We develop a new asymptotic theory for autocorrelation robust tests using a vector autoregressive (VAR) covariance matrix estimator. In contrast to the conventional asymptotics where the VAR order goes to in…nity but at a slower rate than the sample size, we have the VAR order grow at the same rate, as a …xed fraction of the sample size. Under this …xed-smoothing asymptotic speci…cation, the as...

In this paper we consider estimation and inference of common breaks in panel data models via adaptive group fused lasso. We consider two approaches — penalized least squares (PLS) for firstdifferenced models without endogenous regressors, and penalized GMM (PGMM) for first-differenced models with endogeneity. We show that with probability tending to one both methods can correctly determine the ...

This paper examines the very long-run relationship between income and emissions of enteric methane in New Zealand, over the period 1895 to 1996. Controlling the emissions of this particular greenhouse gas is of crucial importance if that country is to meet its obligations as a signatory to the Kyoto Protocol. We use standard parametric regression, nonparametric regression, and a new nonlinear r...

Large aggregation interval asymptotics are used to investigate the relation between Granger causality in disaggregated vector autoregressions (VARs) and associated contemporaneous correlation among innovations of the aggregated system. Our approach allows us to better understand the informational content in non-diagonal error covariance matrices, which play an important role in structural VAR a...

Understanding and quantifying the determinants of the number of sectors or firms exporting in a given country is of relevance for the assessment of trade policies. Estimation of models for the number of sectors, however, poses a challenge because the dependent variable has both a lower and upper bound, implying that the partial effects of the explanatory variables on the conditional mean of the...

This paper develops and estimates mixture models of crop price comovements using copula functions, which allow for departures from normality during extreme market circumstances. The models also account for unique time series patterns inherent in crop price data. The paper reaches two main conclusions. First, mixture models appear to provide an easy-to-estimate approach for capturing real-life c...

This paper presents a new copula-based approach for analyzing panel data in which (1) the observed multivariate outcomes are a mixture of zeros and continuouslymeasured positives, with the zeros accounting for a nontrivial proportion of the sample; (2) both the zero and positive outcomes show state dependence and dynamic interdependence (cross-equation lagged dependence); and (3) the zeros and ...

This paper studies how well a search and matching model can describe aggregate Japanese labor market dynamics in a full information setting. We develop a discretetime search and matching model with productivity and separation shocks and use it as a data-generating process for our empirical analysis. Using Bayesian methods, we estimate the model for data on unemployment and vacancy postings in J...

In this paper we extend the univariate FIGARCH and FIAPARCH models to a bivariate framework. We estimate bivariate error correction FIGARCH and FIAPARCH models between the All Ordinaries Index and its SPI futures using constant correlation and diagonal parameterisations. We therefore employ a flexible estimation approach that captures the long run equilibrium relationship between the two market...

It is well known that strategies that allow investors to allocate their wealth using return and volatility forecasts, the use of which are termed market and volatility timing, are of significant value. In this paper, we show that distribution tim ing, defined here as the ability to use forecasts for moments up to the fourth one, yields significant incremental economic value. By considering the ...