MONTHLY AND QUARTERLY GDP ESTIMATES FOR INTERWAR BRITAIN James - PowerPoint PPT Presentation

MONTHLY AND QUARTERLY GDP ESTIMATES FOR INTERWAR BRITAIN James Mitchell (NIESR) Solomos Solomou (Cambridge) National Institute of Economic and Martin Weale (BOE and NIESR) Social Research

Monthly GDP estimates • Derive monthly series of UK GDP for the inter-war period from a set of monthly indicators that were constructed at the time • Illustrate how the new data can contribute to our understanding of the economic history of the UK in the 1930s • Use the series to draw comparisons between recession profiles in the 1930s and the post- war period

Monthly indicators of economic activity • Work by Burns and Mitchell (1946) anticipated by The Economist • The Economist collected monthly indicators during the period 1924-1938 and published an aggregate indicator computed as the geometric mean of the indicators • These data form the basis of our monthly GDP indicator, together with data for quarterly industrial production

Need for monthly GDP estimates • GDP is the most appropriate indicator of overall economic activity • Preferable to relate these new (or lost) The Economist data directly to GDP • Avoids subjective inference based on simply eye- balling these data - or averaging them • Unlike Rhodes (1937, JRSS) and Stock & Watson (2002, JBES) we reduce these monthly data to an estimator of monthly GDP itself • This involves exploiting available annual GDP data for the interwar period (1924-1938) • Monthly GDP data facilitate historical analysis

Monthly indicators of economic activity • Averaging the indicator variables is not the only method of aggregation • Rhodes (1937, JRSS) – anticipating Stock & Watson (2002, JBES) - suggested instead use of the first principal component of the series • But it is not necessarily the aggregate that is most closely correlated with GDP growth • Some means is needed of selecting from the indicators a composite which is closely linked to GDP rather than one which is simply a summary of the indicator data set • And this must be consistent with the annual GDP data • We derive monthly GDP series exploiting these new monthly data from The Economist and quarterly industrial production

Interpolating GDP using monthly indicators • Chow-Lin (1971, ReStat). Dynamics ad hoc • Dynamic generalisations – accommodate dynamics flexibly via AR polynomial • Model data in logarithms – Implies a nonlinear temporal aggregation constraint • Regression based methods – Mitchell et al. (2005, EJ) • State-space methods – Harvey & Pierse (1984, JASA) – Multivariate extensions: Moauro & Savio (2005, EcsJ) • Dynamic factor model. Useful when N gets big(ish)

Interpolating monthly GDP using a dynamic factor model • Based on Proietti and Moauro (2006, JRSSC) • Work in log-levels rather than growth rates as in Stock and Watson (1991) – Mariano & Murasawa (2003, JAE) - and can handle mixed frequency data • Assumes a latent factor, the business cycle , drives (co)variation in the observed mixed-frequency data – Consistent with Burns and Mitchell’s (1946) characterisation of the business cycle as common movements in different economic indicators – Stone (1947, JRSS) and Stock & Watson (1991) • Provides exact solution to nonlinearity of aggregation constraint

Interpolating monthly GDP using a dynamic factor model y • Model monthly N -vector in levels rather than t m , first differences ⎫ y θ μ * = µ + = = , t 1,..., ; T m 1,...,12 ⎪ t m , t m , t m , ⎪ 2 ⎬ φ Δ µ = η η ∼ σ ( ) L , NID (0, ) η t m , t m , t m , ⎪ D μ β η η 0 Σ * * * Δ = + ∼ ( ) L , NID ( , ) ⎪ ⎭ * t m , t m , t m , η µ • The common trend and the idiosyncratic t m , μ * components are modelled as difference t m , stationary processes Δ β • ( i=1,…,N ) is composed of drift , an y it m , i − 1 * η individual AR plus the business cycle d L ( ) i it m , − 1 φ η ( ) L (common) AR component t m , – This flexibility means the model fits the data

Estimation with constraints • Identification • Estimation by ML exploiting Kalman filter • Partition data vector into y y = ( ' , y , y )' t m , 1 , t m 2 , t m 3 , t m • where y 1t,m represents the observed monthly indicators from The Economist and y 2t,m and y 3t,m are monthly IP and GDP which are latent and the objects we wish to estimate • We observe annual GDP data y 3t such that = ∑ 12 y y 3 t 3 , t m = m 1 • Similarly define a constraint given quarterly IP data – Impose constraints by defining a cumulator variable

The Economist ’s monthly data

Monthly GDP 1924 - 1938 • We present data at both market prices and factor cost, but focus attention on the market price data • Model subjected to diagnostic tests • We could not accept the restriction that the idiosyncratic components shared a common AR coefficient • The factor loadings are all positive and are mostly significantly different from zero at 5% – T he Economist ’s series are coincident indicators of economic activity – Employment, cotton, IP and GDP most sensitive to the business cycle

£mn 1938 prices Factor Cost Monthly GDP at 1938 Market Prices and 250 300 350 400 450 500 1924-1 1924-7 1925-1 1925-7 1926-1 1926-7 1927-1 1927-7 1928-1 1928-7 1929-1 Market prices 1929-7 1930-1 1930-7 1931-1 1931-7 1932-1 Factor cost 1932-7 1933-1 1933-7 1934-1 1934-7 1935-1 1935-7 1936-1 1936-7 1937-1 1937-7 1938-1 1938-7

Historical analysis of the Great Depression • Burns and Mitchell (1946) dated peak at July 1929 • Our GDP data suggest Jan 1930 • They dated the recovery from Aug 1932 • We see no sustained recovery until well into 1933 • These apparently minor differences can have substantial implications – Say if we wish to address the role of particular policies (e.g. devaluation in Sept 1931; monetary expansion in April 1932) in generating recovery – The role of policy change in generating expectational changes – Sargent (1983) and Temin (1989) – Previously absence of British data prevented analysis – Our data indicate a “hesitant recovery path” consistent with the view that the policymakers found it hard to generate a favourable expectational change with a single policy move

The Profiles of Five UK Depressions The postwar monthly GDP data are from 2% NIESR; see Mitchell et al. (2005, EJ) 1% 0% GDP : Change from Peak -1% -2% -3% -4% -5% -6% -7% -8% -9% 0 6 12 18 24 30 36 42 48 Months from Start of Recession 1930-1934 1973-1976 1979-1983 1990-1993 2008-

Conclusion • Rather than use disparate indicators in an ad hoc manner to draw conclusions about the profiles of business cycles, it is preferable to use these variables to construct high- frequency estimates of GDP itself • The high frequency GDP data we provide are, we hope, of use to economists and economic historians addressing a number of questions • The global financial crisis of 2008 has resulted in renewed interest in the homologies between the current events and the Great Depression • But to-date (Eichengreen and O’Rourke, 2009) use has only been made of lower frequency GDP or IP data