Forecasting and Policy Making (Paper) - Center for Financial Studies

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(iv) large-scale structural macro-econometric models. We will show how to employ structural models for identifying the sources of predicted dynamics of key macroeconomic variables. They allow for an instructive interpretation of forecasts. Since central banks often condition forecasts on exogenous paths of future monetary and fiscal policy, we will discuss and implement several different assumptions used at central banks and illustrate two alternative methods for computing such forecasts. Furthermore, we will show how these methods can be used to include judgement in model-based forecasts and how to incorporate a particular nonlinearity that is great importance in current forecasts, that is the zero-lower-bound on nominal interest rates. We use the term ”structural” to mean that model equations and parameters have an economic interpretation that imposes particular parametric restrictions on the forecast relative to the non-structural VAR models of the preceding subsection. Furthermore, the term ”New Keynesian” refers to models that include explicit nominal rigidities, such as for example wage and price contracts, rational expectations of market participants and monetary policy rules. In particular, the latter two features distinguish these models from more traditional Keynesian-style models with purely backward-looking dynamics. Within the class of New-Keynesian models we find it helpful to distinguish between a first generation of the type of models with rational expectations and nominal rigidities published from the late 1970s to the early 1990s. In these models behavioral equations are often inspired by the decision rules of optimizing agents but they are not exclusively derived from optimization problems of households and firms. By contrast, the second generation of New Keynesian models published from the late 1990s onwards explicitly implements all restrictions resulting from optimizing behavior of households and firms subject to concretely specified constraints. They are typically referred to as New Keynesian DSGE models. 27 A well known advantage of structural models with rational expectations and microeconomic foun- dations is that they enable modelers and forecasters to account for the Lucas critique, which empha- sizes that market participants’ expectations and decision-making changes in response to systematic changes in policy making. Thus, such models are particularly useful for counterfactual policy sim- ulations and analysis following policy regime changes such as the change of policy strategy or the formation of a monetary union. 5.1 Non-structural time series models Nowcasting Given that macroeconomic data only become available with some time lag, the first important step in any forecasting exercise is to estimate the currently prevailing state of the economy. This estimate is commonly referred to as the nowcast. To give an example, the first releasse of U.S. GDP is published late in the first month following the quarter it refers to and updated releases are published subsequently. Rather than relying only on GDP numbers from earlier quarters, central banks typically 27 For detailed descriptions of models of these different types see for example Taylor and Wieland (2011) and Schmidt and Wieland (2012, in preparation). 32
use other available information that is related to economic activity. For example, there exist monthly data series on components such as industrial production. Furthermore, surveys and financial variables such as stock prices contain information on perceptions regarding business conditions and economic growth. Leading indicator variables have long been used to obtain a picture of current economic activity (see Stock and Watson, 2002b; Forni et al., 2000, for modern approaches). Going beyond the observation of such indicators, policy institutions typically construct a direct estimate of current GDP. This GDP nowcast is then used as a starting point for GDP forecasts obtained by a variety of methods. Typically, nowcasts are computed with non-structural time series model which make it easier to include high-frequency variables that are not normally included in structural macroeconomic models. The prevalent nowcasting methods are essentially linear projections. For many years, GDP nowcasts have been computed using bridge equations, that is simple regressions of GDP or its components on different monthly data series. Kitchen and Monaco (2003) demonstrate how bridge equations are used by the U.S. treasury staff to forecast GDP. Baffigi et al. (2004) use this method to estimate euro area GDP. This approach can only include a limited number of time series. For example the bridge equation models considered by Baffigi et al. (2004) are regressions of quarterly GDP on its lagged values and current and lagged values of less than ten other variables like industrial production, retail sales and consumer confidence. Kitchen and Monaco (2003) run a large number of bridge equations including different indicators and average the resulting forecasts by R 2 weights obtained from the fit of the different bridge equations. Similarly, Stock and Watson (1999) use different methods including a simple mean to combine forecasts of inflation from different regressions. Complications arise because nowcasting methods need to handle mixed frequency data, unbalanced datasets due to unsynchronized publication dates and missing data. Furthermore, there is a large number of higher frequency data series that are potentially informative for current GDP. Evans (2005) estimates GDP on a daily basis by using the information of daily data releases for many macroeconomic indicator series. To deal with unsynchronized publications with varying publications lags (the so called jagged/ragged edge problem) and different frequencies of the time series considered he uses a state-space model and evaluates it using the Kalman Filter. More recently nowcasts have been computed using dynamic factor models. Factor models can deal with large datasets (see e.g. Stock and Watson, 2002a,b) and have the advantage of being able to extract information signals from inherently noisy data. Giannone et al. (2008) show how to extract the information content from an unbalanced dataset with many time series of differing frequencies. The nowcast is computed by a simple projection of quarterly GDP on the common factors from a large panel of time series with higher frequency data. Nowcasts from these models can be updated with new data releases on a daily basis. Their method has been implemented at the Board of Governors of the Federal Reserve System in a project that started in 2003 and at the European Cental Bank in 2008. The method is also used by national central banks in the euro area, and by the central banks of New Zealand and Norway (see Bańbura et al., 2010, for references to all these different implementations). The Federal Reserve uses a dynamic factor model that exploits information from of about 200 different time series. They estimate 45 specifications that differ by the number of static and dynamic factors. 33
Page 1 and 2: Forecasting and Policy Making ∗ V
Page 3 and 4: 1 Introduction Gouverner c’est pr
Page 5 and 6: to return to target within an accep
Page 7 and 8: In contrast to the CBO forecast the
Page 9 and 10: forecast and for the same projectio
Page 11 and 12: national central banks also publish
Page 13 and 14: In practice, central banks have not
Page 15 and 16: Item (v) on Tinbergen’s list requ
Page 17 and 18: ule and can also be expressed as a
Page 19 and 20: prescription within one quarter. Si
Page 21 and 22: By now there are several studies th
Page 23 and 24: of interest-rate-smoothing (ρ > 0.
Page 25 and 26: These findings provide evidence tha
Page 27 and 28: With the funds rate near zero, the
Page 29 and 30: These results suggest that rules wi
Page 31: clear that all estimates for the eu
Page 35 and 36: VARs. The advantage of non-structur
Page 37 and 38: The notation of equations, variable
Page 39 and 40: ealizations. This state of the econ
Page 41 and 42: librium real interest rate, ηt a v
Page 43 and 44: 2 0 −2 −4 −6 x 104 1.4 1.2 1
Page 45 and 46: 1 0 −1 −2 2 1 0 −1 4 2 0 −2
Page 47 and 48: less flexible than, for example, mo
Page 49 and 50: 4 2 0 −2 Quarterly annualized inf
Page 51 and 52: Figure 13: The figure shows a histo
Page 53 and 54: at the Fed since 1996. Its characte
Page 55 and 56: Advantages and disadvantages of dif
Page 57 and 58: Adolfson et al. (2007a) and Maih (2
Page 59 and 60: ule, the dashed line the constant i
Page 61 and 62: 8 6 4 2 0 −2 −4 −6 −8 data
Page 63 and 64: 1 −1 1 −1 1 −1 1 −1 1 −1
Page 65 and 66: Figure 17 plots the p-values of the
Page 67 and 68: itself might be the crucial factor
Page 69 and 70: Figure 21 indicates that the recess
Page 71 and 72: Only for the 2008/2009 recession th
Page 73 and 74: We start by determining the optimal
Page 75 and 76: β β β 8 6 4 2 h π =0 0 0 0.5 1
Page 77 and 78: Table 11: Characteristics and Perfo
Page 79 and 80: Table 12: Characteristics and Perfo
Page 81 and 82: are even less robust than the outco
Page 83 and 84:
Bernanke, B. S., 2009. Federal Rese
Page 85 and 86:
Edge, R. M., Kiley, M. T., Laforte,
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Hargreaves, D., 1999. SDS-FPS: A sm
Page 89 and 90:
Rotemberg, J. J., Woodford, M., 199
Page 91 and 92:
Wieland, V., 2010. Quantitative eas
Page 93 and 94:
new capital becomes effective with
Page 95:
6 4 2 0 −2 −2 7 6 5 4 3 2 Forec
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