A composite likelihood framework for analyzing singular DSGE models

This paper builds upon the composite likelihood concept of Lindsay (1988) to develop a framework for parameter identification, estimation, inference, and forecasting in DSGE models allowing for stochastic singularity. The framework consists of the following four components. First, it provides a necessary and sufficient condition for parameter identification, where the identifying information is provided by the first and second order properties of nonsingular submodels. Second, it provides an MCMC based procedure for parameter estimation. Third, it delivers confidence sets for structural parameters and impulse responses that allow for model misspecification. Fourth, it gen- erates forecasts for all the observed endogenous variables, irrespective of the number of shocks in the model. The framework encompasses the conventional likelihood analysis as a special case when the model is nonsingular. It enables the researcher to start with a basic model and then gradually incorporate more shocks and other features, meanwhile confronting all the models with the data to assess their implications. The methodology is illustrated using both small and medium scale DSGE models. These models have numbers of shocks ranging between one and seven.Accepted manuscrip

Using arbitrary precision arithmetic to sharpen identification analysis for DSGE models

This paper is at the intersection of macroeconomics and modern computer arithmetic. It seeks to apply arbitrary precision arithmetic to resolve practical di¢ culties arising in the iden- ti cation analysis of log linearized DSGE models. The main focus is on methods in Qu and Tkachenko (2012, 2017) since the framework appears to be the most comprehensive to date. Working with this arithmetic, we develop the following three-step procedure for analyzing local and global identi cation. (1) The DSGE model solution algorithm is modi ed so that all the relevant objects are computed as multiprecision entities allowing for indeterminacy. (2) The rank condition and the Kullback-Leibler distance are computed using arbitrary precision Gauss- Legendre quadrature. (3) Minimization is carried out by combining double precision global and arbitrary precision local search algorithms, where the criterion for convergence is set based on the chosen precision level, so that it can be e¤ectively examined whether the minimized value equals zero. In an application to a model featuring monetary and scal policy interactions (Leeper, 1991 and Tan and Walker, 2015), we nd that the arithmetic removes all ambiguity in the analysis. As a result, we reach clear conclusions showing observational equivalence both within the same policy regime and across di¤erent policy regimes under generic parameter val- ues. We further illustrate the application of the method to medium scale DSGE models by considering the model of Schmitt-Grohé and Uribe (2012), where the use of extended precision again helps remove ambiguity in cases where near observational equivalence is detected.First author draf

Inference on conditional quantile processes in partially linear models with applications to the impact of unemployment benefits

We propose methods to estimate and conduct inference on conditional quantile processes for models with nonparametric and linear components. The estimation procedure uses local linear or quadratic regressions, with the bandwidth allowed to vary across quantiles to adapt to data sparsity. We establish a Bahadur representation that holds uniformly in the covariate value and the quantile index. Then,we show that the proposed estimator converges weakly to a Gaussian process and develop methods for constructing uniform confidence bands and hypothesis testing. Our results also cover locally partially linear models with boundary points, thereby allowing for Sharp Regression Discontinuity Designs (SRD). This allows us to study the effects of unemployment insurance (UI) benefits extensions using the dataset of Nekoei and Weber (2017) who found a statistically significant effect, though of minor economic importance using an SRD focusing on the average effect. Our model allows heterogeneity with respect to both the covariate and the quantile. We find economically strong significant effects in the tail of the distribution,say the 10% quantile of the outcome variable (e.g., the wage change distribution). Under a rank invariance assumption, this implies that individuals who benefited the most are those who would have experienced substantial wage cuts if there were no benefit extension. Since our setup allows for discrete covariates, we also find positive and statistically significant effects for white-collar and female workers and those with a college education, but not for blue-collar male workers without higher education. Hence, while UI benefits reduce the within-group inequality for some subgroups by covariates, they can be viewed as regressive and enhancing between-group inequality, although they also help to bridge the gender gap.First author draf

Seroprevalence and risk factors of Toxoplasma gondii infection in children with leukemia in Shandong Province, Eastern China: a case—control prospective study

Limited information is available concerning the epidemiology of Toxoplasma gondii infection in children with leukemia in Eastern China. Therefore, a case-control study was conducted to estimate the seroprevalence of toxoplasmosis in this patient group and to identify risk factors and possible routes of infection. Serum samples were collected from 339 children with leukemia and 339 age matched health control subjects in Qingdao from September 2014 to March 2018. Enzyme linked immunoassays were used to screen anti- T. gondii IgG and anti- T. gondii IgM antibodies. Forty-eight (14.2%) children with leukemia and 31 (9.1%) control subjects were positive for anti-T. gondii IgG antibodies (P < 0.05), while 13 (3.8%) patients and 14 (4.1%) controls were positive for anti-T. gondii IgM antibodies (P = 0.84). Multivariate analysis showed exposure to soil and a history of blood transfusion were risk factors for T. gondii infection. Compared with IgG, patients with a history of blood transfusion were more likely to present anti- T. gondii IgM (P = 0.003). Moreover, patients with chronic lymphocytic leukemia and acute lymphocytic leukemia had higher T. gondii seroprevalence in comparison to control subjects (P = 0.002 and P = 0.016, respectively). The results indicated that the seroprevalence of T. gondii infection in children with leukemia is higher than that of healthy children in Eastern China. This information may be used to guide future research and clinical management, and further studies are necessary to elucidate the role of T. gondii in children with leukemia

Measuring the bias of technological change

Abstract When technological change occurs, it can increase the productivity of capital, labor, and the other factors of production in equal terms or it can be biased towards a specific factor. Whether technological change favors some factors of production over others is an empirical question that is central to economics. The literatures in industrial organization, productivity, and economic growth rest on very specific assumptions about the bias of technological change. Yet, the evidence is sparse. In this paper we propose a general framework for estimating production functions that allows productivity to be multi-dimensional. Using firm-level panel data, we are able to directly assess the bias of technological change by measuring, at the level of the individual firm, how much of technological change is factor neutral and how much of it is labor augmenting. We further relate the speed and the direction of technological change to firms&apos; R&amp;D activities. * We than