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First-order integer-valued autoregressive processes with Generalized Katz innovations

A new integer-valued autoregressive process (INAR) with Generalised Lagrangian Katz (GLK) innovations is defined. We show that our GLK-INAR process is stationary, discrete semi-self-decomposable, infinite divisible, and provides a flexible modelling framework for count data allowing for under- and over-dispersion, asymmetry, and excess of kurtosis. A Bayesian inference framework and an efficient posterior approximation procedure based on Markov Chain Monte Carlo are provided. The proposed model family is applied to a Google Trend dataset which proxies the public concern about climate change around the world. The empirical results provide new evidence of heterogeneity across countries and keywords in the persistence, uncertainty, and long-run public awareness level.


Introduction
In the recent years there has been a large interest in discrete-time integer-valued models, also due to increased availability of count data in very diverse fields including finance (Liesenfeld et al., 2006;Rydberg and Shephard, 2003;Aknouche et al., 2021), economics (Freeland, 1998;Freeland and McCabe, 2004;Berry and West, 2020), social sciences (Pedeli and Karlis, 2011), sports (Shahtahmassebi and Moyeed, 2016), image processing (Afrifa-Yamoah and Mueller, 2022) and oceanography (Cunha et al., 2018). Among the modelling approaches, integer-valued autoregressive processes (INAR), introduced independently by Al-Osh and Alzaid (1987) and McKenzie (1985), become vary popular. The stochastic construction of the INAR in the recent years by Bayesian methods, the applications to count data models are still limited (e.g., see McCabe and Martin, 2005;Neal and Subba Rao, 2007;Drovandi et al., 2016;Shang and Zhang, 2018;Garay et al., 2020b). Thus, we provide a Bayesian inference procedure for our model and illustrate the efficiency of the procedure on a synthetic dataset. The Bayesian approach to inference entirely considers parameter uncertainty in the prior knowledge about a random process and allows for imposing parameter restrictions through the specification of the prior distribution (Chen and Lee, 2016). The posterior distribution of the parameters quantifies uncertainty in the estimation (Chen and Lee, 2017), which can be included in the prediction. The inference from the Bayesian perspective may result in richer inferences in the case of small samples (Garay et al., 2020a) and extra-sample information and in robust inference in the presence of outliers (Fried et al., 2015). Finally, model selection for both nested and non-nested models can be easily carried out.
We illustrate the model's flexibility with an application to an original Google Trend dataset on measures of public concern about climate change in different countries. Assessing the level of public awareness and knowledge in a specific topic and understanding the dynamics in the social consciousness allows for designing more effective public policies. For this reason, researchers measured and studied the level of awareness about the effects of climate change in different sectors of society such as households (Frondel et al., 2017), winegrowers (Battaglini et al., 2009), farmers (Fahad and Wang, 2018;Singh et al., 2017), mountain peoples (Ullah et al., 2018). Most of these studies rely on surveys conducted in a specific geographical area and sector of society, with a few exceptions. For example, Ziegler (2017) proposed a cross-country analysis of climate change beliefs and attitudes. Lineman et al. (2015) provided a broader and global perspective by exploiting the potentiality of big data provided by Google Trend. This extended climate change perception literature along two lines. First, we consider a multi-country dataset including country-specific measures to capture heterogeneity across the world in public awareness. Moreover, we offer a model-based approach and an inference procedure to analyze these measures, which allow for a better comprehension of the cross-country and cross-keyword heterogeneity in the long-run level of public perception and perception uncertainty and persistence.
The paper is organized as follows. Section 2 introduces the Generalized Lagrangian Katz family, the INAR process, and some of their properties. Section 3 proposes a Bayesian inference procedure and provides some simulation results. Section 4 illustrates the model and inference framework on a multi-country Google Trend dataset on climate change and provides some new findings in the public interest in climate change. Section 5 concludes.

INAR(1) with generalized Katz innovations 2.1 Generalized Lagrangian Katz family
The Generalized Lagrangian Katz (GLK) distribution has been introduced by (Janardan, 1998), which named the distribution Generalized Polya Eggenberger distribution. (Consul and Famoye, 2006) argued that since the distribution it is not related to the Polya, it should be name Generalized Lagrangian Katz distribution. The GLK has probability mass function (pmf), P (X = x) = p x , defined as where (x) k↑ = x(x + 1) . . . (x + k − 1) is the rising factorial with the convention that (x) 0 = 1, and a > 0, c > 0, b ≥ 0 and 0 < β < 1 are the parameters. In the following, we denote the distribution with GLK(a, b, c, β).
• If −c < b < 0 but the additional constraints of the previous point are not satisfied, the terms appearing in the product ((a + bx)/c + 1) x↑ change sing and there is no guarantee that the result is positive. Indeed, for all the x such that x > max{(a + 1)/|b|, (c − a)/(c + b), 2} one has (a + bx)/c + 1 < 0 and (a + bx)/c + x − 1 > 0 and hence there is an integer q = q x such that which is negative whenever q x is odd. For example take a = 10, b = −1 and c = 2, for x = 20 one has q 20 = 5, which shows that p 20 < 0 which clearly is impossible.
It should be noted that alternative definitions for −c < b < 0 can be considered. For example one can set to 0 the p x < 0, i.e. when x > max{(a + 1)/|b|. In this case re-scaling the p x is necessary to get x * x=0 p x = 1. The resulting pmf is no more a generalized Lagrangian distribution (due to the truncation and rescaling) and the normalizing constant is not in closed form. See for example McCabe and Skeels (2020) for a discussion on the parameter values for the Katz distributions. In this paper we restrict our attention on the family of GLK and rule out negative pmf values by assuming b ≥ 0. Our modelling and inference framework can be easily extend to account for these distributions.
The following remarks clarify the relationship with other definitions of Katz distributions and with other discrete distributions. For some values of the parameters, the GLK distribution reduces either to some well known distributions in time series analysis, or to distributions which have not yet been used in count data modelling.
Remark 2. Three distributions related to our research are: (i) the Lagrangian Katz distribution LK(a, b, β) by replacing c with β, (which is called Generalized Katz in (Consul and Famoye, 2006)); (ii) the Katz distribution K(a, β) for b = 0 and by replacing c with β (Katz, 1965); (iii) the Polya-Eggenberger distribution PE(a, c, β) Janardan, 1998). Note that the Katz distribution in Consul and Famoye (2006), Tab. 2.1, is not the Katz distribution of Katz (1965), it corresponds instead to the Generalized Polya Eggenberger of the first type (GPED 1 -I) of Janardan (1998) and can be obtain as the limit of the zero-truncated GLK for a → −c.
The probability mass function of the GLK for different parameter settings given in Fig. 1. In the top-left plot we compare LK(a, c), LK(a, b, β) and GLK(a, b, c, β) with the same mean. The top-right plot illustrates the sensitivity of the GLK(a, b, c, β) pmf with respect to the different parameters. All distributions have the same mean (vertical dashed line). The bottom plots illustrate the effects of the parameters on the tails (log-scale) for a GLK(a, b, c, β) with over-dispersion V MR = 50/15 (left) and under-dispersion V MR = 13/15 (right).
Remark 3. The following standard distributions have been used to define INAR processes: (i) the Negative Binomial distribution N B(r, p) for b = 0 β = 1 − p and r = a/c; (ii) the Binomial distribution Bin(n, p) for c = 1, b = −1, a = n ∈ N and Remark 4. The GLK family includes also the generalizations of the distributions given in the previous remark, that are the Generalized Negative Binomial distribution GN B(r, γ, p) for c = 1, a = r, b = γ−1 and β = p and the Generalized Poisson (GP) distribution GP(θ, λ) for c → 0 s.t. b a = λ and aβ/c = θ > 0 with 0 < λ < θ −1 . The GP limit of the GLK distribution is stated in (Consul and Famoye, 2006) without proof. In Appendix A we provide a proof of the result.
Studying the moments allows for a better understanding of the flexibility of the GLK distribution. Four moments relevant to our analysis are the following.
For a proof, see Janardan (1998) Theorems 1-3. Another quantity of interest is the coefficient of variation which represents a measure of relative dispersion, given by the standard deviation to mean ratio. From the previous result it follows that the coefficient of variation is Another classical measure of dispersion is the Fisher index, given by the varianceto-mean ratio V MR = (1 − β) κ 2 , which does not depend on the parameter a. For a given β, following the values of κ (b and c), the distribution allows for various degree of dispersion: not dispersed (V MR = 0), under-dispersed (V MR < 1), equally dispersed (V MR = 1) and over-dispersed (V MR > 1).
The skewness and the kurtosis of the distribution are respectively. For a given value of θ, there is negative skewness if β < (1 + ξ)/(2κ+ ξ) with ξ = 3θ(b + c)a −1/2 and positive otherwise. Figure 2 illustrates the effect of the parameter values on the mean, index of dispersion, skewness and kurtosis. Increasing the value of β (horizontal axis) the GLK(a, b, c, β) distribution allows for different types of dispersion (panel b), for both negative and positive skewness (panel c) and for various degrees of excess of kurtosis (panel d).
We conclude this section with an important property of the GLK distribution.
See Appendix for a proof based on the properties of the pgf function.

A INAR(1) process
The Generalized Katz INAR(1) process (GLK-INAR(1)) is defined by means of the binomial thinning operator, •. The binomial thinning for a non-negative discrete random variable X is defined as where B i (α) are iid Bernoulli r.v.s with success probability P (B i (α) = 1) = α.
Definition 1 (GLK-INAR process). For α ∈ (0, 1), the GLK-INAR(1) process is defined by where ε t are iid random variables with Generalized Lagrangian Katz distribution GLK(a, b, c, β), independent of X s , s ≤ t − 1. As any INAR process, the GLK-INAR(1) has the following representation and its conditional pgf can be written as where H(u) is defined in Eq. 3 or in Eq. 4. Details are given in Appendix A. The previous representation allows us to derive the following properties, which are useful in forecasting. Theorem 1. The conditional mean and variance of the process GLK- where κ = 1 − β − bβ/c and θ = β/c.
The process {X t } t∈Z is a Markov Chain on N and the transition probability P i,j = P(X t = j|X t−1 = i) can be expressed as where p x is the pmf given in Eq. 1.
Theorem 2. The process {X t } t∈Z is an irreducible, aperiodic and positive recurrent Markov chain. Hence there is a unique stationary distribution for the process See Appendix A for a proof. The result extends to the case of GLK innovations, the stationarity for INAR(1) with power series innovations given in Bourguignon and Vasconcellos (2015) and with Katz distribution given in Kim and Lee (2017).
From Th. 2 it follows that there is a strong stationary process with stationary distribution P j , j ∈ N, given by the marginal distribution Since at stationarity the process satisfies X = α•X+ε, where ε ∼ GLK(a, b, c, β), and the innovation terms are infinite divisible by Theorem 2, the stationary distribution satisfies the following definition of discrete semi-self-decomposability given in Bouzar (2008).
Being the stationary distribution a DSSD, Th. 2 in Bouzar (2008) yields that it is also infinitely divisible.
Theorem 3. The marginal distribution of the stationary process {X t } t∈Z is infinitely divisible.
ε and σ 2 ε the mean, second order non-central moment and variance given in Prop. 1 for a GLK(a, b, c, β). For a GLK-INAR(1) process, the following unconditional moments can be derived: Higher order non-central moments can be derived using the formula: where s(m, k) and S(m, k) denote the Stirling's numbers of the I and II kind, respectively.
From the previous theorem one obtains the unconditional variance of the process σ 2 X = V(X t ) = (σ 2 ε + αµ ε )/(1 − α 2 ) and the dispersion index of the process The autocorrelation function is as in the INAR(1) process (e.g., see Al-Osh and Alzaid, 1987).

Bayesian inference 3.1 Posterior distribution
Let us assume the following prior distributions where Be(κ, τ ) is the beta distribution with shape parameters κ and τ and Ga(κ, τ ) the gamma distribution with shape and scale parameters κ and τ , respectively. In the empirical applications we assume a non-informative hyper-parameter setting for α and β, that is κ α = τ α = κ β = τ β = 1 and an informative prior for a, b and c with κ a = τ a = 1, κ b = κ c = 2 and τ b = τ c = 1/2. Let x 1 , . . . , x T be a sequence of observations for the GLK-INAR(1) process, then the joint posterior distribution is given by where θ = (α, a, b, c, β) is the parameter vector π(θ) the joint prior and Remark 7 (Parameters space). Following the discussion in Remark 1, if the parameter constrain c > 0 is not imposed the coefficients of the Lagrangian expansion can be negative. In this case a truncated GLK can be used, similarly to what is proposed in McCabe and Skeels (2020) for the Katz distribution, and the inference procedure can be easily extended to include this type of distributions. The truncation can be imposed by using the following recursion for the transition probability: The probability p i becomes null for i > j if U(θ) + V (θ)j < 0 at j.
Since the joint posterior is not tractable we follow a Markov Chain Monte Carlo (MCMC) framework to posterior approximation. See Robert and Casella (2013) for an introduction to MCMC methods. We overcome the difficulties in tuning the parameters of the MCMC procedure by applying the Adaptive MCMC sampler (AMCMC) proposed in Andrieu and Thoms (2008). Following a standard procedure, the following reparametrization is considered to impose the constrains to the parameters of the GLK-INAR(1). Let η be the parameter vector of dimension q obtained by the transformation ϕ(θ) and let π(η|x 1 , . . . , the Jacobian of the transformation ϕ given above. Given the adaptation parameters µ j and Σ (j) , at the j-th iterations, the AMCMC consists of the following three steps. First, a candidate η * is generated from the following proposal distribution Second, the candidate is accepted with probability ρ (j) = ρ(η (j−1) , η * ), and third, the adaptive parameters are updated as follows: where ρ * = 0.4 is the target acceptance probability and γ (j) = j −1.6 is the adaptive scale (Andrieu and Thoms, 2008, , Algorithm 4).

Simulation results
We illustrate the effectiveness of the Bayesian procedure in recovering the true value of the parameters and the efficiency of the MCMC procedure through some simulation experiments. We test the efficiency of the algorithm in two different settings, which can be commonly found in the data: low persistence and high persistence (see trajectories in Fig. 3). The true values of the parameters are: α = 0.3, a = 5.3239,b = 0.0592, c = 0.6, β = 0.5917 in the low persistence setting, and α = 0.7, a = 5.3239,b = 0.0592, c = 0.6, β = 0.5917 in the high persistence setting. For each setting we run the Gibbs sampler for 50,000 iterations on each dataset, discard the first 10,000 draws to remove dependence on initial conditions, and finally apply a thinning procedure with a factor of 10, to reduce the dependence between consecutive draws. For illustrative purposes, we show in Figures 4 the MCMC posterior approximation for the parameter α (first row), the unconditional mean of the process (second row), and the marginal likelihood (last row), in one of our experiments for the high-and low-persistence settings. In each plot the true value (solid black line) and the Bayesian estimates approximated by using 4,000 MCMC samples after thinning and burn-in removal (dashed red line). For illustrative purposes, Fig. 10-11 and 12-13 in Appendix B exhibit 10,000 MCMC posterior draws and the MCMC approximation of the posterior distribution for all the parameters, in the high-and low-persistence settings.
In our experiments the acceptance rate is in the range of 40%-53% for both parameter settings. Table 1 shows, for all the parameters the autocorrelation function (ACF), effective sample size (ESS), inefficiency factor (INEFF) and Geweke's convergence diagnostic (CD) before (BT subscript) and after thinning (AT subscript). The numerical standard errors are evaluated using the nse package (Geyer, 1992;Ardia and Bluteau, 2017;Ardia et al., 2018).
The thinning procedure is effective in reducing the autocorrelation levels and in increasing the ESS. The p-values of the CD statistics indicate that the null hypothesis that two sub-samples of the MCMC draws have the same distribution is always accepted. The efficiency of the MCMC after the thinning procedure is generally improved. After thinning, on average the inefficiency measures (5.83), the p-values of the CD statistics (0.36) and the NSE (0.02) achieved the values recommended in the literature (e.g., see Roberts et al., 1997). Overall, this implies that the Gibbs sampler is computationally efficient, and the Monte Carlo estimator of the posterior quantities of interest has low variance.

Application to climate change 4.1 Data description
We used Google Trends data to measure the changes in public concern about climate change. Google Trend represents a source of big data (Choi and Varian, 2012;Scott and Varian, 2014) which have been used in many studies for example Anderberg et al. (2021) studied domestic violence during covid-19, Guolo and Varin (2014) and Yang et al. (2021) studied respectively flu and influenza trends, Schiavoni et al. (2021) and Yi et al. (2021) presented applications to unemployment while Yu et al. (2019) studied oil consumptions. In this study, we follow Lineman et al. (2015) and use Google search volumes as a proxy for public concern about "Climate Change" (CC) and "Global Warming" (GW). The search volume is the traffic for the specific  (1) Table 1: Autocorrelation function (ACF), effective sample size (ESS), inefficiency factor (INEFF), numerical standard errors (NSE) and Geweke's convergence diagnostic (CD) of the posterior MCMC samples for the two settings: low persistence and high persistence. We ran the proposed MCMC algorithm for 50,000 iterations and evaluate the statistics before (subscript BT) and after (subscript AT) removing the first 10,000 burn-in samples, and applying a thinning procedure with a factor of 10. In parenthesis the p-values of the Geweke's convergence diagnostic.

Estimation results
The posterior distribution of the autoregressive coefficient is given in Fig. 6. The coefficient estimate and posterior credible interval (in parenthesis) are α = 0.56 (0.50, 0.62) and α = 0.62 (0.56, 0.67) for the GW and the CC dataset, respectively. This result indicates that the public concern in climate risk is persistent over time worldwide at an aggregate level. The estimated parameter of the innovation process and their 0.95% credible intervals (in parenthesis) are a where p(X|θ) is the likelihood of the model, θ (j) j = 1, . . . , N the MCMC draws after thinning and burn-in sample removal, and θ is the parameter estimate. The DICs for the GLK (NB) INARs fitted on the aggregate CC and GW series are 1.6677 · 10 3 (1.6861 · 10 3 ) and 1.8638 · 10 3 (1.8835 · 10 3 ), respectively. We run the analysis at a disaggregate level. The results are given in Fig. 7-8 and Tab. 2-3. Figure 7 provides evidence of an inverse relationship between estimated persistence α and dispersion V MR cross countries (reference lines in the left plot). There is evidence of this inverse relationship in both the CC (blue dots) and GW (red dots) datasets. The plot on the right indicates an inverse (direct) relationship between the estimated unconditional mean µ ε /(1 − α) and the dispersion index V MR for the GW (CC). We indicate the parameter estimates for the world volume of searches (stars) in the same picture.   (1) models, for the "Climate Change" and "Global Warming" search volumes in different countries. Countries with less than 21% of zeros in the two series. "*" indicate the model with the largest marginal likelihood.
The terms "Climate Change" and "Global Warming" are used interchangeably, nevertheless, they describe different phenomena and can be used to determine the   Table 3: Estimated GLK-INAR(1) autoregressive coefficient ( α) and its 95% credible interval (CI), and marginal likelihood of the GLK-INAR(1) and the INARKF(1) models, for the "Climate Change" and "Global Warming" search volumes in different countries. Countries with more than 21% of zeros in the two series. "*" indicate the model with the largest marginal likelihood.
level of understanding of the public about these two parallel concepts Lineman et al. (2015). We investigate the relationships in the search volumes through the lens of our GLK-INAR(1) model. The left plot in Fig. 8 shows the unconditional mean of the search volumes for the two concepts in all countries (dots). In public attention, the two concepts are connected in the long run. We find a positive association for both countries with large (percentage of zeros < 21%) and low search volumes (percentage of zeros > 21%). There is an asymmetric effect in the overdispersion (right plot), and in all countries, the GW search volume has a larger VMR than the CC volume. This can be explained by the GW larger variability induced by the changes in the use of the GW term in the official communications.
Comparing the coefficients across the rows of Tables 2-3, we find evidence of two types of series, one with high persistence and the other with low persistence. Moreover, for each country the level of persistence is similar across the two datasets (compare columns of Tables 2-3).
Tables 2-3 report the marginal likelihood of the GLK-INAR(1) and Lagrangian Katz INAR(1) in columns GLK and LK respectively. We find evidence of a better fitting of the GLK-INAR(1) for some countries and variables, e.g. CC searches in India and CC and GW searcher in South Africa. In order to get further insights into the results, we study the relationship between the dynamic and dispersion properties of the series and the actual level of climate risk of the countries. We consider the Global Climate Risk Index (CRI), which ranks countries and regions following the impacts of extreme weather events (such as storms, hurricanes, floods, heatwaves, etc.). The lower the index value, the larger the climate risk is. Following the values of the CRI for 2021, based on the events recorded from 2000 to 2019, our dataset includes some of the countries most exposed to the climate risk, such as Japan, Philippines, Germany, South Africa, India, Sri-Lanka and Canada (Eckstein et al., 2021, see).
The left plot in Fig. 9 shows unconditional mean against the CRI. There is evidence of a positive relationship between the public interest in climate-related topics and the actual level of climatic risk. Otherwise said, the lower the CRI level, the larger the Google search volumes are (see dashed lines). For example India has high risk (CRI equal to 7) and very high long-run level of public attention.
The right plot reports the coefficient of variation against the CRI for all countries in the "Climate Change" (blue) and "Global Warming" (red) datasets. The dashed lines represent linear regressions estimated on the data. There is evidence of a negative relationship between dispersion of the public concern and climatic risk; that is, in countries with more significant risk levels, the Google search volumes are less over-dispersed.

Conclusion
A novel integer-valued autoregressive process is proposed with Generalized Lagrangian Katz innovations. Theoretical properties of the model, such as stationarity, moments, and semi-self-decomposability are provided. A Bayesian approach to inference is proposed, and an efficient Gibbs sampling procedure has been proposed. The modeling framework is applied to a Google Trend dataset on the public concern about climate change for 65 countries. New evidence is provided about the long-run level of public attention, its persistence and dispersion in countries with low and high level of climate risk. where u satisfies z = ug(z). The definition of Lagrangian distribution given in Janardan (1998) can is obtained from the one given above by replacing f (z) with f (z)(1 − zg ′ (z)/g(z))). By applying iteratively the derivative ∂ to the product of functions we obtain the coefficient in the j-th term of the expansion where we set ℓ = j to get the result and the following equivalent Lagrangian expansion In particular, for j ≥ 1, be the transformed and the transformer function, respectively. Then Hence while, for k ≥ 2, the k-th coefficient of the Lagrangian expansion in Eq. 37 is where ξ k = a c + k b c + 1 and (x) k↑ = x(x + 1) . . . (x + k − 1) is the rising factorial.
• If −c < b < 0, a/c, b/c ∈ N and (c − a)/(c + b) ≤ (a + c)/|b|, then for k < k * = (a + c)/|b| one has a + bk c + 1 > 0 and hence also proving that p k > 0. For k ≥ k * ≥ (c−a)/(c+b) one has that m k = (|b|k−a)/c is an integer with 1 ≤ m k ≤ k−1 and hence the product k−1 m=1 a c + k b c + m = 0 since for m = m k one has a c + k b c + m = 0. This shows that p k = 0 for every k ≥ k * .

A.3 Proof of the results in Theorem 2
A random variable X is infinite divisible if for every n ∈ N there exists a sequence of random variables X nj , j = 1, . . . , n such that X has the same distribution of X n1 + . . . + X nn . From the pgf of a GLK given in Eq. 4 which is the pgf of the sum of n independent GLKs with distribution GLK(a/n, b, c, β) where a/n > 0 according to the definition of GLK.
A.4 Proofs of Equations (10)- (9) Before proving the general result let us prove the following two properties of the thinning operator where X and Y are two independent discrete-valued random variables. Property (i) follows from the law of iterated expectations and the properties of the probability generating function (pgf): which is the pgf of the r.v. α 2 • X. Property (ii) follows from: where we definedB l = B l+X for l = 1, . . . , Y . Applying iteratively the two properties of the binomial thinning operator returns the following representation where we defined α 0 • X = X.
The conditional pgf follows from the above decomposition where H(u) satisfies Eq. 3 and 4.

A.5 Proof of the results in Theorem 1
Using the expressions for the moments given in Th. 1 and the independent assumption of the innovations on the thinning and on the past values of process one obtains for the case k = 1 and Following the recursive representation in 58 and by independence assumption, the conditional variance writes as where we defined α 0 • X = X. Similarly, the conditional variance writes as A.6 Proof of the result in Remark 5 Setting b = 0, c = β = θ 1 and a = θ 2 , it follows κ = 1 − θ 2 and θ = 1, thus the conditional mean and variance become and A.7 Proof of the result in Remark 6 Under the assumption |α| < 1 the limiting conditional variance becomes: A.8 Proof of the results in Theorem 2 We follow the same argument as in Bourguignon and Vasconcellos (2015). Let p l,k = P(X t = k|X t−1 = l) be the transition probability given in Eq. 13. The process is irreducible since that every k ∈ N can be reached from every l ∈ N. The transition are constant over t, thus the process admits a stationary distribution. We need to show that and bounded. Next we show by induction that where For m = 1 the equation is satisfied since Assume it is satisfied for m then for m + 1 we have P m+1 where p x is the pmf of the GLK and the summation in the last line is the pgf of the GLK. It follows that P m+1 x,0 (iii) Let us denote with (x) m = x(x − 1) . . . (x − m + 1) the falling factorial and with µ (k) = E((X) k ) the m-order falling factorial moment of a random variable X. The following two results will be used. The relationships between non-central moments and falling factorial moments are where s(m, k) and S(m, k) are the Stirling numbers of the I and II kind, respectively (e.g., see Consul and Famoye, 2006, p. 18). Let X and Y be two random variables then which can be proved by induction. Let α • X a binomial thinning with X a discrete random variable, then (117) where |κ| = κ 1 + . . . + κ X . Using the results given above and stationarity (i.e. E((X t ) m ) = µ (m) which implies the m-order falling factorial moment of a INAR(1) is (see also ). + 6µ 2 ε − 12µ ε µ (2) ε + 6µ ε + 6(µ (2) ε ) 2 − 11µ (2) ε + 6µ (3) ε − µ (4) ε α 2 (140) where we used µ ε and µ (4) ε = −6µ ε + 11µ (2) ε − 6µ