Misspecification Errors Research Articles

Deconstructing shocks and persistence in OECD real exchange rates1)

This paper analyzes the persistence of shocks that affect the real exchange rates for a panel of seventeen OECD developed countries during the post-Bretton Woods era. The adoption of a panel data framework allows us to distinguish two different sources of shocks, i.e. the idiosyncratic and the common shocks, each of which may have di¤erent persistence patterns on the real exchange rates. We first investigate the stochastic properties of the panel data set using panel stationarity tests that simultaneously consider both the presence of cross-section dependence and multiple structural breaks that have not received much attention in previous persistence analyses. Empirical results indicate that real exchange rates are non-stationary when the analysis does not account for structural breaks, although this conclusion is reversed when they are modeled. Consequently, misspecification errors due to the non-consideration of structural breaks leads to upward biased shocks' persistence measures. The persistence measures for the idiosyncratic and common shocks have been estimated in this paper always turn out to be less than one year.

The Optimal Size of German Child Care Centers and the Impact of Regulation: Estimating the Cost Function of a Regulated Multi-Product Firm

Summary Using a unique data set on German child care centers, we estimate a long-run multi-product cost function for child care provision in Germanywhile taking into account legal minimal labor input restrictions. For a representative child care center we find economies of scale, a U-shaped average cost curve, and indications of diseconomies of scope. The legally stipulated minimum child-to-staff ratio is manifested in a positive Lagrange multiplier, showing that modeling legal restrictions is necessary to avoid misspecification error. Our study provides a useful tool for policymakers in estimating the effects of future demographic change on child care costs.

Robust Design for Profit Maximization With Aversion to Downside Risk From Parametric Uncertainty in Consumer Choice Models

In new product design, risk averse firms must consider downside risk in addition to expected profitability, since some designs are associated with greater market uncertainty than others. We propose an approach to robust optimal product design for profit maximization by introducing an α-profit metric to manage expected profitability vs. downside risk due to uncertainty in market share predictions. Our goal is to maximize profit at a firm-specified level of risk tolerance. Specifically, we find the design that maximizes the α-profit: the value that the firm has a (1 − α) chance of exceeding, given the distribution of possible outcomes. The parameter α ∈ (0,1) is set by the firm to reflect sensitivity to downside risk (or upside gain), and parametric study of α reveals the sensitivity of optimal design choices to firm risk preference. We account here only for uncertainty of choice model parameter estimates due to finite data sampling when the choice model is assumed to be correctly specified (no misspecification error). We apply the delta method to estimate the mapping from uncertainty in discrete choice model parameters to uncertainty of profit outcomes and identify the estimated α-profit as a closed-form function of decision variables for the multinomial logit model. An example demonstrates implementation of the method to find the optimal design characteristics of a dial-readout scale using conjoint data.

The exponentiated exponential–geometric distribution: a distribution with decreasing, increasing and unimodal failure rate

In this paper, we proposed a new family of distributions namely exponentiated exponential–geometric (E2G) distribution. The E2G distribution is a straightforwardly generalization of the exponential–geometric (EG) distribution proposed by Adamidis and Loukas [A lifetime distribution with decreasing failure rate, Statist. Probab. Lett. 39 (1998), pp. 35–42], which accommodates increasing, decreasing and unimodal hazard functions. It arises on a latent competing risk scenarios, where the lifetime associated with a particular risk is not observable but only the minimum lifetime value among all risks. The properties of the proposed distribution are discussed, including a formal proof of its probability density function and explicit algebraic formulas for its survival and hazard functions, moments, rth moment of the ith order statistic, mean residual lifetime and modal value. Maximum-likelihood inference is implemented straightforwardly. From a mis-specification simulation study performed in order to assess the extent of the mis-specification errors when testing the EG distribution against the E2G, and we observed that it is usually possible to discriminate between both distributions even for moderate samples with presence of censoring. The practical importance of the new distribution was demonstrated in three applications where we compare the E2G distribution with several lifetime distributions.

How Good Are Observational Studies in Assessing Psychiatric Treatment Outcomes?

How Good Are Observational Studies in Assessing Psychiatric Treatment Outcomes?

Advances in pricing commodity futures: Multifactor models

The purpose of this paper is to obtain the risk-neutral drift of the state variables directly from market data in a multifactor commodity futures model. Note that the risk-neutral drift is a key factor in the general asset pricing theory but it is not observable.In this paper, we derive some exact results which relate the risk-neutral drifts to the slope of the commodity futures price jointly with the factors. This fact allows us to estimate some of the coefficients of the pricing partial differential equation directly from the futures data available in the markets. Moreover, we do not have to estimate either the physical drift or the market price of risk. Therefore we considerably reduce the number of functions to estimate and, as a consequence, we reduce the computational cost as well as the misspecification error. In order to investigate the finite sample properties of this approach we carry out some numerical experiments. Finally, an application to crude oil and natural gas futures contracts traded at the New York Mercantile Exchange (NYMEX) is also illustrated.

Bootstrap-based ARMA order selection

Modelling the underlying stochastic process is one of the main goals in the study of many dynamic phenomena, such as signal processing, system identification and time series. The issue is often addressed within the framework of ARMA (Autoregressive Moving Average) paradigm, so that the related task of identification of the ‘true’ order is crucial. As it is well known, the effectiveness of such an approach may be seriously compromised by misspecification errors since they may affect model capabilities in capturing dynamic structures of the process. As a result, inference and empirical outcomes may be heavily misleading. Despite the big number of available approaches aimed at determining the order of an ARMA model, the issue is still open. In this paper, we bring the problem in the framework of bootstrap theory in conjunction with the information-based criterion of Akaike (AIC), and a new method for ARMA model selection will be presented. A theoretical justification for the proposed approach as well as an evaluation of its small sample performances, via simulation study, are given.

The complementary exponential geometric distribution: Model, properties, and a comparison with its counterpart

In this paper, we proposed a new two-parameter lifetime distribution with increasing failure rate, the complementary exponential geometric distribution, which is complementary to the exponential geometric model proposed by Adamidis and Loukas (1998). The new distribution arises on a latent complementary risks scenario, in which the lifetime associated with a particular risk is not observable; rather, we observe only the maximum lifetime value among all risks. The properties of the proposed distribution are discussed, including a formal proof of its probability density function and explicit algebraic formulas for its reliability and failure rate functions, moments, including the mean and variance, variation coefficient, and modal value. The parameter estimation is based on the usual maximum likelihood approach. We report the results of a misspecification simulation study performed in order to assess the extent of misspecification errors when testing the exponential geometric distribution against our complementary one in the presence of different sample size and censoring percentage. The methodology is illustrated on four real datasets; we also make a comparison between both modeling approaches.

DOES REAL INTEREST RATE PARITY HOLD FOR OECD COUNTRIES? NEW EVIDENCE USING PANEL STATIONARITY TESTS WITH CROSS-SECTION DEPENDENCE AND STRUCTURAL BREAKS

This paper tests for real interest rate parity (RIRP) among the 17 major Organisation for Economic Cooperation and Development countries over the period 1978:Q1–2006:Q1. The econometric methods applied consist of combining the use of panel data tests that are valid under cross-section dependence and the presence of multiple structural breaks. This feature is important because the misspecification errors due to not accounting for structural breaks and/or cross-section dependence can lead to misleading conclusions. Our results support the fulfilment of the weak version of the RIRP for short-term interest rate differentials once dependence and structural breaks are considered.

Prediction of Functional Status for the Elderly Based on a New Ordinal Regression Model

The functional mobility of the elderly is a very important factor in aging research, and prognostic information is valuable in making clinical and health care policy decisions. We develop a predictive model for the functional status of the elderly based on data from the Second Longitudinal Study of Aging (LSOA II). The functional status is an ordinal response variable. The ordered probit model has been moderately successful in analyzing such data; however, its reliance on the normal distribution for its latent variable hinders its accuracy and potential. In this paper, we focus on the prediction of conditional quantiles of the functional status based on a more general transformation model. The proposed estimation procedure does not rely on any parametric specification of the conditional distribution functions, aiming to reduce model misspecification errors in the prediction. Cross-validation within the LSOA II data shows that our prediction intervals are more informative than those from the ordered probit model. Monte Carlo simulations also demonstrate the merits of our approach in the analysis of ordinal response variables.

The pungent smell of “red herrings”: Subsoil assets, rents, volatility and the resource curse

Brunnschweiler and Bulte (2008) [1,2] provide cross-country evidence that resource curse is a “red herring” once one corrects for endogeneity of resource exports and allows resource abundance to affect growth. Their results show that resource exports are no longer significant while value of subsoil assets has a significant positive effect on growth. But the World Bank measure of subsoil assets is proportional to current rents, and thus is also endogenous. Furthermore, their results suffer from an unfortunate data mishap, omitted variables bias, weakness of instruments, violation of exclusion restrictions and misspecification error. Correcting for these issues and instrumenting resource exports with values of proven reserves at the beginning of the sample period, there is no evidence for resource curse either and subsoil assets are no longer significant. However, the same evidence suggests that resource exports or rents boost growth in stable countries, but also make especially already volatile countries more volatile and thus indirectly worsen growth prospects. Ignoring the volatility channel may lead one to erroneously conclude that there is no effect of resources on growth.

The Pungent Smell of 'Red Herrings': Subsoil Assets, Rents, Volatility and the Resource Curse

Brunnschweiler and Bulte (2008) provide cross-country evidence that the resource curse is a “red herring” once one corrects for endogeneity of resource exports and allows resource abundance affect growth. Their results show that resource exports are no longer significant while the value of subsoil assets has a significant positive effect on growth. But the World Bank measure of subsoil assets is proportional to current rents, and thus also endogenous. Furthermore, their results suffer from an unfortunate data mishap, omitted variables bias, weakness of the instruments, violation of exclusion restrictions and misspecification error. Correcting for these issues and instrumenting resource exports with values of proven reserves at the beginning of the sample period; there is no evidence for the resource curse either and subsoil assets are no longer significant. However, the same evidence suggests that resource exports or rents boost growth in stable countries, but also make especially already volatile countries more volatile and thus indirectly worsen growth prospects. Ignoring the volatility channel, may lead one to erroneously conclude that there is no effect of resources on growth.

New trade theory, non-price competitiveness and export performance

This paper develops a demand function for Greece's exports of manufactures according to New Trade Theory. Non-price competitiveness plays a vital role in explaining export performance and failure to include it in the export equation may lead to mis-specification error. Foreign income has a moderately high effect on exports in the long run and no effect in the short run. Exports are also sensitive to domestic and competitors' prices in the long run, but cost and price competitiveness elasticities are close to one, indicating that Greek exporters have some ability to compete on the basis of prices.

A Robust Conditional Realized Extended 4-CAPM

In this paper we present and extend the approach of Bollerslev and Zhang (2003) for realized measures and co-measures of risk in some classical asset pricing models, such as the Capital Asset Pricing Model (CAPM) of Sharpe (1964) and the Arbitrage Pricing Theory (APT) model by Ross (1976). These extensions include higher-moments asset pricing models (see Jurczenko and Maillet, 2006), conditional asset pricing models (see Bollerslev et al., 1988, and Jondeau and Rockinger, 2004). Estimations are conducted using several methodologies aiming to neutralize data measurement and model misspecification errors (see Ledoit and Wolf, 2003 and 2004), properly dealing with inter-relations between financial assets in term of returns (see Zellner, 1962), but also in terms of higher conditional moments (see Bollerslev, 1988).

Price and Non-Price Competitiveness of Exports of Manufactures

This paper develops a demand function for Greece's exports of manufactures according to New Trade Theory. The sample covers a rather long period of four decades with exports aggregated based on industrial rather than on trade classification. The study contributes to a better understanding of the effects of export prices, domestic and competitors', as well as of non-price competitiveness approximated with capital stock, on export performance. The empirical estimation uses the Johansen maximum likelihood approach in the long run and a dynamic error-correction equation in the short run. The estimated long-run and short-run relationships follow the economic theory and are remarkably stable. It is shown that non-price competitiveness plays a vital role in explaining export performance in the long run as well as in the short run and that failure to include it in the export equation may lead to mis-specification error. As opposed to conventional models of export demand where income effects are very high, in the present study foreign income has a moderately high effect on exports in the long run and no effect in the short run. Exports are also sensitive to domestic and competitors' prices in the long run, but cost and price competitiveness elasticities are close to one, indicating that Greek exporters have some ability to compete on the basis of prices.

Price and non-price competitiveness of exports of manufactures

This paper develops a demand function for Greece’s exports of manufactures according to New Trade Theory. The sample covers a rather long period of four decades with exports aggregated based on industrial rather than on trade classification. The study contributes to a better understanding of the effects of export prices, domestic and competitors’, as well as of non-price competitiveness approximated with capital stock, on export performance. The empirical estimation uses the Johansen maximum likelihood approach in the long run and a dynamic errorcorrection equation in the short run. The estimated long-run and short-run relationships follow the economic theory and are remarkably stable. It is shown that non-price competitiveness plays a vital role in explaining export performance in the long run as well as in the short run and that failure to include it in the export equation may lead to mis-specification error. As opposed to conventional models of export demand where income effects are very high, in the present study foreign income has a moderately high effect on exports in the long run and no effect in the short run. Exports are also sensitive to domestic and competitors’ prices in the long run, but cost and price competitiveness elasticities are close to one, indicating that Greek exporters have some ability to compete on the basis of prices.

The Cyclical Association of Residential Housing Price and Consumption

Executive Summary.This paper develops a theoretical framework to investigate private consumption changes that are brought about by the residential housing price effect, which in turn is envisaged to comprise the income effect, substitution effect, and expectation effect along with the residential price cycle. A frequency domainbased model that employs cross-spectra analysis is consistent with such a theoretical framework, and helps to validate it, as its model-free characteristics avoid the problems of model misspecification and parameter estimation error. In the Singapore context, the results show that residential price affects consumption significantly, depending on the time scale and frequency without a consistent sign. The expectation effect, operating through the capital gain effect, is important in explaining the residential price-consumption relationship and contributes more during the expansion period than the recession period.

Improved Forecasting of Mutual Fund Alphas and Betas*

This paper proposes a simple back testing procedure that is shown to dramatically improve a panel data model's ability to produce out of sample forecasts. Here the procedure is used to forecast mutual fund alphas. Using monthly data with an OLS model it has been difficult to consistently predict which portfolio managers will produce above market returns for their investors. This paper provides empirical evidence that sorting on the estimated alphas populates the top and bottom deciles not with the best and worst funds, but with those having the greatest estimation error. This problem can be attenuated by back testing the statistical model fund by fund. The back test used here requires a statistical model to exhibit some past predictive success for a particular fund before it is allowed to make predictions about that fund in the current period. Another estimation problem concerns the use of a single statistical model for all available mutual funds. Since no one statistical model is likely to fit every fund, the result is a great deal of misspecification error. This paper shows that the combined use of an OLS and Kalman filter model increases the number of funds with predictable out of sample alphas by about 60%. Overall, a strategy that uses very modest ex-ante filters to eliminate funds whose parameters likely derive primarily from estimation error produces an out of sample risk-adjusted return of over 4% per annum.

The Effect of the Number of Observations per Parameter in Misspecified Confirmatory Factor Analytic Models

Some authors have suggested that sample size in covariance structure modeling should be considered in the context of how many parameters are to be estimated (e.g., Kline, 2005). Previous research has examined the effect of varying sample size relative to the number of parameters being estimated (N:q). Although some support has been found for this effect, the effect size appears to be small compared to other influences, such as indicator reliability and sample size (Jackson, 2003). Efforts to extend this work to the case where models are intentionally misspecified are described in this article. In addition to varying the number of observations per estimated parameter, several other known influences on model fit were varied such as sample size, the degree of misspecification, number of variables per factor, and the communality of the measured variables. The results suggest that decreasing the number of parameters to be estimated while holding sample size constant can help detect misspecification errors, and some fit indexes were more sensitive to this manipulation than others. In general, the effects of N:q were small relative to other experimental effects.

Demographic Issues in Longevity Risk Analysis

AbstractFundamental to the modeling of longevity risk is the specification of the assumptions used in demographic forecasting models that are designed to project past experience into future years, with or without modifications based on expert opinion about influential factors not represented in the historical data. Stochastic forecasts are required to explicitly quantify the uncertainty of forecasted cohort survival functions, including uncertainty due to process variance, parameter errors, and model misspecification errors. Current applications typically ignore the latter two sources although the potential impact of model misspecification errors is substantial. Such errors arise from a lack of understanding of the nature and causes of historical changes in longevity and the implications of these factors for the future. This article reviews the literature on the nature and causes of historical changes in longevity and recent efforts at deterministic and stochastic forecasting based on these data. The review reveals that plausible alternative sets of forecasting assumptions have been derived from the same sets of historical data, implying that further methodological development will be needed to integrate the various assumptions into a single coherent forecasting model. Illustrative calculations based on existing forecasts indicate that the ranges of uncertainty for older cohorts' survival functions will be at a manageable level. Uncertainty ranges for younger cohorts will be larger and the need for greater precision will likely motivate further model development.