The Macroeconomic Controversy Over Price Rigidity — How to Resolve it and How Bayesian Estimation has Led us Astray

Price rigidity plays a central role in macroeconomic models but remains controversial. Those espousing it look to Bayesian estimated models in support, while those assuming price flexibility largely impose it on their models. So controversy continues unresolved by testing on the data. In a Monte Carlo experiment we ask how different estimation methods could help to resolve this controversy. We find Bayesian estimation creates a large potential estimation bias compared with standard estimation techniques. Indirect estimation where the bias is found to be low appears to do best, and offers the best way forward for settling the price rigidity controversy.

Permanent-Transitory decomposition of cointegrated time series via Dynamic Factor Models, with an application to commodity prices

We propose a cointegration-based Permanent-Transitory decomposition for non-stationary Dynamic Factor Models. Our methodology exploits the cointegration relations among the observable variables and assumes they are driven by a common and an idiosyncratic component. The common component is further split into a long-term non-stationary and a short-term stationary part. A Monte Carlo experiment shows that incorporating the cointegration structure into the DFM leads to a better reconstruction of the space spanned by the factors, compared to the most standard technique of applying a factor model in differenced systems. We apply our procedure to a set of commodity prices to analyse the comovement among different markets and find that commodity prices move together mostly due to long-term common forces; while the trend for the prices of most primary goods is declining, metals and energy exhibit an upward or at least stable pattern since the 2000s.

The Robustness of Conditional Logit for Binary Response Panel Data Models with Serial Correlation

We examine the conditional logit estimator for binary panel data models with unobserved heterogeneity. A key assumption used to derive the conditional logit estimator is conditional serial independence (CI), which is problematic when the underlying innovations are serially correlated. A Monte Carlo experiment suggests that the conditional logit estimator is not robust to violation of the CI assumption. We find that higher persistence and smaller time dimension both increase the magnitude of the bias in slope parameter estimates. We also compare conditional logit to unconditional logit, bias corrected unconditional logit, and pooled correlated random effects logit.

Optimal Reconciliation of Seasonally Adjusted Disaggregates Taking Into Account the Difference Between Direct and Indirect Adjustment of the Aggregate

This article presents a new method to reconcile direct and indirect deseasonalized economic time series. The proposed technique uses a Combining Rule to merge, in an optimal manner, the directly deseasonalized aggregated series with its indirectly deseasonalized counterpart. The lastmentioned series is obtained by aggregating the seasonally adjusted disaggregates that compose the aggregated series. This procedure leads to adjusted disaggregates that verify Denton’s movement preservation principle relative to the originally deseasonalized disaggregates. First, we use as preliminary estimates the directly deseasonalized economic time series obtained with the X-13ARIMA-SEATS program applied to all the disaggregation levels. Second, we contemporaneously reconcile the aforementioned seasonally adjusted disaggregates with its seasonally adjusted aggregate, using Vector Autoregressive models. Then, we evaluate the finite sample performance of our solution via a Monte Carlo experiment that considers six Data Generating Processes that may occur in practice, when users apply seasonal adjustment techniques. Finally, we present an empirical application to the Mexican Global Economic Indicator and its components. The results allow us to conclude that the suggested technique is appropriate to indirectly deseasonalize economic time series, mainly because we impose the movement preservation condition to the preliminary estimates produced by a reliable seasonal adjustment procedure.

Large-Sample Variance of Fleiss Generalized Kappa

Cohen’s kappa coefficient was originally proposed for two raters only, and it later extended to an arbitrarily large number of raters to become what is known as Fleiss’ generalized kappa. Fleiss’ generalized kappa and its large-sample variance are still widely used by researchers and were implemented in several software packages, including, among others, SPSS and the R package “rel.” The purpose of this article is to show that the large-sample variance of Fleiss’ generalized kappa is systematically being misused, is invalid as a precision measure for kappa, and cannot be used for constructing confidence intervals. A general-purpose variance expression is proposed, which can be used in any statistical inference procedure. A Monte-Carlo experiment is presented, showing the validity of the new variance estimation procedure.

Ischiofemoral impingement: the evolutionary cost of pelvic obstetric adaptation

The risk for ischiofemoral impingement has been mainly related to a reduced ischiofemoral distance and morphological variance of the femur. From an evolutionary perspective, however, there are strong arguments that the condition may also be related to sexual dimorphism of the pelvis. We, therefore, investigated the impact of gender-specific differences in anatomy of the ischiofemoral space on the ischiofemoral clearance, during static and dynamic conditions. A random sampling Monte-Carlo experiment was performed to investigate ischiofemoral clearance during stance and gait in a large (n = 40 000) virtual study population, while using gender-specific kinematics. Subsequently, a validated gender-specific geometric morphometric analysis of the hip was performed and correlations between overall hip morphology (statistical shape analysis) and standard discrete measures (conventional metric approach) with the ischiofemoral distance were evaluated. The available ischiofemoral space is indeed highly sexually dimorphic and related primarily to differences in the pelvic anatomy. The mean ischiofemoral distance was 22.2 ± 4.3 mm in the females and 29.1 ± 4.1 mm in the males and this difference was statistically significant (P < 0.001). Additionally, the ischiofemoral distance was observed to be a dynamic measure, and smallest during femoral extension, and this in turn explains the clinical sign of pain in extension during long stride walking. In conclusion, the presence of a reduced ischiofemroal distance and related risk to develop a clinical syndrome of ischiofemoral impingement is strongly dominated by evolutionary effects in sexual dimorphism of the pelvis. This should be considered when female patients present with posterior thigh/buttock pain, particularly if worsened by extension. Controlled laboratory study.

Do Prior Type and Sample Size have Effect on Mixtures of Normal? The Monte Carlo evidence

Overtime finite mixtures of Normal in regression have gained popularity and also shown to be useful in modelling heterogeneous data. This study examines the effects of prior and sample size in regression mixtures of Normal models with Bayesian approach. Monte Carlo experiment was carried out on the Normal mixtures model in order to examine the strength of priors and also to know the suitable sample size to produce stable results. Results obtained from the experiment indicate that an informative prior gives a reliable estimate than non-informative prior while large sample sizes maybe needed to obtain stable results.

Government Insurance Against Natural Disasters

This paper estimates insurance requirements against natural disasters (NDs) in the Eastern Caribbean Currency Union (ECCU) using an insurance layering framework. The layers include a government saving fund, as well as market instruments. Each layer is calibrated to cover estimated fiscal cost of NDs according to intensity and expected damage. The results indicate that ECCU countries could target saving fund stocks for relativelly smaller and more frequent events in the range of 6-12 percent of GDP, enough to cover 95 percent of NDs’ fiscal costs. To ensure financially-sustainable saving funds with a low probability of depletion, this requires annual budget savings in the range os 0.5 to 1.9 percent of GDP per year. Additional coverage could be obtained with market instruments for large and less frequent events, albeit at a significant cost.The results are based on a Monte-Carlo experiment that simulates natural disaster shocks and their impact on output and government finances.

NONPARAMETRIC EULER EQUATION IDENTIFICATION AND ESTIMATION

We consider nonparametric identification and estimation of pricing kernels, or equivalently of marginal utility functions up to scale, in consumption-based asset pricing Euler equations. Ours is the first paper to prove nonparametric identification of Euler equations under low level conditions (without imposing functional restrictions or just assuming completeness). We also propose a novel nonparametric estimator based on our identification analysis, which combines standard kernel estimation with the computation of a matrix eigenvector problem. Our estimator avoids the ill-posed inverse issues associated with nonparametric instrumental variables estimators. We derive limiting distributions for our estimator and for relevant associated functionals. A Monte Carlo experiment shows a satisfactory finite sample performance for our estimators.

Effects of Multicollinearity and Correlation between the Error Terms on Some Estimators in a System of Regression Equations

One of the assumptions of a single equation model is that there is one -way causation between the dependent variable Y and the independent variables X. When the assumption is not valid, as, in many econometric models, of lack of correlation between the independent variables and the error terms (U) is further violated, Ordinary Least Square estimator would no longer efficient, that was why this study examined the effects of multicollinearity and a correlation between the error terms on the performance of seven estimators and identified the estimator that yields the most preferred estimates under the separate or joint influence of the two correlation effects under consideration. A two-equation model in which the two correlation problems were introduced was used in this study. The error terms of the two equations were also correlated. The levels of correlation between the error terms and multicollinearity were specified between -1 and +1 at an interval of 0.2 except when the correlation value approached unity. A Monte Carlo experiment of 1000 trials was carried out at five levels of sample sizes 20, 30, 50, 100, and 250 at two runs.