A Method for Taking Models to the Data

ArticleinJournal of Economic Dynamics and Control 28(6):1205-1226 · February 2004with24 Reads
DOI: 10.1016/S0165-1889(03)00080-0 · Source: RePEc
This paper develops a method for combining the power of a dynamic, stochastic, general equilibrium model with the flexibility of a vector autoregressive time-series model to obtain a hybrid that can be taken directly to the data. It estimates this hybrid model via maximum likelihood and uses the results to address a number of issues concerning the ability of a prototypical real business cycle model to explain movements in aggregate output and employment in the postwar US economy, the stability of the real business cycle model's structural parameters, and the performance of the hybrid model's out-of-sample forecasts.
    • "At this stage, it is important to emphasize the reasons behind the choice of our sample period. While the end point of the sample is purely driven by data availability at the time of writing this article, the starting point of the sample is in line with a major break point corresponding to significant changes in US monetary policy (Ireland 2004). In addition, 1980 also roughly coincides with the end of the Volcker stabilization and disinflation era. "
    [Show abstract] [Hide abstract] ABSTRACT: The objective of this article is to predict, both in sample and out of sample, the consumer price index (CPI) of the US economy based on monthly data covering the period of 1980:1–2013:12, using a variety of linear (random walk (RW), autoregressive (AR) and seasonal autoregressive integrated moving average (SARIMA)) and nonlinear (artificial neural network (ANN) and genetic programming (GP)) univariate models. Our results show that, while the SARIMA model is superior relative to other linear and nonlinear models, as it tends to produce smaller forecast errors; statistically, these forecasting gains are not significant relative to higher-order AR and nonlinear models, though simple benchmarks like the RW and AR(1) models are statistically outperformed. Overall, we show that in terms of forecasting the US CPI, accounting for nonlinearity does not necessarily provide us with any statistical gains.
    Article · Mar 2016
    • "Guerron-Quintana (2010) shows that a careful selection of the observables is important, because alternative combinations of observables can lead to very different estimated key parameters and change the economic implications of estimated models substantially. A number of papers have considered that some data series might be imprecisely measured and have therefore included measurement errors in addition to the models' structural shocks (see, e.g., Ireland, 2004; Edge et al., 2008 ). Boivin and Giannoni (2006) question whether economic variables can be properly measured by single indicators at all and introduced techniques to estimate DSGE models based on rich datasets. "
    [Show abstract] [Hide abstract] ABSTRACT: Hours per capita measures based on the private sector as usually included in the set of observables for estimating macroeconomic models are affected by low-frequent demographic trends and sectoral shifts that cannot be explained by standard models. Further, model-based output gap estimates are closely linked to the observable hours per capita series. Hence, hours per capita that are not measured in concordance with the model assumptions can distort output gap estimates. This paper shows that sectoral shifts in hours and the changing share of prime age individuals in the working-age population lead indeed to erroneous output gap dynamics. Regarding the aftermath of the global financial crisis model-based output gaps estimated using standard hours per capita series are persistently negative for the US economy. This is not caused by a permanently depressed economy, but by the retirement wave of baby boomers which lowers aggregate hours per capita. After adjusting hours for changes in the age composition to bring them in line with the model assumptions, the estimated output gap gradually closes in the years following the global financial crisis.
    Full-text · Article · Feb 2016 · Applied Economics
    • "In this section, we compare the estimation results of the two monetary policy rules given their alternative assumptions about the information utilized in making policy decisions. To estimate these area-wide and multi-country variants of the New Keynesian monetary model, we follow the dynamic stochastic general equilibrium (DSGE) approach, as suggested by Ireland (2004). This estimation strategy has become a standard procedure (e.g., Sö derlind, 1999; María-Dolores and Vá zquez, 2006; Jondeau and Sahuc, 2008a,b) for evaluating optimal monetary policy rules. "
    Full-text · Dataset · Sep 2015 · Applied Economics
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