series. The motivation for extending the Markov switching GARCH model, previously developed to capture mean asymmetry, is that the switching variable, assumed to be a first-order Markov process, is unobserved. The proposed model extends this work to incorporate Markov switching in the mean and variance simultaneously. Parameter estimation and inference are performed in a Bayesian framework via a Markov chain Monte Carlo scheme. We compare competing models using Bayesian forecasting in a comparative value-at-risk study. The proposed methods are illustrated using both simulations and eight international stock market return series. The results generally favor the proposed double Markov switching GARCH model with an exogenous variable. Copyright © 2008 John Wiley & Sons, Ltd. [source] Stability of nonlinear AR-GARCH modelsJOURNAL OF TIME SERIES ANALYSIS, Issue 3 2008Mika Meitz Abstract., This article studies the stability of nonlinear autoregressive models with conditionally heteroskedastic errors. We consider a nonlinear autoregression of order p [AR(p)] with the conditional variance specified as a nonlinear first-order generalized autoregressive conditional heteroskedasticity [GARCH(1,1)] model. Conditions under which the model is stable in the sense that its Markov chain representation is geometrically ergodic are provided. This implies the existence of an initial distribution such that the process is strictly stationary and , -mixing. Conditions under which the stationary distribution has finite moments are also given. The results cover several nonlinear specifications recently proposed for both the conditional mean and conditional variance, and only require mild moment conditions. [source] Prediction in ARMA Models with GARCH in Mean EffectsJOURNAL OF TIME SERIES ANALYSIS, Issue 5 2001Menelaos Karanasos This paper considers forecasting the conditional mean and variance from an ARMA model with GARCH in mean effects. Expressions for the optimal predictors and their conditional and unconditional MSEs are presented. We also derive the formula for the covariance structure of the process and its conditional variance. JEL. C22. [source] Rejecting the mean: Estimating the response of fen plant species to environmental factors by non-linear quantile regressionJOURNAL OF VEGETATION SCIENCE, Issue 4 2005Henning K. Schröder Abstract Question: Is quantile regression an appropriate statistical approach to estimate the response of fen species to single environmental factors? Background: Data sets in vegetation field studies are often characterized by a large number of zeros and they are generally incomplete in respect to the factors which possibly influence plant species distribution. Thus, it is problematic to relate plant species abundance to single environmental factors by the ordinary least squares regression technique of the conditional mean. Location: Riparian herbaceous fen in central Jutland (Denmark). Methods: Semi-parametric quantile regression was used to estimate the response of 18 plant species to six environmental factors, 95% regression quantiles were chosen to reduce the impact of multiple unmeasured factors on the regression analyses. Results of 95% quantile regression and ordinary least squares regression were compared. Results: The standard regression of the conditional mean underestimated the rates of change of species cover due to the selected factor in comparison to 95% regression quantiles. The fitted response curves indicated a general broad tolerance of the studied fen species to different flooding durations but a narrower range concerning groundwater amplitude. The cover of all species was related to soil exchangeable phosphate and base-richness. A relationship between soil exchangeable potassium and species cover was only found for 11 species. Conclusion: Considering the characteristics of data sets in vegetation science, non-linear quantile regression is a useful method for gradient analyses. [source] On Business Cycle Asymmetries in G7 CountriesOXFORD BULLETIN OF ECONOMICS & STATISTICS, Issue 3 2004Khurshid M. Kiani Abstract We investigate whether business cycle dynamics in seven industrialized countries (the G7) are characterized by asymmetries in conditional mean. We provide evidence on this issue using a variety of time series models. Our approach is fully parametric. Our testing strategy is robust to any conditional heteroskedasticity, outliers, and/or long memory that may be present. Our results indicate fairly strong evidence of nonlinearities in the conditional mean dynamics of the GDP growth rates for Canada, Germany, Italy, Japan, and the US. For France and the UK, the conditional mean dynamics appear to be largely linear. Our study shows that while the existence of conditional heteroskedasticity and long memory does not have much effect on testing for linearity in the conditional mean, accounting for outliers does reduce the evidence against linearity. [source] Robust modelling of DTARCH modelsTHE ECONOMETRICS JOURNAL, Issue 2 2005Yer Van Hui Summary, Autoregressive conditional heteroscedastic (ARCH) models and its extensions are widely used in modelling volatility in financial time series. One of the variants, the double-threshold autoregressive conditional heteroscedastic (DTARCH) model, has been proposed to model the conditional mean and the conditional variance that are piecewise linear. The DTARCH model is also useful for modelling conditional heteroscedasticity with nonlinear structures such as asymmetric cycles, jump resonance and amplitude-frequence dependence. Since asset returns often display heavy tails and outliers, it is worth studying robust DTARCH modelling without specific distribution assumption. This paper studies DTARCH structures for conditional scale instead of conditional variance. We examine L1 -estimation of the DTARCH model and derive limiting distributions for the proposed estimators. A robust portmanteau statistic based on the L1 -norm fit is constructed to test the model adequacy. This approach captures various nonlinear phenomena and stylized facts with desirable robustness. Simulations show that the L1 -estimators are robust against innovation distributions and accurate for a moderate sample size, and the proposed test is not only robust against innovation distributions but also powerful in discriminating the delay parameters and ARCH models. It is noted that the quasi-likelihood modelling approach used in ARCH models is inappropriate to DTARCH models in the presence of outliers and heavy tail innovations. [source] Do the Central Banks of Australia and New Zealand Behave Asymmetrically?THE ECONOMIC RECORD, Issue 261 2007Evidence from Monetary Policy Reaction Functions We test for evidence of asymmetric behaviour in the monetary policy reaction functions of the central banks of Australia and New Zealand. For the Reserve Bank of New Zealand, we found little evidence of asymmetric behaviour, whereas the Reserve Bank of Australia (RBA) appears to react more aggressively to negative output relative to positive output gaps of the same size. We impose additional structure on our model to help distinguish whether the asymmetric response originates from non-linearity in the inflation equation or from non-linearity in an approximate representation of the RBA's preferences over macroeconomic outcomes. We find that the preferences of the RBA may drive the asymmetry: the RBA appears to dislike negative output gaps more than positive output gaps of the same magnitude. We show this generates only a small increase in the conditional mean of inflation that is statistically indistinguishable from the target rate of inflation. [source] Return and Volatility Dynamics in the Spot and Futures Markets in Australia: An Intervention Analysis in a Bivariate EGARCH-X FrameworkTHE JOURNAL OF FUTURES MARKETS, Issue 9 2001Ramaprasad Bhar This article provides evidence of linkages between the equity market and the index futures market in Australia, where the futures market has experienced a major structural event due to the futures contract respecification. A bivariate Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model is developed that includes a cointegrating residual as an explanatory variable for both the conditional mean and the conditional variance. The conditional mean returns from both markets are influenced by the long-run equilibrium relationship, and these markets are informationally linked through the second moments. The crossmarket spillovers exhibit asymmetric behavior in that the volatility responses to past standardized innovations are different for market advances and market retreats. An intervention analysis shows that some of the parameters describing the return-generating process have shifted after the contract respecification by the futures exchange. © 2001 John Wiley & Sons, Inc. Jrl Fut Mark 21:833,850, 2001 [source] THE DECLINE IN THE VOLATILITY OF THE BUSINESS CYCLES IN THE UKTHE MANCHESTER SCHOOL, Issue 2008CHRISTINA V. ATANASOVA We analyse the sources of the decline of business cycle volatility in the UK using a dynamic factor model that allows for the presence of a structural break in the conditional mean and variance of output, sales, income and unemployment. We augment the factor model with an economic component to investigate the role of structural changes and improved monetary policy in the volatility decline of the series. Our results suggest that the dominant cause for the observed volatility decline is the reduced variability of shocks. [source] Pricing Weather Derivatives using a Predicting Power Time Series Process,ASIA-PACIFIC JOURNAL OF FINANCIAL STUDIES, Issue 6 2009Chuang-Chang Chang Abstract This paper extended the Cao-Wei (2004, JFM) model to construct a theoretical model for pricing weather derivatives in two significant ways. One adopted a time series model developed by Campbell and Diebold (2005, JASA) to describe the dynamics of temperature. The advantage of using Campbell and Diebold's time series model to describe the temperature dynamics is that it can not only take the conditional mean of temperature coming from trend, seasonal, and cyclical components but also allow for the conditional variance dynamics. The other purpose of this paper is to use an extended power utility function, instead of Cao and Wei's constant proportional risk aversion (CPRA) utility function. The extended power utility function could exhibit decreasing, constant, and increasing relative risk aversion. Eventually, we find that the prices of weather derivatives can be determined by weather conditions, discount factors, and forward premiums. Additionally, these sources have close relations with some risk aversion parameters. Furthermore, the results are consistent with Cao and Wei's condition under some specific parameter assumptions. [source] Second-Order Noncausality in Multivariate GARCH ProcessesJOURNAL OF TIME SERIES ANALYSIS, Issue 5 2000Fabienne Comte Typical multivariate economic time series may exhibit co-behavior patterns not only in the conditional means, but also in the conditional variances. In this paper we give two new definitions of variance noncausality in a multivariate setting a Granger-type noncausality and a linear Granger noncausality through projections on Hilbert spaces. Both definitions are related to a previous second-order noncausality concept defined by Granger et al. in a bivariate setting. The implications of second-order noncausality on multivariate ARMA processes with GARCH-type errors are investigated. We derive exact testable restrictions on the parameters of the processes considered, implied by this type of noncausality. Conditions for the finiteness of the fourth-order moment of the multivariate GARCH process are derived and related to earlier results in the univariate framework. We include an illustration of second-order noncausality in a trivariate model of daily financial returns. [source] Looking for contagion in currency futures marketsTHE JOURNAL OF FUTURES MARKETS, Issue 10 2003Chu-Sheng Tai This article tests whether there are pure contagion effects in both conditional means and volatilities among British pound, Canadian dollar, Deutsche mark, and Swiss franc futures markets during the 1992 ERM crisis. A conditional version of international capital asset pricing model (ICAPM) in the absence of purchasing power parity (PPP) is used to control for economic fundamentals. The empirical results indicate that overall there are no mean spillovers among those futures markets, but they are detected during the crisis period. That is, past return shocks originating in any one of the four markets have no impact on the other three markets during the entire sample period, suggesting that these markets are weak-form efficient. However, this weak-form market efficiency fails to hold during the market turmoil, especially for British pound and Swiss franc, and the sources of contagion-in-mean effects are mainly due to the return shocks originating in three European currency futures markets. As for the contagion-in-volatility, it is detected for British pound only because its conditional volatility is influenced by the negative volatility shocks from Canadian dollar, Deutsche mark, and Swiss franc, with Deutsche mark playing the dominant role in generating these shocks. JEL Classifications: C32; F31; G12. © 2003 Wiley Periodicals, Inc. Jrl Fut Mark 23:957,988, 2003 [source] Extensions of the Penalized Spline of Propensity Prediction Method of ImputationBIOMETRICS, Issue 3 2009Guangyu Zhang SummaryLittle and An (2004,,Statistica Sinica,14, 949,968) proposed a penalized spline of propensity prediction (PSPP) method of imputation of missing values that yields robust model-based inference under the missing at random assumption. The propensity score for a missing variable is estimated and a regression model is fitted that includes the spline of the estimated logit propensity score as a covariate. The predicted unconditional mean of the missing variable has a double robustness (DR) property under misspecification of the imputation model. We show that a simplified version of PSPP, which does not center other regressors prior to including them in the prediction model, also has the DR property. We also propose two extensions of PSPP, namely, stratified PSPP and bivariate PSPP, that extend the DR property to inferences about conditional means. These extended PSPP methods are compared with the PSPP method and simple alternatives in a simulation study and applied to an online weight loss study conducted by Kaiser Permanente. [source] | ||||||||||||||||||||||||||||