Can a simple DSGE model outperform Professional Forecasters?

DSGE models have recently become one of the most frequently used tools in policy analysis. Nevertheless, their forecasting proprieties are still unexplored. In this article we address this problem by examining the quality of forecasts from a small size DSGE model, a trivariate VAR model and the Philadelphia Fed Survey of Professional Forecasters. The forecast performance of these methods is analysed for the key U.S. economic variables: the three month Treasury bill yield, the GDP growth rate and the GDP price index inflation. We evaluate the ex post forecast errors on the basis of the data from the period of 1994–2006. We apply the Philadelphia Fed “Real-Time Data Set for Macroeconomists,” described by Croushore and Stark (2001a), to ensure that the information available to the SPF was exactly the same as the data used to estimate the DSGE and VAR models. Overall, the results are mixed. It appears that when comparing the root mean squared errors for some forecast horizons the DSGE model seems to outperform the SPF in forecasting the GDP growth rate. However, this characteristic turned out to be not statistically significant. In principle most forecasts of the GDP price index inflation and the short term interest rate by the SPF are significantly better than those from the DSGE model. The forecast quality of the VAR model turned out to be the worst one.forecasting, real-time data, Survey of Professional Forecasters, DSGE, VAR

Market Power with Dynamic Inventory Constraints: The Bias in Standard Measures

This paper incorporates inventory dynamics into an analysis of market power. Using a model in which each firm accounts for the effect of its current action on the current and future actions of itself and its competitors, we show that measures of market power that ignore inventory dynamics are biased. We then apply the model to the beef-packing industry using annual data on cattle stocks, slaughter and prices from 1933-1999. Our estimates suggest that static measures overestimate the amount of market power exerted by beef-packing firms.market power dynamic cattle conjectural variations stock

The flexible coefficient multinomial logit (FC-MNL) model of demand for differentiated products

We show FC-MNL is flexible in the sense of Diewert (1974), thus its parameters can be chosen to match a well-defined class of possible own- and cross-price elasticities of demand. In contrast to models such as Probit and Random Coefficient-MNL models, FC-MNL does not require estimation via simulation; it is fully analytic. Under well-defined and testable parameter restrictions, FC-MNL is shown to be an unexplored member of McFadden’s class of Multivariate Extreme Value discrete-choice models. Therefore, FC-MNL is fully consistent with an underlying structural model of heterogeneous, utility-maximizing consumers. We provide a Monte-Carlo study to establish its properties and we illustrate the use by estimating the demand for new automobiles in Italy

Application of a structural model to a wholesale electricity market: The Spanish market from January 1999 to June 2007

The aim of this work is to analyse the agents’ behaviour in highly concentrated and strongly regulated electricity wholesale markets with rigid demand. In order to accomplish this aim, the analysis was based on the former Spanish electricity generation market, between January 1999 and June 2007, before the MIBEL (Iberian Electricity Market) has started. The analysis is carried out in the theoretical framework of the structural models. The result of the structural model supports the apparently competitive nature of the market analysed for the period 1999 to 2003, despite than fact that the Lerner index average was high during this period. It will therefore be important in future work to analyse whether the high average mark-up verified accords with the CTCs (stranded costs compensation which have the characteristics of contracts for difference) which frame the activities of the electricity producers.electricity market, rigid demand, structural model, market power

R&D and productivity : estimating production functions when productivity is endogenous

We develop a simple estimator for production functions in the presence of endogenous productivity change that allows us to retrieve productivity and its relationship with R&D at the firm level. By endogenizing the productivity process we build on the recent literature on structural estimation of production functions. Our dynamic investment model can be viewed as a generalization of the knowledge capital model (Griliches 1979) that has remained a cornerstone of the productivity literature for more than 25 years. We relax the assumptions on the R&D process and examine the impact of the investment in knowledge on the productivity of firms. We illustrate our approach on an unbalanced panel of more than 1800 Spanish manufacturing firms in nine industries during the 1990s. Our findings indicate that the link between R&D and productivity is subject to a high degree of uncertainty, nonlinearity, and heterogeneity across firms. By accounting for uncertainty and nonlinearity, we extend the knowledge capital model. Moreover, capturing heterogeneity gives us the ability to assess the role of R&D in determining the differences in productivity across firms and the evolution of firmlevel productivity over time

An Economic Analysis of Mountain Pine Beetle Impacts in a Global Context

The economic effects of the mountain pine beetle outbreak in British Columbia are simulated using a multi-region spatial price equilibrium model coupled with a stochastic dynamic updating procedure. The simulation captures expected changes in the B.C. timber supply, growth of plantation forests in the southern hemisphere and an escalating Russian log export tax. The results indicate lumber and log prices will rise in B.C., offsetting some of the economic loss to timber producers. However, on net producers in the B.C. forest industry will experience a decrease in economic surplus.Mountain pine beetle; spatial price equilibirum; trade modeling

Sticky Information in General Equilibrium

This paper develops and analyzes a general-equilibrium model with sticky information. The only rigidity in goods, labor, and financial markets is that agents are inattentive, sporadically updating their information sets, when setting prices, wages, and consumption. After presenting the ingredients of such a model, the paper develops an algorithm to solve this class of models and uses it to study the model’s dynamic properties. It then estimates the parameters of the model using U.S. data on five key macroeconomic time series. It finds that information stickiness is present in all markets, and is especially pronounced for consumers and workers. Variance decompositions show that monetary policy and aggregate demand shocks account for most of the variance of inflation, output, and hours.

Technology and Firm Size Distribution:Evidence from Italian Manufacturing

This paper explores the relationship between firm size distribution and technology. Similarly to Crosato and Ganugi (2006), we focus on six industries from the Micro1 survey by the Italian Statistical National Office (ISTAT). Firm technology is analysed across selected industries by means of a non-parametric production analysis, the Free Disposal Hull approach (Deprins et al., 1984; Kerstens and Vanden Eeckaut, 1999). The existence of a link between technical efficiency and size on the one hand, and between scale elasticity and size on the other is investigated. Graphical analyses show the absence of a clear-cut relation in the first case, while an inverse relation is found in the second one. Building on this relation, we inquire whether the shape of the firm size-distribution is related to a particular pattern of returns to scale. This problem is studied through the Zipf Plot (Stanley et al., 1995) of the Pareto IV distribution, which is concave for firms up to a given threshold, and then becomes linear. Results show that firms in the concave part of the plot experience increasing returns to scale. On the contrary, firms in the linear part are mainly characterised by constant returns to scale.Italian manufacturing; Free Disposal Hull; Pareto distributions; Returns to scale;

Toward a Taylor rule for fiscal policy

This paper presents a procedure to determine policy feedback rules in dynamic stochastic general equilibrium (DSGE) models. We illustrate our approach with fiscal feedback rules for tax instruments in a standard medium-scale DSGE model. First, we approximate the optimal dynamic behavior of the economy using simple linear feedback rules. Then we calculate the elasticities of the model variables' moments with respect to the feedback coefficients. The feedback coefficients associated with the highest elasticities form the policy feedback rules to be estimated. Our results stress the importance of carefully modeled fiscal tax policy in two dimensions: (i) with respect to the dynamic responses of fiscal policy to exogenous shocks and (ii) with respect to the historical shock decomposition of fiscal policy. --Fiscal policy,Bayesian model estimation,Identification

Estimation and Inference about Heterogeneous Treatment Effects in High-Dimensional Dynamic Panels

This paper provides estimation and inference methods for a large number of heterogeneous treatment effects in a panel data setting with many potential controls. We assume that heterogeneous treatment is the result of a low-dimensional base treatment interacting with many heterogeneity-relevant controls, but only a small number of these interactions have a non-zero heterogeneous effect relative to the average. The method has two stages. First, we use modern machine learning techniques to estimate the expectation functions of the outcome and base treatment given controls and take the residuals of each variable. Second, we estimate the treatment effect by l1-regularized regression (i.e., Lasso) of the outcome residuals on the base treatment residuals interacted with the controls. We debias this estimator to conduct pointwise inference about a single coefficient of treatment effect vector and simultaneous inference about the whole vector. To account for the unobserved unit effects inherent in panel data, we use an extension of correlated random effects approach of Mundlak (1978) and Chamberlain (1982) to a high-dimensional setting. As an empirical application, we estimate a large number of heterogeneous demand elasticities based on a novel dataset from a major European food distributor