### Abstract

- Top of page
- Abstract
- 1. INTRODUCTION
- 2. TWO FRAMEWORKS TO EVALUATE THE LIKELIHOOD
- 3. AN APPLICATION
- 4. THE ESTIMATION ALGORITHM
- 5. FINDINGS
- 6. CONCLUSIONS
- Acknowledgements
- REFERENCES
- Supporting Information

This paper compares two methods for undertaking likelihood-based inference in dynamic equilibrium economies: a sequential Monte Carlo filter and the Kalman filter. The sequential Monte Carlo filter exploits the nonlinear structure of the economy and evaluates the likelihood function of the model by simulation methods. The Kalman filter estimates a linearization of the economy around the steady state. We report two main results. First, both for simulated and for real data, the sequential Monte Carlo filter delivers a substantially better fit of the model to the data as measured by the marginal likelihood. This is true even for a nearly linear case. Second, the differences in terms of point estimates, although relatively small in absolute values, have important effects on the moments of the model. We conclude that the nonlinear filter is a superior procedure for taking models to the data. Copyright © 2005 John Wiley & Sons, Ltd.

### 1. INTRODUCTION

- Top of page
- Abstract
- 1. INTRODUCTION
- 2. TWO FRAMEWORKS TO EVALUATE THE LIKELIHOOD
- 3. AN APPLICATION
- 4. THE ESTIMATION ALGORITHM
- 5. FINDINGS
- 6. CONCLUSIONS
- Acknowledgements
- REFERENCES
- Supporting Information

Recently, a growing literature has focused on the formulation and estimation of dynamic equilibrium models using a likelihood-based approach. Examples include the seminal paper of Sargent (1989) and, more recently, Bouakez *et al.* (2002), DeJong *et al.* (2000), Dib (2001), Fernández-Villaverde and Rubio-Ramírez (2003), Hall (1996), Ireland (2002), Kim (2000), Landon-Lane (1999), Lubik and Schorfheide (2003), McGrattan *et al.* (1997), Moran and Dolar (2002), Otrok (2001), Rabanal and Rubio-Ramírez (2003), Schorfheide (2000), Smets and Wouters (2003), to name just a few. Most of these papers have used the Kalman filter to estimate a linear approximation to the original model.

This paper studies the effects of estimating the nonlinear representation of a dynamic equilibrium model instead of working with its linearized version. We document how the estimation of the nonlinear solution of the economy substantially improves the empirical fitting of the model. The marginal likelihood of the economy, i.e., the probability that the model assigns to the data, increases by two orders of magnitude. This is true even for our application, the neoclassical growth model, which is nearly linear. We also report that, although the effect of linearization on point estimates is small, the impact on the moments of the model is of first-order importance. This finding is key for applied economists because quantitative models are widely judged by their ability to match data moments.

Dynamic equilibrium models have become a standard tool in quantitative economics (see Cooley, 1995 for a summary of applications). These models can be described as a likelihood function for observables, given the model's structural parameters—those characterizing preferences and technology. The advantage of thinking about models as a likelihood function is that, once we can evaluate the likelihood, inference is a direct exercise. In a classical environment we maximize this likelihood function to get point estimates and standard errors. A Bayesian researcher can use the likelihood and priors about the parameters to find the posterior. The advent of Markov chain Monte Carlo algorithms has facilitated this task. In addition, we can compare models by likelihood ratios (Vuong, 1989) or Bayes factors (Geweke, 1998) even if the models are misspecified and nonnested.

The previous discussion points out the need to evaluate the likelihood function. The task is conceptually simple, but its implementation is more cumbersome. Dynamic equilibrium economies do not have a ‘paper and pencil’ solution. This means that we can study only an approximation to them, usually generated by a computer. The lack of a closed form for the solution of the model complicates the process of finding the likelihood.

The literature shows how to write this likelihood analytically only in a few cases (see Rust, 1994 for a survey). Outside those, Sargent (1989) proposed an approach that has become popular. Sargent noticed that a standard procedure for solving dynamic models is to linearize them. This can be done either directly in the equilibrium conditions or by generating a quadratic approximation to the utility function of the agents. Both approaches imply that the optimal decision rules are linear in the states of the economy. The resulting linear system of difference equations can be solved with standard methods (see Anderson *et al.*, 1996; Uhlig, 1999 for a detailed explanation).

For estimation purposes, Sargent emphasized that the resulting system has a linear representation in a state-space form. If, in addition, we assume that the shocks exogenously hitting the economy are normal, we can use the Kalman filter to evaluate the likelihood. It has been argued (for example, Kim *et al.*, 2003) that this linear solution is likely to be accurate enough for fitting the model to the data.

However, exploiting the linear approximation to the economy can be misleading. For instance, linearization may be an inaccurate approximation if the nonlinearities of the model are important or if we are travelling far away from the steady state of the model. Also, accuracy in terms of the policy function of the model does not necessarily imply accuracy in terms of the likelihood function. Finally, the assumption of normal innovations may be a poor representation of the dynamics of the shocks in the data.

An alternative to linearization is to work instead with the nonlinear representation of the model and to apply a nonlinear filter to evaluate the likelihood. This is possible thanks to the recent development of sequential Monte Carlo methods (see the seminal paper of Gordon *et al.*, 1993 and the review of the literature by Doucet *et al.*, 2000 for extensive references). Fernández-Villaverde and Rubio-Ramírez (2004) build on this literature to show how a sequential Monte Carlo filter delivers a consistent and efficient evaluation of the likelihood function of a nonlinear and/or nonnormal dynamic equilibrium model.

The presence of the two alternative filters begets the following question: how different are the answers provided by each of them? We study this question with the canonical stochastic neoclassical growth model with leisure choice. We estimate the model using both simulated and real data and compare the results obtained with the sequential Monte Carlo filter and the Kalman filter.

Why do we choose the neoclassical growth model for our comparison? First, this model is the workhorse of modern macroeconomics. Since any lesson learned in this paper is conditional on our particular model, we want to select an economy that is the foundation of numerous applications. Second, even if the model is nearly linear for the standard calibration, the answers provided by each of the filters are nevertheless quite different. In this way, we make our point that linearization has a nontrivial impact on estimation in the simplest possible environment.

Our main finding is that, while linearization may have a second-order effect on the accuracy of the policy function given some parameter values, it has a first-order impact on the model's likelihood function. Both for simulated and for real data, the sequential Monte Carlo filter generates an overwhelmingly better fit of the model as measured by the marginal likelihood. This is true even if most differences in the point estimates of the parameters generated by each of the two filters are small.

Why is the marginal likelihood so much higher for the sequential Monte Carlo? Because this filter delivers point estimates for the parameters that imply model's moments closer to the moments of the data. This result is crucial in applied work because models are often judged by their ability to match empirical moments.

Our finding is not the first in the literature that suggests accounting for nonlinearities substantially improves the measures of fit of a model. For example, Sims and Zha (2002) report that the ability of a structural VAR to account for the dynamics of the output and monetary policy increases by several orders of magnitude when they allow the structural equation variances to change over time. A similar finding is emphasized by the literature on regime switching (Kim and Nelson, 1999) and by the literature on the asymmetries of the business cycle (Kim and Piger, 2002).

The rest of the paper is organized as follows. In Section 2 we discuss the two alternatives to evaluate the likelihood of a dynamic equilibrium economy. Section 3 presents our application. Section 4 describes the estimation algorithm, and Section 5 reports our main findings. Section 6 concludes.

### 4. THE ESTIMATION ALGORITHM

- Top of page
- Abstract
- 1. INTRODUCTION
- 2. TWO FRAMEWORKS TO EVALUATE THE LIKELIHOOD
- 3. AN APPLICATION
- 4. THE ESTIMATION ALGORITHM
- 5. FINDINGS
- 6. CONCLUSIONS
- Acknowledgements
- REFERENCES
- Supporting Information

Now we explain how to incorporate the likelihood functions in a Bayesian estimation algorithm. In the Bayesian approach, the main inference tool is the parameters' posterior distribution given the data, π(γ|*y*^{T}). The posterior density is proportional to the likelihood times the prior. Therefore, we need to specify priors on the parameters, π(γ), and to evaluate the likelihood function.

We specify our priors in Section 5.1, and the likelihood function is evaluated either by (6) or by (7), depending on how we solve the model. Since none of these posteriors have a closed-form, we use a Metropolis–Hasting algorithm to draw from them. We call π_{SMC}(γ|*y*^{T}) the posterior implied by the sequential Monte Carlo filter and π_{KF}(γ|*y*^{T}) the posterior derived from the Kalman filter. To simplify the notation, we let *f*_{SMC}(·, ·;γ_{i}) and *g*_{SMC}(·, ·;γ_{i}) be defined by (1) and (2), and *f*_{KF}(·, ·;γ_{i}) and *g*_{KS}(·, ·;γ_{i}) by (3) and (4).

The algorithm to draw a chain from is as follows:

**Step 0, Initialization:**Set*i*0 and initial γ_{i}. Compute functions*f*_{j}(·, ·;γ_{i}) and*g*_{j}(·, ·;γ_{i}). Evaluate π(γ_{i}) and*p*_{j}(*y*^{T};γ_{i}) using(6)or(7). Set*i**i* + 1.

**Step 1, Proposal draw:**Get a proposal draw, where*v*_{i}∼*N*(0, Ψ).

**Step 3, Evaluating the proposal:**Evaluateand tt using either (6) or (7).

We used standard methods to check the convergence of the chain generated by the Metropolis–Hasting algorithm (see Mengersen *et al.*, 1999). Also, we selected the variance of the innovations in the proposals for the parameters to achieve an acceptance rate of proposals of around 40%.

We concentrate in this paper on Bayesian inference. However, we could also perform classical inference, maximizing the likelihood function obtained in the previous section, and building an asymptotic variance–covariance matrix using standard numerical methods. Also, the value of the likelihood function at its maximum would be useful to compute likelihood ratios for model comparison purposes.

### 5. FINDINGS

- Top of page
- Abstract
- 1. INTRODUCTION
- 2. TWO FRAMEWORKS TO EVALUATE THE LIKELIHOOD
- 3. AN APPLICATION
- 4. THE ESTIMATION ALGORITHM
- 5. FINDINGS
- 6. CONCLUSIONS
- Acknowledgements
- REFERENCES
- Supporting Information

We undertake two main exercises. In the first one, we simulate ‘artificial’ data using the nonlinear solution of the model for a particular choice of values of γ*. Then, we define some priors over γ, and draw from its posterior distribution implied by both *p*_{SMC}(*y*^{T};γ) and *p*_{KF}(*y*^{T};γ). Finally, we compute the marginal likelihood of the ‘artificial’ data implied by each likelihood approximation. This exercise answers the following two questions: (1) How accurate is the estimation of the ‘true’ parameter values, γ*, implied by each filter? (2) How big is the improvement delivered by the sequential Monte Carlo filter over the Kalman filter? From the posterior means, we address the first question. From the marginal likelihoods, we respond to the second.

Since the difference between the policy functions implied by the finite elements and the linear method depends greatly on γ*, we perform the described exercise for two different values of γ*, one with low risk aversion and low variance, , when both policies are close, and another with high risk aversion and high variance, , when the policies are farther away.

Our second exercise uses US data to estimate the model with the sequential Monte Carlo and the Kalman filters. This exercise answers the following question: Is the Sequential Monte Carlo providing a better explanation of the real data?

We divide our exposition into three parts. First, we specify the priors for the parameters. Second, we present results from the ‘artificial’ data experiment. Finally, we present the results with US data.

#### 5.1. The Priors

We postulate flat priors for all 10 parameters, subject to some boundary constraints to make the priors proper. This choice is motivated by two considerations. First, since we are going to estimate our model using ‘artificial’ data generated at some value γ*, we do not want to bias the results in favour of any alternative by our choice of priors. Second, with a flat prior, the posterior is proportional to the likelihood function (except for the very small issue of the bounded support of the priors). As a consequence, our experiment can be interpreted as a classical exercise in which the mode of the likelihood function is the maximum likelihood estimate. A researcher who prefers more informative priors can always reweight the likelihood to accommodate her priors (see Geweke, 1998).

The parameter governing labour supply, θ, follows a uniform distribution between 0 and 1. That constraint imposes only a positive marginal utility of leisure. The persistence of the technology shock, ρ, also follows a uniform distribution between 0 and 1. The parameter τ follows a uniform distribution between 0 and 100. That choice rules out only risk-loving behaviour and risk aversions that will predict differences in interest rates several orders of magnitude higher than the observed ones. The prior for the technology parameter, α, is uniform between 0 and 1. The prior on the depreciation rate ranges between 0 and 0.05, covering all national accounts estimates of quarterly depreciation. The discount factor, β, is allowed to vary between 0.75 and 1, implying steady-state annual interest rates between 0 and 316%. The standard deviation of the innovation of productivity, σ_{ϵ}, follows a uniform distribution between 0 and 0.1, a bound 15 times higher than the usual estimates. We also pick this prior for the three standard deviations of the measurement errors. Table I summarizes our discussion.

Table I. Priors for the parameters of the modelParameter | Distribution | Hyperparameters |
---|

θ | Uniform | 0, 1 |

ρ | Uniform | 0, 1 |

τ | Uniform | 0, 100 |

α | Uniform | 0, 1 |

δ | Uniform | 0, 0.05 |

β | Uniform | 0.75, 1 |

σ_{ϵ} | Uniform | 0, 0.1 |

σ_{1} | Uniform | 0, 0.1 |

σ_{2} | Uniform | 0, 0.1 |

σ_{3} | Uniform | 0, 0.1 |

#### 5.2. Results with ‘Artificial’ Data

We simulate observations from the model and use them as data for the estimation. We generate data from two different calibrations.

First, to make our experiment as realistic as possible, we present a benchmark calibration of the model. The discount factor β = 0.9896 matches an annual interest rate of 4.27%. The risk aversion τ = 2 is a common choice in the literature. θ = 0.357 matches the micro evidence of labour supply. We reproduce the labour share of national income with α = 0.4. The depreciation rate δ = 0.02 fixes the investment/output ratio and ρ = 0.95 and σ = 0.007 match the historical properties of the Solow residual of the US economy. With respect to the standard deviations of the measurement errors, we set them equal to 0.01% of the steady-state value of output, 0.35% of the steady-state value of hours and 0.2% of the steady-state value of investment based on our priors regarding the relative importance of measurement errors in the National Income and Product Accounts. We summarize the chosen values in Table II.

Table II. Calibrated parametersParameter | θ | ρ | τ | α | δ | β | σ_{ϵ} | σ_{1} | σ_{2} | σ_{3} |
---|

Value | 0.357 | 0.95 | 2.0 | 0.4 | 0.02 | 0.99 | 0.007 | 1.58 × 10^{−4} | 0.0011 | 8.66 × 10^{−4} |

The second calibration, which we will call extreme, maintains the same parameters except that it increases τ to 50 (implying a relative risk aversion of 24.5) and σ_{ϵ} to 0.035. This high risk aversion and variance introduce a strong nonlinearity to the economy. This particular choice of parameters allows us to check the differences between the sequential Monte Carlo filter and the Kalman filter in a highly nonlinear world while maintaining a familiar framework. We justify our choice, thus, not basing it on empirical considerations but on its usefulness as a ‘test’ case.

After generating a sample of size 100 for each of the two calibrations,2 we apply our priors and our likelihood evaluation algorithms. For the sequential Monte Carlo filter, we use 60,000 particles to get 50,000 draws from the posterior distribution. Since we do not suffer from an attrition problem, we do not replenish the swarm. See Fernández-Villaverde and Rubio-Ramírez (2004) for further details of this issue and of convergence. For the Kalman filter, we also get 50,000 draws. In both cases, we have a long burn-in period.

In Figure 1 we plot the likelihood function in logs of the model, given our simulated data for the sequential Monte Carlo filter (continuous line) and the Kalman filter (discontinuous line). We draw in each panel the likelihood function for an interval around the calibrated value of the structural parameter, keeping all the other parameters fixed at their calibrated values. We can think of each panel then as a transversal cut of the likelihood function. To facilitate the comparison, we show the ‘true’ value for the parameter corresponding to the direction being plotted with a vertical line, and we do not draw values lower than −20,000.

Figure 1 reveals two points. First, both likelihoods have the same shape and are centred on the ‘true’ value of the parameter, although the Kalman filter delivers a slight bias for four parameters (α, δ, β and θ). Note that, since we are assuming flat priors, none of the curvature of the likelihoods is coming from the prior. Second, there is a difference in level between the likelihood generated by the sequential Monte Carlo filter and the one delivered by the Kalman filter. This is a first proof that the nonlinear model fits the data better even for this nearly linear economy.

Table III conveys similar information: the point estimates are approximately equal regardless of the filter. However, the sequential Monte Carlo delivers estimates that are better in the sense of being closer to their pseudotrue value.3

Table III. Nonlinear versus linear posterior distributions benchmark caseNonlinear (SMC filter) | Linear (Kalman filter) |
---|

Parameter | Mean | s.d. | Parameter | Mean | s.d. |
---|

θ | 0.357 | 0.10 × 10^{−3} | θ | 0.374 | 0.06 × 10^{−3} |

ρ | 0.950 | 0.29 × 10^{−3} | ρ | 0.914 | 0.24 × 10^{−3} |

τ | 2.000 | 0.92 × 10^{−3} | τ | 3.536 | 1.17 × 10^{−3} |

α | 0.400 | 0.10 × 10^{−3} | α | 0.443 | 0.08 × 10^{−3} |

δ | 0.020 | 0.02 × 10^{−3} | δ | 0.030 | 0.02 × 10^{−3} |

β | 0.990 | 0.02 × 10^{−3} | β | 0.978 | 0.03 × 10^{−3} |

σ_{ϵ} | 0.007 | 0.04 × 10^{−4} | σ_{ϵ} | 0.011 | 0.36 × 10^{−4} |

σ_{1} | 1.58 × 10^{−4} | 0.15 × 10^{−6} | σ_{1} | 1.86 × 10^{−4} | 4.64 × 10^{−7} |

σ_{2} | 1.12 × 10^{−3} | 0.68 × 10^{−6} | σ_{2} | 5.55 × 10^{−4} | 2.01 × 10^{−6} |

σ_{3} | 5.64 × 10^{−4} | 0.81 × 10^{−6} | σ_{3} | 2.42 × 10^{−3} | 1.75 × 10^{−6} |

Table IV reports the logmarginal likelihood differences between the nonlinear and the linear case. We compute the marginal likelihood with Geweke's (1998) harmonic mean proposal. Consequently, we need to specify a bound on the support of the weight density. To ensure robustness, we report the distances for a range of values of the truncation value *p* from 0.1 to 0.9. All the values convey the same message: the nonlinear solution method fits the data two orders of magnitude better than the linear approximation. A good way to read this number is to use Jeffreys' (1961) rule: if one hypothesis is more than 100 times more likely than the other, the evidence is decisive in its favour. This translates into differences in logmarginal likelihoods of 4.6 or higher. Our value of 73.6 is, then, well beyond decisiveness in favour of nonlinear filtering.

Table IV. Logmarginal likelihood difference benchmark case*p* | Nonlinear vs linear |
---|

0.1 | 73.631 |

0.5 | 73.627 |

0.9 | 73.603 |

Fernández-Villaverde *et al.* (2004) provide a theoretical explanation for this finding. They show how the bound on the error in the likelihood induced by the linear approximation of the policy function gets compounded with the size of the sample. The intuition is as follows. Small errors in the policy function accumulate at the same rate as the sample size grows. This means that, as the sample size goes to infinity, a linear approximation will deliver an approximation of the likelihood that will fail to converge.

We now move to study the results for the extreme calibration. Figure 2 is equivalent to Figure 1 for the extreme case. First, note how the likelihood generated by the sequential Monte Carlo filter is again centred on the ‘true’ value of the parameter. In comparison, the likelihood generated by the Kalman filter is not. These differences will have an important impact on the marginal likelihood. Table V recasts the same information in terms of means and standard deviations of the posteriors. As in the benchmark case, the sequential Monte Carlo delivers better estimates of the parameters of the model.

Table V. Nonlinear versus linear posterior distributions extreme caseNonlinear (SMC filter) | Linear (Kalman filter) |
---|

Parameter | Mean | s.d. | Parameter | Mean | s.d. |
---|

θ | 0.357 | 0.08 × 10^{−3} | θ | 0.337 | 0.06 × 10^{−3} |

ρ | 0.950 | 0.17 × 10^{−3} | ρ | 0.894 | 0.19 × 10^{−3} |

τ | 50.000 | 0.24 × 10^{−1} | τ | 67.70 | 0.10 × 10^{−1} |

α | 0.400 | 0.05 × 10^{−3} | α | 0.346 | 0.04 × 10^{−3} |

δ | 0.020 | 0.05 × 10^{−4} | δ | 0.010 | 0.02 × 10^{−4} |

β | 0.990 | 0.08 × 10^{−4} | β | 0.996 | 0.08 × 10^{−4} |

σ_{ϵ} | 3.50 × 10^{−2} | 0.03 × 10^{−4} | σ_{ϵ} | 3.61 × 10^{−2} | 0.09 × 10^{−4} |

σ_{1} | 1.58 × 10^{−4} | 0.06 × 10^{−6} | σ_{1} | 1.72 × 10^{−4} | 0.05 × 10^{−6} |

σ_{2} | 1.12 × 10^{−3} | 0.05 × 10^{−5} | σ_{2} | 9.08 × 10^{−4} | 0.03 × 10^{−5} |

σ_{3} | 8.66 × 10^{−4} | 0.02 × 10^{−5} | σ_{3} | 2.64 × 10^{−3} | 0.04 × 10^{−5} |

Table VI reports the logmarginal likelihood differences between the nonlinear and the linear case for the extreme calibration for different *p*'s. Again, we can see how the evidence in favour of the nonlinear filter is overwhelming.

Table VI. Logmarginal likelihood difference extreme case*p* | Nonlinear vs linear |
---|

0.1 | 117.608 |

0.5 | 117.592 |

0.9 | 117.564 |

As a conclusion, our exercise shows how even for a nearly linear case such as the neoclassical growth model, an estimation that respects the nonlinear structure of the economy improves substantially the ability of the model to fit the data. This may indicate that we greatly handicap dynamic equilibrium economies when we linearize them before taking them to the data and that some empirical rejections of these models may be due to the biases introduced by linearization.

Our results do not imply, however, that we should completely abandon linear methods. We have also shown that their accuracy for point estimates is acceptable. For some exercises where only point estimates are required, the extra computational cost of the sequential Monte Carlo filter may not compensate for the reduction in bias. Practitioners should weight the advantages and disadvantages of each procedure in their particular application.

#### 5.3. Results with Real Data

Now we estimate the neoclassical growth model with US quarterly data. We use real output per capita, average hours worked and real gross fixed investment per capita from 1964:Q1 to 2003:Q1. We first remove a trend from the data using an H-P filter. In this way, we do not need to model explicitly the presence of a trend and its possible changes.

Table VII presents the results from the posterior distributions from 100,000 draws for each filter, again after a long burn-in period. The discount factor, β, is estimated to be 0.997 with the nonlinear filter and 0.973 with the Kalman filter. This is an important difference when using quarterly data. The linear model compensates for the lack of curvature induced by its certainty equivalence with more impatience. The parameter controlling the elasticity of substitution, τ, is estimated by the nonlinear filter to be 1.717 and by the Kalman filter to be 1.965. The parameter α is close to the canonical value of one-third in the case of the sequential Monte Carlo, and higher (0.412) in the case of the Kalman filter. Finally, we note how the standard deviation of the parameters is estimated to be much higher when we use the nonlinear filter than when we employ the Kalman filter, indicating that the nonlinear likelihood is more dispersed.

Table VII. Nonlinear versus linear posterior distributions real dataNonlinear (SMC filter) | Linear (Kalman filter) |
---|

Parameter | Mean | s.d. | Parameter | Mean | s.d. |
---|

θ | 0.390 | 0.11 × 10^{−2} | θ | 0.423 | 0.19 × 10^{−3} |

ρ | 0.978 | 0.52 × 10^{−2} | ρ | 0.941 | 0.27 × 10^{−3} |

τ | 1.717 | 0.12 × 10^{−1} | τ | 1.965 | 0.12 × 10^{−2} |

α | 0.324 | 0.71 × 10^{−3} | α | 0.412 | 0.37 × 10^{−3} |

δ | 0.006 | 0.36 × 10^{−4} | δ | 0.019 | 0.79 × 10^{−5} |

β | 0.997 | 0.92 × 10^{−4} | β | 0.973 | 0.43 × 10^{−5} |

σ_{ϵ} | 0.020 | 0.11 × 10^{−3} | σ_{ϵ} | 0.009 | 0.43 × 10^{−5} |

σ_{1} | 0.45 × 10^{−1} | 0.42 × 10^{−3} | σ_{1} | 0.11 × 10^{−3} | 0.18 × 10^{−6} |

σ_{2} | 0.15 × 10^{−1} | 0.25 × 10^{−3} | σ_{2} | 0.83 × 10^{−2} | 0.34 × 10^{−5} |

σ_{3} | 0.38 × 10^{−1} | 0.39 × 10^{−3} | σ_{3} | 0.26 × 10^{−1} | 0.18 × 10^{−4} |

It is difficult to assess whether the differences in point estimates documented in Table VII are big or small. A possible answer is based on the impact of the different estimates on the moments generated by the model. Macroeconomists often use these moments to evaluate the model's ability to account for the data. Table VIII presents the moments of the real data and reports the moments that the stochastic neoclassical growth model generates by simulation when we calibrated it at the mean of the posterior distribution of the parameters given by each of the two filters.

Table VIII. Nonlinear versus linear moments real data | Real data | Nonlinear (SMC filter) | Linear (Kalman filter) |
---|

| Mean | s.d. | Mean | s.d. | Mean | s.d. |
---|

*output* | 1.95 | 0.073 | 1.91 | 0.129 | 1.61 | 0.068 |

*hours* | 0.36 | 0.014 | 0.36 | 0.023 | 0.34 | 0.004 |

*inv* | 0.42 | 0.066 | 0.44 | 0.073 | 0.28 | 0.044 |

We highlight two observations from Table VIII. First, the nonlinear model matches the data much better than the linearized one. This difference is significant because the moments are nearly identical if we simulate the model using the linear or the nonlinear solution method with the same set of parameter values. The differences come thus from the point estimates delivered by each procedure. The nonlinear estimation nails down the mean of each of the three observables and does a fairly good job with the standard deviations. Second, the estimation by the nonlinear filter implies a higher output, investment and hours worked than the estimation by the linear filter.

The main reason for these two differences is the higher β estimated by the sequential Monte Carlo. The lower discount factor induces a higher accumulation of capital and, consequently, a higher output, investment and hours worked. The differences for the standard deviation of the economy are also important. The nonlinear economy is also more volatile than the linearized model in terms of the standard deviation of output and hours.

Table IX reports the logmarginal likelihood differences between the nonlinear and the linear case. As in the previous cases, the real data strongly support the nonlinear version of the economy with differences in log terms of around 93. The differences in moments discussed above are one of the main driving forces behind the finding. A second force is that the likelihood function generated by the sequential Monte Carlo is less concentrated than that coming from the Kalman filter.4

Table IX. Logmarginal likelihood difference real data*p* | Nonlinear vs linear |
---|

0.1 | 93.65 |

0.5 | 93.55 |

0.9 | 93.55 |