CYCLE

Business cycles—the recurring fluctuations in economic activity characterised by periods of expansion and contraction—are fundamental to understanding economic dynamics. These cycles impact employment, inflation, and production, and studying their patterns helps economists forecast economic health and advise on policy responses. In recent years, macro-level DSGE models and micro-level econometric analysis have become essential in comprehending business cycles, but the there is still a gap between the two. Let's explore insights from three studies to understand business cycles better by both theoretical and empirical microfoundation for macroeconomic models.

price rigidity

Price rigidity, or the resistance of prices to adjust promptly to changes in demand or supply, plays a central role in shaping business cycles in New Keynesian DSGE models. However, are prices rigid in the first place? Zhou and Dixon (2019) explore this phenomenon in the UK using a micro-data approach, analysing both consumer and producer prices through hazard functions and survival analysis (see Fig 4 below). By studying the frequency and conditions under which prices adjust, they reveal significant insights into how prices respond to macroeconomic shifts, such as inflation or interest rate changes. This approach, integrating micro-level price dynamics into a macroeconomic model, enhances the accuracy of simulations of macro-level DSGE models and provides a robust foundation for understanding business cycles. 

Key findings include:

• Finding 1: For both consumer and producer prices, macroeconomic factors (e.g. inflation, interest rate) have a significant effect on the probability of a price change (the hazard rate). Producer price’s hazard rates are more sensitive to shifts in inflation and the interest rate than retail prices.

• Finding 2: Hazard rates have a downward sloping trend (becoming smaller as the price spell gets older), supporting the hypothesis of the “selection effect” (older prices are likely to belong to products with a lower frequency of adjustment). 

• Finding 3: There is also a 4-month cycle of spikes in the hazard rates of both consumer and producer prices, and this pattern is stronger for the goods sector and independent/local shops.

• Finding 4: When we use the microdata evidence in a simple DSGE model, we find that allowing for sectoral heterogeneity in price setting behaviour yields the best results. In particular, we find that best model is one in which the service sector (as defined by the ONS) has Taylor pricing and the goods sector is Calvo.

A DSGE Model of China

While much of the business cycle literature focuses on developed economies, Dai, Minford, and Zhou (2015) examine China using a DSGE model tailored to its unique economic structure. We adapt the model by accounting for China's relatively high degree of state intervention and its evolving market structures. This research underscores the flexibility needed when applying DSGE models to diverse economic contexts, showing that China’s business cycles can still align with core DSGE assumptions under certain modifications.

We compare the predictive power of New Keynesian and hybrid models, ultimately favouring a hybrid model that combines rigid and competitive pricing sectors (see Fig 10 below). This setup better reflects China's segmented economy, where some sectors experience price flexibility, while others remain highly regulated. Through this lens, we illuminate the adaptability of DSGE models for developing economies, emphasising that incorporating specific institutional and market characteristics into these models improves their forecasting accuracy for business cycles.

model evaluation

Accurate forecasting is vital for assessing business cycles, especially in uncertain economic climates. Minford, Xu, and Zhou (2015) tackle this challenge by comparing DSGE models with traditional VAR models using out-of-sample forecasting (OSF) tests. We highlight the strengths and limitations of DSGE models in predicting business cycles. Through rigorous statistical testing, they demonstrate that while DSGE models generally outperform unrestricted VARs in forecasting major economic variables, their success heavily depends on the model's specification accuracy.

Interestingly, we find that an incorrectly specified DSGE model may still outperform a VAR model in certain cases, suggesting the robustness of DSGE structures for forecasting (see Fig 5 below). This study provides a critical perspective on the power of forecasting tests, showing that indirect inference tests are particularly effective for ensuring model accuracy. Thus, for policymakers and economists aiming to forecast business cycles, well-specified DSGE models hold considerable promise, but only if their underlying assumptions align closely with the real-world economy they represent.

References

• Zhou, Peng; Huw Dixon. 2019. The Determinants of Price Rigidity in the UK: Analysis of the CPI and PPI Microdata and Application to Macrodata Modelling. The Manchester School, 87(5): 640-677. DOI: 10.1111/manc.12263.

• Dai, Li; Minford, Patrick; Zhou, Peng. 2015. A DSGE Model of China. Applied Economics, 47(59), 6438-60. DOI: 10.1080/00036846.2015.1071477. 

• Minford, Patrick; Xu, Yongdeng; Zhou, Peng. 2015. How Good Are out of Sample Forecasting Tests on DSGE Models? Italian Economic Journal, 1(3), 333-51. DOI: 10.1007/s40797-015-0020-9.