Structural information in recursive VAR orderings

This paper investigates structural models that will permit a Cholesky decomposition of the covariance matrix of VAR residuals to identify some structural impulse response functions. Cholesky decompositions are found to be useful identification tools for the set of partially recursive structural models. A partially recursive structure is defined as any block recursive system where the equations in one block can be recursively ordered and where the structural shocks are uncorrelated. Using this class of models, we derive necessary and sufficient conditions for the moving average representation from a Cholesky decomposition to identify structure. The paper concludes by discussing implications of these results for empirical research.     

Exogeneity in structural equation models

The practical relevance of several concepts of exogeneity of treatments for the estimation of causal parameters based on observational data are discussed. We show that the traditional concepts, such as strong ignorability and weak and super-exogeneity, are too restrictive if interest lies in average effects (i.e. not on distributional effects of the treatment). We suggest a new definition of exogeneity, KL-exogeneity. It does not rely on distributional assumptions and is not based on counterfactual random variables. As a consequence it can be empirically tested using a proposed test that is simple to implement and is distribution-free.     

Restrictions on variables

Accounting identities impose exact restrictions on the endogenous variables of econometric models. Such restrictions are usually met by choosing a closing entry or by building an allocation model. Selecting a closing entry may be difficult or arbitrary, while allocation models admit little flexibility in the choice of explanatory variables and lagged adjustment schemes. This paper studies a third solution which in a sense lies inbetween: freely chosen equations for all variables are adjusted additively such that the restrictions will hold. The adjustment involves some new parameters which can be estimated simultaneously with the original parameters using Maximum Likelihood techniques.An application is provided for a financial model of the Dutch private sector. Our approach here proves superior to any choice of closing entry in the system.     

汇率制度外生性检验——基于人民币实际有效汇率的分析

本文基于条件ECM模型对人民币汇率与宏观经济变量关注参数的内生性或外生性进行检验。结果表明,人民币实际有效汇率贬值从总体上有利于GDP的增长,但是由于人民币实际有效汇率具有弱外生性、强外生性和超外生性等特点,汇率制度缺乏弹性不利于我国国民经济的发展,因此加快人民币汇率形成机制的市场化进程,是大势所趋。     

Common cyclical features analysis in VAR models with cointegration

The paper considers n-dimensional VAR models for variables exhibiting cointegration and common cyclical features. Two specific reduced rank vector error correction models are discussed. In one, named the “strong form” and denoted by SF, the collection of all coefficient matrices of a VECM has rank less than n, in the other, named the “weak form” and denoted by WF, the collection of all coefficient matrices except the matrix of coefficient of error correction terms has rank less than n. The paper explores the theoretical connections between these two forms, suggests asymptotic tests for each form and examines the small sample properties of these tests by Monte Carlo simulations.     

On DS-optimal Design Matrices with Restrictions on Rows or Columns

The distance stochastic optimality criterion is considered in a linear regression setting with two possible experimental regions. The first region consists of design matrices with restrictions on their rows, while the second consists of design matrices with restrictions on their columns.     

Forecasting mortality with a hyperbolic spatial temporal VAR model

Accurate forecasts of mortality rates are essential to various types of demographic research like population projection, and to the pricing of insurance products such as pensions and annuities. Recent studies have considered a spatial–temporal vector autoregressive (STVAR) model for the mortality surface, where mortality rates of each age depend on the historical values for that age (temporality) and the neighboring cohorts ages (spatiality). This model has sound statistical properties including co-integrated dependent variables, the existence of closed-form solutions and a simple error structure. Despite its improved forecasting performance over the famous Lee–Carter (LC) model, the constraint that only the effects of the same and neighboring cohorts are significant can be too restrictive. In this study, we adopt the concept of hyperbolic memory to the spatial dimension and propose a hyperbolic STVAR (HSTVAR) model. Retaining all desirable features of the STVAR, our model uniformly beats the LC, the weighted functional demographic model, STVAR and sparse VAR counterparties for forecasting accuracy, when French and Spanish mortality data over 1950–2016 are considered. Simulation results also lead to robust conclusions. Long-term forecasting analyses up to 2050 comparing the four models are further performed. To illustrate the extensible feature of HSTVAR to a multi-population case, a two-population illustrative example using the same sample is further presented.     

Minimum Distance Estimation and Testing of DSGE Models from Structural VARs*

The aim of this paper is to complement the minimum distance estimation–structural vector autoregression approach when the weighting matrix is not optimal. In empirical studies, this choice is motivated by stochastic singularity or collinearity problems associated with the covariance matrix of impulse response functions. Consequently, the asymptotic distribution cannot be used to test the economic model's fit. To circumvent this difficulty, we propose a simple simulation method to construct critical values for the test statistics. An empirical application with US data illustrates the proposed method.     

Evaluating Economic Justifications for Alcohol Restrictions

Abstract . Does economics justify restricting alcohol consumption? A new line of research concludes that alcohol involves significant social costs and that various restrictions would lead to net social gains. This article focuses on Levy and Miller (1995 ), who conduct a cost‐benefit analysis of serving‐intoxicated‐patron laws. We administer a survey of taverns in Washtenaw County, Michigan, to investigate the plausibility of some of Levy and Miller’s claims. We find a number of problems with their economic discussion: in addition to a number of problematic assumptions, they count private costs as social costs and completely ignore consumer and producer surplus associated with alcohol. We find their assumptions bias the results in favor of the restrictions. Despite their popularity in public policy debates, these economic justifications for restricting alcohol are dubious.     

Modelling factor demands with SEM and VAR: an empirical comparison

The empirical analysis of the economic interactions between factors of production, output and corresponding prices has received much attention over the last two decades. Most contributions in this area have agreed on the neoclassical principle of a representative optimizing firm and typically use theory-based structural equation models (SEM). A popular alternative to SEM is the vector autoregression (VAR) methodology. The most recent attempts to link the SEM approach with VAR analysis in the area of factor demands concentrate on single-equation models, whereas no effort has been devoted to compare these alternative approaches when a firm is assumed to face a multi-factor technology and to decide simultaneously the optimal quantity for each input. This paper bridges this gap. First, we illustrate how the SEM and the VAR approaches can both represent valid alternatives to model systems of dynamic factor demands. Second, we show how to apply both methodologies to estimate dynamic factor demands derived from a cost-minimizing capital-labour-energy-materials (KLEM) technology with adjustment costs (ADC) on the quasi-fixed capital factor. Third, we explain how to use both models to calculate some widely accepted indicators of the production structure of an economic sector, such as price and quantity elasticities, and alternative measures of ADC. In particular, we propose and discuss some theoretical and empirical justifications of the differences between observed elasticities, measures of ADC, and the assumption of exogeneity of output and/or input prices. Finally, we offer some suggestions for the applied researcher.      

RISE OF VAR MODELLING APPROACH*

Abstract This paper surveys the rise of the Vector AutoRegressive (VAR) approach from a historical perspective. It shows that the VAR approach arises from a fusion of the Cowles Commission tradition and time series statistical methods, catalysed by the rational expectations (RE) movement, that the approach offers a systematic solution to the issue of ‘model choice’ bypassed by Cowles researchers, hence essentially inheriting and enhancing the Cowles legacy rather than abandoning or opposing it. By tackling model choice, however, the VAR approach helps reform econometrics by shifting the research focus from measurement of given theories to identification/verification of data‐coherent theories.     

Combining VAR and DSGE forecast densities

A popular macroeconomic forecasting strategy utilizes many models to hedge against instabilities of unknown timing; see (among others) Stock and Watson (2004), Clark and McCracken (2010), and Jore et al. (2010). Existing studies of this forecasting strategy exclude dynamic stochastic general equilibrium (DSGE) models, despite the widespread use of these models by monetary policymakers. In this paper, we use the linear opinion pool to combine inflation forecast densities from many vector autoregressions (VARs) and a policymaking DSGE model. The DSGE receives a substantial weight in the pool (at short horizons) provided the VAR components exclude structural breaks. In this case, the inflation forecast densities exhibit calibration failure. Allowing for structural breaks in the VARs reduces the weight on the DSGE considerably, but produces well-calibrated forecast densities for inflation.     

The Determinants and Impact of State Abortion Restrictions

This paper shows that a state's abortion policy is determined by the strength of interest advocacy groups and political forces. The greater the membership in the National Abortion Rights Action League, the percentage of female state legislators and the percentage of Democratic female legislators, the less restrictive a state's abortion policy. The greater a state's population that are Roman Catholics, the more restrictive a state's abortion policy. The paper also estimates the impact abortion restrictions have on a state's abortion rate. The empirical results show that abortion restrictions have no statistically significant impact on a state's abortion rate. A state's abortion restrictions do not significantly increase out-of-state abortions.     

Bayesian stochastic search for VAR model restrictions

We propose a Bayesian stochastic search approach to selecting restrictions for vector autoregressive (VAR) models. For this purpose, we develop a Markov chain Monte Carlo (MCMC) algorithm that visits high posterior probability restrictions on the elements of both the VAR regression coefficients and the error variance matrix. Numerical simulations show that stochastic search based on this algorithm can be effective at both selecting a satisfactory model and improving forecasting performance. To illustrate the potential of our approach, we apply our stochastic search to VAR modeling of inflation transmission from producer price index (PPI) components to the consumer price index (CPI).     

Forecasting with vector autoregressive (VAR) models subject to business cycle restrictions

In the last decade VAR models have become a widely-used tool for forecasting macroeconomic time series. To improve the out-of-sample forecasting accuracy of these models, Bayesian random-walk prior restrictions are often imposed on VAR model parameters. This paper focuses on whether placing an alternative type of restriction on the parameters of unrestricted VAR models improves the out-of-sample forecasting performance of these models. The type of restriction analyzed here is based on the business cycle characteristics of U.S. macroeconomic data, and in particular, requires that the dynamic behavior of the restricted VAR model mimic the business cycle characteristics of historical data. The question posed in this paper is: would a VAR model, estimated subject to the restriction that the cyclical characteristics of simulated data from the model “match up” with the business cycle characteristics of U.S. data, generate more accurate out-of-sample forecasts than unrestricted or Bayesian VAR models?     

风险测量VAR及其原理

用公式可表示为:Prob(△P>VAR}=1-a(其中Prob表示:资产价值损失小于可能损失上限的概率;△P表示:某一金融资产在一定持有期△t的价值失额;VAR表示:给定置信水平a下的在险价值,即可能的损失上限;a表示:给定的置信水平。)     

住房交易约束与农户习惯形成

本文分析了现行农村住房制度对农户习惯偏好的影响。利用2000~2008年中国29个省区市的农村住户调查数据得到的估计结果表明,住房交易约束是中国农户习惯形成的重要原因,但不是唯一原因。由于不能通过市场进行交易、不能作为抵押物而获得贷款,中国农户的住房资产不仅未能产生显著的财富效应,反而降低了农户的短期边际消费倾向。     

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We suggest a new two-step model selection procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties and we prove its consistency. A Monte Carlo study explores the finite sample performance of this procedure and evaluates the forecasting accuracy of models selected by this procedure. Two empirical applications confirm the usefulness of the model selection procedure proposed here for forecasting.