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We consider the problem of estimating a Markov process that is endogenously sampled. We observe a discrete-time Markov process { p t } at a set of random times { t 1 , … , t n } that depend on the outcome of a probabilistic sampling rule that depends on the state of the process and other observed covariates x t . We focus on a particular example where p t is the daily wholesale price of a standardized steel product. The endogenous sampling problem arises from the fact that we only observe p t on the days the firm purchases steel. We show how to solve this problem under two different assumptions about firm behavior: (1) optimality: the timing of steel purchases is governed by an optimal purchasing strategy that maximizes expected discounted profits, and (2) potential suboptimality: we allow the firm to use any randomized, Markovian purchasing strategy. In the latter case, the estimation problem becomes semi-parametric and we use the method of sieves to estimate a flexible parametric approximation to the firm’s purchasing behavior that best fits the data without imposing optimality. We show how estimation of this model becomes tractable under either of these assumptions using the method of simulated moments (MSM). We simulate realizations of wholesale steel prices and sample them in the same way as they are sampled in the actual data, i.e. only on days where purchases occur. We use the MSM estimator to estimate a truncated lognormal AR(1) model of the wholesale price processes for particular types of steel plate and test and reject the assumption that the firm is behaving optimally.

The elasticity of substitution between capital and labor plays an important role in the analysis of economic and policy issues such as factors' share in national income and tax policies on business capital formation. Rather than focusing on long-run relationships to estimate this elasticity, I exploit the short-run variations in the labor income share due to changes in capital-embodied technology. Using the simulated method of moments approach, I obtain an elasticity estimate that is clearly less than one. The study indicates that estimates based on the long-run relationship of factor's share may tend to be significantly larger.

This study extends the hybrid version of the baseline New-Keynesian model with heterogeneous agents who may adopt various forecast heuristics. With a focus on consumer expectations, we identify the most appropriate pairs of forecast heuristics that can lead to an equivalent fit to the data compared with the model specification under rational expectations. The competing specifications are estimated using the simulated method of moments. Our empirical results suggest that expectations under bounded rationality in the United States are grounded on consumers’ emotional state, while for the Euro Area they are technical in nature. This observation questions the need for a hybrid model specification under rational expectations.

We propose a new monitoring procedure based on moving sums (MOSUM) for detecting single or multiple structural breaks in factor copula models. The test compares parameter estimates from a rolling window to those from a historical data set and analyzes the behavior under the null hypothesis of no parameter change. The case of multiple breaks is also treated. In the model, the joint copula is given by the copula of random variables which arise from a factor model. This is particularly useful for analyzing high dimensional data. Parameters are estimated with the simulated method of moments (SMM). We analyze the behavior of the monitoring procedure in Monte Carlo simulations and a real data application. We consider an online procedure for predicting the day-ahead Value-at-risk based on the suggested monitoring procedure.

Within the seminal asset-pricing model by Brock and Hommes (Journal of Economic Dynamics Control 22, 1235–1274, 1998), heterogeneous boundedly rational agents choose between a fixed number of expectation rules to forecast asset prices. However, agents’ heterogeneity is limited in the sense that they typically switch between a representative technical and a representative fundamental expectation rule. Here, we generalize their framework by considering that all agents follow their own time-varying technical and fundamental expectation rules. Estimating our model using the method of simulated moments reveals that it is able to explain the statistical properties of the daily and monthly behavior of the S&P500 quite well. Moreover, our analysis reveals that heterogeneity is not only a realistic model property but clearly helps to explain the intricate dynamics of financial markets.

Consumers may face demand uncertainty when choosing a service plan under three‐part tariffs, and preferences for multiple services may be inter‐dependent. To examine such a demand system, we construct a two‐stage discrete/continuous choice model for service bundles, allowing for interactive utility and preference correlations. Implementing a piecewise maximization approach to consumers’ non‐differentiable utility maximization problem, we estimate the model via simulated method of moments. We empirically illustrate the model using data from a Chinese wireless service provider. Our counterfactual analysis shows that the three‐part tariffs with interchangeable units show no significant loss of revenue, compared to existing tariffs.

This paper focuses on investigating financial asset returns' extreme risks, which are defined as the negative log-returns over a certain threshold. A simple agent-based model is constructed to explain the behavior of the market traders when extreme risks occur. We consider both the volatility clustering and the heavy tail characteristics when constructing the model. Empirical study uses the China securities index 300 daily level data and applies the method of simulated moments to estimate the model parameters. The stationarity and ergodicity tests provide evidence that the proposed model is good for estimation and prediction. The goodness-of-fit measures show that our proposed model fits the empirical data well. Our estimated model performs well in out-of-sample Value-at-Risk prediction, which contributes to the risk management.

This paper studies the allocation of heterogeneous agents to levels of educational attainment. The goal is to understand the magnitudes and sources of mismatch in this assignment, both in theory and in the data. The paper presents evidence of substantial mismatch between ability and educational attainment across 21 OECD countries, with a focus on Germany, Italy, Japan and the US. In the model, mismatch originates from: (i) taste shocks, (ii) binding borrowing constraints and (iii) noisy measures of ability in test scores. The model is estimated using a simulated method of moments approach. The main finding is that measured mismatch arises largely from noise in test scores and does not reflect borrowing constraints. Differences in tastes for education across households play a minor role in explaining mismatch. Further, the estimation allows us to decompose the college wage premium, isolating cross-country differences in selection effects from the return to education.

We propose new fluctuation tests for detecting structural breaks in factor copula models and analyse the behaviour under the null hypothesis of no change. In the model, the joint copula is given by the copula of random variables which arise from a factor model. This is particularly useful for analysing data with high dimensions. Parameters are estimated with the simulated method of moments (SMM). The discontinuity of the SMM objective function complicates the derivation of a functional limit theorem for the parameters. We analyse the behaviour of the tests in Monte Carlo simulations and a real data application. It turns out that our test is more powerful than nonparametric tests for copula constancy in high dimensions.

This paper estimates the importance of temptation (Gul and Pesendorfer, 2001) for consumption smoothing and asset accumulation in a structural life-cycle model. We use two complementary estimation strategies: ?rst, we estimate the Euler equation of this model; and second we match liquid and illiquid wealth accumulation using the Method of Simulated Moments. We ?nd that the utility cost of temptation is one-quarter of the utility bene?t of consumption. Further, we show that allowing for temptation is crucial for correctly estimating the elasticity of intertemporal substitution: estimates of the EIS are substantially higher than without temptation. Finally, our Method of Simulated Moments estimation is able to match well the life-cycle accumulation pro?les for both liquid and illiquid wealth only if temptation is part of the preference speci?cation. Our ?ndings on the importance of temptation are robust to the di?erent estimation strategies. Revised July 2020

We propose a new simulation-based estimation method, adversarial estimation, for structural models. The estimator is formulated as the solution to a minimax problem between a generator (which generates simulated observations using the structural model) and a discriminator (which classifies whether an observation is simulated). The discriminator maximizes the accuracy of its classification while the generator minimizes it. We show that, with a sufficiently rich discriminator, the adversarial estimator attains parametric efficiency under correct specification and the parametric rate under misspecification. We advocate the use of a neural network as a discriminator that can exploit adaptivity properties and attain fast rates of convergence. We apply our method to the elderly's saving decision model and show that our estimator uncovers the bequest motive as an important source of saving across the wealth distribution, not only for the rich., 56 pages, 3 tables, 11 figures

We study the estimation of dynamic economic models for which some of the state variables are observed only occasionally by the econometrician—a common problem in many fields, ranging from industrial organization to marketing to finance. If such occasional state observations are serially correlated, the likelihood function of the model becomes a potentially high-dimensional integral over a nonstandard domain. We propose a method that generalizes the recursive likelihood function integration procedure (RLI; Reich, 2018) to numerically approximate this integral, and demonstrate its statistical efficiency in several well-understood examples from finance and industrial organization. In extensive Monte Carlo studies, we compare the performance of our approach to a recently suggested method of simulated moments. In all our demonstrations, we consistently and efficiently identify all model parameters, and we find that the additional variance of our estimator when going from full to occasional state observations is small for the parameters of interest.

This paper studies the positive and normative implication of extreme shocks for monetary policy. The analysis is based on a small-scale new Keynesian model with sticky prices and wages where shocks are drawn from asymmetric generalized extreme value (GEV) distributions. A nonlinear perturbation of the model is estimated by the simulated method of moments. Under both the Taylor and Ramsey policies, the central bank responds nonlinearly and asymmetrically to shocks. The trade-off between targeting a gross inaation rate above 1 as insurance against extreme shocks and strict price stability is unambiguously decided in favour of strict price stability.

A model of how personality traits affect household time and resource allocation decisions and wages is developed and estimated. In the model, households choose between two behavioral modes: cooperative or noncooperative. Spouses receive wage offers and allocate time to supplying labor market hours and to producing a public good. Personality traits, measured by the so-called Big Five traits, can affect household bargaining weights and wage offers. Model parameters are estimated by Simulated Method of Moments using the Household Income and Labor Dynamics in Australia (HILDA) data. Personality traits are found to be important determinants of household bargaining weights and of wage offers and to have substantial implications for understanding the sources of gender wage disparities.

This paper develops a general equilibrium model to study the link between the amount of capital invested in housing assets and the term structure of interest rates. In the model, the production of housing assets is irreversible and housing assets can be used as collateral for borrowing funds. Agents’ decisions about consumption and investments in housing and non-housing assets generate a time-varying market price of risk that drives the dynamics of the term structure. The calibration to U.S. data using the simulated method of moments technique captures the dynamics of consumption, and the short- and long-term interest rates.

Because the data show that market tightness is not orthogonal to unemployment, this paper identifies the many empirical difficulties caused by adopting the free entry of vacancies assumption in the Diamond-Mortensen-Pissarides (DMP) framework. Relaxing the free entry assumption and using Simulated Method of Moments (SMM) finds the vacancy creation process is less than infinitely elastic. Because a recession-leading job separation shock then causes vacancies to fall as unemployment increases, the ad hoc restriction to zero job separation shocks (to generate Beveridge curve dynamics) becomes redundant. In contrast to standard arguments, the calibrated model finds the job separation process drives unemployment volatility over the cycle. (JEL E24, E32, J24, J63, J64)

Estimation of agent-based models is currently an intense area of research. Recent contributions have to a large extent resorted to simulation-based methods mostly using some form of simulated method of moments estimation (SMM). There is, however, an entire branch of statistical methods that should appear promising, but has to our knowledge never been applied so far to estimate agent-based models in economics and finance: Markov chain Monte Carlo methods designed for state space models or models with latent variables. This latter class of models seems particularly relevant as agent-based models typically consist of some latent and some observable variables since not all the characteristics of agents would mostly be observable. Indeed, one might often not only be interested in estimating the parameters of a model, but also to infer the time development of some latent variable. However, agent-based models when interpreted as latent variable models would be typically characterized by non-linear dynamics and non-Gaussian fluctuations and, thus, would require a computational approach to statistical inference. Here we resort to Sequential Monte Carlo (SMC) estimation based on a particle filter. This approach is used here to numerically approximate the conditional densities that enter into the likelihood function of the problem. With this approximation we simultaneously obtain parameter estimates and filtered state probabilities for the unobservable variable(s) that drive(s) the dynamics of the observable time series. In our examples, the observable series will be asset returns (or prices) while the unobservable variables will be some measure of agents’ aggregate sentiment. We apply SMC to two selected agent-based models of speculative dynamics with somewhat different flavor. The empirical application to a selection of financial data includes an explicit comparison of the goodness-of-fit of both models.

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.

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

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.

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.

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.

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.

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.

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.

We extend prior evidence that naively using intraday agent-based models that involve realistic order-matching processes for modeling continuous-time double auction markets seems to fail to be able to provide a robust link between data and many model parameters, even when these models are able to reproduce a number of well-known stylized facts of return time series. We demonstrate that while the parameters of intraday agent-based models rooted in market microstructure can be meaningfully calibrated, those exclusively related to agent behaviors and incentives remain problematic. This could simply be a failure of the calibration techniques used but we argue that the observed parameter degeneracies are most likely a consequence of the realistic matching processes employed in these models. This suggests that alternative approaches to linking data, phenomenology and market structure may be necessary and that it is conceivable that one could construct a useful model that does not directly depend on the nuances of agent behaviors, even when it is known that the real agents engage in complex behaviors., 17 pages, 8 figures

We investigate the effects of assuming a fully permanent income shock in a standard buffer-stock consumption model, when the true income process is only highly persistent. This assumption is computationally very advantageous, and thus often used, but might be problematic due to the implied misspecification. Across most parameterizations, and using the method of simulated moments, we find a relatively large estimation bias in preference parameters. For example, assuming a unit root process when the true AR(1) coefficient is 0.97, leads to an estimation bias of up to 30 percent in the constant relative risk aversion (CRRA) coefficient. If used for calibration, misspecified preferences could, for example, lead to a serious misjudgment in the value of social insurance mechanisms. Economic behavior, such as the marginal propensity to consume (MPC), of households simulated from the estimated (misspecified) model is, on the other hand, rather close to that from the correctly specified model.

Motivated by the observation that a country tends to have more official foreign reserves if its crude oil endowment is large (small) but its productivity is low (high), we develop and estimate a dynamic stochastic optimization model to show that precautionary incentives against oil price shocks can account for a substantial part of the effects of productivity and oil endowment on a country’s foreign reserves. We demonstrate how oil price shocks are absorbed by changes in foreign reserves which, in turn, leads to less variation in aggregate consumption. Along with productivity and oil endowment, we also consider as determinants of foreign reserves conventional variables such as trade-to-GDP ratio and capital openness. Overall, our results suggest that ignoring productivity and oil endowment may lead to large bias in the estimates of some conventional determinants of a country’s foreign reserves.

This paper examines the implications of investor expectations for the joint determination of earnings management and asset prices. I propose a dynamic stochastic model of financial reporting, in which a firm manager opportunistically shifts earnings in order to manipulate investor expectations, subject to a constraint on accumulated discretionary accruals. Four alternative investor expectation schemes are studied: rational expectations, constant-gain learning, regime-shifting beliefs, and accounting-information-system beliefs. The main findings are threefold. First, there does not exist a stationary rational expectations equilibrium; under the other three expectation schemes, optimal reporting decisions are characterized by threshold rules based on investor beliefs. Second, regression analysis using simulated data indicates that the relationships among earnings, discretionary accruals, and stock returns critically depend on investor expectations. Third, structural estimation uncovers considerable heterogeneity in investors' belief-formation processes, with accounting-information-system beliefs providing the best model fit for the majority of S&P 500 firms.

This paper studies the application of the simulated method of moments (SMM) to the estimation of nonlinear dynamic stochastic general equilibrium (DSGE) models. Monte-Carlo analysis is employed to examine the small-sample properties of SMM in specifications with different curvatures and departures from certainty equivalence. Results show that SMM is computationally efficient and delivers accurate estimates, even when the simulated series are relatively short. However, the small-sample distribution of the estimates is not always well approximated by the asymptotic Normal distribution. An empirical application to the macroeconomic effects of skewed disturbances shows that negatively skewed productivity shocks induce agents to accumulate additional capital and can generate asymmetric business cycles.

This paper constructs a rich model of saving for retired single people. Our framework allows for bequest motives and heterogeneity in medical expenses and life expectancies. We estimate the model using AHEAD data and the method of simulated moments. The data show that out-of-pocket medical expenses rise quickly with both age and permanent income. For many elderly people the risk of living long and requiring expensive medical care is a more important driver of old age saving than the desire to leave bequests. Social insurance programs such as Medicaid rationalize the low asset holdings of the poorest. These government programs, however, also benefit the rich because they insure them against their worst nightmares about their very old age: either not being able to afford the medical care that they need, or being left destitute by huge medical bills.

A cross validation method for selection of statistics for Approximate Bayesian Computing, and for related estimation methods such as the Method of Simulated Moments, is presented. The method uses simulated annealing to minimize the cross validation criterion over a combinatorial search space that may contain an extremely large number of elements. A first simple example, for which optimal statistics are known from theory, shows that the method is able to select these optimal statistics out of a large set of candidate statistics. A second example of selection of statistics for a stochastic volatility model illustrates the method in a more complex case. Code to replicate the results, or to use the method for other applications, is provided at http://www.runmycode.org/companion/view/1116.

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 incorporates home production into a real business cycle (RBC) model of a small open economy to provide a parsimonious explanation of the empirical pattern of international business cycles in developed economies and emerging markets. It is well known in the literature that in order for the RBC model to replicate quantita- tively plausible empirical moments of small open economies, the model needs to feature counterfactually a small income effect on labor supply. This paper provides a plausible solution to this puzzle by considering home production that introduces substitutability between market consumption and home consumption, which in turn generates a high volatility in market consumption in accordance with the data, even in the presence of a sizable income effect on labor supply. Furthermore, the model with estimated parameter values based on the simulated method of moments is able to match other empirical moments, such as the standard deviations of output, investment and the trade balance and the correlations between output and other standard macroeconomic variables. Given that home production is more prevalent in emerging markets than in developed economies, the model is also able to replicate empirical differences between emerging markets and developed economies in the volatility of market consumption and the volatility/countercyclicality of the trade balance. JEL Classification-JEL: D13, E32, F41, O16

We examine the behavior of members of an industry-wide pension fund to assess both the prevalence of defaults and their impact on retirement savings. Our empirical investigations show that preferences, demographic characteristics and labor mobility matter greatly when it comes to the extent of active plan choice (or high defaulting) and overall level of pension benefits. Using a structural dynamic life-cycle model, we then evaluate the ability of these empirically motivated factors to explain retirement savings patterns. In our model, individuals decide how much to save in a setting that combines irreversible automatic enrolment with an active decision regime. After automatic enrolment, there is an initial choice between two pension plans (defined benefit vs. defined contribution), and then members decide (possibly by default) their voluntary contributions and type of investment allocation. We estimate the model using the simulated method of moments on administrative data from a large Australian pension fund. Our results show that default settings strongly influence wealth accumulation. Such settings are also highly persistent, both over time and across decisions. Overall, our findings suggest that if defaults (particularly the irreversible ones) are not carefully designed, retirement savings can be severely affected. This article is protected by copyright. All rights reserved.

We develop an empirical search-matching model which is suitable for analyzing the wage, employment and welfare impact of regulation in a labor market with heterogeneous workers and jobs. To achieve this we develop an equilibrium model of wage determination and employment which extends the current literature on equilibrium wage determination with matching and provides a bridge between some of the most prominent macro models and microeconometric research. The model incorporates productivity shocks, long-term contracts, on-the-job search and counter-oers. Importantly, the model allows for the possibility of assortative matching between workers and jobs due to complementarities between worker and job characteristics. We use the model to estimate the potential gain from optimal regulation and we consider the potential gains and redistributive impacts from optimal unemployment insurance policy. The model is estimated on the NLSY using the method of moments.

The paper considers two rival models referring to the new macroeconomic consensus: a standard three-equation model of the New-Keynesian variety versus dynamic adjustments of a business and an inflation climate in an ‘Old-Keynesian’ tradition. Over the two subperiods of the Great Inflation and Great Moderation, both of them are estimated by the method of simulated moments. An innovative feature is here that the moments do not only include the autocovariances up to eight lags of quarterly output, inflation and the interest rate, but optionally also a measure of the raggedness of the three variables. In short, the performance of the Old-Keynesian model is very satisfactory and similar to the New-Keynesian model, or even better. In particular, the Old-Keynesian model is better suited to match the new moments without deteriorating the original second moments too much.

We develop an empirical search-matching model which is suitable for analyzing the wage, employment and welfare impact of regulation in a labor market with heterogeneous workers and jobs. To achieve this we develop an equilibrium model of wage determination and employment which extends the current literature on equilibrium wage determination with matching and provides a bridge between some of the most prominent macro models and microeconometric research. The model incorporates productivity shocks, long-term contracts, on-the-job search and counter-offers. Importantly, the model allows for the possibility of assortative matching between workers and jobs due to complementarities between worker and job characteristics. We use the model to estimate the potential gain from optimal regulation and we consider the potential gains and redistributive impacts from optimal unemployment insurance policy. Here optimal policy is defined as that which maximizes total output and home production, accounting for the various constraints that arise from search frictions. The model is estimated on the NLSY using the method of moments.

This study proposes a new way of solving standard dynamic problem based on Simulated Method of Moments (SMM) approach. It uses a newly introduced model of stock returns involving latent state variables and the regime-switching fundamentals and estimates three key preference parameters namely the Coefficient of Relative Risk Aversion, the Elasticity of Intertemporal Substitution and the subjective discount factor by suitably applying SMM and without directly using noisy consumption data. The estimates we found here seem to be relatively better than prevalent studies and very close to the true values of the parameters.

Summary This paper uses extreme value theory to study the implications of skewness risk for nominal loan contracts in a production economy. Productivity and inflation innovations are drawn from generalized extreme value distributions. The model is solved using a third-order perturbation and estimated by the simulated method of moments. Results show that the data reject the hypothesis that innovations are drawn from normal distributions and favor instead the alternative that they are drawn from asymmetric distributions. Estimates indicate that skewness risk accounts for 12% of the risk premia and reduces bond yields by approximately 55 basis points. For a bond that pays 1 dollar at maturity, the adjustment factor associated with skewness risk ranges from 0.15 cents for a 3-month bond to 2.05 cents for a 5-year bond. Copyright © 2016 John Wiley & Sons, Ltd.

This paper studies the application of the simulated method of moments (SMM) for the estimation of nonlinear dynamic stochastic general equilibrium (DSGE) models. Monte Carlo analysis is employed to examine the small-sample properties of SMM in specifications with different curvature. Results show that SMM is computationally efficient and delivers accurate estimates, even when the simulated series are relatively short. However, asymptotic standard errors tend to overstate the actual variability of the estimates and, consequently, statistical inference is conservative. A simple strategy to incorporate priors in a method of moments context is proposed. An empirical application to the macroeconomic effects of rare events indicates that negatively skewed productivity shocks induce agents to accumulate additional capital and can endogenously generate asymmetric business cycles.