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.     

Shifts in autonomy, responsibility and control from centre to periphery in public administration: the case of the ministry of finance in Italy

In tackling administrative reform and in the hope of improving the effective allocation of resources, most European governments have shown a growing interest in adopting private sector management models in the public administration. The assumption underlying this paper is that the decisive variables in the different national contexts have to do with the relationships between the central and the peripheral administrative levels, and the way in which administrative actors at the two levels interpret their roles and participate in the reform process. The paper examines the case of the reform of the Italian Ministry of Finance. In seeking to improve its performance and the services it provides, the ministry reform is intended to introduce a management system in which the key concepts are the planning, programming and control of administrative action and results. According to reform rhetoric, shaping a new class of administrative managers at the local level is the crux of the question. However, research results hint that the “creation” of this new local executive staff is yet to be completed. The working hypothesis advanced is that this is due to local executives’ lack of confidence in the “system”, inasmuch as the reform process has so far been characterised by a tendency to give them responsibility without autonomy and autonomy without control. The greater their lack of trust, the lesser their willingness to risk the consequences of failure and the greater their tendency to stick to defensive positions and to return to previous “bureaucratic” conceptions and ways of operating.     

Performance evaluation of the LDR and the PSH with forecast errors

Despite its ability to produce optimal solutions, the Linear Decision Rule (LDR) has not had a significant impact in the business environment. The Production Switching Heuristic (PSH), which has shown promising results when compared with the LDR, has experienced some business application because of its practicability and flexibility. During aggregate production planning, forecast errors are almost unavoidable, but the sensitivity of these models to such errors has not been thoroughly tested. Insufficient attention has been paid to truly understand the cost effects of forecast errors and other important interactions. The study investigates these issues by analyzing the results of 740 simulated problems.Using the famous “paint factory” cost data, the sensitivity of the LDR and the PSH are examined under various experimental conditions. The factors controlled at different levels are: forecast error mean, forecast error standard deviation, demand pattern, demand variability, and cost coefficients. The results show that 1) the PSH is generally less sensitive than the LDR to forecast errors, 2) both forecast error mean and standard deviation effectively measure the severity of forecast errors, and 3) underforecasts cause less cost penalty than overforecasts.The outcome of the study has helpful managerial implications for aggregate planning related decisionmaking. It suggests that the use of the PSH could result in potential cost savings even if significant forecast errors are envisioned as long as the period-to-period demand variability is not substantially high. Also, BIAS warrants more attention than MSE in evaluating the extent of forecast errors and their eventual cost impact on aggregate production planning.     

Urban size (dis)amenities revisited

An article by Kelley in this Journal has argued that most studies of urban-size amenities built around the analysis of wage differentials have ignored the interaction of supply and demand forces. The result, it was argued, has been serious simultaneous equation bias which suggested that larger cities had lower-valued non-pecuniary amenities. If the bias is eliminated, the opposite is shown to be correct: urban amenities increase in value with city size. This note argues that Kelley's conclusions are themselves based on serious theoretical and econometric errors: (i) He ignores the equilibrium conditions of the model. (ii) He treats variables as exogenous which are not. (iii) He ignores almost perfect collinearity between the variables he uses.     

A simple estimator for nonlinear error in variable models

We propose a simple estimator for nonlinear method of moment models with measurement error of the classical type when no additional data, such as validation data or double measurements, are available. We assume that the marginal distributions of the measurement errors are Laplace (double exponential) with zero means and unknown variances and the measurement errors are independent of the latent variables and are independent of each other. Under these assumptions, we derive simple revised moment conditions in terms of the observed variables. They are used to make inference about the model parameters and the variance of the measurement error. The results of this paper show that the distributional assumption on the measurement errors can be used to point identify the parameters of interest. Our estimator is a parametric method of moments estimator that uses the revised moment conditions and hence is simple to compute. Our estimation method is particularly useful in situations where no additional data are available, which is the case in many economic data sets. Simulation study demonstrates good finite sample properties of our proposed estimator. We also examine the performance of the estimator in the case where the error distribution is misspecified.     

Estimating structural equation models with non-normal variables by using transformations

We discuss structural equation models for non-normal variables. In this situation the maximum likelihood and the generalized least-squares estimates of the model parameters can give incorrect estimates of the standard errors and the associated goodness-of-fit chi-squared statistics. If the sample size is not large, for instance smaller than about 1000, asymptotic distribution-free estimation methods are also not applicable. This paper assumes that the observed variables are transformed to normally distributed variables. The non-normally distributed variables are transformed with a Box–Cox function. Estimation of the model parameters and the transformation parameters is done by the maximum likelihood method. Furthermore, the test statistics (i.e. standard deviations) of these parameters are derived. This makes it possible to show the importance of the transformations. Finally, an empirical example is presented.     

Spatial Autocorrelation: A Primer

Regression error terms are likely to be spatially autocorrelated in any situation in which “location matters.” While both the precision of the estimates and the reliability of hypothesis testing can be improved by making a correction for spatial autocorrelation, the techniques for making such a correction are not widely understood. The purpose of this paper is to explore some of the issues involved in estimating models with spatially autocorrelated error terms. One of the two most common methods of handling spatial autocorrelation is the weight matrix approach, in which the process generating the errors is modeled. The resulting correlation structure is then derived from the hypothesized process. The second method models the correlation structure itself, rather than the underlying process. The bulk of this paper is concerned with comparing these two methods and their resulting correlation structures. Other issues are discussed at the end of the paper.     

An extension of the monocentric urban spatial equilibrium model to a multicenter setting: The case of the two-center city

Firms in a monocentric city conforming in substance to the “new urban economics” produce an export commodity under agglomeration economies and employ homogeneous labor and capital. Workers reside about the CBD in decreasing densities with distance. A developer establishes a second export production center within the city's residential area. Conditions for economic viability and growth of the subcenter are examined, and its impacts on short-run and long-run city location patterns are discussed. A limiting condition on subcenter employment size is provided.     

Externalities, Indivisibility, Nonreplicability, and Agglomeration

In this paper we argue that the impact of external scale economies and diseconomies on city size is not nearly as clear-cut as it is tacitly believed in urban economics. Similarly, city-size distortions are not caused by externalities alone. Indivisibility and nonreplicability, which prevent establishing the “right” number of cities, may represent a source for city-size distortions which can be stronger than the standard resource misallocation resulting from external scale economies and diseconomies. It follows that a direct population dispersion policy is not just an inferior substitute to Pigouvian taxes and subsidies but rather a useful complement.     

Forecasting with vector autoregressive (VAR) models subject to business cycle restrictions

In the last decade VAR models have become a widely-used tool for forecasting macroeconomic time series. To improve the out-of-sample forecasting accuracy of these models, Bayesian random-walk prior restrictions are often imposed on VAR model parameters. This paper focuses on whether placing an alternative type of restriction on the parameters of unrestricted VAR models improves the out-of-sample forecasting performance of these models. The type of restriction analyzed here is based on the business cycle characteristics of U.S. macroeconomic data, and in particular, requires that the dynamic behavior of the restricted VAR model mimic the business cycle characteristics of historical data. The question posed in this paper is: would a VAR model, estimated subject to the restriction that the cyclical characteristics of simulated data from the model “match up” with the business cycle characteristics of U.S. data, generate more accurate out-of-sample forecasts than unrestricted or Bayesian VAR models?     

Community income distributions in a metropolitan area

We extend de Bartolome and Ross [C.A.M. de Bartolome, S.L. Ross, Equilibrium with local governments and commuting: Income sorting vs. income mixing, Journal of Urban Economics 54 (2003) 1–20] to the case when the income distribution in the metropolitan area is a continuous distribution. In particular, we consider a circular central city surrounded by a suburban community. All households must commute to the metropolitan center and public service levels differ in the two jurisdictions. There is intra-jurisdictional and inter-jurisdictional capitalization. Our model has an equilibrium in which the income distributions of the central city and of the suburban community do overlap. Our finding contrasts with the traditional finding of Alonso–Mills–Muth-type models of spatial sorting and of Tiebout-type models of fiscal sorting, both of which have been shown to predict that the income distributions of the two communities do not overlap. In addition, the model explains the fixedness in jurisdictional boundaries.     

An integrative model of effort propensity

Over twenty years ago, Mitchell [Mitchell, T.R. (1982) Motivation: New directions for theory, research, and practice. Academy of Management Review, 7:80–88.] called for research which integrates and competitively tests the multitude of motivation theories competitively. Yet, with few exceptions, theories of motivation tend to be narrow in focus. However, many motivation theories incorporate similar predictor variables such as job satisfaction, perceived equity, and organizational commitment, suggesting that theory integration is warranted. In this paper, several literatures are reviewed which deal with employee effort at different levels (e.g., withholding effort, offering extra effort). “Effort propensity” is offered as an appropriate integrating variable, and an integrative model of effort propensity which pulls these various literatures together and stimulates the type of research described by Mitchell [Mitchell, T.R. (1982) Motivation: New directions for theory, research, and practice. Academy of Management Review, 7:80–88.] is proposed.     

A flexible parametric approach for estimating switching regime models and treatment effect parameters

In this paper, we propose a flexible, parametric class of switching regime models allowing for both skewed and fat-tailed outcome and selection errors. Specifically, we model the joint distribution of each outcome error and the selection error via a newly constructed class of multivariate distributions which we call generalized normal mean–variance mixture distributions. We extend Heckman’s two-step estimation procedure for the Gaussian switching regime model to the new class of models. When the distributions of the outcome errors are asymmetric, we show that an additional correction term accounting for skewness in the outcome error distribution (besides the analogue of the well known inverse mill’s ratio) needs to be included in the second step regression. We use the two-step estimators of parameters in the model to construct simple estimators of average treatment effects and establish their asymptotic properties. Simulation results confirm the importance of accounting for skewness in the outcome errors in estimating both model parameters and the average treatment effect and the treatment effect for the treated.     

Estimating panel data models in the presence of endogeneity and selection

We consider estimation of panel data models with sample selection when the equation of interest contains endogenous explanatory variables as well as unobserved heterogeneity. Assuming that appropriate instruments are available, we propose several tests for selection bias and two estimation procedures that correct for selection in the presence of endogenous regressors. The tests are based on the fixed effects two-stage least squares estimator, thereby permitting arbitrary correlation between unobserved heterogeneity and explanatory variables. The first correction procedure is parametric and is valid under the assumption that the errors in the selection equation are normally distributed. The second procedure estimates the model parameters semiparametrically using series estimators. In the proposed testing and correction procedures, the error terms may be heterogeneously distributed and serially dependent in both selection and primary equations. Because these methods allow for a rather flexible structure of the error variance and do not impose any nonstandard assumptions on the conditional distributions of explanatory variables, they provide a useful alternative to the existing approaches presented in the literature.     

A mixed index approach to identifying hedonic price models

Recent literature suggests identifying house price hedonic regressions by using instrumental variables, spatial statistics, the borders approach, panel data, and other techniques. We present an empirical application of a mixed index model, first proposed by Bowden [Bowden, R.J., 1992. Competitive selection and market data: the mixed-index problem. The Review of Economic Studies 59(3):625–633.] to identify hedonic price regressions. We compare the performance of the mixed index model to a traditional hedonic model and to a hedonic model that includes characteristics of the buyer of each house. We find the mixed index model outperforms the other models based on bootstrap distributions of predicted housing values, prediction variance, and predicted policy effects. The mixed index model distributions are less skewed and kurtotic than the other models, suggesting it more closely satisfies the classical linear regression assumption of normally distributed errors. Compared to the mixed index model, the traditional hedonic overstates the importance of lot size and school quality to house price and understates the importance of environmental quality.     

Service Guarantee Strength: The key to service quality

While most authors describe a service guarantee as a “zero-one variable” indicating the presence or absence of an explicit written service guarantee, this paper develops a construct called “Service Guarantee Strength” (SGS) that is a continuous variable. This construct measures the degree to which a firm sets clear service quality standards for itself on dimensions that customers care about, and has an formal policy for quickly giving meaningful compensation to customers when these standards are not met.The paper builds upon established micro-level behavioral theory to develop the “Service Guarantee Strength Framework”. This framework posits that high Service Guarantee Strength leads to improved service quality, customer satisfaction, and loyalty through three intervening variables—marketing communications impact, employee motivation and vision, and learning through service failure.An empirical investigation was conducted to test the SGS Framework using both employee and customer data from three pairs of firms, with each pair in a different industry. None of these firms had an explicit service guarantee. Unlike many behavioral research studies, this study measured both employee and customer perceptual data and compared the two. The research finds that Service Guarantee Strength is positively related to customer perceptions of service quality, customer satisfaction, and loyalty.     

Transport subsidies, system choice, and urban sprawl

This paper analyzes the effect of transport subsidies on the spatial expansion of cities, asking whether such subsidies are a source of undesirable urban sprawl. Even though the cost-reducing effect of transport subsidies is offset by a higher general tax burden (which reduces the demand for space), the analysis shows that subsidies nevertheless lead to spatial expansion of cities. If the transport system exhibits constant returns to scale, the subsidies are inefficient, making the urban expansion they entail undesirable. The paper also studies transport “system choice,” with the city portrayed as selecting its transport system from along a continuum of money cost/time cost choices. The analysis shows that subsidies inefficiently bias choice in the direction of a high money cost/low time cost option. Lastly, the paper considers system choice in a city with rich and poor groups, showing that the rich favor a system with a high money cost and low time cost, but that their choice, if implemented, leads to a city whose spatial size is smaller than optimal. Thus, rich control of system choice does not lead to urban sprawl.     

Copula-based multivariate GARCH model with uncorrelated dependent errors

Multivariate GARCH (MGARCH) models are usually estimated under multivariate normality. In this paper, for non-elliptically distributed financial returns, we propose copula-based multivariate GARCH (C-MGARCH) model with uncorrelated dependent errors, which are generated through a linear combination of dependent random variables. The dependence structure is controlled by a copula function. Our new C-MGARCH model nests a conventional MGARCH model as a special case. The aim of this paper is to model MGARCH for non-normal multivariate distributions using copulas. We model the conditional correlation (by MGARCH) and the remaining dependence (by a copula) separately and simultaneously. We apply this idea to three MGARCH models, namely, the dynamic conditional correlation (DCC) model of Engle [Engle, R.F., 2002. Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics 20, 339–350], the varying correlation (VC) model of Tse and Tsui [Tse, Y.K., Tsui, A.K., 2002. A multivariate generalized autoregressive conditional heteroscedasticity model with time-varying correlations. Journal of Business and Economic Statistics 20, 351–362], and the BEKK model of Engle and Kroner [Engle, R.F., Kroner, K.F., 1995. Multivariate simultaneous generalized ARCH. Econometric Theory 11, 122–150]. Empirical analysis with three foreign exchange rates indicates that the C-MGARCH models outperform DCC, VC, and BEKK in terms of in-sample model selection and out-of-sample multivariate density forecast, and in terms of these criteria the choice of copula functions is more important than the choice of the volatility models.