Estimating dynamic panel data discrete choice models with fixed effects

This paper considers the estimation of dynamic binary choice panel data models with fixed effects. It is shown that the modified maximum likelihood estimator (MMLE) used in this paper reduces the order of the bias in the maximum likelihood estimator from O(T^-1) to O(T^-2), without increasing the asymptotic variance. No orthogonal reparametrization is needed. Monte Carlo simulations are used to evaluate its performance in finite samples where T is not large. In probit and logit models containing lags of the endogenous variable and exogenous variables, the estimator is found to have a small bias in a panel with eight periods. A distinctive advantage of the MMLE is its general applicability. Estimation and relevance of different policy parameters of interest in this kind of models are also addressed.     

The asymmetric predictability of high-yield bonds

This study examines the relationship between the high-yield bonds market and the stock market and indicates that stock returns lead high-yield bond returns. Specifically, this study further shows that this lead–lag relationship is more solid during bear market periods since a downward trend in the stock market implies a high likelihood of the exercise of the equity put in short position embedded in a high-yield bond at maturity. We also conducted out-of-sample forecast using a VAR model, an AR model and naïve estimation during bear market and non-bear market periods. Our results demonstrate that high-yield bond returns are better predicted by a VAR model that includes past stock returns than by an AR model or naive estimation during bear market periods, but such is not the case during non-bear market periods.     

Moment-based estimation of smooth transition regression models with endogenous variables

Nonlinear regression models have been widely used in practice for a variety of time series and cross-section datasets. For purposes of analyzing univariate and multivariate time series data, in particular, smooth transition regression (STR) models have been shown to be very useful for representing and capturing asymmetric behavior. Most STR models have been applied to univariate processes, and have made a variety of assumptions, including stationary or cointegrated processes, uncorrelated, homoskedastic or conditionally heteroskedastic errors, and weakly exogenous regressors. Under the assumption of exogeneity, the standard method of estimation is nonlinear least squares. The primary purpose of this paper is to relax the assumption of weakly exogenous regressors and to discuss moment-based methods for estimating STR models. The paper analyzes the properties of the STR model with endogenous variables by providing a diagnostic test of linearity of the underlying process under endogeneity, developing an estimation procedure and a misspecification test for the STR model, presenting the results of Monte Carlo simulations to show the usefulness of the model and estimation method, and providing an empirical application for inflation rate targeting in Brazil. We show that STR models with endogenous variables can be specified and estimated by a straightforward application of existing results in the literature.     

Changes in urban land values: 1836–1970

A model is developed for measuring the return to holding land and those returns are examined using a random coefficient estimation procedure for specific periods from 1836 to 1970. This statistical model provides a mean rate of return for land and a predictor for each time period. The results suggest that the long-term return to holding land is no higher than the rate of return to holding high-grade bonds. For shorter holding periods, the returns vary significantly.     

Employment effects of the Football World Cup 1974 in Germany

This study demonstrates that the Football World Cup 1974 in Germany was not able to generate any medium to long-term employment effects that were significantly different from zero. It is the first work to examine the employment effects of Football World Cup tournaments. It is also the first work to undertake a multivariate analysis of the employment effects of a major sporting event outside of the USA. In addition, this study does not arbitrarily determine the time period for the potential positive effects of a major sporting event but instead examines several alternative periods. Furthermore, the study tests for method sensitivity by analysing the data set in parallel with the approaches used in the studies of sporting events in the USA as well as in a fourth modifying estimation approach. In contrast to the conclusions reached in comparable studies, the results are not regarded as a clear refutation of the positive effects of major sporting events.     

Nonparametric estimation in case of endogenous selection

This paper addresses the problem of estimation of a nonparametric regression function from selectively observed data when selection is endogenous. Our approach relies on independence between covariates and selection conditionally on potential outcomes. Endogeneity of regressors is also allowed for. In the exogenous and endogenous case, consistent two-step estimation procedures are proposed and their rates of convergence are derived. Pointwise asymptotic distribution of the estimators is established. In addition, bootstrap uniform confidence bands are obtained. Finite sample properties are illustrated in a Monte Carlo simulation study and an empirical illustration.     

Stochastic specification and the estimation of share equations

The standard stochastic specification for a system of share equations is obtained by assuming that the shares have a joint normal distribution with means depending upon exogenous variables and a constant covariance matrix. This specification ignores the requirement that shares be between zero and unity by giving positive probability to shares outside this range. An alternative stochastic specification involving the Dirichlet distribution, which automatically limits shares to the unit simplex, is suggested. A comparison of results obtained from the two specifications is made using sampling experiments and data from three different empirical studies. The sampling experiments and empirical applications show that the results are generally quite close, thus providing some justification for the continued use of the normal distribution specification in the estimation of share equations.     

A small linear model of wage- and price-inflation in the Norwegian economy

This paper presents a model of inflation in a small open economy which features both wage-wage linkages and a wage-price spiral. Hence we have a simultaneous structure which contains the conventional Scandinavian model of inflation as a special case. Full system estimation results are reported. Great emphasis is placed on data coherency and on parameter stability. One interesting finding is that both wage growth and the wage level in the exposed (E)-sector are strongly influenced by the outside wage. This contradicts the predictions of the Scandinavian model, which defines the wage-leading role of the E-sector by the absence of outside wage effects in E-sector wage formation. Another result is that the speed of adjustment to exogenous shocks is greater for prices than for wages. This finding may be important in explaining real wage flexibility, which is often seen as the hallmark of low unemployment economies such as the Norwegian.     

Departures from marginal-cost pricing in the American automobile industry: Estimates for 1977–1978

How are prices set in the American automobile oligopoly? This paper seeks empirical estimates of the extent of departure from marginal-cost pricing and of the effects of foreign competition. The model estimated has product differentiation, multiproduct firms, and heterogeneity in consumer tastes. The estimation presumes that product type (proxied by engineering specifications) is exogenous to the price/quantity market equilibrium. Cross-section results for the 1977 and 1978 model years yield price-cost margins around 10%. Import competition has the effect of lowering equilibrium margins for compact and subcompact models.     

Finite‐sample comparison of alternative methods for estimating dynamic panel data models

The Wooldridge method is based on a simple and novel strategy to deal with the initial values problem in nonlinear dynamic random‐effects panel data models. The characteristic of the method makes it very attractive in empirical applications. However, its finite sample performance and robustness are not fully known as of yet. In this paper we investigate the performance and robustness of this method in comparison with an ideal case in which the initial values are known constants; the worst scenario is based on an exogenous initial values assumption, and the Heckman's reduced‐form approximation method, which is widely used in the literature. The dynamic random‐effects probit and Tobit (type I) models are used as working examples. Various designs of the Monte Carlo experiments and two further empirical illustrations are provided. The results suggest that the Wooldridge method works very well only for the panels of moderately long duration (longer than 5–8 periods). Heckman's reduced‐form approximation is suggested for short panels (shorter than 5 periods). It is also found that all the methods tend to perform equally well for panels of long duration (longer than 15–20 periods). Copyright © 2011 John Wiley & Sons, Ltd.     

A note on the evaluation of long-run investment decisions using the sharpe ratio

This paper reexamines the use of the Sharpe ratio to measure the performance of large and small company stocks along with corporate bonds over different holding periods. It builds on previous research which cites the effects of serial correlation and non-normality in the creation of estimation error in the calculation of the Sharpe ratio. It finds that higher order moments such as skewness and kurtosis are a further source of error that must be accounted for when making inferences about asset performance.     

Errors-in-variables in demand systems

The rejection of symmetry and other restrictions in demand systems may be due to measurement errors in the exogenous variables. It is shown that symmetry conditions can be used to identify and consistently estimate a linear model's parameters when measurement error exists. Several identification rules are derived and estimation of identified models is considered. Results are applied to estimation of the Almost Ideal Demand System for the United Kingdom.     

Zero expenditures and the estimation of engel curves

Over the brief interview periods typical of budget surveys (such as the U.K. Family Expenditure Survey) households' recorded expenditures are liable to be misleading indicators of their underlying consumption. Most starkly, a household may not be observed to purchase commodities that it nevertheless consumes. This paper considers the implications of such measurement errors for the estimation of demand systems, and derives a consistent estimator for the special case of linear Engel curves.     

Can a stochastic cusp catastrophe model explain housing market crashes?

Like stock market prices, housing prices often exhibit temporary booms and busts. A possible explanation for the observed abrupt changes is offered by the stochastic catastrophe model. This paper addresses the question whether the catastrophe model can describe and predict the dynamics of housing markets. We fit a stochastic cusp catastrophe model to empirical housing market data for six OECD countries, US, JP, UK, NL, SE and BE. Two different estimation approaches are considered – Cobb׳s method and Euler discretization. The analysis shows that while Cobb׳s approach describes the long-run stationary density better, Euler discretization is more tailored for time series, as it provides better one-step-ahead predictions. Proceeding using the Euler discretization method we discuss the dynamics of housing markets in terms of the multiple equilibria cusp catastrophe model. By considering the long-term interest rate as an exogenous variable we obtain new insights into the policy implications of interest rate levels, in particular concerning the stability of housing markets.     

Identification and estimation of econometric models with group interactions,contextual factors and fixed effects

This paper considers identification and estimation of structural interaction effects in a social interaction model. The model allows unobservables in the group structure, which may be correlated with included regressors. We show that both the endogenous and exogenous interaction effects can be identified if there are sufficient variations in group sizes. We consider the estimation of the model by the conditional maximum likelihood and instrumental variables methods. For the case with large group sizes, the possible identification can be weak in the sense that the estimates converge in distribution at low rates.     

Weak Identification of Forward‐looking Models in Monetary Economics*

Recently, single‐equation estimation by the generalized method of moments (GMM) has become popular in the monetary economics literature, for estimating forward‐looking models with rational expectations. We discuss a method for analysing the empirical identification of such models that exploits their dynamic structure and the assumption of rational expectations. This allows us to judge the reliability of the resulting GMM estimation and inference and reveals the potential sources of weak identification. With reference to the New Keynesian Phillips curve of Galí and Gertler [Journal of Monetary Economics (1999) Vol. 44, 195] and the forward‐looking Taylor rules of Clarida, Galí and Gertler [Quarterly Journal of Economics (2000) Vol. 115, 147], we demonstrate that the usual ‘weak instruments’ problem can arise naturally, when the predictable variation in inflation is small relative to unpredictable future shocks (news). Hence, we conclude that those models are less reliably estimated over periods when inflation has been under effective policy control.     

Selection of estimation window in the presence of breaks

In situations where a regression model is subject to one or more breaks it is shown that it can be optimal to use pre-break data to estimate the parameters of the model used to compute out-of-sample forecasts. The issue of how best to exploit the trade-off that might exist between bias and forecast error variance is explored and illustrated for the multivariate regression model under the assumption of strictly exogenous regressors. In practice when this assumption cannot be maintained and both the time and size of the breaks are unknown, the optimal choice of the observation window will be subject to further uncertainties that make exploiting the bias–variance trade-off difficult. To that end we propose a new set of cross-validation methods for selection of a single estimation window and weighting or pooling methods for combination of forecasts based on estimation windows of different lengths. Monte Carlo simulations are used to show when these procedures work well compared with methods that ignore the presence of breaks.     

Market analysis with rational expectations: Theory and estimation

In a dynamic market structure the rational expectations hypothesis implies the existence of a ‘fixed point’ or‘equilibrium’ price sequence. For a prototype market structure we develop the This approach displays the relationships between exogenous structure, expectations, and behavior. The resulting framework suggests effective estimation procedures and clarifies analysis. Results for an application are presented and discussed.     

Value at risk models for volatile emerging markets equity portfolios

This paper investigates the issue of market risk quantification for emerging and developed market equity portfolios. A very wide spectrum of popular and widely used in practice Value at Risk (VaR) models are evaluated and compared with Extreme Value Theory (EVT) and adaptive filtered models, during normal, crises, and post-crises periods. The results are interesting and indicate that despite the documented differences between emerging and developed markets, the most successful VaR models are common for both asset classes. Furthermore, in the case of the (fatter tailed) emerging market equity portfolios, most VaR models turn out to yield conservative risk forecasts, in contrast to developed market equity portfolios, where most models underestimate the realized VaR. VaR estimation during periods of financial turmoil seems to be a difficult task, particularly in the case of emerging markets and especially for the higher loss quantiles. VaR models seem to be affected less by crises periods in the case of developed markets. The performance of the parametric (non-parametric) VaR models improves (deteriorates) during post-crises periods due to the inclusion of extreme events in the estimation sample.     

The economic performance of Swiss drinking water utilities

This paper measures the performance in terms of costs of Swiss drinking water utilities accounting for environmental factors. We estimate a translog stochastic variable cost frontier using two different techniques on an unbalanced panel of 141 water distribution utilities over the years 2002–2009, for a total of 745 observations. Results show that exogenous factors have an impact on variable cost. More precisely, we find that the share of pumped over total extracted water, population density, altitude and meteorological factors (maximum 30 days temperature and extreme precipitation events) have a significant impact on variable cost. Likelihood ratio tests emphasize the importance to include observed heterogeneity in the estimations. Efficiency rankings provided by models accounting for exogenous factors and their counterparts without them are however relatively similar. On the contrary, the efficiency ranks differ strongly between alternative estimation techniques. In assessing the economic performance of utilities, the most important choice thus seems to be about the way unobserved heterogeneity is treated.