Christian M. Dahl

The Effects of Birth Inputs on Birthweight

Unobserved heterogeneity among childbearing women makes it difficult to isolate the causal effects of smoking and prenatal care on birth outcomes (such as birthweight). Whether a mother smokes, for instance, is likely to be correlated with unobserved characteristics of the mother. This article controls for such unobserved heterogeneity by using state-level panel data on maternally linked births. A quantile-estimation approach, motivated by a correlated random-effects model, is used to estimate the effects of smoking and other observables (number of prenatal-care visits, years of education, and so on) on the entire birthweight distribution.

Testing for neglected nonlinearity in regression models based on the theory of random fields

Abstract Within a flexible regression model (J.D. Hamilton, Econometrica 69 (3) (2001) 537) we offer a battery of new Lagrange multiplier statistics that circumvent the problem of unidentified nuisance parameters under the null hypothesis of linearity and that are robust to the specification of the covariance function that defines the random field. These advantages are the result of (i) switching from the L 2 to the L 1 norm; and (ii) assuming that the random field is sufficiently smooth for its covariance function to be locally approximated by a high order Taylor expansion. A Monte Carlo simulation suggests that our statistics have superior power performance on detecting bilinear, neural network, and smooth transition autoregressive specifications. We also provide an application to the Industrial Production Index of sixteen OECD countries.

Wage Dispersion and Decentralization of Wage Bargaining

This article studies how decentralization of wage bargaining from sector to firm level influences wage levels and wage dispersion. We use detailed panel data covering a period of decentralization in the Danish labor market. The decentralization process provides variation in the individual worker’s wage-setting system that facilitates identification of the effects of decentralization. We find a wage premium associated with firm-level bargaining relative to sector-level bargaining and that the return to skills is higher under the more decentralized wage-setting systems. Using quantile regression, we also find that wages are more dispersed under firm-level bargaining compared to more centralized wage-setting systems.

An Investigation of Tests For Linearity and the Accuracy of Likelihood Based Inference Using Random Fields

We analyze the random field regression model approach recently suggested by Hamilton (2001, Econometrica, 69, 537--73). We show through extensive simulation studies that although the random field approach is indeed very closely related to the non-parametric spline smoother it seems to offer several advantages over the latter. First, tests for neglected nonlinearity based on Hamiltons random field approach seem to be more powerful than existing test statistics developed within the context of the multivariate spline smoother approach. Second, the convergence properties of the random field approach in limited samples appear to be significantly better than those of the multivariate spline smoother. Finally, when compared to the popular neural network approach the random field approach also performs very well. These results provide strong support for the view of Harvey and Koopman (2000, Econometrics Journal, 3, 84--107) that model-based kernels or splines have a sounder statistical justification than those typically used in non-parametric work. Copyright Royal Economic Society, 2002

Short-Run Exchange-Rate Dynamics: Theory and Evidence

Recent research has revealed a wealth of information about the microeconomics of currency markets and thus the determination of exchange rates at short horizons. This information is valuable to us as scientists since, like evidence of macroeconomic regularities, it can provide critical guidance for designing exchange-rate models. This paper presents an optimizing model of short-run exchange-rate dynamics consistent with both the micro evidence and the macro evidence, the first such model of which we are aware. With respect to microeconomics, the model is consistent with the institutional structure of currency markets, it accurately reflects the constraints and objectives faced by the major participants, and it fits key stylized facts concerning returns and order flow. With respect to macroeconomics, the model is consistent with most of the major puzzles that have emerged under floating rates.

The Formation of Inflation Expectations under Changing Inflation Regimes

The present article offers a careful description of empirical identification of possible multiple changes in regime. We apply recently developed tools designed to select among regime-switching models among a broad class of linear and nonlinear regression models and provide a discussion of the impact on the formation of inflation expectations in the presence of multiple and recurrent changes in inflation regimes. Our empirical findings give a plausible explanation as to why the rational-expectations hypothesis based on direct measures of inflation expectations from survey series is typically rejected because of large systematic differences between actual and expected inflation rates. In particular, our results indicate that in the case of changing and not perfectly observed inflation regimes, inference about rationality and unbiasedness based on a comparison of ex ante forecasts from survey series and actual inflation rate based on ex post realizations will be ambiguous because of the presence of an ex post bias. The empirical findings are based on Danish inflation rates covering 1957-1998. We show that it is not possible to reject the hypothesis of multiple inflationary regimes and that the actual inflation rate can be represented by a two-state Markov regime-switching model. It turns out that the real-time forecasts produced from this model exhibit a large degree of similarity when compared to the direct measures of inflation expectations. The result illustrates the important impact of switching regimes on the formation of actual and expected inflation and hence of ex post bias as a main contributor to the difference between actual and expected inflation observed directly from survey series.

The Limiting Properties of the QMLE in a General Class of Asymmetric Volatility Models

In this paper we analyze the limiting properties of the estimated parameters in a general class of asymmetric volatility models which are closely related to the traditional exponential GARCH model. The new representation has three main advantages over the traditional EGARCH: (1) It allows a much more flexible representation of the conditional variance function. (2) It is possible to provide a complete characterization of the asymptotic distribution of the QML estimator based on the new class of nonlinear volatility models, something which has proven very difficult even for the traditional EGARCH. (3) It can produce asymmetric news impact curves where, contrary to the traditional EGARCH, the resulting variances do not excessively exceed the ones associated with the standard GARCH model, irrespectively of the sign of an impact of moderate size. Furthermore, the new class of models considered can create a wide array of news impact curves which provide the researcher with a richer choice set relative to the traditional. We also show in a Monte Carlo experiment the good finite sample performance of our asymptotic theoretical results and we compare them with those obtained from a parametric and the residual based bootstrap. Finally, we provide an empirical illustration.

An Investigation of Tests for Linearity and the Accuracy of Flexible Nonlinear Inference

A new approach recently suggested by Hamilton for flexible parametric inference in nonlinear models is examined through simulation studies. Hamilton suggests a new test for neglected nonlinearity and we compare it with the neural network test, Tsays test, Whites dynamic misspecification test, Ramseys Reset test, the so-called V23 test, and the nonparametric BDS test. With respect to size and power properties, the results on the relative performance of Hamiltons test are very encouraging. In particular, we find that against almost all the nonlinear alternatives where the size and power properties of the popular neural network test are good the size and power properties of Hamiltons new test are even better. Secondly, we examine the convergence properties of Hamiltons estimator of the conditional mean function. Our finding suggest that in the case of a true linear relationship, the costs of using the flexible nonlinear approach in terms of efficiency and speed of convergence are minor. We also show that for many nonlinear models the percentage improvement in fit relative to the linear least squared estimator can be substantial. Finally, we present evidence showing that in finite samples the flexible regression approach suggested by Hamilton clearly outperforms the neural network regression approach in terms of accuracy.

Specifying Nonlinear Econometric Models by Flexible Regression Models and Relative Forecast Performance

The paper considers the task of selecting a flexible nonlinear model which can be used as a baseline model. The baseline model may be used as a testing ground for more structural models which are congruent with economic theory. From the limited empirical evidence obtained here it is tentatively suggested to find a baseline nonlinear flexible form for a univariate time series by following the procedure: 1. Recursively, based on h extra periods at a time specify and estimate a linear form by use of model selection criteria like Cross Validation and/or BIC. 2. After a preliminary test for linearity, recursively, specify and estimate flexible regression models like the FNL suggested by Hamilton (1999) and the Projection Pursuit model suggested by Aldrin, Boelviken and Schweder (1993) for cases of moderate nonlinearities. Use the Cross Validation and the BIC criteria. 3. Based on the remaining part of the data set select the best nonlinear flexible form by use of forecast criteria measuring the absolute forecast performance and the directional forecast performance in h-steps ahead predictions, and compare the best flexible form to the linear specification by use of the Diebold Mariano tests, see Deibold and Mariano (1995) and the forecast encompassing tests suggested by Harvey, Lebourne, and Newhold (1998). The results indicate that the FNL method and the Projection Pursuit Model are the preferable models to apply and that the CV and BIC are the best selection criteria, while the forecast encompassing tests properly modified as suggested by Harvey et. al. (1998) possess better power properties than the Diebold- Mariano test.

Identifying Nonlinear Components by Random Fields in the US GNP Growth. Implications for the Shape of the Business Cycle

Within a flexible parametric regression framework (Hamilton, 2001) we provide further evidence on the existence of a nonlinear component in the quarterly growth rate of the US real GNP. We implement a battery of new tests for neglected nonlinearity based on the theory of random fields (Dahl and Gonzalez-Rivera, 2003). We find that the nonlinear component is driven by the fifth lag of the growth rate. We show that our model is superior to linear and nonlinear parametric specifications because it produces a business cycle that when dissected with the BBQ algorithm mimics very faithfully the characteristics of the actual US business cycle. On understanding the relevance of the fifth lag, we find that the nonparametrically estimated conditional mean supports parametric specifications that allow for three phases in the business cycle: rapid linear contractions, aggressive short-lived convex early expansions, and moderate/slow relatively long concave late expansions.