DISENTANGLING DEMAND AND SUPPLY SHOCKS IN THE CRUDE OIL MARKET: HOW TO CHECK SIGN RESTRICTIONS IN STRUCTURAL VARS

Sign restrictions have become increasingly popular for identifying shocks in structural vector autoregressive (SVAR) models. So far there are no techniques for validating the shocks identified via such restrictions. Although in an ideal setting the sign restrictions specify shocks of interest, sign restrictions may be invalidated by measurement errors, data adjustments or omitted variables. We model changes in the volatility of the shocks via a Markov switching (MS) mechanism and use this device to give the data a chance to object to sign restrictions. The approach is illustrated by considering a small model for the market of crude oil. Earlier findings that oil supply shocks explain only a very small fraction of movements in the price of oil are confirmed and it is found that the importance of aggregate demand shocks for oil price movements has declined since the mid 1980s. Copyright © 2013 John Wiley & Sons, Ltd.     

Smooth minimum distance estimation and testing with conditional estimating equations: Uniform in bandwidth theory

To study the influence of a bandwidth parameter in inference with conditional moments, we propose a new class of estimators and establish an asymptotic representation of our estimator as a process indexed by a bandwidth, which can vary within a wide range including bandwidths independent of the sample size. We study its behavior under misspecification. We also propose an efficient version of our estimator. We develop a procedure based on a distance metric statistic for testing restrictions on parameters as well as a bootstrap technique to account for the bandwidth’s influence. Our new methods are simple to implement, apply to non-smooth problems, and perform well in our simulations.     

Aggregate shocks and macroeconomic fluctuations: A bayesian approach

This paper describes Bayesian techniques for analysing the effects of aggregate shocks on macroeconomic time-series. Rather than calculate point estimates of the response of a time-series to an aggregate shock, we calculate the whole probability density function of the response and use Monte-Carlo or Gibbs sampling techniques to evaluate its properties. The proposed techniques impose identification restrictions in a way that includes the uncertainty in these restrictions, and thus are an improvement over traditional approaches that typically use least-squares techniques supplemented by bootstrapping. We apply these techniques in the context of two different models. A key finding is that measures of uncertainty, such as posterior standard deviations, are much larger than are their classical counterparts.     

What are the effects of fiscal policy shocks?

We propose and apply a new approach for analyzing the effects of fiscal policy using vector autoregressions. Specifically, we use sign restrictions to identify a government revenue shock as well as a government spending shock, while controlling for a generic business cycle shock and a monetary policy shock. We explicitly allow for the possibility of announcement effects, i.e., that a current fiscal policy shock changes fiscal policy variables in the future, but not at present. We construct the impulse responses to three linear combinations of these fiscal shocks, corresponding to the three scenarios of deficit‐spending, deficit‐financed tax cuts and a balanced budget spending expansion. We apply the method to US quarterly data from 1955 to 2000. We find that deficit‐financed tax cuts work best among these three scenarios to improve GDP, with a maximal present value multiplier of five dollars of total additional GDP per each dollar of the total cut in government revenue 5 years after the shock. Copyright © 2009 John Wiley & Sons, Ltd.     

An options pricing approach to ramping rate restrictions at hydro power plants

This paper investigates the impact of ramping rate restrictions imposed on hydro operations to protect aquatic ecosystems. The optimal ramping decision is specified as an optimal control problem which results in a Hamilton Jacobi Bellman (HJB) equation. Electricity prices are modelled as a regime switching stochastic process. The optimal control is determined by solving the HJB equation numerically using a fully implicit finite difference approach with semi-Lagrangian time stepping. The paper focuses on the effect of ramping restrictions on a hydro plant׳s value and optimal operations, and provides an analysis of which factors cause ramping restrictions to have a greater or lesser impact on profitability. It is shown that hydro plant value is negatively affected by ramping restrictions, but the extent of the impact depends on key parameters which determine the desirability of frequent changes in water release rates. Interestingly for the case considered, value is not sensitive to ramping restrictions over a large range of restrictions. The results point to the importance of accurately modelling electricity prices in gauging the trade offs involved in imposing restrictions on hydro operators which may hinder their ability to respond to volatile electricity prices and meet peak demands.     

The demand for and supply of assurance

Consumers want products and services that are safe and of good quality. Corresponding to such demand is the demand for assurance , before the fact, that the quality and safety will be as promised. his demand for assurance creates opportunities for entrepreneurs to profit by providing assurance - and they do so in a wide and largely unappreciated variety of ways. The essential dialectic of the free enterprise system does apply to assurance. Governments'quality and safety restrictions on the freedom of contract, known to be so costly, are, therefore, unredeemed and should be repealed.     

Methods for Generating Longitudinally Correlated Binary Data

The analysis of longitudinally correlated binary data has attracted considerable attention of late. Since the estimation of parameters in models for such data is based on asymptotic theory, it is necessary to investigate the small‐sample properties of estimators by simulation. In this paper, we review the mechanisms that have been proposed for generating longitudinally correlated binary data. We compare and contrast these models with regard to various features, including computational efficiency, flexibility and the range restrictions that they impose on the longitudinal association parameters. Some extensions to the data generation mechanism originally suggested by Kanter (1975) are proposed.     

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.     

Estimating restricted structural change models

This paper considers issues related to multiple structural changes, occurring at unknown dates, in the linear regression model when restrictions are imposed on the parameters. This includes, for example, the important special case where different nonadjacent regimes are the same. The estimates are constructed as global minimizers of the restricted sum of squared residuals and we provide an extension of the algorithm discussed in Bai and Perron [2003b, Computation and analysis of multiple structural change models. Journal of Applied Econometrics 18, 1–22] to efficiently compute them. We show that the estimates of the break dates have the same asymptotic properties with or without the restrictions imposed; that is, in large samples, there is no efficiency gain from imposing valid restrictions as far as the estimates of the break dates are concerned. Of course, efficiency gains occur for the other parameters of the model. Simulations show that in small samples, all parameters are more efficiently estimated using the restrictions. We also consider tests of the null hypothesis of no structural change. These are also more powerful when the restrictions are imposed. A Gauss code for all the procedures discussed in this paper is available from the authors.     

The Government Spending Multiplier at the Zero Lower Bound: Evidence from the United States

We estimate state-dependent government spending multipliers for the United States. We use a factor-augmented interacted vector autoregression (FAIVAR) model. This allows us to capture the time-varying monetary policy characteristics including the recent zero interest rate lower bound (ZLB) state, to account for the state of the business cycle and to address the limited information problem typically inherent in VARs. We identify government spending shocks by sign restrictions and use a government spending growth forecast series to account for the effects of anticipated fiscal policy. In our baseline specification, we find that government spending multipliers in a recession range from 3.56 to 3.79 at the ZLB. Away from the ZLB, multipliers in recessions range from 2.31 to 3.05. Several robustness analyses confirm that multipliers are higher, when the interest rate is lower and that multipliers in recessions exceed multipliers in expansions. Our results are consistent with theories that predict larger multipliers at the ZLB.     

The Government Spending Multiplier at the Zero Lower Bound: Evidence from the United States

We estimate state‐dependent government spending multipliers for the United States. We use a factor‐augmented interacted vector autoregression (FAIVAR) model. This allows us to capture the time‐varying monetary policy characteristics including the recent zero interest rate lower bound (ZLB) state, to account for the state of the business cycle and to address the limited information problem typically inherent in VARs. We identify government spending shocks by sign restrictions and use a government spending growth forecast series to account for the effects of anticipated fiscal policy. In our baseline specification, we find that government spending multipliers in a recession range from 3.56 to 3.79 at the ZLB. Away from the ZLB, multipliers in recessions range from 2.31 to 3.05. Several robustness analyses confirm that multipliers are higher, when the interest rate is lower and that multipliers in recessions exceed multipliers in expansions. Our results are consistent with theories that predict larger multipliers at the ZLB.     

Mean square error tests for restrictions in singular linear models

Weak and strong mean square error tests of restrictions presented in Wallace (1972) are generalized to apply to singular linear models. The singularity necessitates a slight change in the strong m.s.e. criterion and the requirement that the restrictions be estimable, but otherwise the tests are applied in a fashion analogous to the non-singular case. Use of those tests implies that the solution for the linear model parameter vector is contingent on a test result. The risk behavior of these contingent solutions is discussed.     

The Early Millennium Slowdown: Replicating the Peersman (2005) Results

This paper undertakes both a narrow and wide replication of the constant coefficients vector autoregression (VAR) identified with sign restrictions considered by Peersman (Journal of Applied Econometrics 2005; 20 (2): 185–207. His results for the US are robust to an increase in the sample period from 2002:Q2 to 2014:Q2, but the extension to time‐varying parameters highlights the importance of testing the robustness of results against time variation. In particular, there are differences across models regarding the role of individual shocks during the 2001 US slowdown. Copyright © 2015 John Wiley & Sons, Ltd.     

Identification of rational expectations models

This paper deals with the problem of the identification of simultaneous Rational Expectations (RE) models. In the case of RE models with current expectations of the endogenous variables, the necessary and sufficient conditions for the global identification are derived explicitly in terms of the structural parameters and the linear homogenous identifying restrictions. It is shown that in the absence of a priori restrictions on the processes generating the exogenous variables and the disturbances, RE models and general distributed lag models are ‘observationally equivalent’. In the case of RE models with future expectations of the endogenous variables, a general solution that highlights the ‘non-uniqueness’ problem and from which other solutions such as forward or backward solutions can be obtained, is derived. It is shown that untestable and often quite arbitrary restrictions are needed if RE models with future expectations are to be identifiable. Certain order conditions similar to those obtained for the identification of RE models with current expectations are also derived for this case.     

Testing equality restrictions in generalized linear models for multinomial data

Based on f{\phi } -divergences an estimator of the generalized linear models for multinomial data under linear restrictions on the parameters is considered. New test statistics, also based on f{\phi } -divergences are considered as alternatives to the classical ones for testing a hypothesis about linear restrictions on the parameters. The asymptotic distribution of them is obtained under the null hypothesis as well as under contiguous local hypotheses. An application of the estimators and the tests is illustrated in a numerical example and in simulation studies.     

The role of credit supply shocks in pacific alliance countries: A TVP-VAR-SV approach

This paper analyzes the effect of loan supply shocks on the real economic activity of Pacific Alliance countries using a Time-Varying Parameter VAR with Stochastic Volatility (TVP-VAR-SV) model which is identified by sign restrictions. Two main results arise from the analysis. First, loan supply shocks have an important impact on real economic activity in all Pacific Alliance countries: about 1% in Colombia, Mexico, and Peru, and about 0.5% in Chile. Thus, its contribution to business cycle fluctuations is similar to that of aggregate supply shocks and aggregate demand shocks in both stability and slowdown periods. Second, the power of loan supply shocks to affect economic activity do not remain constant over the time and its evolution across periods is heterogeneous among all Pacific Alliance countries. The sensitivity analysis indicates that the results of the model are robust to different priors specifications and to multiple sets of sign restrictions.     

Nonparametric state price density estimation using constrained least squares and the bootstrap

The economic theory of option pricing imposes constraints on the structure of call functions and state price densities. Except in a few polar cases, it does not prescribe functional forms. This paper proposes a nonparametric estimator of option pricing models which incorporates various restrictions (such as monotonicity and convexity) within a single least squares procedure. The bootstrap is used to produce confidence intervals for the call function and its first two derivatives and to calibrate a residual regression test of shape constraints. We apply the techniques to option pricing data on the DAX.     

Structural vector autoregressions with Markov switching: Combining conventional with statistical identification of shocks

In structural vector autoregressive (SVAR) analysis a Markov regime switching (MS) property can be exploited to identify shocks if the reduced form error covariance matrix varies across regimes. Unfortunately, these shocks may not have a meaningful structural economic interpretation. It is discussed how statistical and conventional identifying information can be combined. The discussion is based on a VAR model for the US containing oil prices, output, consumer prices and a short-term interest rate. The system has been used for studying the causes of the early millennium economic slowdown based on traditional identification with zero and long-run restrictions and using sign restrictions. We find that previously drawn conclusions are questionable in our framework.     

Low-frequency robust cointegration testing

Standard inference in cointegrating models is fragile because it relies on an assumption of an I(1)$I\left(1\right)$ model for the common stochastic trends, which may not accurately describe the data’s persistence. This paper considers low-frequency tests about cointegrating vectors under a range of restrictions on the common stochastic trends. We quantify how much power can potentially be gained by exploiting correct restrictions, as well as the magnitude of size distortions if such restrictions are imposed erroneously. A simple test motivated by the analysis in Wright (2000) is developed and shown to be approximately optimal for inference about a single cointegrating vector in the unrestricted stochastic trend model.     

Monetary Policy Indeterminacy and Identification Failures in the U.S.: Results from A Robust Test

We propose a novel identification‐robust test for the null hypothesis that an estimated New Keynesian model has a reduced form consistent with the unique stable solution against the alternative of sunspot‐driven multiple equilibria. Our strategy is designed to handle identification failures as well as the misspecification of the relevant propagation mechanisms. We invert a likelihood ratio test for the cross‐equation restrictions (CER) that the New Keynesian system places on its reduced‐form solution under determinacy. If the CER are not rejected, sunspot‐driven expectations can be ruled out from the model equilibrium and we accept the structural model. Otherwise, we move to a second‐step and invert an Anderson and Rubin‐type test for the orthogonality restrictions (OR) implied by the system of structural Euler equations. The hypothesis of indeterminacy and the structural model are accepted if the OR are not rejected. We investigate the finite‐sample performance of the suggested identification‐robust two‐step testing strategy by some Monte Carlo experiments and then apply it to a New Keynesian AD/AS model estimated with actual US data. In spite of some evidence of weak identification as for the ‘Great Moderation’ period, our results offer formal support to the hypothesis of a switch from indeterminacy to a scenario consistent with uniqueness occurring in the late 1970s. Our identification‐robust full‐information confidence set for the structural parameters computed on the ‘Great Moderation’ regime turns out to be more precise than the intervals previously reported in the literature through ‘limited‐information’ methods. Copyright © 2014 John Wiley & Sons, Ltd.