DSGE pileups

The sampling distribution of estimators for DSGE structural parameters tends to be non-normal and/or pile up on the boundary of the theoretically admissible parameter space. This calls into question both the reliability of asymptotic approximations and the presumption of correct specification. This paper seeks to develop a conceptual framework for understanding how these phenomena arise, and to provide pragmatic methods for dealing with them in practice. The results are presented in three examples and a medium scale DSGE model.     

Regression with errors in variables: estimators based on third order moments

In this paper consistent and, in a well–defined sense, optimal moment–estimators of the regression coefficient in a simple regression model with errors in variables are derived. The asymptotic variance and other asymptotic properties of these estimators are given. As is known for a long time, serious estimation problems exist in this model. There are two ways out of this problem: using either additional assumptions or additional information in the data. A lot of attention has been paid to the use of additional assumptions. However, quite often this leads to rather unrealistic models. In this paper we use additional information in the data. That means here that, besides first and second order moments, third order moments are formulated as functions of the model parameters. Besides theoretical derivations a small study with generated data is discussed. This study shows that for samples larger than 50 the estimates we consider behave nicely.     

Block recursion and structural vector autoregressions

In applications of structural VAR modeling, finite-sample properties may be difficult to obtain when certain identifying restrictions are imposed on lagged relationships. As a result, even though imposing some lagged restrictions makes economic sense, lagged relationships are often left unrestricted to make statistical inference more convenient. This paper develops block Monte Carlo methods to obtain both maximum likelihood estimates and exact Bayesian inference when certain types of restrictions are imposed on the lag structure. These methods are applied to two examples to illustrate the importance of imposing restrictions on lagged relationships.     

An approximation to the limit distribution of the epps-pulley test statistic for normality

Summary The approach of Epps and Pulley (1983) based on the empirical characteristic function is one of the most powerful tools for detecting any departures from normality. We obtain the first four moments of the limiting null distribution of the Epps-Pulley Statistic. Johnson- and Pearson curve fitting yields excellent approximations to simulated quantiles, and by modifying the test statistic the procedure may be carried out easily without the use of extensive tables for all sample sizes. Research done while the author was on leave at the University of Gie?en.     

The expected real return to equity

The expected return to equity – typically measured as a historical average – is a key variable in the decision making of investors. A recent literature uses analysts' forecasts, investor surveys or present-value relationships and finds estimates of expected returns that are sometimes much lower than historical averages. This study extends the present-value approach to a dynamic optimizing framework. Given a model that captures this relationship, one can use data on dividends, earnings and valuations to infer the model-implied expected return. Using this method, the estimated expected real return to equity ranges from 4.9% to 5.6% . Furthermore, the analysis indicates that expected returns have declined by about 3 percentage points over the past 40 years. These results indicate that future returns to equity may be lower than past realized returns.     

Moment-based estimation of smooth transition regression models with endogenous variables

Nonlinear regression models have been widely used in practice for a variety of time series and cross-section datasets. For purposes of analyzing univariate and multivariate time series data, in particular, smooth transition regression (STR) models have been shown to be very useful for representing and capturing asymmetric behavior. Most STR models have been applied to univariate processes, and have made a variety of assumptions, including stationary or cointegrated processes, uncorrelated, homoskedastic or conditionally heteroskedastic errors, and weakly exogenous regressors. Under the assumption of exogeneity, the standard method of estimation is nonlinear least squares. The primary purpose of this paper is to relax the assumption of weakly exogenous regressors and to discuss moment-based methods for estimating STR models. The paper analyzes the properties of the STR model with endogenous variables by providing a diagnostic test of linearity of the underlying process under endogeneity, developing an estimation procedure and a misspecification test for the STR model, presenting the results of Monte Carlo simulations to show the usefulness of the model and estimation method, and providing an empirical application for inflation rate targeting in Brazil. We show that STR models with endogenous variables can be specified and estimated by a straightforward application of existing results in the literature.     

The density of the time to ruin for a Sparre Andersen process with Erlang arrivals and exponential claims

We derive expressions for the density of the time to ruin given that ruin occurs in a Sparre Andersen model in which individual claim amounts are exponentially distributed and inter-arrival times are distributed as Erlang(n,?β). We provide numerical illustrations of finite time ruin probabilities, as well as illustrating features of the density functions.     

Effects of EU dairy policy reform for Dutch dairy farming: a primal approach using GMM estimation

This paper estimates a production function for milk using ageneralised method of moments estimator to avoid the endogeneityproblem. Using the first-order conditions for profit maximisation,the economic effects for individual Dutch dairy farms of the2003 EU dairy policy reform are analysed. With an expected milkprice decrease of 21 per cent, profit decreases on average by22 per cent. EU direct payments compensate for roughly 53 percent of this fall in profit. The profit reduction means that69 per cent of all small farms have negative income from farming,compared with 15 per cent in the initial situation.     

Financial development,economic growth and convergence clubs

To study the role of financial development in economic growth, we apply an array of convergence tests designed to capture nonlinear transitional dynamics to real outputs per capita. Strong evidence of multiple convergence clubs is observed, implying that the clubs are formed based on the initial level of real output per capita and average growth rate. Our empirical results show that the stage of economic growth of each country plays an important role for the composition of the convergence clubs. Furthermore, financial development emerges to be a significant determinant, albeit plays differently in the economic growth of each convergence club.     

TESTING FOR STATIONARITY WITH A BREAK IN PANELS WHERE THE TIME DIMENSION IS FINITE

In this paper, we consider the case of finite time dimension in the panel stationarity tests with structural breaks. By fixing T, the finite sample properties of the tests for both micro (T small and N large) and macro (both T and N large) panel data are generally greatly improved. More importantly, the derivation of the tests for finite T and , as opposed to joint asymptotic where N and simultaneously, avoids the imposition of the rate condition making the test valid for any (T, N) blend. Four models corresponding to the usual combination of breaks are considered. The asymptotic distributions of the test are derived under the null hypothesis and are shown to be normally distributed. Their moments for T fixed are derived analytically employing Ghazal’s corollary 1. The case with unknown breaks is also considered. The proposed tests have generally empirical sizes that are very close to the nominal size. The Monte Carlo simulations show that the power of the test statistics increases substantially with N and T.