Functional coefficient instrumental variables models

We consider estimation of nonparametric structural models under a functional coefficient representation for the regression function. Under this representation, models are linear in the endogenous components with coefficients given by unknown functions of the predetermined variables, a nonparametric generalization of random coefficient models. The functional coefficient restriction is an intermediate approach between fully nonparametric structural models that are ill posed when endogenous variables are continuously distributed, and partially linear models over which they have appreciable flexibility. We propose two-step estimators that use local linear approximations in both steps. The first step is to estimate a vector of reduced forms of regression models and the second step is local linear regression using the estimated reduced forms as regressors. Our large sample results include consistency and asymptotic normality of the proposed estimators. The high practical power of estimators is illustrated via both a Monte Carlo simulation study and an application to returns to education.     

The Impact of the 2008 Crisis on UK Prices: What We Can Learn from the CPI Microdata1

This paper takes the locally collected price quotes used to construct the CPI index in the UK for the period 1996–2013 and explores the impact of the Great Recession (2008-9) on the pricing behaviour of firms. We develop a time series framework which captures the link between macroeconomic variables and the behaviour of prices in terms of the frequency of price change, the dispersion of price levels and the size, dispersion and kurtosis of price-growth. We find strong evidence for inflation having an effect, but not output. The change in the behaviour of prices during the Great Recession is largely explained by the changes in inflation and VAT. Nevertheless, the magnitude of the inflation effect is sufficiently small that it need not influence monetary policy.     

The Impact of the 2008 Crisis on UK Prices: What We Can Learn from the CPI Microdata1

This paper takes the locally collected price quotes used to construct the CPI index in the UK for the period 1996–2013 and explores the impact of the Great Recession (2008‐9) on the pricing behaviour of firms. We develop a time series framework which captures the link between macroeconomic variables and the behaviour of prices in terms of the frequency of price change, the dispersion of price levels and the size, dispersion and kurtosis of price‐growth. We find strong evidence for inflation having an effect, but not output. The change in the behaviour of prices during the Great Recession is largely explained by the changes in inflation and VAT. Nevertheless, the magnitude of the inflation effect is sufficiently small that it need not influence monetary policy.     

Identifying local influential observations in Liu estimator

Identifying influential observations is an important step in the right direction of the linear regression model building process. Various influence measures based on different motivational arguments are designed to measure the influence of observations through different aspects of various regression models. This paper is on the result of the research studies on the local influence of minor perturbation on the Liu estimator in linear regression model. The diagnostics under the perturbations of constant variance, individual explanatory variables and assessing the influence on the selection of Liu estimator biasing parameter are derived for Liu estimator. Two real data sets are employed to illustrate our methodologies.     

Three Corrected Score Tests for Generalized Linear Models with Dispersion Covariates

We develop three corrected score tests for generalized linear models with dispersion covariates, thus generalizing the results of Cordeiro , Ferrari and Paula (1993) and Cribari-Neto and Ferrari (1995) . We present, in matrix notation, general formulae for the coefficients which define the corrected statistics. The formulae only require simple operations on matrices and can be used to obtain analytically closed-form corrections for score test statistics in a variety of special generalized linear models with dispersion covariates. They also have advantages for numerical purposes since our formulae are readily computable using a language supporting numerical linear algebra. Two examples, namely, iid sampling without covariates on the mean or dispersion parameter oand one-way classification models, are given. We also present some simulations where the three corrected tests perform better than the usual score test, the likelihood ratio test and its Bartlett corrected version. Finally, we present a numerical example for a data set discussed by Simonoff and Tsai (1994) .     

Commodity house prices

This paper studies how commodity price movements have affected the local house prices in commodity-dependent economies, Australia and New Zealand. We build a geographically hierarchical empirical model and find that the commodity prices influence local house prices directly and also indirectly through macroeconomic variables. The impacts of commodity price changes are analogous to “income shocks” rather than “cost shocks”. Regional heterogeneity is also observed in terms of differential dynamic responses of local house prices to energy versus non-energy commodity price movements. The results are robust to alternative approaches. Directions for future research are also discussed.     

Combining Forecasts from Nested Models*

Motivated by the common finding that linear autoregressive models often forecast better than models that incorporate additional information, this paper presents analytical, Monte Carlo and empirical evidence on the effectiveness of combining forecasts from nested models. In our analytics, the unrestricted model is true, but a subset of the coefficients is treated as being local‐to‐zero. This approach captures the practical reality that the predictive content of variables of interest is often low. We derive mean square error‐minimizing weights for combining the restricted and unrestricted forecasts. Monte Carlo and empirical analyses verify the practical effectiveness of our combination approach.     

Estimating class‐specific parametric models under class uncertainty: local polynomial regression clustering in an hedonic analysis of wine markets

We introduce a method for estimating multiple class regression models when class membership is uncertain. The procedure—local polynomial regression clustering—first estimates a nonparametric model via local polynomial regression, and then identifies the underlying classes by aggregating sample observations into data clusters with similar estimates of the (local) functional relationships between dependent and independent variables. Finally, parametric functions specific to each class are estimated. The technique is applied to the estimation of a multiple‐class hedonic model for wine, resulting in the identification of four distinct wine classes based on differences in implicit prices of the attributes. Copyright © 2009 John Wiley & Sons, Ltd.     

Financial information and macroeconomic forecasts

We study the forecasting power of financial variables for macroeconomic variables in 62 countries between 1980 and 2013. We find that financial variables such as credit growth, stock prices, and house prices have considerable predictive power for macroeconomic variables at the one- to four-quarter horizons. A forecasting model that includes financial variables outperforms the World Economic Outlook (WEO) forecasts in up to 85% of our sample countries at the four-quarter horizon. We also find that cross-country panel models produce more accurate out-of-sample forecasts than individual country models.     

Panel data estimates of the effects of different types of crime on housing prices

One of the most studied effects of crime is the impact that neighborhood crime has on housing values. A major drawback of these studies is that, although crime is undoubtedly endogenous in property value models because of either simultaneity, omitted variables or measurement error, the vast majority of studies treat crime measures as exogenous independent variables. We exploit a unique nine-year crime panel at the neighborhood level to estimate models that properly address the endogeneity of crime and allow us to overcome other specification errors that have plagued previous studies. Of the seven different types of crime we investigate, only robbery and aggravated assault crimes (per acre) exert a meaningful influence upon neighborhood housing values.     

What does money and credit tell us about real activity in the United States?

We analyse the forecasting power of different monetary aggregates and credit variables for US GDP. Special attention is paid to the influence of the recent financial market crisis. For that purpose, in the first step we use a three-variable single-equation framework with real GDP, an interest rate spread and a monetary or credit variable, in forecasting horizons of one to eight quarters. This first stage thus serves to pre-select the variables with the highest forecasting content. In a second step, we use the selected monetary and credit variables within different VAR models, and compare their forecasting properties against a benchmark VAR model with GDP and the term spread (and univariate AR models). Our findings suggest that narrow monetary aggregates, as well as different credit variables, comprise useful predictive information for economic dynamics beyond that contained in the term spread. However, this finding only holds true in a sample that includes the most recent financial crisis. Looking forward, an open question is whether this change in the relationship between money, credit, the term spread and economic activity has been the result of a permanent structural break or whether we might return to the previous relationships.     

Nonparametric model validations for hidden Markov models with applications in financial econometrics

We address the nonparametric model validation problem for hidden Markov models with partially observable variables and hidden states. We achieve this goal by constructing a nonparametric simultaneous confidence envelope for transition density function of the observable variables and checking whether the parametric density estimate is contained within such an envelope. Our specification test procedure is motivated by a functional connection between the transition density of the observable variables and the Markov transition kernel of the hidden states. Our approach is applicable for continuous-time diffusion models, stochastic volatility models, nonlinear time series models, and models with market microstructure noise.     

Using survey data to study capitalization of local public services

We use surveys in which respondents evaluate local amenities in Norway to compute proxy variables for the quality of local public services as well as other local amenities relevant to location decisions. Average satisfaction reported by the respondents is computed for each amenity and each municipality, adjusted for sample variation in personal characteristics and included as explanatory variables in a cross-section study of house prices. We find that house prices are increasing in satisfaction with cultural activities, health care, care for the elderly and public transportation.     

Rationality,specification tests,and macroeconomic models

Rational expectation models embody cross-equation restrictions that are implied by the theory of rational expectations. In this paper we illustrate how tests of these restrictions may be implemented in terms of general macroeconomic models by employing the models of Taylor and Sargent as examples. In addition, the more important issue of the proper interpretation of the results of these tests is addressed. We contend that tests for rationality should become part of the model-building process as they are akin to specification tests for models in which rational expectations is treated as the maintained hypothesis. A procedure is suggested when the restriction are inconsistent with data. Special emphasis is placed upon examining how changes in specifications of the model's exogenous variables can influence test results.     

The Ordinal Controversy Revisited

We present a discussion of the different dimensions of the ongoing controversy about the analysis of ordinal variables. The source of this controversy is traced to the earliest possible stage, measurement theory. Three major approaches in analyzing ordinal variables, called the non-parametric, the parametric, and the underlying variable approach, are identified and the merits and drawbacks of each of these approaches are pointed out. We show that the controversy on the exact definition of an ordinal variable causes problems with regard to defining ordinal association, and therefore to the interpretation of many recently designed models for ordinal variables, e.g., structure equation models using polychoric correlations, latent class models and ordinal response models. We conclude that the discussion with regard to ordinal variable modeling can only be fruitful if one makes a distinction between different types of ordinal variables. Five types of ordinal variables were identified. The problems concerning the analysis of these five types of ordinal variables are solved in some cases and remain a problem for others.     

The construction and estimation of continuous time models and discrete approximations in econometrics

We consider econometric models involving variables that are defined continuously over time, or more frequently than they are observed. Separate but analogous treatment is given to both closed models (involving no exogenous variables) and open models (involvingexogenous variables). Justification for the use of standard discrete time models is given. Some exact discrete time models, and some computationally convenient approximate ones, are considered. Asymptotically efficient estimation procedures for a wide class of models are described.     

The Prophecy of Place: A Labor Market Study of Young Women and Education

This study demonstrates that place—defined in this article as labor market area (LMA)— provides a useful context for examining how youth manage gendered situations. Places vary by conditions in which gender is more, less or differently salient, and a particular mix of factors accommodates different individual outcomes. This study utilizes multi–level modeling to examine influence of LMA characteristics on over–time educational measures for young women (Center for Human Resource Research 1994). Hierarchical models determine place–level effects on both average outcomes (within and between LMAs) and attainment processes. A major finding of this study is that aggregate place effects channel personal decisions and outcomes of young women. Young women's educational aspirations are dependent on gender– specific variables such as the number of women in college or the number of young women married in a local area. Attainment depends on the percentage of women in higher education and a local labor market's average age at first marriage. Further, what are assumed to be positive environmental effects (e.g., manufacturing dominance) are based on structural advantages for men and actually depress outcomes for women. Notably, the influence of place is independent of strong individual–level determinants, including social class.     

A simple mean–dispersion model of ambiguity attitudes

Several characterizations of ambiguity aversion decompose preferences into the expected utility of an act and an adjustment factor, an ambiguity index, or a dispersion function. In each of these cases, the adjustment factor has very little structure imposed on it, and thus these models provide little guidance as to which function to use from the infinite class of possible alternatives. In this paper, we provide a simple axiomatic characterization of mean–dispersion preferences which uniquely determines a subjective probability distribution over a set of possible priors and which uniquely identifies the dispersion function. We provide an algorithm for determining this subjective probability distribution and the coefficient in the dispersion function from experimental data. We also demonstrate that the model accommodates ambiguity aversion in the Ellsberg paradox.     

Local tax interaction and endogenous spatial weights based on quality of life

This paper studied the role of municipal quality of life as a driver of strategic tax interactions among local governments. A number of studies point out the existence of strategic interactions using spatial econometric models in which the spatial weights are mostly based on non-economic criteria or basic economic variables such as per capita gross domestic product (GDP). We propose the more sophisticated well-being indicator of municipal quality of life as the driver for these interactions. To deal with the potential endogeneity, we rely on instrumental variable estimators. The empirical analysis focuses on the main local tax in Spain (property) and on municipalities with more than 50,000 inhabitants, and it confirms the relevance of differentials in quality of life for the understanding of tax choices.