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We examine the interaction of uncertainty and credit frictions in a New Keynesian framework. The model considers credit frictions arising from costly-state verification in the provision of loans to fund the acquisition of capital by entrepreneurs and includes three types of time-varying stochastic volatility shocks related to monetary policy uncertainty, financial risk (micro uncertainty), and macro uncertainty. Key parameters are estimated by the simulated method of moments using data from the United States from 1984:Q1 until 2014:Q4. We find that (1) micro uncertainty has first-order effects that are significantly larger than the effects of macro uncertainty and monetary policy uncertainty; (2) poor credit conditions exacerbate the economic drag from micro uncertainty shocks, amplify the effects of monetary policy shocks, and mitigate the impact of total factor productivity (TFP) shocks; (3) a degree of asymmetry and nonscalability appears in response to monetary policy shocks depending on the degree of nominal rigidities and initial conditions; and (4) monetary policy uncertainty accounts for about one-third of business cycle volatility largely by affecting the size of monetary policy shocks.

This study examines the role of sectoral productivity in explaining the structural change process and relative sectoral prices in an economy. A simple two-sector general equilibrium model was developed to show that improvement in agricultural productivity relocates labor away from the agriculture sector under certain conditions. In contrast to the conventional wisdom that relative sectoral prices reflect relative sectoral productivities, this study shows the possibility of a U-shaped relationship between the two factors and found a structural break in their relationship based on the US data during 1920–1965. We estimate our model parameters by using the simulated method of moments after constructing a suitable structural model with historical data on the US economy for the previous two centuries (1820–2017). For the most plausible set of parameter values, our model could primarily replicate the relative price of farm movement and the agricultural labor share of the US economic history.

In Italy, young adults tend to postpone their transition to adulthood and live with their parents until very late compared with other countries. A dynamic discrete choice model is proposed in which agents choose residential arrangements, together with labor supply and marital status, conditional on the economic and institutional framework and on other agents’ choices. The model is structurally estimated with the Simulated Method of Moments for non-student high-school graduate males and then used to assess, through a variety of counterfactual experiments, the relative importance of factors that are claimed to influence the choice to leave home in the existing literature: labor market conditions, parental resources, housing market conditions and social interaction. Results suggest that Italians choose to remain with their parents due to a combination of poor labor market conditions and high housing costs. The relatively high income of parents could contribute to the patterns observed by acting as an insurance against unemployment. Finally, estimates indicate that individuals tend to conform to a social norm, especially in the South of the country were family ties and the costs in terms of utility from not complying with expected behaviors appear to be stronger.

Purpose The purpose of this paper is to quantify the impact of Wal-Mart Supercenters (WMSs) on supermarkets’ profit margins using fluid milk in the Dallas/Fort Worth metropolitan area in the USA as a case study. Design/methodology/approach The authors develop a two-stage dynamic entry game to model market competition in the pre- and post-WMS stages, and test the theoretical model using the method of simulated moments and milk scanner data from Dallas/Fort Worth supermarkets. Findings The empirical findings show that the entry of WMSs accounts for an average of about 16.29-25.69 percent decline in milk profit margins of nearby, or incumbent, supermarkets. Economies of scale and chain synergies are found to be five times more significant for WMSs than for incumbent supermarkets, granting Wal-Mart a significant competitive edge. Originality/value This paper quantifies the impact of WMS’s entry on incumbent supermarkets’ profit margins through a structural model of entry. In addition, this paper assesses the effect economies of scale stemming from the ability to provide shopping convenience to consumers as well as chain economies from operating stores near each other.

Macroeconomic models often generate nominal price rigidity via menu costs. This paper provides empirical evidence that treating menu costs as a structural explanation for sticky prices may be spurious. Using scanner data, I note two empirical facts: (1) price points, embodied in nine-ending prices, account for approximately two-thirds of prices; and (2) at the conclusion of sales, post-sale prices return to their pre-sale levels more than three-fourths of the time. I construct a model that nests roles for menu costs and price points and estimate model variants. Excluding the two facts yields a statistically and economically significant role for menu costs in generating price rigidity. Incorporating the two facts yields an incentive to set nine-ending prices two orders of magnitude larger than the menu costs. In this setting, the price point model can match the two stylized facts, but menu costs are effectively irrelevant as a source of price rigidity. The choice of a mechanism for price rigidity matters for aggregate dynamics.

We benchmark seven global optimization algorithms by comparing their performance on challenging multidimensional test functions as well as a method of simulated moments estimation of a panel data model of earnings dynamics. Five of the algorithms are taken from the popular NLopt open-source library: (i) Controlled Random Search with local mutation (CRS), (ii) Improved Stochastic Ranking Evolution Strategy (ISRES), (iii) Multi-Level Single-Linkage (MLSL) algorithm, (iv) Stochastic Global Optimization (StoGo), and (v) Evolutionary Strategy with Cauchy distribution (ESCH). The other two algorithms are versions of TikTak, which is a multistart global optimization algorithm used in some recent economic applications. For completeness, we add three popular local algorithms to the comparison—the Nelder-Mead downhill simplex algorithm, the Derivative-Free Non-linear Least Squares (DFNLS) algorithm, and a popular variant of the Davidon-Fletcher-Powell (DFPMIN) algorithm. To give a detailed comparison of algorithms, we use a set of benchmarking tools recently developed in the applied mathematics literature. We find that the success rate of many optimizers vary dramatically with the characteristics of each problem and the computational budget that is available. Overall, TikTak is the strongest performer on both the math test functions and the economic application. The next-best performing optimizers are StoGo and CRS for the test functions and MLSL for the economic application.

Yes, most likely. The firm-level evidence on costly reversibility is even stronger than the prior evidence at the plant level. The firm-level investment rate distribution is highly skewed to the right, with a small fraction of negative investments, 5.79%, a tiny fraction of inactive investments, 1.46%, and a large fraction of positive investments, 92.75%. When estimated via simulated method of moments, the standard investment model explains the average value premium, while simultaneously matching the key properties of the investment rate distribution, including the cross-sectional volatility, skewness, and the fraction of negative investments. The combined effect of costly reversibility and operating leverage is the key driving force behind the model’s quantitative performance.

In the United States, both taxes and old age Social Security benefits depend on one's marital status and tend to discourage the labor supply of the secondary earner. To what extent are these provisions holding back female labor supply? We estimate a rich life cycle model of labor supply and savings for couples and singles using the method of simulated moments (MSM) on the 1945 and 1955 birth-year cohorts and use it to evaluate what would happen without these provisions. Our model matches well the life cycle profiles of labor market participation, hours, and savings for married and single people and generates plausible elasticities of labor supply. Eliminating marriage-related provisions drastically increases the participation of married women over their entire life cycle, reduces the participation of married men after age 60, and increases the savings of couples in both cohorts, including the later one, which has similar participation to that of more recent generations. If the resulting government surplus were used to lower income taxation, there would be large welfare gains for the vast majority of the population. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

This paper studies the interdependence between labor force at- tachment to the teaching sector and fertility decisions of female teachers. A joint dynamic model of labor supply and fertility with unobserved heterogene- ity in preferences for children is formulated and estimated. The model assumes that these women behave as though they were solving a nite-horizon, discrete- choice, dynamic programming problem under uncertainty. The structural pa- rameters of the model are estimated by the Simulated Method of Moments using data from the NLS-72, and are used to predict the eects that wage increases and reductions in the cost of childcare would have on teachers' em- ployment and fertility choices. By jointly modeling employment and fertility, my estimates unpack important features of the interdependence between these two choices and contradict previous ndings. Wage policies have a negative eect on fertility, and thus decrease exits related to childbirth. Reductions in the cost of childcare, on the other hand, increase fertility and are shown to be eective in increasing job attachment to the teaching sector among beginner

The practice of selling multiple products or services under nonlinear pricing has a long history in the business community. Consumers may face demand uncertainty when choosing a service plan, and preferences for multiple products or services may be inter-dependent. To examine a demand system with these features, we construct a two-stage discrete/continuous choice model for service bundle demand under three-part tariffs, allowing for interactive utility and preference correlations. Implementing a piecewise maximization approach to the consumer utility maximization problem, which is non-differentiable under three-part tariffs, we estimate the model via simulated method of moments. We then apply our model to data from a major wireless service provider in China. Finally, our counterfactual analysis simulates outcomes under three-part tariffs with interchangeable units. Compared to existing tariffs, the proposed ones contain fewer instruments without a significant loss of revenue. We also illustrate the implications of incorporating ex-post usage shocks in model estimates, elasticities and counterfactual outcomes.

This study proposes a method to economically evaluate environmental quality using property auction data. Our approach is consistent with microeconomic auction theory and can directly estimate the value function from the data, a task that is often difficult when using conventional revealed preference methods such as the hedonic approach. We use a method of simulated moments to estimate the value function, which in turn is used to calculate the economic value of environmental quality changes. We apply this method to data on foreclosure property auctions in Tokyo in order to economically evaluate hypothetical improvements in neighborhood fire risks.

This paper constructs a heterogeneous agent exchange rate model of speculators and non-speculators from a simple monetary framework. The model replaces rational expectations with an adaptive learning rule that forecasts future exchange rates with an econometric model, and assumes two types of market participants, speculators and non-speculators, that differ by their forecasting model. Speculators employ a correctly specified forecasting model, are relatively short-term oriented, and are subject to momentum and herding effects via an expectation shock; non-speculators utilize a simple forecasting model, have no incentive to be short-term oriented, and are not subject to herding effects. Parameters are calibrated and estimated using the method of simulated moments, and simulation results show that the model is able to replicate foreign exchange market stylized facts better than a model of representative agent rational expectations. Furthermore, the dynamics of the model are shown to derive from both agent heterogeneity and the expectation shock.

The article applies unit-level logit mixed models to estimating small-area weighted sums of probabilities. The model parameters are estimated by the method of simulated moments (MSM). The empirical best predictor (EBP) of weighted sums of probabilities is calculated and compared with plug-in estimators. An approximation to the mean-squared error (MSE) of the EBP is derived and a bias-corrected MSE estimator is given and compared with parametric bootstrap alternatives. Some simulation experiments are carried out to study the empirical behavior of the model parameter MSM estimators, the EBP and plug-in estimators and the MSE estimators. An application to the estimation of poverty proportions in the counties of the region of Valencia, Spain, is given.

We propose an importance-sampling procedure to improve the computational performance of the simulated method of moments (SMM) for the estimation of structural models with fixed parameter heterogeneity. The main advantage of the procedure is that it does not require to simulate observations every time that the structural parameters change during the minimization of the SMM criterion function. We illustrate the use of our method by estimating a neoclassical model of investment for a sample of US manufacturing companies, allowing the technological parameters to vary across firms.

This paper adds persistent shocks into the adaptive learning expectation formation process in stochastic growth asset pricing production and endowment economies. These expectation shocks, designed to capture psychological elements which can arise from news, changes in sentiment, herding and bandwagon effects, generate waves of optimism and pessimism in equity price forecasts. The paper estimates parameters of the expectation shock and adaptive learning process with the method of simulated moments, and compares simulation results to U.S. economic and financial market stylized facts. Numerical results for both the estimated production and endowment economies show that the expectation shock model matches several of the stylized facts better than does a model that assumes rational expectations or adaptive learning alone.

This article examines whether adjustment frictions help account for the patterns of household consumption expenditures observed in the Consumer Expenditure Survey, namely, that the variance of log durable expenditure is four times larger than that of log nondurable expenditure for annual data and this gap substantially widens for quarterly data. Estimating a structural model of household consumption with nondurable and durable goods with the simulated method of moments, I find that the fixed costs associated with durable adjustments are important in matching the cross-sectional moments. Using the estimated model, I also examine the response of nondurable and durable expenditures to income shocks. (JEL D12, D91, E21)

We take the model of Alfarano et al. (J Econ Dyn Control 32:101–136, 2008) as a prototype agent-based model that allows reproducing the main stylized facts of financial returns. The model does so by combining fundamental news driven by Brownian motion with a minimalistic mechanism for generating boundedly rational sentiment dynamics. Since we can approximate the herding component among an ensemble of agents in the aggregate by a Langevin equation, we can either simulate the model in full at the micro level, or via an approximate aggregate law of motion. In the simplest version of our model, only three parameters need to be estimated. We explore the performance of a simulated method of moments (SMM) approach for the estimation of this model. As it turns out, sensible parameter estimates can only be obtained if one first provides a rough “mapping” of the objective function via an extensive grid search. Due to the high correlations of the estimated parameters, uninformed choices will often lead to a convergence to any one of a large number of local minima. We also find that the efficiency of SMM is relatively insensitive to the size of the simulated sample over a relatively large range of sample sizes and the SMM estimates converge to their GMM counterparts only for large sample sizes. We believe that this feature is due to the limited range of moments available in univariate asset pricing models, and that the sensitivity of the present model to the specification of the SMM estimator could carry over to many related agent-based models of financial markets as well as to similar diffusion processes in mathematical finance.

Indirect inference (II) is a classical method for estimating the parameter of a complex model when the likelihood is unavailable or too expensive to evaluate. The idea was popularised several years prior to the main developments in ABC by Gourieroux et al. (1993); Smith (1993), where interest was in calibrating complex time series models used in financial applications. The II method became a very popular approach in the econometrics literature (e.g. Smith (1993); Monfardini (1998); Dridi et al. (2007)) in a similar way to the ubiquitous application of ABC to models in population genetics. However, the articles by Jiang and Turnbull (2004) and Heggland and Frigessi (2004) have allowed the II approach to be known and appreciated by the wider statistical community. In its full generality, the II approach can be viewed as a classical method to estimate the parameter of a statistical model on the basis of a so-called indirect or auxiliary summary of the observed data (Jiang and Turnbull, 2004). A special case of II is the simulated method of moments (McFadden, 1989), where the auxiliary statistic is a set of sample moments. In this spirit, the traditional ABC method may be viewed as a Bayesian version of II, where prior information about the parameter may be incorporated and updated using the information about the parameter contained in the summary statistic. However, much of the II literature has concentrated on developing the summary statistic from an alternative parametric auxiliary model that is analytically and/or computationally more tractable. The major focus of this book chapter is on approximate Bayesian methods that harness such an auxiliary model. These are referred to as parametric Bayesian indirect inference (pBII) methods by Drovandi et al. (2014a)...

This paper studies the implications of household financial choices for the effects of monetary policy on consumption. Based on data from four major euro area countries, the paper estimates the key structural parameters using a simulated method of moments approach to match moments related to asset market participation rates, portfolio shares and wealth to income ratios by education and country. The policy functions based upon the estimation are used to characterize the distributions of the marginal propensity to consume across heterogenous households for each of the four countries. Due to this heterogeneity in consumption responses, monetary policy, operating through its effects on household income and asset market returns, has a differential impact on individuals within and across countries. Generally, poor households respond more to the income variations produced by monetary policy innovations while rich households respond more to policy-induced variations in stock returns. Monetary policy has a larger impact on consumption in Italy and Spain compared to France and Germany.

We present a dynamic lifecycle model of women’s choices with respect to partnership status, labour supply and fertility when a male partner’s true tendency for abusive behaviour is unobserved. The model is estimated by the method of simulated moments using longitudinal data from the Avon Longitudinal Study of Parents and Children. The results indicate that uncertainty about a partner’s abusive type creates incentives for women to delay fertility, reduce fertility overall, divorce more often and increase labour supply. We also study the impact of higher female wages, income support to single mothers, and subsidized childcare when the mother is working. While higher wages reduce women’s overall exposure to abuse, both income support and subsidized childcare fail to do so because they encourage early fertility. Income support also leads to less accumulated labour market experience and hence higher abuse rates.

I estimate a dynamic agency model to quantify the importance of dismissals in CEO incentives -vis-a-vis pecuniary compensation. The model features endogenous dynamics in deferred and flow compensation, as well as exogenous departures, and endogenous dismissals after poor firm performance. Thus, the model functions as a classification device for CEO turnover events that exploits information from all the departures in the data. I estimate the model via the Simulated Method of Moments, using data for CEOs in U.S. public firms appointed from 1993 to 2013. The estimated CEO dismissal rate is 1.2 percent, and the CEO replacement cost represents 3.4 percent of firm assets, 64 million in 2015 U.S. dollars for the median firm. Poor governance, proxied by director independence, increases the replacement costs for big firms. The relationship reverses in small firms, so board independence must also capture better hiring policies or career concerns of directors. The results confirm that CEO dismissals are infrequent. However, changes in the cost of replacements that generate small increases in the underlying dismissal rate lead to substantial reductions in the size of incentive compensation.

This paper assesses the quantitative importance of including sectoral heterogeneity in computing the gains from trade. Our framework draws from Caliendo and Parro (2015) and Alvarez and Lucas (2007) and has sectoral heterogeneity along five dimensions, including the elasticity of trade to trade costs, the value-added share, and the input-output structure. The key parameter we estimate is the sectoral trade elasticity, and we use the Simonovska and Waugh (2014) simulated method of moments estimator with micro price data. Our estimates range from 2.97 to 8.94, considerably lower than those obtained with the Eaton and Kortum (2002) price-based method. Our benchmark model is calibrated to 21 OECD countries and 20 sectors. We compute the gains from trade in our benchmark model, and in several re-calibrated versions of the model in which we eliminate one or more sources of sectoral heterogeneity. Our main result is that sectoral heterogeneity does not always lead to an increase in the gains from trade. There are two reasons for this. First, the magnitudes of our estimated sectoral trade elasticities are relatively high, while the magnitude of our estimated aggregate trade elasticity is low. All else equal, this will lead to higher gains for the aggregate, one-sector model. Second, the sectors with low trade elasticities (hence, implying high gains from trade) are not the sectors with low value-added shares and with high initial trade shares (which would magnify the gains). Hence, the sectoral heterogeneity in our calibrated model does not exert complementary gains from trade effects.

This paper examines changes in married people's allocation of time since 1980, a period in which female labor supply increased substantially, men's share of household work rose, and the gender wage gap narrowed down. I develop a life-cycle collective household model for market and non-market work, consumption and asset accumulation, which also features lack of commitment to lifetime marriage. Wages in the model shift intra-family bargaining power and induce bargaining effects on outcomes in addition to standard income and substitution effects. I estimate gender-specific preferences and how intra-family bargaining power changes with a narrowing gender gap using data from the PSID. The results suggest that a narrowing gender gap improved women's bargaining power in the family resulting in a shift of household work to their husbands. It also contributed to the increase in female labor market participation. If the gender gap is counterfactually eliminated, the proportion of women in full-time work rises throughout the lifecycle to match approximately that of men. The increase comes from women who cut down household chores and enter the labor market when they previously did not participate.

This paper studies the rates of convergence for learning distributions implicitly with the adversarial framework and Generative Adversarial Networks (GANs), which subsume Wasserstein, Sobolev, MMD GAN, and Generalized/Simulated Method of Moments (GMM/SMM) as special cases. We study a wide range of parametric and nonparametric target distributions under a host of objective evaluation metrics. We investigate how to obtain valid statistical guarantees for GANs through the lens of regularization. On the nonparametric end, we derive the optimal minimax rates for distribution estimation under the adversarial framework. On the parametric end, we establish a theory for general neural network classes (including deep leaky ReLU networks) that characterizes the interplay on the choice of generator and discriminator pair. We discover and isolate a new notion of regularization, called the generator-discriminator-pair regularization, that sheds light on the advantage of GANs compared to classical parametric and nonparametric approaches for explicit distribution estimation. We develop novel oracle inequalities as the main technical tools for analyzing GANs, which are of independent interest., Comment: Journal of Machine Learning Research, to appear

In this paper, I show how gradient-based optimization methods can be used to estimate stochastic dynamic models in economics. By extending the state space to include all model parameters, I show that we need to solve the model only once to do structural estimation. Parameters are then estimated by minimizing the distance between key empirical moments and the model-implied ones. Unlike the Simulated Method of Moments, the model-implied moments are estimated without the computation of a single moment. Instead, a neural network learns the corresponding moments using raw simulated observations. Once a network learned the (differentiable) mapping between parameters and moments, a Newton-Raphson routine is coupled with simulated annealing to find the set of parameters that globally minimizes the objective function. I illustrate the algorithm by solving and estimating a benchmark macroeconomic model with stochastic volatility, endogenous labor supply, and irreversible investment.

In this article, we empirically examine a hybrid New-Keynesian model with heterogeneous bounded rational agents who may adopt an optimistic or pessimistic attitude—so called animal spirits—toward future movements of the output and inflation gap. The model is estimated via the simulated method of moments using Euro Area data from 1975Q1 to 2009Q4. In addition, we compare its empirical performance to the standard model with rational expectations. Our empirical results show that the model-generated auto- and cross-covariances of the output, inflation and nominal interest rate gap can provide a good approximation of the empirical second moments.

We compare the performance of maximum likelihood (ML) and simulated method of moments (SMM) estimation for dynamic discrete choice models. We construct and estimate a simplified dynamic structural model of education that captures some basic features of educational choices in the United States in the 1980s and early 1990s. We use estimates from our model to simulate a synthetic dataset and assess the ability of ML and SMM to recover the model parameters on this sample. We investigate the performance of alternative tuning parameters for SMM.

This paper provides a different perspective on the firm-level empirical analysis of the relation between foreign ownership and labour demand adjustment in host countries. We estimate a dynamic structural model of employment on a sample of 8215 Belgian firms observed between 2003 and 2010 that permits to distinguish the ‘ownership status’ of firms. We consider a dynamic discrete choice model of a general specification of adjustment costs including convex, non-convex and “disruption of production” components. We use a method of simulated moments procedure to estimate the structural parameters. Our results indicate that multinational’s affiliates face lower convex and fixed adjustment costs but higher “disruption of production” adjustment costs than national firms.

Two difficulties arise in the estimation of AB models: (i) the criterion function has no simple analytical expression, (ii) the aggregate properties of the model cannot be analytically understood. In this paper we show how to circumvent these difficulties and under which conditions ergodic models can be consistently estimated by simulated minimum distance techniques, both in a long-run equilibrium and during an adjustment phase.

Vehicle-to-Vehicle communications provide the opportunity to create an internet of cars through the recent advances in communication technologies, processing power, and sensing technologies. A connected vehicle receives real-time information from surrounding vehicles; such information can improve drivers’ awareness about their surrounding traffic condition and lead to safer and more efficient driving maneuvers. Lane-changing behavior,as one of the most challenging driving maneuvers to understand and to predict, and a major source of congestion and collisions, can benefit from this additional information.This paper presents a lane-changing model based on a game-theoretical approach that endogenously accounts for the flow of information in a connected vehicular environment.A calibration approach based on the method of simulated moments is presented and a simplified version of the proposed framework is calibrated against Next Generation Simulation (NGSIM) data. The prediction capability of the simplified model is validated. It is concluded the presented framework is capable of predicting lane-changing behavior with limitations that still need to be addressed. Finally, a simulation framework based on the fictitious play is proposed. The simulation results revealed that the presented lane-changing model provides a greater level of realism than a basic gap-acceptance model.

In the U.S., both taxes and old age Social Security benefits depend on one's marital status and tend to discourage the labor supply of the secondary earner. We study the effects of eliminating these marriage-related provisions on the labor supply and savings of two different cohorts. To do so, we estimate a rich life-cycle model of couples and singles using the Method of Simulated Moments (MSM) on the 1945 and 1955 birth-year cohorts. Our model matches well the life cycle profiles of labor market participation, hours, and savings for married and single people and generates plausible elasticities of labor supply. We find that these marriage-related provisions reduce the participation of married women over their life cycle, the participation of married men after age 55, and the savings of couples. These effects are large for both the 1945 and 1955 cohorts, even though the latter had much higher labor market participation of married women to start with.

This paper develops measures of transaction costs in the absence of transaction timestamps and information about who initiates transactions, which are data limitations that often arise in studies of over-the-counter markets. I propose new measures of the effective spread and study the performance of all estimators analytically, in simulations, and present an empirical illustration with small-cap stocks for the 2005-2014 period. My theoretical, simulation, and empirical results provide new insights into measuring transaction costs and may help guide future empirical work.

Generalized method of moments (GMM) refers to a class of estimators constructed from the sample moment counterparts of population moment conditions (sometimes known as orthogonality conditions) of the data generating model. GMM estimators have become widely used, for the following reasons: 1. GMM estimators have large sample properties that are easy to characterize. A family of such estimators can be studied simultaneously in ways that make asymptotic efficiency comparisons easy. The method also provides a natural way to construct tests which take account of both sampling and estimation error. 2. In practice, researchers find it useful that GMM estimators may be constructed without specifying the full data generating process (which would be required to write down the maximum likelihood estimator). This characteristic has been exploited in analysing partially specified economic models, studying potentially misspecified dynamic models designed to match target moments, and constructing stochastic discount factor models that link asset pricing to sources of macroeconomic risk.

In the United States, both taxes and old age Social Security benefits explicitly depend on one's marital status. We study the effects of eliminating these marriage-related provisions on the labor supply and savings of two different cohorts. To do so, we estimate a rich life-cycle model of couples and singles using the method of simulated moments (MSM) on the 1945 and 1955 birth-year cohorts. Our model matches well the life-cycle profiles of labor market participation, hours, and savings for married and single people and generates plausible elasticities of labor supply. We find that these marriage-related provisions reduce the participation of married women over their life cycle, the participation of married men after age 55, and the savings of couples. These effects are large for both the 1945 and 1955 cohorts, even though to start with the latter had much higher labor market participation of married women.

Despite recent advances in bringing agent-based models (ABMs) to the data, the estimation or calibration of model parameters remains a challenge, especially when it comes to large-scale agent-based macroeconomic models. Most methods, such as the method of simulated moments (MSM), require in-the-loop simulation of new data, which may not be feasible for such computationally heavy simulation models. The purposes of this paper is to provide a proof-of-concept of a generic empirical validation methodology for such large-scale simulation models. We introduce an alternative 'large-scale' empirical validation approach, and apply it to the Eurace@Unibi macroeconomic simulation model (Dawid, et al., 2016). This model was selected because it displays strong emergent behaviour and is able to generate a wide variety on nonlinear economic dynamics, including endogenous business- and financial cycles. In addition, it is a computationally heavy simulation mode, so it fits our targeted use-case. The validation protocol consists of three stages. At the first stage we use Nearly-Orthogonal Latin Hypercube sampling (NOLH) in order to generate a set of 513 parameter combinations with good space-filling properties. At the second stage we use the recently developed Markov Information Criterion (MIC) to score the simulated data against empirical data. Finally, at the third stage we use stochastic kriging to construct a surrogate model if the MIC response surface, resulting in an interpolation of the response surface as a function of the parameters. The parameter combinations providing the best fit to the data are then identified as the local minima of the interpolated MIC response surface. The Model Confidence Set (MCS) procedure of Hansen et al. (2011) is used to restrict the set of model calibrations to those models that cannot be rejected to have equal predictive ability, at a given confidence level. Validation of the surrogate model is carried out by re-running the second stage of the analysis on the so identified optima and cross-checking that the realised MIC scores equal the MIC scores predicted by the surrogate model. The results we obtain so far look promising as a first proof-of-concept for the empirical validation methodology since we are able to validate the model using empirical data series for 30 OECD countries and the euro area. The internal validation procedure of the surrogate model also suggests that the combination of NOLH sampling, MIC measurement and stochastic kriging yields reliable predictions of the MIC scores for samples not included in the original NOLH sample set. In our opinion, this is a strong indication that the method we propose could provide a viable statistical machine learning technique for the empirical validation of (large-scale) ABMs.

This study proposes a novel approach to facilitate the estimation of the preference parameters of a two consumption good C-CAPM that accounts for multi-period disasters, partial government defaults, and the possible destruction of the stock of the durable good. The maximum likelihood estimation of the disaster process parameters requires a cross-country panel of historical consumption data and international business cycle dates. The estimation of the risk aversion coefficient and the intertemporal elasticity of substitution (IES) is facilitated by the simulated method of moments. The results show that the empirical equity premium can be explained with economically plausible and quite precise risk aversion and IES estimates. This conclusion withstands a battery of robustness checks.

A structural model is developed and estimated by a maximum likelihood routine to investigate interrelated factor demand subject to nonconvex adjustment costs. The dataset concerns Norwegian plants operating in manufacturing industries and it covers the period 1993-2005. The estimates indicate that it is advantageous to adjust the stock of labour and capital simultaneously. The cost advantage of simultaneous changes is small for capital but is large for labour. The empirical results suggest that when estimating separate factor demand models the bias of parameter estimates is most severe in case of labour demand.

We evaluate empirically two sources of large takeover premiums: preemptive bidding and target resistance. We develop an auction model that features costly sequential entry of bidders in takeover contests and encompasses both explanations. We estimate the model parameters by simulated method of moments for a sample of US takeovers. Our estimates imply that target resistance explains the entire magnitude of the premium in 74% of successful single-bidder contests. Simulation experiments show that initial bidders have, on average, a higher valuation for the target than rival bidders, so that a relatively low initial bid is sufficient to deter a rival from entry.

The paper proposes an elementary agent-based asset pricing model that, invoking the two trader types of fundamentalists and chartists, comprises four features: (i) price determination by excess demand; (ii) a herding mechanism that gives rise to a macroscopic adjustment equation for the market fractions of the two groups; (iii) a rush towards fundamentalism when the price misalignment becomes too large; and (iv) a stronger noise component in the demand per chartist trader than in the demand per fundamentalist trader, which implies a structural stochastic volatility in the returns. Combining analytical and numerical methods, the interaction between these elements is studied in the phase plane of the price and a majority index. In addition, the model is estimated by the method of simulated moments, where the choice of the moments reflects the basic stylized facts of the daily returns of a stock market index. A (parametric) bootstrap procedure serves to set up an econometric test to evaluate the model's goodness-of-fit, which proves to be highly satisfactory. The bootstrap also makes sure that the estimated structural parameters are well identified.

In a small open economy fluctuations in the real exchange rate can affect plant turnover, and thus aggregate productivity, by altering the makeup of plants that populate the market. An appreciation of the local currency increases the level of competition in the domestic market as import competition intensifies and export opportunities shrink, forcing less productive plants from the market and compelling new entrants to be more competitive than they otherwise would have been. Depreciations have the opposite effect, as import competition weakens and new export opportunities arise, less competitive plants are able to continue to operate in the market and crowd out new, more productive entrants. This paper develops a dynamic structural model that captures the effect of plantlevel productivity and real exchange rate fluctuations on plant entry and exit decisions in the Canadian agricultural implements industry, and how this, in turn, affects aggregate productivity. The model's dynamic parameters are estimated in two stages. Variable profit parameters and the per-period fixed cost of operation are estimated first using the Nested Pseudo Likelihood (NPL) algorithm, and then the parameters characterizing the distribution of unobserved potential entrant productivity, along with the cost of entry, are estimated in a second stage using the Method of Simulated Moments (MSM). Finally, simulations of the model are used to investigate the effects of shocks to the exchange rate process on aggregate industry productivity., Dans une petite économie ouverte, les variations du taux de change réel peuvent influer sur la rotation des usines, et donc sur la productivité globale, par les modifications qu’elles entraînent dans la configuration du marché. Lorsque la monnaie du pays s’apprécie, le niveau de concurrence augmente sur le marché intérieur du fait que la concurrence des importations s’intensifie et que les possibilités d’exportation s’amenuisent, chassant ainsi les usines moins productives du marché et obligeant les nouvelles venues à se montrer plus compétitives qu’elles ne l’auraient fait autrement. À l’inverse, les dépréciations rendent les importations moins concurrentielles et ouvrent de nouveaux débouchés à l’étranger, ce qui permet aux usines moins compétitives de rester sur le marché et de tenir à l’écart de nouvelles concurrentes, plus productives. L’auteur élabore un modèle structurel dynamique qui reproduit l’incidence de la productivité individuelle des usines et des variations du taux de change réel sur les décisions des usines d’entrer dans le secteur canadien du matériel agricole ou d’en sortir, et les répercussions de ces décisions sur la productivité globale. L’estimation des paramètres dynamiques du modèle se fait en deux temps. L’auteur procède tout d’abord à l’estimation des paramètres des bénéfices variables et des charges d’exploitation fixes par période au moyen de l’algorithme imbriqué mis au point par Aguirregabiria et Mira (2002) pour maximiser la pseudo-vraisemblance. Il estime ensuite les paramètres caractérisant la distribution de la productivité non observée des entrants éventuels, de même que les coûts d’entrée, par la méthode des moments simulés. Enfin, par des simulations du modèle, il étudie les effets des chocs touchant le processus de détermination des taux de change sur la productivité sectorielle globale.

Investment frictions reduce, delay or protract investment expenditure that is necessary for firms to capture growth opportunities. Using a capital adjustment costs framework, this article estimates the gap between China's actual and frictionless aggregate output. It applies the method of simulated moments to a fully structural investment model on a panel of Chinese firms and takes into account potential unobserved heterogeneities and measurement error in the data. The estimated capital adjustment costs imply that if Chinese firms had faced a lower level of adjustment costs such as in the US, China's aggregate output would be 25% higher.

The expected return to equity--typically measured as a historical average--is a key variable in the decision making of investors. A recent literature based on analysts' forecasts and practitioner surveys finds estimates of expected returns that are sometimes much lower than historical averages. This study presents a novel method that estimates the expected return to equity using only observable data. The method builds on a present value relationship that links dividends, earnings, and investment to market values via expected returns. Given a model that captures this relationship, one can infer the expected return. Using this method, the estimated expected real return to equity ranges from 4 to 5.5 percent. Furthermore, the analysis indicates that expected returns have declined by about 2 percentage points over the past forty years. These results indicate that future returns to equity may be lower than past realized returns.

We develop a dynamic structural model to better understand how corporate payout policy is determined in conjunction with other corporate decisions. In a first-best model, a manager maximizes equity value by choosing the firm's optimal financing, investment, dividends, and cash holdings. By using simulated method of moments, we show that, on average, firms excessively smooth their payout while making corporate savings overly volatile and retaining excess cash. We then extend the model to capture the effect of a manager, who perceives a cost to cutting payouts. Estimating the model, we infer the magnitude of this cost. We find that a managerial preference for consistent payout explains the smooth payout and high volatility of cash holdings. [ABSTRACT FROM AUTHOR]

This article uses a game-theoretic, two-sided matching model and method of simulated moments estimation to study factors affecting the match of elementary teachers to their first jobs. We find that employers demonstrate preferences for teachers having stronger academic achievement (e.g., attended a more selective college) and for teachers living in closer proximity to the school. Teachers show preferences for schools that are closer geographically, are suburban, have a smaller proportion of students in poverty, and, for white teachers, have a smaller proportion of minority students. These results appear predictable but contradict findings from prior research estimating hedonic wage equations for teacher labor markets.