Diagnostics cannot have much power against general alternatives

Model diagnostics are shown to have little power unless alternative hypotheses can be narrowly defined. For example, the independence of observations cannot be tested against general forms of dependence. Thus, the basic assumptions in regression models cannot be inferred from the data. Equally, the proportionality assumption in proportional-hazards models is not testable. Specification error is a primary source of uncertainty in forecasting, and this uncertainty will be difficult to resolve without external calibration. Model-based causal inference is even more problematic.     

Spatial modelling of claim frequency and claim size in non-life insurance

In this paper, models for claim frequency and average claim size in non-life insurance are considered. Both covariates and spatial random effects are included allowing the modelling of a spatial dependency pattern. We assume a Poisson model for the number of claims, while claim size is modelled using a Gamma distribution. However, in contrast to the usual compound Poisson model, we allow for dependencies between claim size and claim frequency. A fully Bayesian approach is followed, parameters are estimated using Markov Chain Monte Carlo (MCMC). The issue of model comparison is thoroughly addressed. Besides the deviance information criterion and the predictive model choice criterion, we suggest the use of proper scoring rules based on the posterior predictive distribution for comparing models. We give an application to a comprehensive data set from a German car insurance company. The inclusion of spatial effects significantly improves the models for both claim frequency and claim size, and also leads to more accurate predictions of the total claim sizes. Further, we detect significant dependencies between the number of claims and claim size. Both spatial and number of claims effects are interpreted and quantified from an actuarial point of view.     

The Significance of Credit Rationing on Real Investment Activity – A Test for the Significance of Capital-Market Imperfection in Japan

Does bank credit matter to the real investment finance of firms? If so, to what kind of firms does it matter, and why? Despite the progress of research in this field, there is still little empirical consensus on these problems. This paper contributes to the understanding of capital-market imperfection in Japan by answering these questions. I introduce new methods to test the significance of credit rationing and show that bank credit is indeed a very important determinant of real investment activity of small firms. The availability of long-term loans has particularly significant effects on the investment opportunities of small firms, although large firms with capital of more than 1 billion yen are free from credit rationing. JEL Classification Numbers: C51, E44, G21.     

On estimating long-run effects in models with lagged dependent variables

A common procedure in economics is to estimate long-run effects from models with lagged dependent variables. For example, macro panel studies frequently are concerned with estimating the long-run impacts of fiscal policy, international aid, or foreign investment.Our analysis points out the hazards of this practice. We use Monte Carlo experiments to demonstrate that estimating long-run impacts from dynamic models produces unreliable results.Biases can be substantial, sample ranges very wide, and hypothesis tests can be rendered useless in realistic data environments. There are three reasons for this poor performance. First, OLS estimates of the coefficient of a lagged dependent variable are downwardly biased in finite samples. Second, small biases in the estimate of the lagged, dependent variable coefficient are magnified in the calculation of long-run effects. And third, and perhaps most importantly, the statistical distribution associated with estimates of the LRP is complicated, heavy-tailed, and difficult to use for hypothesis testing. While many of the underlying problems have been long-known in the literature, the continued widespread use of the associated empirical procedures suggests that researchers are unaware of the extent and severity of the estimation problems. This study aims to illustrate their practical importance for applied research.     

The cointegrated vector autoregressive model with general deterministic terms

In the cointegrated vector autoregression (CVAR) literature, deterministic terms have until now been analyzed on a case-by-case, or as-needed basis. We give a comprehensive unified treatment of deterministic terms in the additive model $X_t=\gamma Z_t+Y_t$, where $Z_t$ belongs to a large class of deterministic regressors and $Y_t$ is a zero-mean CVAR. We suggest an extended model that can be estimated by reduced rank regression, and give a condition for when the additive and extended models are asymptotically equivalent, as well as an algorithm for deriving the additive model parameters from the extended model parameters. We derive asymptotic properties of the maximum likelihood estimators and discuss tests for rank and tests on the deterministic terms. In particular, we give conditions under which the estimators are asymptotically (mixed) Gaussian, such that associated tests are ${\chi }^2$-distributed.     

CAUSAL INFERENCE ON EDUCATION POLICIES: A SURVEY OF EMPIRICAL STUDIES USING PISA,TIMSS AND PIRLS

The identification of the causal effects of educational policies is the top priority in recent education economics literature. As a result, a shift can be observed in the strategies of empirical studies. They have moved from the use of standard multivariate statistical methods, which identify correlations or associations between variables only, to more complex econometric strategies, which can help to identify causal relationships. However, exogenous variations in databases have to be identified in order to apply causal inference techniques. This is a far from straightforward task. For this reason, this paper provides an extensive and comprehensive overview of the literature using quasi‐experimental techniques applied to three well‐known international large‐scale comparative assessments, such as PISA, PIRLS or TIMSS, over the period 2004–2016. In particular, we review empirical studies employing instrumental variables, regression discontinuity designs, difference in differences and propensity score matching to the above databases. Additionally, we provide a detailed summary of estimation strategies, issues treated and profitability in terms of the quality of publications to encourage further potential evaluations. The paper concludes with some operational recommendations for prospective researchers in the field.     

TOOLS FOR RE‐RANDOMIZATION

With the rising popularity of field experiments in economics, re‐randomization schemes have emerged as tools to induce balance in observable variables across treatment groups. However, re‐randomization is not fully understood and the methodologies to estimate its effects on the distribution of parameters are still under‐developed. This paper helps to close that gap by suggesting an asymptotically normal re‐randomization scheme and bootstrapping procedure to carry inference under a wide range of estimators and re‐randomization schemes.     

Heteroskedasticity-consistent covariance matrix estimators for spatial autoregressive models

In the presence of heteroskedasticity, conventional test statistics based on the ordinary least squares (OLS) estimator lead to incorrect inference results for the linear regression model. Given that heteroskedasticity is common in cross-sectional data, the test statistics based on various forms of heteroskedasticity-consistent covariance matrices (HCCMs) have been developed in the literature. In contrast to the standard linear regression model, heteroskedasticity is a more serious problem for spatial econometric models, generally causing inconsistent extremum estimators of model coefficients. This paper investigates the finite sample properties of the heteroskedasticity-robust generalized method of moments estimator (RGMME) for a spatial econometric model with an unknown form of heteroskedasticity. In particular, it develops various HCCM-type corrections to improve the finite sample properties of the RGMME and the conventional Wald test. The Monte Carlo results indicate that the HCCM-type corrections can produce more accurate results for inference on model parameters and the impact effects estimates in small samples.     

How to Construct a Random Probability Measure

This article presents an overview of several constructive methods for generating random probability measures. Applications of random probability measures include Bayesian statistics, average optimal control problems, average error bounds for numerical equation solving methods, and models for random distributions of mass in space.     

Term structure and the estimated monetary policy rule in the Eurozone

In this paper we estimate a standard version of the New Keynesian Monetary (NKM) model augmented with term structure in order to analyze two issues. First, we analyze the effect of introducing an explicit term structure channel in the NKM model on the estimated parameter values of the model, with special emphasis on the interest rate smoothing parameter using data for the Eurozone. Second, we study the ability of the model to reproduce some stylized facts such as highly persistent dynamics, the weak comovement between economic activity and inflation, and the positive, strong comovement between interest rates observed in actual Eurozone data. The Sect. 3 implemented is a classical structural method based on the indirect inference principle. We are grateful to Eduardo Ley, two anonymous referees and seminar participants at the XXXI Simposio de Análisis Económico (Oviedo, Spain) and Bank of Spain for their useful comments. Financial support from Ministerio de Ciencia y Tecnología and Universidad del País Vasco (Spain) and Fundación Séneca through projects SEJ2004-04811/ECON, 9/UPV00035.321-13511/2001 and I02937/PHCS/05, respectively, is gratefully acknowledged. The first author also thanks Fundación Ramón Areces for financial support.