半参数趋势阈值面板模型及其参数估计

半参数趋势面板数据模型在社会经济问题的实证分析中具有很强的适用性,但现有的研究中,半参数趋势面板模型考虑了时间趋势的非线性,但没有考虑政策等因素对参数的影响。本文将结构突变理论引入截面相关下的半参数趋势面板模型,并基于PPLE方法,建立了有效估计量和识别程序。通过仿真实验和实证应用,验证了对于含有突变点的半参数趋势面板模型,EPPLE方法的参数估计是有效的。     

Political Instability, Uncertainty and Economics

This paper focuses on the relationship between political instability, policy–making and macroeconomic outcomes. The theoretical section explores various models that explain the effect of instability (and political uncertainty) on growth, budget formation, inflation and monetary policy. The empirical section discusses the evidence on the predictions generated by theoretical models. Preliminary to this discussion, however, is the analysis of a few general issues concerning the specification and estimation of econometric models with political variables. Some new results are then produced on the empirical relevance of theories of strategic use of fiscal deficit.     

Discrete Hours Labour Supply Modelling: Specification, Estimation and Simulation

Abstract.  The assumption behind discrete hours labour supply modelling is that utility‐maximising individuals choose from a relatively small number of hours levels, rather than being able to vary hours worked continuously. Such models are becoming widely used in view of their substantial advantages, compared with a continuous hours approach, when estimating and their role in tax policy microsimulation. This paper provides an introduction to the basic analytics of discrete hours labour supply modelling. Special attention is given to model specification, maximum likelihood estimation and microsimulation of tax reforms. The analysis is at each stage illustrated by the use of numerical examples. At the end, an empirical example of a hypothetical policy change to the social security system is given to illustrate the role of discrete hours microsimulation in the analysis of tax and transfer policy changes.     

Estimation with overidentifying inequality moment conditions

This paper derives limit distributions of empirical likelihood estimators for models in which inequality moment conditions provide overidentifying information. We show that the use of this information leads to a reduction of the asymptotic mean-squared estimation error and propose asymptotically uniformly valid tests and confidence sets for the parameters of interest. While inequality moment conditions arise in many important economic models, we use a dynamic macroeconomic model as a data generating process and illustrate our methods with instrumental variable estimators of monetary policy rules. The results obtained in this paper extend to conventional GMM estimators.     

Behavioural Microsimulation with Labour Supply Responses

This paper provides a technical survey of recent developments in behavioural microsimulation. We discuss the criteria by which models of labour supply may be chosen for application to behavioural microsimulation, and consider how such models may be augmented to control for fixed costs, child–related work costs, preference heterogeneity and endogeneity in wages. We describe methods by which non–linear budget constraints may be accommodated in estimation, policy simulations and welfare analysis, and discuss how stochastic terms may be factored into the simulation of behavioural responses to a policy shock.     

Robustness and the general dynamic factor model with infinite-dimensional space: Identification,estimation, and forecasting

General dynamic factor models have demonstrated their capacity to circumvent the curse of dimensionality in the analysis of high-dimensional time series and have been successfully considered in many economic and financial applications. As second-order models, however, they are sensitive to the presence of outliers—an issue that has not been analyzed so far in the general case of dynamic factors with possibly infinite-dimensional factor spaces (Forni et al. 2000, 2015, 2017). In this paper, we consider this robustness issue and study the impact of additive outliers on the identification, estimation, and forecasting performance of general dynamic factor models. Based on our findings, we propose robust versions of identification, estimation, and forecasting procedures. The finite-sample performance of our methods is evaluated via Monte Carlo experiments and successfully applied to a classical data set of 115 US macroeconomic and financial time series.     

Mining big data using parsimonious factor,machine learning,variable selection and shrinkage methods

A number of recent studies in the economics literature have focused on the usefulness of factor models in the context of prediction using “big data” (see Bai and Ng, 2008; Dufour and Stevanovic, 2010; Forni, Hallin, Lippi, & Reichlin, 2000; Forni et al., 2005; Kim and Swanson, 2014a; Stock and Watson, 2002b, 2006, 2012, and the references cited therein). We add to this literature by analyzing whether “big data” are useful for modelling low frequency macroeconomic variables, such as unemployment, inflation and GDP. In particular, we analyze the predictive benefits associated with the use of principal component analysis (PCA), independent component analysis (ICA), and sparse principal component analysis (SPCA). We also evaluate machine learning, variable selection and shrinkage methods, including bagging, boosting, ridge regression, least angle regression, the elastic net, and the non-negative garotte. Our approach is to carry out a forecasting “horse-race” using prediction models that are constructed based on a variety of model specification approaches, factor estimation methods, and data windowing methods, in the context of predicting 11 macroeconomic variables that are relevant to monetary policy assessment. In many instances, we find that various of our benchmark models, including autoregressive (AR) models, AR models with exogenous variables, and (Bayesian) model averaging, do not dominate specifications based on factor-type dimension reduction combined with various machine learning, variable selection, and shrinkage methods (called “combination” models). We find that forecast combination methods are mean square forecast error (MSFE) “best” for only three variables out of 11 for a forecast horizon of $h=1$, and for four variables when $h=3$ or $12$. In addition, non-PCA type factor estimation methods yield MSFE-best predictions for nine variables out of 11 for $h=1$, although PCA dominates at longer horizons. Interestingly, we also find evidence of the usefulness of combination models for approximately half of our variables when $h>1$. Most importantly, we present strong new evidence of the usefulness of factor-based dimension reduction when utilizing “big data” for macroeconometric forecasting.     

Estimating dynamic panel data discrete choice models with fixed effects

This paper considers the estimation of dynamic binary choice panel data models with fixed effects. It is shown that the modified maximum likelihood estimator (MMLE) used in this paper reduces the order of the bias in the maximum likelihood estimator from O(T^-1) to O(T^-2), without increasing the asymptotic variance. No orthogonal reparametrization is needed. Monte Carlo simulations are used to evaluate its performance in finite samples where T is not large. In probit and logit models containing lags of the endogenous variable and exogenous variables, the estimator is found to have a small bias in a panel with eight periods. A distinctive advantage of the MMLE is its general applicability. Estimation and relevance of different policy parameters of interest in this kind of models are also addressed.     

Predicting housing prices in China based on modified Holt's exponential smoothing incorporating whale optimization algorithm

The forecast of the real estate market is an important part of studying the Chinese economic market. Most existing methods have strict requirements on input variables and are complex in parameter estimation. To obtain better prediction results, a modified Holt's exponential smoothing (MHES) method was proposed to predict the housing price by using historical data. Unlike the traditional exponential smoothing models, MHES sets different weights on historical data and the smoothing parameters depend on the sample size. Meanwhile, the proposed MHES incorporates the whale optimization algorithm (WOA) to obtain the optimal parameters. Housing price data from Kunming, Changchun, Xuzhou and Handan were used to test the performance of the model. The housing prices results of four cities indicate that the proposed method has a smaller prediction error and shorter computation time than that of other traditional models. Therefore, WOA-MHES can be applied efficiently to housing price forecasting and can be a reliable tool for market investors and policy makers.     

The impact of non-normality,sample size and estimation technique on goodness-of-fit measures in structural equation modeling: evidence from ten empirical models of travel behavior

Ten empirical models of travel behavior are used to measure the variability of structural equation model goodness-of-fit as a function of sample size, multivariate kurtosis, and estimation technique. The estimation techniques are maximum likelihood, asymptotic distribution free, bootstrapping, and the Mplus approach. The results highlight the divergence of these techniques when sample sizes are small and/or multivariate kurtosis high. Recommendations include using multiple estimation techniques and, when sample sizes are large, sampling the data and reestimating the models to test both the robustness of the specifications and to quantify, to some extent, the large sample bias inherent in the χ ² test statistic.     

Hungary's entry into the euro area: Lessons for prospective members from a monetary policy perspective

This paper evaluates the conduct of monetary policy in Hungary using standard Taylor rules as well as extended rules that incorporate real exchange rate effects. Moreover, we explicitly consider the impact of future euro area entry by estimating instrument rules that permit an influence from Maastricht Treaty inflation requirements via the estimation of Markov switching models as well as by estimating a differential rule vis-à-vis the existing euro area. Lastly, the paper also considers the impact on policy rules from the large data revision that affects real exchange rate and output estimates. I find that interest rate setting behavior in Hungary does not resemble that of the euro area. Also, counterfactual experiments reveal that the potential macroeconomic costs of entry into the euro area sooner rather than later may be lower than if membership in the single currency area is delayed beyond 2008.     

Introduction to internally consistent modeling,aggregation, inference,and policy

This special issue of the Journal of Econometrics honors William A. Barnett’s exceptional contributions to unifying economic theory with rigorous statistical inference to interpret economic data and inform public policy. It is devoted to papers that advance microeconometrics, macroeconometrics, and financial econometrics to build models to interpret evidence.     

Learning, monetary policy rules, and macroeconomic stability

Several papers have documented a regime switch in US monetary policy from ‘passive’ and destabilizing in the pre-1979 period to ‘active’ and stabilizing afterwards. These studies typically work with DSGE models with rational expectations.This paper relaxes the assumption of rational expectations and allows for learning instead. Economic agents form expectations from simple models and update the parameters through constant-gain learning. In this setting, the paper aims to test whether monetary policy may have been a source of macroeconomic instability in the 1970s by inducing unstable learning dynamics.The model is estimated by Bayesian methods. The constant-gain coefficient is jointly estimated with the structural and policy parameters in the system.The results show that monetary policy was respecting the Taylor principle also in the pre-1979 period and, therefore, did not trigger macroeconomic instability.     

Trade,competitiveness and investment: an empirical assessment

ABSTRACT

The Eurozone crisis has exposed several weaknesses of the European Monetary Union economies. This paper aims to assess the impact on external competitiveness of an expansionary capital stock policy that could contribute to reduce the trade balance asymmetries within the EU and help European exporters to recover their competitive role in international markets. A policy action to increase capital stock accumulation through investment in selected European countries could generate a double dividend: increasing both price and nonprice competitiveness, so stimulating their competitive position as exporters, and consolidating the growth path of EU economy. The analysis employs a bilateral trade model built at INFORUM with several distinguishing characteristics: a comprehensive bilateral data set, econometric estimation of key parameters, and emphasis on sectoral details. Our findings show that a capital stock increase is effective in narrowing trade imbalances within EU. Heterogeneous effects are estimated for commodities in China and the US.     

Interactive R&D spillovers: An estimation strategy based on forecasting-driven model selection

This paper proposes an estimation strategy that exploits recent non-parametric panel data methods that allow for a multifactor error structure and extends a recently proposed data-driven model-selection procedure, which has its roots in cross validation and aims to test whether two competing approximate models are equivalent in terms of their expected true error. We extend this procedure to a large panel data framework by using moving block bootstrap resampling techniques in order to preserve cross-sectional dependence in the bootstrapped samples. Such an estimation strategy is illustrated by revisiting an analysis of international technology diffusion. Model selection procedures clearly conclude in the superiority of a fully non-parametric (non-additive) specification over parametric and even semi-parametric (additive) specifications. This work also refines previous results by showing threshold effects, non-linearities, and interactions that are obscured in parametric specifications and which have relevant implications for policy.     

NONLINEAR GROWTH EFFECTS OF TAXATION: A SEMI‐PARAMETRIC APPROACH USING AVERAGE MARGINAL TAX RATES

One of the major challenges of empirical tax research is the identification and calculation of appropriate tax data. While there is consensus that average marginal tax rates are most suitable for studying the effects of tax policy on economic growth, because of data limitations the calculation of marginal tax rates has been limited to the USA and the UK. This paper provides calculations of average marginal tax rates for the four Scandinavian countries using the methodologies of Seater (1982, 1985) and Barro and Sahasakul (1983, 1986). Then, by pooling the newly calculated tax rates for the Scandinavian countries with the data for the USA and the UK, we investigate the effects of tax policy shocks on the per capita GDP growth rate. Our results suggest that an increase in average marginal tax rates has a negative impact on economic growth. Employing additive mixed panel models with penalized splines as estimation approach, we show that changes in tax rates have nonlinear effects. Increasing average marginal tax rates turn out to be the most distorting at relatively moderate tax rates. Copyright © 2013 John Wiley & Sons, Ltd.     

Productivity and Efficiency Measurement in Our “New Economy”: Determinants,Interactions, and Policy Relevance

Interpreting and using productivity and efficiency measures requires establishing the determinants and implications of observed performance. This is not a simple task, however, given the complex interactions among economic entities in modern markets. Identifying and quantifying performance drivers often involves parametric estimation of models with explicit performance factors build into functions representing technology and behavior. Here I overview some such studies in the literature on food system economic performance, to emphasize the potential to empirically represent a more complex web of technological and market phenomena than in standard productivity analyses, to enhance the measures' interpretability and relevance for policy guidance.     

WORDS, DEEDS AND OUTCOMES: A SURVEY ON THE GROWTH EFFECTS OF EXCHANGE RATE REGIMES

Abstract.  Recent studies on the growth effects of exchange rate regimes offer a wide range of different, sometimes contradictory results. In this paper, we systematically compare three prominent contributions in this field. Using a common data set, a common specification and common estimation methods, we argue that the contradictory findings can be explained by the fact that these studies use regime classifications which reflect fundamentally different aspects of exchange rate policy.     

Data,Linkages and Models: US National Income and Product Accounts in the Framework of a Social Accounting Matrix

In policy analysis, there is a continuing tension and interplay between issues, models and data. Issues and models have changed in recent years, and there is a need for evolution in the underlying economy-wide economic data base. We discuss accounting frameworks for integrating micro-survey data with macro-data from the national economic accounts. We take a modeler's perspective, arguing that new accounts are needed to support policy modeling. We discuss in detail the use of a social accounting matrix (SAM). A SAM provides a data framework which reflects an actor/transaction view of the economy and supports disaggregated economy-wide modeling. We discuss the relationship between a SAM and the existing national economic accounts for the USA, including the national income and product accounts and the input–output accounts.