It suffers, however, from a large drawback: the data set must be balanced, where the start and end points of the sample are the same across all observable time series. This method is easy to compute, and is consistent under quite general assumptions as long as both the cross-section and time dimension grow large. The parameters of dynamic factor models can be estimated by the method of principal components.
2015a, b, c, d), a software specialized in time series analysis that is broadly used by economists, econometricians, and statisticians. In this paper, we illustrate how to, by means of programming, set up the popular two-step estimator of Doz et al. Despite the attractiveness of dynamic factor models for macroeconomists, statistical or econometric software do not in general provide these models within standard packages. However, first official estimates of GDP are published with a significant delay, usually about 6–8 weeks after the reference quarter, which makes nowcasting very useful. Information of the economic activity is of great importance for decision makers in, for instance, governments, central banks and financial markets. 2016), forecasting (e.g., Stock and Watson 2002a, b) and nowcasting the state of the economy, that is, forecasting of the very recent past, the present, or the very near future of indicators for economic activity, such as the gross domestic product (GDP) (see, e.g., Banbura et al. 2009), business cycle analysis (e.g., Forni and Reichlin 1998 Eickmeier 2007 Ritschl et al. 2006), financial risk-return analysis (Ludvigson and Ng 2007), monetary policy analysis (e.g., Bernanke et al. Areas of economic analysis using dynamic factor models include, for example, yield curve modeling (e.g., Diebold and Li 2006 Diebold et al. They have therefore become popular among macroeconometricians see, e.g., Breitung and Eickmeier ( 2006), for an overview. In Economics, dynamic factor models are motivated by theory, which predicts that macroeconomic shocks should be pervasive and affect most variables within an economic system. The common component is assumed to be driven by a few common factors, thereby reducing the dimension of the system. The basic idea is to separate a possibly large number of observable time series into two independent and unobservable, yet estimable, components: a ‘common component’ that captures the main bulk of co-movement between the observable series, and an ‘idiosyncratic component’ that captures any remaining individual movement. Meas <- as.matrix((arima.sim(n=200, list(ar=0.6), innov = rnorm(200)*sqrt(0.Dynamic factor models are used in data-rich environments. For complex models and/or large data, I would recommend using your self-written objective function (with help of logLik method) and your favourite numerical optimization routines manually for maximum performance.
The main goal of fitSSM is just to get started with simple stuff. The default behaviour can only handle NA's in covariance matrices H and Q. If you want to use fitSSM for estimating general state space models, you need to provide your own model updating function. It seems that you are missing something in your example, as your error message comes from the function fitSSM. Moreover, I am migrating my Kalman filter codes from EViews to R, so I need to learn SSMcustom for other models that are more complicated. and Q_t this way, I still cannot estimate the \eps_t variance (H_t). Although I am able to estimate the AR(1) coef. Note that I am aware of the SSMarima function, which eases the definition of the transition equation as ARIMA models. However, it seems this cannot be done for the AR coefficients. The NA definitions for the variances works well, as documented in the package's paper.
Ss_model <- SSModel(meas ~ -1 + SSMcustom(Z = Zt, T = Tt, R = Rt,įit <- fitSSM(ss_model, inits = c(0,0.6,0), method = 'L-BFGS-B')īut it returns: "Error in is.SSModel(do.call(updatefn, args = c(list(inits, model), update_args)),: System matrices (excluding Z) contain NA or infinite values, covariance matrices contain values larger than 1e+07" So, I want to estimate the variances H_t and Q_t, but also T_t, the AR(1) coefficient. X_t = T_t * x_ + R_t * \eta_t (transition), My measurement and transition equations are: I am using 'KFAS' package from R to estimate a state-space model with the Kalman filter.