The Phillips curve,parameter instability and the Lucas critique

Empirical estimation of Phillips curve relationships typically indicates the presence of parameter instability. This is argued to be due to the fact that the parameters of these equations are reduced form rather than structural parameters. Estimation of a Phillips curve model by methods which allow for time-varying parameters permits investigation of the nature and timing of the structural breaks which generate instability. This paper estimates such a model by the Kalman filter using quarterly data over the period 1972.4 to 1993.3. We find evidence of a gradual decline in the private sector's assessment of the steady-state inflation rate during the 1980s, but little evidence of the sort of dramatic regime shift predicted by some of the more extreme rational expectations models.     

Identifiability of the misspecified split hazard models

Unlike standard models, a split population hazard model allows the exit probability to be less than one. Although conceptually attractive, split models are prone to identification problems. In the reduced form estimation of the hazard function, the influence of split may not be distinguishable from that of neglected heterogeneity. For illustration, I use Monte Carlo simulations to highlight the problem of interpreting the structural parameters of the split Weibull and the Weibull-gamma models.     

Bayesian Estimation and Unit Root Test for Logistic Smooth Transition Autoregressive Process

The paper considers nonlinear logistic smooth transition autoregressive (LSTAR) process and aims to detect the unit root under the null hypothesis of a random walk process against the alternative of a stationary LSTAR process and to estimate the parameters of the process in Bayesian framework using MCMC. The simulation study is carried out for investigating the performance of the Bayes estimators for parameters and Bayesian unit root test and it has been observed that the estimates of parameters of the LSTAR process are close to the true parameter values. It has been observed that the Bayesian unit root test performs well and the power of the test is high even for the boundary cases having root close to unity, at least when the sample size is large. Since the LSTAR models are widely applied for real exchange rate modeling, the theoretical results are illustrated empirically for the real exchange rates of ten OCED countries.

     

Was the recent downturn in US real GDP predictable?

This article uses a small set of variables – real GDP, the inflation rate and the short-term interest rate – and a rich set of models – atheoretical (time series) and theoretical (structural), linear and nonlinear, as well as classical and Bayesian models – to consider whether we could have predicted the recent downturn of the US real GDP. Comparing the performance of the models to the benchmark random-walk model by root mean-square errors, the two structural (theoretical) models, especially the nonlinear model, perform well on average across all forecast horizons in our ex post, out-of-sample forecasts, although at specific forecast horizons certain nonlinear atheoretical models perform the best. The nonlinear theoretical model also dominates in our ex ante, out-of-sample forecast of the Great Recession, suggesting that developing forward-looking, microfounded, nonlinear, dynamic stochastic general equilibrium models of the economy may prove crucial in forecasting turning points.     

Adaptive ARFIMA models with applications to inflation

Many previous analyses of inflation have used either long memory or nonlinear time series models. This paper suggests a simple adaptive modification of the basic ARFIMA model, which uses a flexible Fourier form to allow for a time varying intercept. Simulation evidence suggests that the model provides a good representation of various forms of structural breaks and also that the new model can be efficiently estimated by a QMLE approach. We investigate monthly CPI inflation series for the G7 countries and find evidence of stable long memory parameters across regimes and also of significant nonlinear effects. The estimated adaptive ARFIMA models generally have less persistent long memory parameters than previous studies, with the estimated time dependent intercept being an important component. The model is also supplemented with an adaptive FIGARCH component, yielding a double nonlinear long memory model.     

Estimation of Structural Nonlinear Errors‐in‐Varibles Models by Simulated Least‐Squares Method

This article proposes a simulation approach to obtain least‐squares or generalized least‐squares estimators of structural nonlinear errors‐in‐variables models. The proposed estimators are computationally attractive because they do not need numerical integration nor huge numbers of simulations per observable. In addition, the asymptotic covariance matrix of the estimator has a simple decomposition that may be used to guide selection of appropriate simulation sizes. The method is also useful for models with missing data or imperfect surrogate covariates, where application of conventional least‐squares and maximum‐likelihood methods is restricted by numerical multidimensional integrations.     

Optimal design of experiments to identify latent behavioral types

Bayesian optimal experiments that maximize the information gained from collected data are critical to efficiently identify behavioral models. We extend a seminal method for designing Bayesian optimal experiments by introducing two computational improvements that make the procedure tractable: (1) a search algorithm from artificial intelligence that efficiently explores the space of possible design parameters, and (2) a sampling procedure which evaluates each design parameter combination more efficiently. We apply our procedure to a game of imperfect information to evaluate and quantify the computational improvements. We then collect data across five different experimental designs to compare the ability of the optimal experimental design to discriminate among competing behavioral models against the experimental designs chosen by a “wisdom of experts” prediction experiment. We find that data from the experiment suggested by the optimal design approach requires significantly less data to distinguish behavioral models (i.e., test hypotheses) than data from the experiment suggested by experts. Substantively, we find that reinforcement learning best explains human decision-making in the imperfect information game and that behavior is not adequately described by the Bayesian Nash equilibrium. Our procedure is general and computationally efficient and can be applied to dynamically optimize online experiments.

     

Calendar effects in structural time series models with trend and season

Calendar effects are analysed in the class of structural time series models one of the two main model based approaches in time series decomposition. While Bell and Hillmer (1983) modeled calendar variation in the ARIMA model based approach, we represent structural models in the generalized regression form which allows to apply classical estimation and test procedures. It turns out that the expected high computaional complexity 0(T ³) in the generalized regression model can be reduced to 0(T). As all parameters are estimated by maximizing the likelihood the Likelihood Ratio statistics can be used to test effects of the calendar composition.     

Generalized cointegration: a new concept with an application to health expenditure and health outcomes

We propose a new generalization of the concept of cointegration that allows for the possibility that a set of variables are involved in an unknown nonlinear relationship. Although these variables may be unit-root non-stationary, there exists a nonlinear combination of them that takes account of such non-stationarity. We then introduce an estimation technique that allows us to test for the presence of this generalized cointegration in the absence of knowledge as to the true nonlinear functional form and the full set of regressors. We outline the basic stages of the technique and discuss how the issue of unit-root non-stationarity and cointegration affects each stage of the estimation procedure. We then apply this technique to the relationship between health expenditure and health outcomes, which is an important but controversial issue. A number of studies have found very little or no relationship between the level of health expenditure and outcomes. In econometric terms, if there is such a relationship, then there should exist a cointegrating relationship between these two variables and possibly many others. The problem that arises is that we may be either unable to measure these other variables or that we do not know about them, in which case we may incorrectly find no relationship between health expenditures and outcomes. We then apply the concept of generalized cointegration; we obtain a highly significant relationship between health expenditure and health outcomes.     

Forecasting Time Series Subject to Multiple Structural Breaks

This paper provides a new approach to forecasting time series that are subject to discrete structural breaks. We propose a Bayesian estimation and prediction procedure that allows for the possibility of new breaks occurring over the forecast horizon, taking account of the size and duration of past breaks (if any) by means of a hierarchical hidden Markov chain model. Predictions are formed by integrating over the parameters from the meta-distribution that characterizes the stochastic break-point process. In an application to U.S. Treasury bill rates, we find that the method leads to better out-of-sample forecasts than a range of alternative methods.     

Review of Solution and Estimation Methods for Nonlinear Dynamic Stochastic General Equilibrium Models with the Zero Lower Bound

We review solution and estimation methods for nonlinear dynamic stochastic general equilibrium models and their application, with a special focus on the zero lower bound on the nominal interest rate. In a fully nonlinear setting, both the solution and estimation methods involve iterative procedures, and their computational expense grows rapidly with an increase in the dimensionality of state variables and parameters. We describe how the procedures deal with the dimensionality problem.     

Repeated Dichotomous Choice Formats for Elicitation of Willingness to Pay: Simultaneous Estimation and Anchoring Effect

Repeated dichotomous choice contingent valuation data are generated from responses to a succession of binary questions regarding alternative prices for an environmental good. In this paper we propose a simultaneous equation model that allows for endogeneity and error correlation across the responses at each stage of the bidding process. The model allows us to study the evolution of anchoring effects after the second dichotomous choice question. Estimation involves the Bayesian techniques of Gibbs sampling and data augmentation, and the application focuses on the preservation value of a natural area. The results for a data set involving up to four successive dichotomous choice questions show that restricted multiple-bounded models are rejected by the data with the general model. In addition, willingness to pay tends to stabilize after the second stage in the elicitation process for the general unrestricted model. When taking anchoring effects into consideration, it is revealed that individuals’ responses in the latter stages are influenced by the sequence of bid prices offered in earlier questions. Nevertheless, they do not have a significant effect on welfare estimates.      

Model Uncertainty in Characterizing Recreation Demand

A Bayesian variable selection procedure is used to control for uncertainty in the specification of a recreational demand model. Specifically, we propose a model that draws on the Bayesian paradigm to integrate the variable selection process into model estimation and to reflect the accompanying uncertainty about which is the best specification in subsequent counterfactual predictions. The advantage of this procedure over previous non-Bayesian approaches is that it overcomes the problem of pre-testing in specification searches. In our application, evaluating demand for recreational lake usage in Iowa, we find clear evidence that site attributes, such as lakes size, handicap facilities and wake restrictions, do impact lake usage. There is also evidence that water quality matters in household recreation choices. Indeed, contrary to Abidoye et?al. (Am J Agricult Econ, 2012), in which only a single functional form is considered, we find clear evidence that water quality matters, with posterior probability of less that 10 % associated with a model without any water quality variables. This suggests that the flexibility that the Bayesian variable selection model affords in capturing the linkage between recreation demand and site characteristics can be important.     

A test for the presence of central bank intervention in the foreign exchange market with an application to the Bank of Canada

In this article, a general nonlinear simultaneous equations framework for the econometric analysis of models of intervention in foreign exchange markets by central banks in response to deviations of exchange rates from target levels is proposed. The instrumental variables estimation of possibly nonlinear response functions and tests of intervention, when the functional form may be nonlinear, asymmetric, and may contain unknown shape parameters, is considered. The methodology applies techniques developed for testing in the presence of nuisance parameters unidentified under a null hypothesis to a nonlinear simultaneous equations model. The results of an empirical analysis of stabilization activity of the Bank of Canada, for the period from 1953 to 2009, with regard to the Canada–U.S exchange rate are reported here. A nonlinear specification is found necessary to capture activity after 1998.     

An extension of mixed estimation,with an application to forecasting new product growth

This paper extends the mixed estimation technique to handle a case in which the coefficients of a set of linear constraints are known nonlinear functions of an unknown parameter vector for which an extraneous unbiased estimate is available. This novel form of the mixed estimation technique is illustrated by applying it to the Bass innovation/diffusion model of new product growth. It is suggested that this is superior to the traditional method whereby managerial intuition is incorporated into this type of model, and is an attractive alternative to recently-suggested Bayesian methods.I thank an anonymous referee for helpful comments and references.     

基于MCMC方法的我国银行业技术效率评价

以动态随机前沿模型为基础,采用基于吉布斯抽样的马尔科夫链蒙特卡洛方法,运用1998—2007年我国银行类金融机构的基本数据对模型的各参数进行贝叶斯估计,得到我国银行类金融机构的技术效率评价结果。结果表明:我国银行类金融机构的技术效率均值为96.68%,运行效率较高,这说明我国推行的银行业改革卓有成效。     

Important overlooked IVs in spatial models

Spatial models often contain additional endogenous variables as regressors. The complete system determining these variables is typically not known to the researcher, and so maximum likelihood or Bayesian estimation methods are precluded. This leaves instrumental variable estimation. In all likelihood, the system may contain certain forms of nonlinearities. These nonlinearities might arise because of endogenous weighting matrices, functional form differences in the endogenous variables, etc. The existence of such nonlinearities strongly suggests the use of nonlinear forms of the instruments. Issues of this sort were pointed out in Kelejian and Piras (Spatial econometrics, Elsevier, Amsterdam, 2017) and Kelejian (Lett Spat Resour Sci 9(1):113–136, 2016). However, thus far Monte Carlo results relating to efficiencies gained by the use of nonlinear instrumental variables are not available. This is unfortunate because these efficiencies can be quite extensive. The purpose of this paper is to fill this void.     

Model Selection in Spatial Autoregressive Models with Varying Coefficients

Spatial autoregressive (SAR) models with varying coefficients are useful for capturing heterogeneous effects of the impacts of covariates as well as spatial interaction in empirical studies, and a wide range of popular models can be seen as its special cases, such as linear SAR models. In this study, we will propose a unified model selection method for the SAR model with varying coefficients to achieve two targets simultaneously: (1) variable selection (eliminate irrelevant covariates), and (2) identification of the covariates with constant effect among the relevant covariates. To do so, we follow the idea of group LASSO to incorporate two penalty functions to simultaneously do model selection and estimation. Monte Carlo experiments show that the proposed method performs well in finite samples. Finally, we illustrate the method with an application to the housing data of Chinese cities.     

Learning Under Ambiguity

This paper considers learning when the distinction between risk and ambiguity matters. It first describes thought experiments, dynamic variants of those provided by Ellsberg, that highlight a sense in which the Bayesian learning model is extreme—it models agents who are implausibly ambitious about what they can learn in complicated environments. The paper then provides a generalization of the Bayesian model that accommodates the intuitive choices in the thought experiments. In particular, the model allows decision-makers' confidence about the environment to change—along with beliefs—as they learn. A portfolio choice application compares the effect of changes in confidence under ambiguity vs. changes in estimation risk under Bayesian learning. The former is shown to induce a trend towards more stock market participation and investment even when the latter does not.     

Financial distress,financial constraint and investment decision: Evidence from Brazil

This paper analyses the presence of financial constraint in the investment decisions of 367 Brazilian firms from 1997 to 2004, using a Bayesian econometric model with group-varying parameters. The motivation for this paper is the use of clustering techniques to group firms in a totally endogenous form. In order to classify the firms we used a hybrid clustering method, that is, hierarchical and non-hierarchical clustering techniques jointly. To estimate the parameters a Bayesian approach was considered. Prior distributions were assumed for the parameters, classifying the model in random or fixed effects. Ordinate predictive density criterion was used to select the model providing a better prediction. We tested thirty models and the better prediction considers the presence of 2 groups in the sample, assuming the fixed effect model with a Student t distribution with 20 degrees of freedom for the error. The results indicate robustness in the identification of financial constraint when the firms are classified by the clustering techniques.