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Should the central bank target asset price inflation? In their 1999 paper Bernanke and Gertler claimed that price stability and financial stability are “mutually consistent objectives” in a flexible inflation targeting regime which “dictates that central banks … should not respond to changes in asset prices.” This conclusion is straightforward within their framework in which asset price inflation shows up as a factor “augmenting” the IS curve. In this chapter, we pursue a different modeling strategy so that, in the end, asset price dynamics will be incorporated into the NK Phillips curve. We put ourselves, therefore, in the best position to obtain a significant stabilizing role for asset price targeting. It turns out, however, that inflation volatility is higher in the asset price targeting case. After all, therefore, targeting asset prices may not be a good idea.

This chapter presents several approaches for identifying and dating the speculative bubble on real estate market. Using the real estate price index (IPAI), statistical and structural approaches were combined in order to detect the existence of a bubble on the Moroccan real estate market. The results obtained affirm that the Moroccan real estate market experienced a speculative bubble during the period 2006–2008 explained mainly by the boom of credit during the same period. The use of the Markov switching model affirmed that the speculative bubble on Morocco is cyclic and consequently corroborates the critic formulated by Evans (1991) concerning the traditional approaches for the detection of financial bubbles. Thus, the analysis of the series of the bubble, extracted using the Kalman filter, affirms the existence of two regimes, namely an explosive regime and a normal regime. The first regime describes the periods of explosion of the bubble and lasts for about 9 quarters, while the second, lasting for 14 quarters, describes the periods of return to the average cycle.

We present a computable general equilibrium model of the interface between the Great Salt Lake (GSL) ecosystem and both the international and regional economy that impacts the ecosystem. International trade is accounted for in the simplest of terms, involving the export of each of the ecosystem's main commodities and importation of a composite good, as well as equilibrium balances in the savings-investment and current accounts. With respect to the ecosystem, the model treats the various representative species as net energy maximizers and bases population dynamics on the period-by-period sizes of surplus net energy. Energy markets – where predators and prey exchange biomass – determine equilibrium energy prices. With respect to the regional economy, we model five production sectors (at the aggregate industry level) – brine cyst harvesters, the mineral-extraction industry, agriculture, recreation, and a composite-good industry – as well as the household sector. By performing dynamic simulations of the joint ecosystem–regional economy model, we isolate the effects of period-by-period stochastic changes in salinity levels and an initial shock to species-population levels on the ecological and economic variables of the model.

This article reviews the literature on the econometric relationship between DSGE and VAR models from the point of view of estimation and model validation. The mapping between DSGE and VAR models is broken down into three stages: (1) from DSGE to state-space model; (2) from state-space model to VAR( ∞ ); (3) from VAR( ∞ ) to finite-order VAR. The focus is on discussing what can go wrong at each step of this mapping and on critically highlighting the hidden assumptions. I also point out some open research questions and interesting new research directions in the literature on the econometrics of DSGE models. These include, in no particular order: understanding the effects of log-linearization on estimation and identification; dealing with multiplicity of equilibria; estimating nonlinear DSGE models; incorporating into DSGE models information from atheoretical models and from survey data; adopting flexible modeling approaches that combine the theoretical rigor of DSGE models and the econometric model’s ability to fit the data.

We study the correlates of immigrant location and migration choices to address the following questions: What location-specific, economic, and demographic factors are associated with these choices? Does the influence of these factors differ by immigrant characteristics? What are the factors associated with the observed increase in immigrant geographic dispersion during the 1990s? Our analysis suggests that: (1) There is significant heterogeneity in the correlates of immigrant location and migration choices; associations vary by immigrant birthplace, age, gender, education, and duration of residence in the United States. (2) Economic factors are, for the most part, weakly associated with immigrant location decisions. (3) Immigrants appear to be more attracted to states with large (growing) populations; less attracted to states with a high proportion of other foreign-born persons; more attracted to states with high unionization, and less attracted to states with high crime. (4) The association between location-specific characteristics and immigrant location choices changed between 1990 and 2000 for some immigrant groups and this explains most of the increase in geographic dispersion during the 1990s. In contrast, changes in location attributes and changes in immigrant composition explain relatively little of the increase in dispersion.

Multisector growth (MSG) models have a special aura that is shared with computable general equilibrium (CGE) models. Both of them, with their many sectors (industries and goods), are known as trying to convert Walrasian general equilibrium systems from an abstract economy representation into workable models with industrial structures changing as actually observed. Yet, they are plagued by severe problems. First, they are difficult subjects involving systems of nonlinear equations. Second, their prevalent numerical (algorithmic) methodology offers little in the way of showing a clear overall picture and understanding the plethora of numbers pouring out from model simulations. The great wood is not seen for all the trees. Hence, the main objective is to set out comparative static and dynamic systems for succinctly stating and explicitly solving MSG models. The Walrasian general equilibrium is completely stated by one equation and the multisector dynamics by one differential equation. Benchmark solutions are shown for three Constant Elasticity of Substitution (CES) parameter regimes of a 10-sector general equilibrium model.

A univariate GARCH(p,q) process is quickly transformed to a univariate autoregressive moving-average process in squares of an underlying variable. For positive integer m, eigenvalue restrictions have been proposed as necessary and sufficient restrictions for existence of a unique mth moment of the output of a univariate GARCH process or, equivalently, the 2mth moment of the underlying variable. However, proofs in the literature that an eigenvalue restriction is necessary and sufficient for existence of unique 4th or higher even moments of the underlying variable, are either incorrect, incomplete, or unnecessarily long. Thus, the paper contains a short and general proof that an eigenvalue restriction is necessary and sufficient for existence of a unique 4th moment of the underlying variable of a univariate GARCH process. The paper also derives an expression for computing the 4th moment in terms of the GARCH parameters, which immediately implies a necessary and sufficient inequality restriction for existence of the 4th moment. Because the inequality restriction is easily computed in a finite number of basic arithmetic operations on the GARCH parameters and does not require computing eigenvalues, it provides an easy means for computing “by hand” the 4th moment and for checking its existence for low-dimensional GARCH processes. Finally, the paper illustrates the computations with some GARCH(1,1) processes reported in the literature.

We examine the allocation of a pre-determined amount of aid from a donor to two recipient countries. The donor suffers from cross-border pollution resulting from production activities in the recipient countries. It is shown that the recipient with the higher fraction of aid allocated to public abatement and with the lower emission tax, receives a higher share of the aid when the donor allocates aid so as to maximize its own welfare. Competition for aid reduces cross-border pollution to the donor when recipients use the fraction of aid allocated to pollution abatement as a policy to divert aid from each other. But, it increases cross-border pollution when recipients use the emission tax to divert aid from each other.