Does neighborhood income mix affect earnings of adults? New evidence from Sweden

This paper contributes to the literature on obtaining unbiased estimates of neighborhood effects, explored in the context of a centralized social welfare state. We employ a longitudinal database comprised of all working age adults in metropolitan Sweden 1991–1999 to investigate the degree to which neighborhood income mix relates to subsequent labor incomes of adults and how this relationship varies by gender and employment status. We control for unobserved, time-invariant individual characteristics by estimating a first-difference equation of changes in average incomes between the 1991–1995 and 1996–1999 periods. We further control for unobserved time varying characteristics through an analysis of non-movers. These methods substantially reduce the magnitude of the apparent effect of neighborhood shares of low-, middle- and high-income males. Nevertheless, statistically and substantively significant neighborhood effects persist, though relationships are nonlinear and vary by gender and employment status. Males who are not fully employed appear most sensitive to neighborhood economic mix in all contexts.     

A two-stage realized volatility approach to estimation of diffusion processes with discrete data

This paper motivates and introduces a two-stage method of estimating diffusion processes based on discretely sampled observations. In the first stage we make use of the feasible central limit theory for realized volatility, as developed in [Jacod, J., 1994. Limit of random measures associated with the increments of a Brownian semiartingal. Working paper, Laboratoire de Probabilities, Universite Pierre et Marie Curie, Paris] and [Barndorff-Nielsen, O., Shephard, N., 2002. Econometric analysis of realized volatility and its use in estimating stochastic volatility models. Journal of the Royal Statistical Society. Series B, 64, 253–280], to provide a regression model for estimating the parameters in the diffusion function. In the second stage, the in-fill likelihood function is derived by means of the Girsanov theorem and then used to estimate the parameters in the drift function. Consistency and asymptotic distribution theory for these estimates are established in various contexts. The finite sample performance of the proposed method is compared with that of the approximate maximum likelihood method of [Aït-Sahalia, Y., 2002. Maximum likelihood estimation of discretely sampled diffusion: A closed-form approximation approach. Econometrica. 70, 223–262].     

Econometric analysis of structural systems with permanent and transitory shocks

This paper considers the implications of the permanent/transitory decomposition of shocks for identification of structural models in the general case where the model might contain more than one permanent structural shock. It provides a simple and intuitive generalization of the influential work of Blanchard and Quah [1989. The dynamic effects of aggregate demand and supply disturbances. The American Economic Review 79, 655–673], and shows that structural equations with known permanent shocks cannot contain error correction terms, thereby freeing up the latter to be used as instruments in estimating their parameters. The approach is illustrated by a re-examination of the identification schemes used by Wickens and Motto [2001. Estimating shocks and impulse response functions. Journal of Applied Econometrics 16, 371–387], Shapiro and Watson [1988. Sources of business cycle fluctuations. NBER Macroeconomics Annual 3, 111–148], King et al. [1991. Stochastic trends and economic fluctuations. American Economic Review 81, 819–840], Gali [1992. How well does the ISLM model fit postwar US data? Quarterly Journal of Economics 107, 709–735; 1999. Technology, employment, and the business cycle: Do technology shocks explain aggregate fluctuations? American Economic Review 89, 249–271] and Fisher [2006. The dynamic effects of neutral and investment-specific technology shocks. Journal of Political Economy 114, 413–451].     

Two-stage rank estimation of quantile index models

This paper estimates a class of models which satisfy a monotonicity condition on the conditional quantile function of the response variable. This class includes as a special case the monotonic transformation model with the error term satisfying a conditional quantile restriction, thus allowing for very general forms of conditional heteroscedasticity. A two-stage approach is adopted to estimate the relevant parameters. In the first stage the conditional quantile function is estimated nonparametrically by the local polynomial estimator discussed in Chaudhuri (Journal of Multivariate Analysis 39 (1991a) 246–269; Annals of Statistics 19 (1991b) 760–777) and Cavanagh (1996, Preprint). In the second stage, the monotonicity of the quantile function is exploited to estimate the parameters of interest by maximizing a rank-based objective function. The proposed estimator is shown to have desirable asymptotic properties and can then also be used for dimensionality reduction or to estimate the unknown structural function in the context of a transformation model.     

Large time-varying parameter VARs

In this paper, we develop methods for estimation and forecasting in large time-varying parameter vector autoregressive models (TVP-VARs). To overcome computational constraints, we draw on ideas from the dynamic model averaging literature which achieve reductions in the computational burden through the use forgetting factors. We then extend the TVP-VAR so that its dimension can change over time. For instance, we can have a large TVP-VAR as the forecasting model at some points in time, but a smaller TVP-VAR at others. A final extension lies in the development of a new method for estimating, in a time-varying manner, the parameter(s) of the shrinkage priors commonly-used with large VARs. These extensions are operationalized through the use of forgetting factor methods and are, thus, computationally simple. An empirical application involving forecasting inflation, real output and interest rates demonstrates the feasibility and usefulness of our approach.     

On the forecasting performance of a small-scale DSGE model

Dynamic stochastic general equilibrium (DSGE) models have recently become standard tools for policy analysis. Nevertheless, their forecasting properties have still barely been explored. In this article, we address this problem by examining the quality of forecasts of the key U.S. economic variables: the three-month Treasury bill yield, the GDP growth rate and GDP price index inflation, from a small-size DSGE model, trivariate vector autoregression (VAR) models and the Philadelphia Fed Survey of Professional Forecasters (SPF). The ex post forecast errors are evaluated on the basis of the data from the period 1994–2006. We apply the Philadelphia Fed “Real-Time Data Set for Macroeconomists” to ensure that the data used in estimating the DSGE and VAR models was comparable to the information available to the SPF.Overall, the results are mixed. When comparing the root mean squared errors for some forecast horizons, it appears that the DSGE model outperforms the other methods in forecasting the GDP growth rate. However, this characteristic turned out to be statistically insignificant. Most of the SPF's forecasts of GDP price index inflation and the short-term interest rate are better than those from the DSGE and VAR models.     

An econometric model of the petroleum industry

This paper describes a forty-two nonlinear equation model of the U.S. petroleum industry estimated over the period 1946 to 1973. The model specifies refinery outputs and prices as being simultaneously determined by market forces while the domestic output of crude oil is determined in a block-recursive segment of the model. The simultaneous behavioral equations are estimated with nonlinear two-stage least-squares adjusted to reflect the implications of autocorrelation for those equations where it appears to be a problem. A multi-period sample simulation, together with forecasts for 1974 and 1975 are used to evaluate the model's performance. Finally it is used to forecast to 1985 under two scenarios and compared with the Federal Energy Administration's forecast for the same period.     

Autoregresive conditional volatility, skewness and kurtosis

This paper proposes a GARCH-type model allowing for time-varying volatility, skewness and kurtosis. The model is estimated assuming a Gram–Charlier (GC) series expansion of the normal density function for the error term, which is easier to estimate than the non-central t distribution proposed by [Harvey, C. R. & Siddique, A. (1999). Autorregresive Conditional Skewness. Journal of Financial and Quantitative Analysis 34, 465–487). Moreover, this approach accounts for time-varying skewness and kurtosis while the approach by Harvey and Siddique [Harvey, C. R. & Siddique, A. (1999). Autorregresive Conditional Skewness. Journal of Financial and Quantitative Analysis 34, 465–487] only accounts for non-normal skewness. We apply this method to daily returns of a variety of stock indices and exchange rates. Our results indicate a significant presence of conditional skewness and kurtosis. It is also found that specifications allowing for time-varying skewness and kurtosis outperform specifications with constant third and fourth moments.     

Scale Efficiency in Canadian Trust Companies

A new method for estimating a relative scale function is derived which does not require the specification or estimation of the firm's cost function. The method is applied to a panel of Canadian trust companies for the years 1985–1988. Results show that the industry scale function is convex with respect to firm size, as measured by the firm's assets. The method avoids a number of shortcomings associated with traditional studies of scale economies in financial institutions.     

Explaining the real exchange rate in Kazakhstan, 1996–2003: Is Kazakhstan vulnerable to the Dutch disease?

Kazakhstan possesses extensive natural resource reserves expected to yield significant export revenues. Since independence in 1991, the composition of exports has changed in favor of energy-related sectors. In the context of such evidence and considerable expected future revenues, researchers have pointed to possible Dutch disease effects. This paper examines whether Kazakhstan is vulnerable to this condition. Using an extended version of the Balassa–Samuelson model including the price of oil, we find evidence that changes in those terms had a significant effect on the real exchange rate during 1996–2003, suggesting symptoms of significant Dutch disease effects in Kazakhstan.     

Forecasting the Value-at-Risk of REITs using realized volatility jump models

This paper examines jump risk in the time series of Real Estate Investment Trusts (REITs). Using high-frequency index-level and firm-level data, the econometric model in this paper integrates jumps into the volatility forecast by estimating jump augmented Heterogeneous Autoregressive (HAR) models of realized volatility. To assess the information value of these specifications, their forecasting accuracies for generating one-step ahead daily Value-at-Risk are also compared with other VaR specifications, including those generated from historical returns, bootstrap technique, and severity loss distribution.     

Modeling tourism: A fully identified VECM approach

System-based cointegration methods have become popular tools for economic analysis and forecasting. However, the identification of structural relationships is often problematic. Using a theory-directed sequential reduction method suggested by Hall, Henry and Greenslade [Hall, S. G., Henry, S., & Greenslade, J. (2002). On the identification of cointegrated systems in small samples: A modelling strategy with an application to UK wages and prices. Journal of Economic Dynamics and Control, 26, 1517–1537], we estimate a vector error correction model of Hawaii tourism, where both demand and supply-side influences are important. We identify reasonable long-run equilibrium relationships, and Diebold–Mariano tests for forecast accuracy demonstrate satisfactory forecasting performance.     

Explaining economic growth in the Soviet Union, 1950–86

Production function estimates are provided for Soviet industrial production and gross national product for the period 1950–86. A variety of alternative specifications is tested, including Cobb-Douglas, constant elasticity of substitution and variable elasticity of substitution production functions, and an error correction mechanism is used to investigate the long-run properties of the estimated equation. The structural stability of the estimates is also examined. Constant-returns-to-scale Cobb-Douglas production functions suggest that the rate of total factor productivity growth in the Soviet economy has declined steadily over time, becoming negative sometime in the period between 1970 and 1980. However the extensive statistical tests can doubt on the validity of any production function estimated on Soviet data.     

Damping seasonal factors: Shrinkage estimators for the X-12-ARIMA program

We examine the effect of damping X-12-ARIMA's estimated seasonal variation on the accuracy of its seasonal adjustments of time series. Two methods for damping seasonals are proposed. In a simulation experiment, we generated time series data for each of 90 distinct experimental conditions that, in aggregate, characterize the variety of monthly series in the M3-competition. X-12-ARIMA consistently overestimated the actual seasonal variation by an amount consistent with statistical theory. Damping seasonals reduced X-12-ARIMA's estimation error by as much as 79% and under no conditions was estimation error increased beyond a trivial amount. Improvement depended primarily on the degree to which random variation in a series dominated seasonal variation. When the multiplicative X-12-ARIMA model did not match the data-generating model, overestimation was less for trend series than for series with no trend; otherwise the presence of trend had no discernible effect. One of the proposed methods was somewhat more accurate and robust, but more complex, than the other. In an analysis of real data—the 1428 monthly series of the M3-competition-damping X-12-ARIMA seasonals prior to forecasting (1) reduced the average forecasting MAPE by 4.9–1.4% and (2) improved forecasting accuracy for 59–65% of the series, depending on the forecasting horizon. This research suggests that damping X-12-ARIMA seasonals leads to more accurate seasonal adjustments of time series, thus providing a more reliable basis for policy-making, forecasting, and the evaluation of forecasting methods by researchers.     

Management of a capital stock by Strotz's naive planner

The capital management problem posed by R.H. Strotz is analyzed for the case of the ‘naive’ planner who fails to anticipate changes in his own preferences. By imposing progressively stronger restrictions on the primitives of the problem – namely, the discounting function, the utility index function, and the investment technology – the planner's behavior is characterized first as the solution to an ordinary differential equation and then via explicit formulae. Inasmuch as these characterizations leave the discounting function essentially unrestricted, the theory can accommodate, in particular, decision makers who discount time according to the hyperbolic and ‘quasi-hyperbolic’ curves used in applied work and said to be supported by psychological studies. Comparative statics of the model are discussed, as are extensions of the analysis to allow for credit constraints, limited foresight, and partial commitment.     

The impact of bank supervision on loan growth

This paper quantifies the short-term and long-term impact of bank supervision (measured using CAMEL composite and component ratings) on different categories of loan growth: (a) commercial and industrial loans, (b) consumer loans, and (c) real estate loans. For each of these categories, we perform dynamic loan growth equations at the state-level augmented by the inclusion of CAMEL ratings for all banks in the state, after controlling for banking and economic conditions. We perform these regressions for two distinct sub-periods: (1) 1985–1993 (which covers the credit crunch period) and (2) 1994–2004 (which covers the sustained recovery period). For the first period, 1985–1993, we find that out of the three loan categories considered, business lending is the most sensitive to changes in CAMEL ratings (both the composite and the components), although the other loan categories also show some sensitivity. Overall, however, we find little evidence suggesting that the effects of changes in any of the components of CAMEL ratings differ systematically from the effects of changes in the composite CAMEL. For the second period, we find little evidence that changes in CAMEL ratings (the composite or its components) had any systematic effect on loan growth for any of the loan categories considered.     

The empirical relevance of the competitive storage model

The empirical relevance of models of competitive storage arbitrage in explaining commodity price behavior has been seriously challenged in a series of pathbreaking papers by [Deaton and Laroque, 1992], [Deaton and Laroque, 1995] and [Deaton and Laroque, 1996]. Here we address their major criticism, that the model is in general unable to explain the degree of serial correlation observed in the prices of twelve major commodities. First, we present a simple numerical version of their model which, contrary to Deaton and Laroque (1992), can generate the high levels of serial correlation observed in commodity prices, if it is parameterized to generate realistic levels of price variation. Then, after estimating the [Deaton and Laroque, 1995] and [Deaton and Laroque, 1996] model using their data set, model specification and econometric approach, we show that the use of a much finer grid to approximate the equilibrium price function yields quite different estimates for most commodities. Results are obtained for coffee, copper, jute, maize, palm oil, sugar and tin that support the specifications of the storage model with positive constant marginal storage cost and no deterioration as in Gustafson (1958a). Consumption demand has a low response to price and, except for sugar, stockouts are infrequent. The observed magnitudes of serial correlation of price match those implied by the estimated model.     

Estimating efficiencies from frontier models with panel data: A comparison of parametric,non-parametric and semi-parametric methods with bootstrapping

The aim of this article is first to review how the standard econometric methods for panel data may be adapted to the problem of estimating frontier models and (in)efficiencies. The aim is to clarify the difference between the fixed and random effect model and to stress the advantages of the latter. Then a semi-parametric method is proposed (using a non-parametric method as a first step), the message being that in order to estimate frontier models and (in)efficiences with panel data, it is an appealing method. Since analytic sampling distributions of efficiencies are not available, a bootstrap method is presented in this framework. This provides a tool allowing to assess the statistical significance of the obtained estimators. All the methods are illustrated in the problem of estimating the inefficiencies of 19 railway companies observed over a period of 14 years (1970–1983).Article presented at the ORSA/TIMS joint national meeting, Productivity and Global Competition, Philadelphia, October 29–31, 1990. An earlier version of the paper was presented at the European Workshop on Efficiency and Productivity Measurement in the Service Industries held at CORE, October 20–21, 1989. Helpful comments of Jacques Mairesse, Benoît Mulkay, Sergio Perelman, Michel Mouchart, Shawna Grosskopf and Rolf Färe, at various stages of the paper, are gratefully acknowledged.     

Housing capital-gains taxation and homeowner mobility: Evidence from the Taxpayer Relief Act of 1997

We provide new evidence on the impact of housing capital-gains taxation on homeowner behavior by examining residential mobility before and after the Taxpayer Relief Act of 1997 (TRA97), which generated the most sweeping reform of capital-gains taxation in the last two decades. In addition to lowering marginal tax rates on long-term capital gains for all assets, TRA97 also eliminated any differential treatment of housing gains above and below age 55, allowing all homeowners to qualify for capital-gains exclusions. Utilizing data drawn from the Current Population Survey (CPS) on either side of the law change (1996 and 1998) on homeowners just above (56–58 year olds) and below (52–54 year olds) the age-55 threshold and a reduced-form, difference-in-difference empirical approach, our estimates suggest that the repeal of the differential capital-gains tax treatment by age embodied in TRA97 had an economically important and statistically significant impact on the residential mobility of under-55 homeowners. Across a variety of specifications, the repeal raised the mobility rate by around 1–1.4 percentage points, which, for a mean mobility rate of 4 percentage points, represented an increase in the mobility rate of homeowners in their early 50s by 22–31%. Furthermore, the bulk of this effect was concentrated among highly mobile homeowners who a priori were more likely to have wanted to trade down (e.g., divorced, empty nesters), those facing higher capital gains tax rates, and those living in states that had experienced higher rates of nominal appreciation.