Determining the Number of Factors in the General Dynamic Factor Model

By Marc Hallin, R. Liska. 2007

592 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

Citations in the corpus

Sufficient Forecasting Using Factor Models (2015, methodology): “There are many existing approaches to determining K in the literature, e.g., Bai and Ng (2002), Hallin and Liˇska (2007), Alessi et al. (2010).”
Generalized dynamic factor models and volatilities: recovering the market volatility shocks (2016, unknown): “Use the Hallin and Liška (2007) information criterion to select the number qT of level-common shocks, and compute the eigenvectors pY;n,1(θ), .”
Estimating Number of Factors by Adjusted Eigenvalues Thresholding (2019, unknown): “Case The model is from Harding (2013).”
A Shadow Policy Rate to Calibrate US Monetary Policy at the Zero Lower Bound (2014, unknown): “According to the criterion proposed by Hallin and Liska (2007), a total of seven factors appear to be appropriate in our empirical analysis.”
New Eurocoin: Tracking Economic Growth in Real Time (2007, methodology): “Our procedure can be summarized as follows: (i) We start by estimating q, the dimension of ut in (9), by means of the criterion proposed in Hallin and Lǐska (2007).”
Improved penalization for determining the number of factors in approximate factor models (2010, methodology): “We follow the procedure proposed by Hallin and Liška (2007).1 The only available information about the asymptotic behavior of r̂Tc,n comes from considering subsamples of sizes (nj , τj) with j = 0, . . . , J such that n0 = 0 n1 n2 . . . nJ = n and τ0 = 0 τ1 τ2 . . . < τJ = T .”
Identifying the number of factors from singular values of a large sample auto-covariance matrix (2014, unknown): “Based on the number of diverging eigenvalues of spectral density matrix of the observations, Hallin and Liska [14] develops an information criterion to choose the number of common factors for generalized dynamic factor models.”
Efficient Estimation of Approximate Factor Models (2012, methodology): “In practice, r can be estimated from the data, and there has been a large literature addressing its consistent estimation, e.g., Bai and Ng (2002), Kapetanios (2010), Onatski (2010), Alessi et al. (2010), Hallin and Lǐska (2007), Lam and Yao (2012), among others.”
OPENING THE BLACK BOX: STRUCTURAL FACTOR MODELS WITH LARGE CROSS SECTIONS (2009, methodology): “Regarding q, the criterion proposed by Hallin and Liška (2007), non–log criterion I C1, for different choices of the parameters and the penalty functions, produces values of q within the range 2–5.”
Do Euro Area Countries Respond Asymmetrically to the Common Monetary Policy (2014, unknown): “The latter is estimated by means of the test proposed by Onatski (2009), which suggests q ∈ {4, 5} (Table 3) and the criterion by Hallin and Liška (2007) suggesting q ∈ {2, 3}.”
Fitting dynamic factor models to non-stationary time series (2009, methodology): “This fact could be, for example, exploited for selecting the dimension r of the factor process (Hallin and Lĭska 2007).”
A network analysis of the volatility of high dimensional financial series (2017, methodology): “As already explained, the Hallin and Liška (2007) criterion yields q =1, i.e. the level common component is driven by a one-dimensional common shock. By estimating expressions (20) and (21), we recover the estimated level common component χn , the rank 1 shocks η T n and the level idiosyncratic comp”
Panel Data Models with Grouped Factor Structure Under Unknown Group Membership (2013, background): “…dynamic factor model (Geweke, 1977; Sargent and Sims, 1977), a static approximate factor model (Chamberlain and Rothschild, 1983), or a special model of the generalized dynamic factor model (Forni et al., 2000), also see, Forni and Lippi, 2001; Amengual and Watson, 2007; Hallin and Liska, 2007.”
The Dynamic Effects of Monetary Policy: A Structural Factor Model Approach (2010, methodology): “The strategy adopted here is to repeat the analysis with different specifications of r. Section 3.6 below shows results for r̂ = 10 and r̂ = 4.5 To determine the number of dynamic factors three criteria are used: Bai and Ng (2007), Amengual and Watson (2007) and Hallin and Liska (2007).”
Shrinkage Estimation of Dynamic Panel Data Models with Interactive Fixed Effects (2016, unknown): “Hallin and Líska (2007) study the determination of the number of factors in general dynamic factor models.”
On the Marčenko-Pastur law for linear time series (2013, background): “This question has been investigated by Bai and Ng [4], Stock and Watson [33] and Hallin and Liška [18].”