Research Database

This memo describes a revision to the mixed-frequency vector autoregression (MF-VAR) model originally constructed by Schorfheide and Song (2012) and subsequently revised by Beauchemin (2013). In this most recent version, the 14-variable model is expanded to include nonfarm payroll employment. The forecast performance of the augmented model is compared with that of its predecessor.

In this paper I develop a practical extension of the Method of Simulated Moments (MSM) estimator for limited dependent variable models to the panel data case. The method is based on a factorization of the MSM first order condition into transition probabilities, along with the development of a new highly accurate method for simulating these transition probabilities. A series of Monte-Carlo tests show that this MSM estimator performs quite well relative to quadrature-based ML estimators, even when large numbers of quadrature points are employed. The estimator also performs well relative to simulated ML, even when a highly accurate method is used to simulate the choice probabilities. In terms of computational speed, complex panel data models involving random effects and ARMA errors may be estimated via MSM in times similar to those necessary for estimation of simple random effects models via ML-quadrature.

This paper critically reevaluates recent claims that the postwar U.S. price level exhibits countercyclicality. While overall countercyclicality is confirmed, temporal disaggregation suggests a shift from pro- to countercyclicality in the early 1970s. Furthermore, the countercyclicality is markedly more pronounced for negative than for positive output innovations. The evidence thus casts doubt on single-source business cycle explanations.

This paper builds a simple but complete model of a political system to analyze the effects of intergenerational conflicts on capital and labor income tax rates, transfers, and government spending. I show how the different nature of tax liabilities for the young and the old can explain why the old receive large gross lump-sum transfers through social security, while the young receive little or none. I also show that there is a natural link between the size of the government as a provider of public goods and the magnitude of transfers that the same government will implement.

A general class of Markov switching regime time series models is presented that allows one to estimate the nontrivial interdependencies between different types of cycles which make the economy grow at an unsteady rate. The paper further explores results obtained in Ghysels (1991b) suggesting that the economy transits from recessions to expansions with an uneven propensity throughout the year. It is also built on the work of Hamilton (1989) who proposed a stochastic switching-regime model for GNP and has important connections with hidden periodic structures discussed by Tiao and Grupe (1980) or Hansen and Sargent (1990), for instance. The time series models we present may have periodic transition probabilities and the drifts may be seasonal. In the latter case, the model exhibits seasonal dummy variation that may change with the stage of the business cycle. While the model is intrinsically nonlinear and stochastic, it produces a linear representation with seasonal effects that appear to be deterministic. The paper provides an elaborate discussion of the regularity conditions for a well-defined covariance structure including explicit formula for characterizing first and second moments. Finally, we present empirical evidence using U.S. GNP data series which tends to support a periodic structure for switching probabilities. The most significant result is the following: it is found that the seasonal in GNP growth significantly affects switching probabilities for regime switches in the nonseasonal growth of GNP. We also analyze the out-of-sample forecast performance of the different models and find that the models exploiting seasonality in transition probabilities perform best.

Despite wide recognition of their significant role in explaining sustained growth and economic development, uncompensated knowledge spillovers have not yet been fully modeled with a microeconomic foundation. The main purpose of this paper is to illustrate the exchange of knowledge as well as its consequences on agglomerative activity in a general-equilibrium search-theoretic framework. Agents, possessing differentiated types of knowledge, search for partners to exchange ideas and create new knowledge in order to improve production efficacy. When individuals’ types of knowledge are too diverse, a match is less likely to generate significant innovations. We demonstrate the extent of agglomeration has significant implications for the patterns of information flows in economies. Further, by simultaneously determining the patterns of knowledge exchange and the spatial agglomeration of an economy we identify additional channels for interaction between agglomerative activity and knowledge exchange. Finally, contrary to previous work in spatial agglomeration, our model suggests that agglomerative environments may be either under-specialized and under-populated or over-specialized and over-populated relative to the social optimum.

This paper studies the accuracy of two versions of the procedure proposed by Kydland and Prescott (1980, 1982) for approximating the optional decision rules in problems in which the objective fails to be quadratic and the constraints linear. The analysis is carried out in the context of a particular example: a version of the Brock-Mirman (1972) model of optimal economic growth. Although the model is not linear quadratic, its solution can nevertheless be computed with arbitrary accuracy using a variant of the value function iteration procedures described in Bertsekas (1976). I find that the Kydland-Prescott approximate decision rules are very similar to those implied by value function iteration.